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\n\n

\"PyPI\"\n\"Python\n\"License\"

\n\n

\"pages\"

\n\n

Welcome to senselab! This is a Python package for streamlining the processing and analysis of behavioral data, such as voice and speech patterns, with robust and reproducible methodologies.

\n\n

Caution:: this package is still under development and may change rapidly over the next few weeks.

\n\n

Installation

\n\n

Install this package via:

\n\n
\n
pip install senselab\n
\n
\n\n

Or get the newest development version via:

\n\n
\n
pip install git+https://github.com/sensein/senselab.git\n
\n
\n\n

Quick start

\n\n
\n
from senselab.audio.data_structures.audio import Audio\nfrom senselab.audio.tasks.preprocessing.preprocessing import resample_audios\n\naudio1 = Audio.from_filepath('path_to_audio_file.wav')\n\nprint("The original audio has a sampling rate of {} Hz.".format(audio1.sampling_rate))\n[audio1] = resample_audios([audio1], resample_rate=16000)\nprint("The resampled audio has a sampling rate of {} Hz.".format(audio1.sampling_rate))\n
\n
\n\n

For more detailed information, check out our Getting Started Tutorial.

\n\n

Why should I use senselab?

\n\n
    \n
  • Modular Design: Easily integrate or use standalone transformations for flexible data manipulation.
    • \n
  • Pre-built Pipelines: Access pre-configured pipelines to reduce setup time and effort.
    • \n
  • Reproducibility: Ensure consistent and verifiable results with fixed seeds and version-controlled steps.
    • \n
  • Easy Integration: Seamlessly fit into existing workflows with minimal configuration.
    • \n
  • Extensible: Modify and contribute custom transformations and pipelines to meet specific research needs.
    • \n
  • Comprehensive Documentation: Detailed guides, examples, and documentation for all features and modules.
    • \n
  • Performance Optimized: Efficiently process large datasets with optimized code and algorithms.
    • \n
  • Interactive Examples: Jupyter notebooks provide practical examples for deriving insights from real-world datasets.
    • \n
\n\n

Contributing

\n\n

Please see CONTRIBUTING.md before contributing.

\n\n

To find out what's currently in progress, please check the Project Board.

\n"}, "senselab.audio": {"fullname": "senselab.audio", "modulename": "senselab.audio", "kind": "module", "doc": "

This module contains implementations related to audio processing.

\n"}, "senselab.audio.data_structures": {"fullname": "senselab.audio.data_structures", "modulename": "senselab.audio.data_structures", "kind": "module", "doc": "

This module contains data structures for audio processing.

\n"}, "senselab.audio.data_structures.audio": {"fullname": "senselab.audio.data_structures.audio", "modulename": "senselab.audio.data_structures.audio", "kind": "module", "doc": "

Data structures relevant for handling audio files and metadata.

\n\n

The most basic unit is the Audio object which represents the necessary information of a loaded audio\nfile and its corresponding metadata. Other functionality and abstract data types are provided for\nease of maintaining the codebase and offering consistent public APIs.

\n"}, "senselab.audio.data_structures.audio.Audio": {"fullname": "senselab.audio.data_structures.audio.Audio", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio", "kind": "class", "doc": "

Pydantic model for audio and its corresponding metadata.

\n\n

Pydantic model for audio that holds the necessary attributes, the actual decoded audio data\nand the sampling rate, to work with audio in python. Contains metadata information as needed\nand has a unique identifier for every audio.

\n\n
Attributes:
\n\n
    \n
  • waveform: The actual audio data read from an audio file, stored as a torch.Tensor\nof shape (num_channels, num_samples)
    • \n
  • sampling_rate: The sampling rate of the audio file
    • \n
  • orig_path_or_id: Optional str for the original path or an ID to track file over time
    • \n
  • metadata: Optional metadata dictionary of information associated with this Audio instance\n(e.g. participant demographics, audio settings, location information)
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "senselab.audio.data_structures.audio.Audio.waveform": {"fullname": "senselab.audio.data_structures.audio.Audio.waveform", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.waveform", "kind": "variable", "doc": "

\n", "annotation": ": torch.Tensor"}, "senselab.audio.data_structures.audio.Audio.sampling_rate": {"fullname": "senselab.audio.data_structures.audio.Audio.sampling_rate", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.sampling_rate", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "senselab.audio.data_structures.audio.Audio.orig_path_or_id": {"fullname": "senselab.audio.data_structures.audio.Audio.orig_path_or_id", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.orig_path_or_id", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "senselab.audio.data_structures.audio.Audio.metadata": {"fullname": "senselab.audio.data_structures.audio.Audio.metadata", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.metadata", "kind": "variable", "doc": "

\n", "annotation": ": Dict"}, "senselab.audio.data_structures.audio.Audio.model_config": {"fullname": "senselab.audio.data_structures.audio.Audio.model_config", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "default_value": "{'arbitrary_types_allowed': True}"}, "senselab.audio.data_structures.audio.Audio.convert_to_tensor": {"fullname": "senselab.audio.data_structures.audio.Audio.convert_to_tensor", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.convert_to_tensor", "kind": "function", "doc": "

Converts the audio data to torch.Tensor of shape (num_channels, num_samples).

\n", "signature": "(\tcls,\tv: Union[List[float], List[List[float]], numpy.ndarray, torch.Tensor],\t_: pydantic_core.core_schema.ValidationInfo) -> torch.Tensor:", "funcdef": "def"}, "senselab.audio.data_structures.audio.Audio.from_filepath": {"fullname": "senselab.audio.data_structures.audio.Audio.from_filepath", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.from_filepath", "kind": "function", "doc": "

Creates an Audio instance from an audio file.

\n\n
Arguments:
\n\n
    \n
  • filepath: Filepath of the audio file to read from
    • \n
  • metadata: Additional information associated with the audio file
    • \n
\n", "signature": "(\tcls,\tfilepath: str,\tmetadata: Dict = {}) -> senselab.audio.data_structures.audio.Audio:", "funcdef": "def"}, "senselab.audio.data_structures.audio.Audio.generate_path": {"fullname": "senselab.audio.data_structures.audio.Audio.generate_path", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.generate_path", "kind": "function", "doc": "

Generate a path like string for this Audio.

\n\n

Generates a path like string for the Audio by either utilizing the orig_path_or_id, checking\nif it is a path (has an extension), otherwise using the id if orig_path_or_id is not an ID\nand giving an extension and relative to the current directory.

\n", "signature": "(self) -> str:", "funcdef": "def"}, "senselab.audio.data_structures.audio.Audio.id": {"fullname": "senselab.audio.data_structures.audio.Audio.id", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.id", "kind": "function", "doc": "

Generate a unique identifier for the Audio.

\n\n

Generate a unique identifier for the Audio where equivalent waveforms and sampling\nrates generate the same IDs.

\n\n

Returns: String UUID of the Audio generated by an MD5 hash of the waveform and the sampling_rate

\n", "signature": "(self) -> str:", "funcdef": "def"}, "senselab.audio.data_structures.audio.Audio.model_fields": {"fullname": "senselab.audio.data_structures.audio.Audio.model_fields", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'waveform': FieldInfo(annotation=Tensor, required=True), 'sampling_rate': FieldInfo(annotation=int, required=True), 'orig_path_or_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'metadata': FieldInfo(annotation=Dict, required=False, default={})}"}, "senselab.audio.data_structures.audio.Audio.model_computed_fields": {"fullname": "senselab.audio.data_structures.audio.Audio.model_computed_fields", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.audio.data_structures.audio.batch_audios": {"fullname": "senselab.audio.data_structures.audio.batch_audios", "modulename": "senselab.audio.data_structures.audio", "qualname": "batch_audios", "kind": "function", "doc": "

Batches the Audios together into a single Tensor, keeping individual Audio information separate.

\n\n

Batch all of the Audios into a single Tensor of shape (len(audios), num_channels, num_samples).\nKeeps the Audio information related to each sampling rate and metadata separate for each Audio to\nallow for unbatching after running relevant functionality.

\n\n
Arguments:
\n\n
    \n
  • audios: List of audios to batch together. NOTE: Should all have the same number of channels\nand is generally advised to have the same sampling rates if running functionality\nthat relies on the sampling rate.
    • \n
\n\n
Returns:
\n\n
\n

Returns a tuple of a Tensor that will have the shape (len(audios), num_channels, num_samples),\n the sampling rate (an integer if all have the same sampling rate), and a list of each individual\n audio's metadata information.

\n
\n\n
Raises:
\n\n
    \n
  • RuntimeError: if all of the Audios do not have the same number of channels
    • \n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio]) -> Tuple[torch.Tensor, Union[int, List[int]], List[Dict]]:", "funcdef": "def"}, "senselab.audio.data_structures.audio.unbatch_audios": {"fullname": "senselab.audio.data_structures.audio.unbatch_audios", "modulename": "senselab.audio.data_structures.audio", "qualname": "unbatch_audios", "kind": "function", "doc": "

Unbatches Audios into a List of Audio objects.

\n\n

Uses the batched Audios, their respective sampling rates, and their corresponding metadatas to create\na list of Audios.

\n\n
Arguments:
\n\n
    \n
  • batched_audio: torch.Tensor of shape (batch_size, num_channels, num_samples) to unstack
    • \n
  • sampling_rates: The sampling rate of each batched audio if they differ or a single sampling rate for all of them
    • \n
  • metadatas: The respective metadata for each of the batched audios
    • \n
\n\n
Returns:
\n\n
\n

List of Audio objects representing each of the Audios that were previously batched together

\n
\n\n
Raises:
\n\n
    \n
  • ValueError if the batched_audio is not in the correct shape or if the number of batched_audios does not\nmatch the amount of metadata and sampling rates (if they were provided as a List) that were provided.
    • \n
\n", "signature": "(\tbatched_audio: torch.Tensor,\tsampling_rates: Union[int, List[int]],\tmetadatas: List[Dict]) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks": {"fullname": "senselab.audio.tasks", "modulename": "senselab.audio.tasks", "kind": "module", "doc": "

Tasks for audio processing.

\n"}, "senselab.audio.tasks.classification": {"fullname": "senselab.audio.tasks.classification", "modulename": "senselab.audio.tasks.classification", "kind": "module", "doc": "

This module defines the APIs for doing generalized audio classification tasks.

\n"}, "senselab.audio.tasks.classification.speech_emotion_recognition": {"fullname": "senselab.audio.tasks.classification.speech_emotion_recognition", "modulename": "senselab.audio.tasks.classification.speech_emotion_recognition", "kind": "module", "doc": "

This module defines APIs for the user to run Speech Emotion Recognition (SER) on sets of audios.

\n"}, "senselab.audio.tasks.classification.speech_emotion_recognition.audio_classification_with_hf_models": {"fullname": "senselab.audio.tasks.classification.speech_emotion_recognition.audio_classification_with_hf_models", "modulename": "senselab.audio.tasks.classification.speech_emotion_recognition", "qualname": "audio_classification_with_hf_models", "kind": "function", "doc": "

General audio classification functionality utilitzing HuggingFace pipelines.

\n\n

Classifies all audios, with no underlying assumptions on what the classification labels are,\nand returns the output that the pipeline gives.

\n\n
Arguments:
\n\n
    \n
  • audios: List of Audio objects that we want to run classification on
    • \n
  • model: The HuggingFace model that will be used for running the inference
    • \n
  • device: The device to run inference on
    • \n
\n\n
Returns:
\n\n
\n

List of Lists of Dictionaries where each corresponds to the audio that it was ran on and the List of\n Dictionaries are of the form [{'label': 'some_label', 'score': some_value},...]

\n
\n\n
Raises:
\n\n
    \n
  • ValueError if the given model does not have the audio-classification pipeline tag
    • \n
  • UserWarning if the model tags don't include endpoints_compatible (seen on HuggingFace as Inference Endpoints)\nas the behavior of the model might not output as expected as a result.
    • \n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.HFModel,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[List[Dict]]:", "funcdef": "def"}, "senselab.audio.tasks.classification.speech_emotion_recognition.speech_emotion_recognition_with_hf_models": {"fullname": "senselab.audio.tasks.classification.speech_emotion_recognition.speech_emotion_recognition_with_hf_models", "modulename": "senselab.audio.tasks.classification.speech_emotion_recognition", "qualname": "speech_emotion_recognition_with_hf_models", "kind": "function", "doc": "

Function for running speech emotion recognition tasks using HuggingFace models.

\n\n

Uses an audio classification pipeline to run speech emotion recognition for every audio in audios using the\nspecified model. For each audio, we return the emotion that the model suggested has the highest probability\nfor single label classification as well as the full emotion output for multi-label classification. Function can\nalso be used for continuous speech emotion recognition, where the second element of each tuple will contain a\ndictionary of the predicted continuous values.

\n\n
Arguments:
\n\n
    \n
  • audios: List of Audio objects that we want to run speech emotion recognition on. If you wish to get the\nemotion at different segments of the audio (e.g. when different speaker talk) please run the audios\nthrough an appropriate segmentation task.
    • \n
  • model: The HuggingFace model that will be used for running the inference
    • \n
  • device: The device to run inference on
    • \n
\n\n
Returns:
\n\n
\n

List of tuples where the first value is the single label classification of the audio (if appropriate) and the\n second value is the full model output, which might be the probabilities for each evaluated emotion\n (for discrete SER) or the continuous emotion predictions.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError if the given model is not properly tagged with 'speech-emotion-recognition' or 'emotion-recognition'\nor the model configuration does not contain an id2label property that predicts commonly used\nemotions (happy, sad, neutral, positive, negative, etc.)
    • \n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.HFModel,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[Tuple[str, Dict]]:", "funcdef": "def"}, "senselab.audio.tasks.data_augmentation": {"fullname": "senselab.audio.tasks.data_augmentation", "modulename": "senselab.audio.tasks.data_augmentation", "kind": "module", "doc": "

This module provides the API of the senselab audio data augmentation.

\n"}, "senselab.audio.tasks.data_augmentation.data_augmentation": {"fullname": "senselab.audio.tasks.data_augmentation.data_augmentation", "modulename": "senselab.audio.tasks.data_augmentation.data_augmentation", "kind": "module", "doc": "

This module implements some utilities for audio data augmentation.

\n"}, "senselab.audio.tasks.data_augmentation.data_augmentation.augment_audios": {"fullname": "senselab.audio.tasks.data_augmentation.data_augmentation.augment_audios", "modulename": "senselab.audio.tasks.data_augmentation.data_augmentation", "qualname": "augment_audios", "kind": "function", "doc": "

Augments all provided audios with a given augmentation, either individually or all batched together.

\n\n

Augment all audios with a user defined augmentation that can be a composition of multiple augmentations. This\naugmentation is either performed on each audio individually or all of the audios provided are batched together\nand run at once. NOTE: if batching, all audios must have the same sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audios: List of Audios whose data will be augmented with the given augmentations
    • \n
  • augmentation: A Composition of augmentations to run on each audio (uses torch-audiomentations), should have its\noutput_type set to \"dict\"
    • \n
  • device_options: The device, or a List of possible devices, to use for augmenting. If the chosen device\nis MPS or CUDA then the audios are all batched together, so for optimal performance, batching should\nbe done by passing a batch_size worth of audios ar a time
    • \n
\n\n
Returns:
\n\n
\n

List of audios that has passed the all of input audios through the provided augmentation. This does\n not necessarily mean that the augmentation has been run on every audio. For more information,\n see the torch-audiomentations documentation.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\taugmentation: torch_audiomentations.core.composition.Compose,\tdevice_options: Union[senselab.utils.data_structures.device.DeviceType, List[senselab.utils.data_structures.device.DeviceType]] = [<DeviceType.CPU: 'cpu'>]) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.data_augmentation.data_augmentation.augment_audios_pt": {"fullname": "senselab.audio.tasks.data_augmentation.data_augmentation.augment_audios_pt", "modulename": "senselab.audio.tasks.data_augmentation.data_augmentation", "qualname": "augment_audios_pt", "kind": "function", "doc": "

Augments all provided audios with a given augmentation, either individually or all batched together.

\n\n

Augment all audios with a user defined augmentation that can be a composition of multiple augmentations. This\naugmentation is either performed on each audio individually or all of the audios provided are batched together\nand run at once. NOTE: if batching, all audios must have the same sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audios: List of Audios whose data will be augmented with the given augmentations
    • \n
  • augmentation: A Composition of augmentations to run on each audio (uses torch-audiomentations), should have its\noutput_type set to \"dict\"
    • \n
  • device_options: The device, or a List of possible devices, to use for augmenting. If the chosen device\nis MPS or CUDA then the audios are all batched together, so for optimal performance, batching should\nbe done by passing a batch_size worth of audios ar a time
    • \n
\n\n
Returns:
\n\n
\n

List of audios that has passed the all of input audios through the provided augmentation. This does\n not necessarily mean that the augmentation has been run on every audio. For more information,\n see the torch-audiomentations documentation.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\taugmentation: torch_audiomentations.core.composition.Compose,\tdevice_options: Union[senselab.utils.data_structures.device.DeviceType, List[senselab.utils.data_structures.device.DeviceType]] = [<DeviceType.CPU: 'cpu'>]) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction": {"fullname": "senselab.audio.tasks.features_extraction", "modulename": "senselab.audio.tasks.features_extraction", "kind": "module", "doc": "

This module provides the API of the senselab audio features extraction.

\n"}, "senselab.audio.tasks.features_extraction.opensmile": {"fullname": "senselab.audio.tasks.features_extraction.opensmile", "modulename": "senselab.audio.tasks.features_extraction.opensmile", "kind": "module", "doc": "

This module contains functions for extracting openSMILE features.

\n"}, "senselab.audio.tasks.features_extraction.opensmile.OpenSmileFeatureExtractorFactory": {"fullname": "senselab.audio.tasks.features_extraction.opensmile.OpenSmileFeatureExtractorFactory", "modulename": "senselab.audio.tasks.features_extraction.opensmile", "qualname": "OpenSmileFeatureExtractorFactory", "kind": "class", "doc": "

A factory for managing openSMILE feature extractors.

\n"}, "senselab.audio.tasks.features_extraction.opensmile.OpenSmileFeatureExtractorFactory.get_opensmile_extractor": {"fullname": "senselab.audio.tasks.features_extraction.opensmile.OpenSmileFeatureExtractorFactory.get_opensmile_extractor", "modulename": "senselab.audio.tasks.features_extraction.opensmile", "qualname": "OpenSmileFeatureExtractorFactory.get_opensmile_extractor", "kind": "function", "doc": "

Get or create an openSMILE feature extractor.

\n\n
Arguments:
\n\n
    \n
  • feature_set (str): The openSMILE feature set.
    • \n
  • feature_level (str): The openSMILE feature level.
    • \n
\n\n
Returns:
\n\n
\n

opensmile.Smile: The openSMILE feature extractor.

\n
\n", "signature": "(cls, feature_set: str, feature_level: str) -> opensmile.core.smile.Smile:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.opensmile.extract_opensmile_features_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.opensmile.extract_opensmile_features_from_audios", "modulename": "senselab.audio.tasks.features_extraction.opensmile", "qualname": "extract_opensmile_features_from_audios", "kind": "function", "doc": "

Apply feature extraction across a list of audio files.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to extract features from.
    • \n
  • feature_set (str): The openSMILE feature set (default is \"eGeMAPSv02\").
    • \n
  • feature_level (str): The openSMILE feature level (default is \"Functionals\").
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, Any]]: The list of feature dictionaries for each audio.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tfeature_set: str = 'eGeMAPSv02',\tfeature_level: str = 'Functionals') -> List[Dict[str, Any]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.opensmile.extract_opensmile_features_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.opensmile.extract_opensmile_features_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.opensmile", "qualname": "extract_opensmile_features_from_audios_pt", "kind": "function", "doc": "

Apply feature extraction across a list of audio files.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to extract features from.
    • \n
  • feature_set (str): The openSMILE feature set (default is \"eGeMAPSv02\").
    • \n
  • feature_level (str): The openSMILE feature level (default is \"Functionals\").
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, Any]]: The list of feature dictionaries for each audio.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tfeature_set: str = 'eGeMAPSv02',\tfeature_level: str = 'Functionals') -> List[Dict[str, Any]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "kind": "module", "doc": "

This module contains functions that extract features from audio files using the PRAAT library.

\n"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_durations": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_durations", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_durations", "kind": "function", "doc": "

Returns the duration of the Audio objects.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio]) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_f0_descriptors": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_f0_descriptors", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_f0_descriptors", "kind": "function", "doc": "

Returns the fundamental frequency descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float,\tunit: str = 'Hertz') -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_harmonicity_descriptors": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_harmonicity_descriptors", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_harmonicity_descriptors", "kind": "function", "doc": "

Returns the harmonicity descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_jitter_descriptors": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_jitter_descriptors", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_jitter_descriptors", "kind": "function", "doc": "

Returns the jitter descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_shimmer_descriptors": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_shimmer_descriptors", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_shimmer_descriptors", "kind": "function", "doc": "

Returns the shimmer descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_durations_pt": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_durations_pt", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_durations_pt", "kind": "function", "doc": "

Returns the duration of the Audio objects.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio]) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_f0_descriptors_pt": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_f0_descriptors_pt", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_f0_descriptors_pt", "kind": "function", "doc": "

Returns the fundamental frequency descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float,\tunit: str = 'Hertz') -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_harmonicity_descriptors_pt": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_harmonicity_descriptors_pt", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_harmonicity_descriptors_pt", "kind": "function", "doc": "

Returns the harmonicity descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_jitter_descriptors_pt": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_jitter_descriptors_pt", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_jitter_descriptors_pt", "kind": "function", "doc": "

Returns the jitter descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_shimmer_descriptors_pt": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_shimmer_descriptors_pt", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_shimmer_descriptors_pt", "kind": "function", "doc": "

Returns the shimmer descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "kind": "module", "doc": "

This module provides the implementation of torchaudio utilities for audio features extraction.

\n"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_spectrogram_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_spectrogram_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_spectrogram_from_audios", "kind": "function", "doc": "

Extract spectrograms from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing spectrograms.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_fft: int = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_spectrogram_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_spectrogram_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mel_spectrogram_from_audios", "kind": "function", "doc": "

Extract mel spectrograms from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing mel spectrograms.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_fft: Optional[int] = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None,\tn_mels: int = 128) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mfcc_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mfcc_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mfcc_from_audios", "kind": "function", "doc": "

Extract MFCCs from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_mfcc (int): Number of MFCCs to return. Default is 40.
    • \n
  • n_ftt (int): Size of FFT, creates n_ftt // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_ftt.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing MFCCs.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_mfcc: int = 40,\tn_ftt: Optional[int] = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None,\tn_mels: int = 128) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_filter_bank_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_filter_bank_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mel_filter_bank_from_audios", "kind": "function", "doc": "

Extract mel filter bank from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
  • n_stft (int): Number of bins in STFT. Default is 201.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing mel filter banks.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_mels: int = 128,\tn_stft: int = 201,\tn_fft: int = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_pitch_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_pitch_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_pitch_from_audios", "kind": "function", "doc": "

Extract pitch from a list of audio objects.

\n\n

Pitch is detected using the detect_pitch_frequency function from torchaudio.\nIt is implemented using normalized cross-correlation function and median smoothing.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • freq_low (int): Lowest frequency that can be detected (Hz). Default is 85.
    • \n
  • freq_high (int): Highest frequency that can be detected (Hz). Default is 3400.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing pitches.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tfreq_low: int = 85,\tfreq_high: int = 3400) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_spectrogram_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_spectrogram_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_spectrogram_from_audios_pt", "kind": "function", "doc": "

Extract spectrograms from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing spectrograms.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_fft: int = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_spectrogram_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_spectrogram_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mel_spectrogram_from_audios_pt", "kind": "function", "doc": "

Extract mel spectrograms from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing mel spectrograms.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_fft: Optional[int] = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None,\tn_mels: int = 128) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mfcc_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mfcc_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mfcc_from_audios_pt", "kind": "function", "doc": "

Extract MFCCs from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_mfcc (int): Number of MFCCs to return. Default is 40.
    • \n
  • n_ftt (int): Size of FFT, creates n_ftt // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_ftt.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing MFCCs.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_mfcc: int = 40,\tn_ftt: Optional[int] = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None,\tn_mels: int = 128) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_filter_bank_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_filter_bank_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mel_filter_bank_from_audios_pt", "kind": "function", "doc": "

Extract mel filter bank from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
  • n_stft (int): Number of bins in STFT. Default is 201.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing mel filter banks.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_mels: int = 128,\tn_stft: int = 201,\tn_fft: int = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_pitch_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_pitch_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_pitch_from_audios_pt", "kind": "function", "doc": "

Extract pitch from a list of audio objects.

\n\n

Pitch is detected using the detect_pitch_frequency function from torchaudio.\nIt is implemented using normalized cross-correlation function and median smoothing.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • freq_low (int): Lowest frequency that can be detected (Hz). Default is 85.
    • \n
  • freq_high (int): Highest frequency that can be detected (Hz). Default is 3400.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing pitches.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tfreq_low: int = 85,\tfreq_high: int = 3400) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio_squim": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "kind": "module", "doc": "

This module provides the implementation of torchaudio squim utilities for audio features extraction.

\n"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.objective_model": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.objective_model", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "objective_model", "kind": "variable", "doc": "

\n", "default_value": "SquimObjective(\n (encoder): Encoder(\n (conv1d): Conv1d(1, 256, kernel_size=(64,), stride=(32,), bias=False)\n )\n (dprnn): DPRNN(\n (row_rnn): ModuleList(\n (0-1): 2 x SingleRNN(\n (rnn): LSTM(256, 256, batch_first=True, bidirectional=True)\n (proj): Linear(in_features=512, out_features=256, bias=True)\n )\n )\n (col_rnn): ModuleList(\n (0-1): 2 x SingleRNN(\n (rnn): LSTM(256, 256, batch_first=True, bidirectional=True)\n (proj): Linear(in_features=512, out_features=256, bias=True)\n )\n )\n (row_norm): ModuleList(\n (0-1): 2 x GroupNorm(1, 256, eps=1e-08, affine=True)\n )\n (col_norm): ModuleList(\n (0-1): 2 x GroupNorm(1, 256, eps=1e-08, affine=True)\n )\n (conv): Sequential(\n (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n (1): PReLU(num_parameters=1)\n )\n )\n (branches): ModuleList(\n (0-1): 2 x Sequential(\n (0): TransformerEncoderLayer(\n (self_attn): MultiheadAttention(\n (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n )\n (linear1): Linear(in_features=256, out_features=1024, bias=True)\n (dropout): Dropout(p=0.0, inplace=False)\n (linear2): Linear(in_features=1024, out_features=256, bias=True)\n (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n (dropout1): Dropout(p=0.0, inplace=False)\n (dropout2): Dropout(p=0.0, inplace=False)\n )\n (1): AutoPool(\n (softmax): Softmax(dim=1)\n )\n (2): Sequential(\n (0): Linear(in_features=256, out_features=256, bias=True)\n (1): PReLU(num_parameters=1)\n (2): Linear(in_features=256, out_features=1, bias=True)\n (3): RangeSigmoid(\n (sigmoid): Sigmoid()\n )\n )\n )\n (2): Sequential(\n (0): TransformerEncoderLayer(\n (self_attn): MultiheadAttention(\n (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n )\n (linear1): Linear(in_features=256, out_features=1024, bias=True)\n (dropout): Dropout(p=0.0, inplace=False)\n (linear2): Linear(in_features=1024, out_features=256, bias=True)\n (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n (dropout1): Dropout(p=0.0, inplace=False)\n (dropout2): Dropout(p=0.0, inplace=False)\n )\n (1): AutoPool(\n (softmax): Softmax(dim=1)\n )\n (2): Sequential(\n (0): Linear(in_features=256, out_features=256, bias=True)\n (1): PReLU(num_parameters=1)\n (2): Linear(in_features=256, out_features=1, bias=True)\n )\n )\n )\n)"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.subjective_model": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.subjective_model", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "subjective_model", "kind": "variable", "doc": "

\n", "default_value": "SquimSubjective(\n (ssl_model): Wav2Vec2Model(\n (feature_extractor): FeatureExtractor(\n (conv_layers): ModuleList(\n (0): ConvLayerBlock(\n (layer_norm): GroupNorm(512, 512, eps=1e-05, affine=True)\n (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False)\n )\n (1-4): 4 x ConvLayerBlock(\n (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False)\n )\n (5-6): 2 x ConvLayerBlock(\n (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False)\n )\n )\n )\n (encoder): Encoder(\n (feature_projection): FeatureProjection(\n (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n (projection): Linear(in_features=512, out_features=768, bias=True)\n (dropout): Dropout(p=0.1, inplace=False)\n )\n (transformer): Transformer(\n (pos_conv_embed): ConvolutionalPositionalEmbedding(\n (conv): ParametrizedConv1d(\n 768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16\n (parametrizations): ModuleDict(\n (weight): ParametrizationList(\n (0): _WeightNorm()\n )\n )\n )\n )\n (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n (dropout): Dropout(p=0.1, inplace=False)\n (layers): ModuleList(\n (0-11): 12 x EncoderLayer(\n (attention): SelfAttention(\n (k_proj): Linear(in_features=768, out_features=768, bias=True)\n (v_proj): Linear(in_features=768, out_features=768, bias=True)\n (q_proj): Linear(in_features=768, out_features=768, bias=True)\n (out_proj): Linear(in_features=768, out_features=768, bias=True)\n )\n (dropout): Dropout(p=0.1, inplace=False)\n (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n (feed_forward): FeedForward(\n (intermediate_dense): Linear(in_features=768, out_features=3072, bias=True)\n (intermediate_dropout): Dropout(p=0.1, inplace=False)\n (output_dense): Linear(in_features=3072, out_features=768, bias=True)\n (output_dropout): Dropout(p=0.1, inplace=False)\n )\n (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n )\n )\n )\n )\n )\n (projector): Linear(in_features=768, out_features=32, bias=True)\n (predictor): Predictor(\n (att_pool_layer): AttPool(\n (linear1): Linear(in_features=64, out_features=1, bias=True)\n (linear2): Linear(in_features=64, out_features=5, bias=True)\n )\n )\n)"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_objective_quality_features_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_objective_quality_features_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "extract_objective_quality_features_from_audios", "kind": "function", "doc": "

Extracts objective audio features from a list of Audio objects.

\n\n

Currently, Torchaudio-Squim model only supports mono audio at 16000 Hz sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audio_list (List[Audio]): List of Audio objects.
    • \n
\n\n
Returns:
\n\n
\n

Dict[str, Any]: Dictionary containing extracted features.

\n
\n", "signature": "(\taudio_list: List[senselab.audio.data_structures.audio.Audio]) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_subjective_quality_features_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_subjective_quality_features_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "extract_subjective_quality_features_from_audios", "kind": "function", "doc": "

Extracts subjective audio features from a list of Audio objects.

\n\n

Currently, Torchaudio-Squim model only supports mono audio at 16000 Hz sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audio_list (List[Audio]): List of Audio objects.
    • \n
  • non_matching_references (List[Audio]): Reference Audio objects for the subjective model.
    • \n
\n\n
Returns:
\n\n
\n

Dict[str, Any]: Dictionary containing extracted features.

\n
\n", "signature": "(\taudio_list: List[senselab.audio.data_structures.audio.Audio],\tnon_matching_references: List[senselab.audio.data_structures.audio.Audio]) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_objective_quality_features_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_objective_quality_features_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "extract_objective_quality_features_from_audios_pt", "kind": "function", "doc": "

Extracts objective audio features from a list of Audio objects.

\n\n

Currently, Torchaudio-Squim model only supports mono audio at 16000 Hz sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audio_list (List[Audio]): List of Audio objects.
    • \n
\n\n
Returns:
\n\n
\n

Dict[str, Any]: Dictionary containing extracted features.

\n
\n", "signature": "(\taudio_list: List[senselab.audio.data_structures.audio.Audio]) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_subjective_quality_features_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_subjective_quality_features_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "extract_subjective_quality_features_from_audios_pt", "kind": "function", "doc": "

Extracts subjective audio features from a list of Audio objects.

\n\n

Currently, Torchaudio-Squim model only supports mono audio at 16000 Hz sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audio_list (List[Audio]): List of Audio objects.
    • \n
  • non_matching_references (List[Audio]): Reference Audio objects for the subjective model.
    • \n
\n\n
Returns:
\n\n
\n

Dict[str, Any]: Dictionary containing extracted features.

\n
\n", "signature": "(\taudio_list: List[senselab.audio.data_structures.audio.Audio],\tnon_matching_references: List[senselab.audio.data_structures.audio.Audio]) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.audio.tasks.forced_alignment": {"fullname": "senselab.audio.tasks.forced_alignment", "modulename": "senselab.audio.tasks.forced_alignment", "kind": "module", "doc": "

This module provides the API for forced alignment.

\n"}, "senselab.audio.tasks.forced_alignment.constants": {"fullname": "senselab.audio.tasks.forced_alignment.constants", "modulename": "senselab.audio.tasks.forced_alignment.constants", "kind": "module", "doc": "

Provides constants for align functionality.

\n"}, "senselab.audio.tasks.forced_alignment.constants.SAMPLE_RATE": {"fullname": "senselab.audio.tasks.forced_alignment.constants.SAMPLE_RATE", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "SAMPLE_RATE", "kind": "variable", "doc": "

\n", "default_value": "16000"}, "senselab.audio.tasks.forced_alignment.constants.MINIMUM_SEGMENT_SIZE": {"fullname": "senselab.audio.tasks.forced_alignment.constants.MINIMUM_SEGMENT_SIZE", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "MINIMUM_SEGMENT_SIZE", "kind": "variable", "doc": "

\n", "default_value": "400"}, "senselab.audio.tasks.forced_alignment.constants.PUNKT_ABBREVIATIONS": {"fullname": "senselab.audio.tasks.forced_alignment.constants.PUNKT_ABBREVIATIONS", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "PUNKT_ABBREVIATIONS", "kind": "variable", "doc": "

\n", "default_value": "['dr', 'vs', 'mr', 'mrs', 'prof']"}, "senselab.audio.tasks.forced_alignment.constants.LANGUAGES_WITHOUT_SPACES": {"fullname": "senselab.audio.tasks.forced_alignment.constants.LANGUAGES_WITHOUT_SPACES", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "LANGUAGES_WITHOUT_SPACES", "kind": "variable", "doc": "

\n", "default_value": "['ja', 'zh']"}, "senselab.audio.tasks.forced_alignment.constants.DEFAULT_ALIGN_MODELS_TORCH": {"fullname": "senselab.audio.tasks.forced_alignment.constants.DEFAULT_ALIGN_MODELS_TORCH", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "DEFAULT_ALIGN_MODELS_TORCH", "kind": "variable", "doc": "

\n", "default_value": "{'fr': TorchAudioModel(path_or_uri='VOXPOPULI_ASR_BASE_10K_FR', revision='main'), 'de': TorchAudioModel(path_or_uri='VOXPOPULI_ASR_BASE_10K_DE', revision='main'), 'es': TorchAudioModel(path_or_uri='VOXPOPULI_ASR_BASE_10K_ES', revision='main'), 'it': TorchAudioModel(path_or_uri='VOXPOPULI_ASR_BASE_10K_IT', revision='main')}"}, "senselab.audio.tasks.forced_alignment.constants.DEFAULT_ALIGN_MODELS_HF": {"fullname": "senselab.audio.tasks.forced_alignment.constants.DEFAULT_ALIGN_MODELS_HF", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "DEFAULT_ALIGN_MODELS_HF", "kind": "variable", "doc": "

\n", "default_value": "{'en': HFModel(path_or_uri='facebook/wav2vec2-base-960h', revision='main', info=None), 'ja': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-japanese', revision='main', info=None), 'zh': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn', revision='main', info=None), 'nl': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-dutch', revision='main', info=None), 'uk': HFModel(path_or_uri='Yehor/wav2vec2-xls-r-300m-uk-with-small-lm', revision='main', info=None), 'pt': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-portuguese', revision='main', info=None), 'ar': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-arabic', revision='main', info=None), 'cs': HFModel(path_or_uri='comodoro/wav2vec2-xls-r-300m-cs-250', revision='main', info=None), 'ru': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-russian', revision='main', info=None), 'pl': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-polish', revision='main', info=None), 'hu': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-hungarian', revision='main', info=None), 'fi': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-finnish', revision='main', info=None), 'fa': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-persian', revision='main', info=None), 'el': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-greek', revision='main', info=None), 'tr': HFModel(path_or_uri='mpoyraz/wav2vec2-xls-r-300m-cv7-turkish', revision='main', info=None), 'da': HFModel(path_or_uri='saattrupdan/wav2vec2-xls-r-300m-ftspeech', revision='main', info=None), 'he': HFModel(path_or_uri='imvladikon/wav2vec2-xls-r-300m-hebrew', revision='main', info=None), 'vi': HFModel(path_or_uri='nguyenvulebinh/wav2vec2-base-vi', revision='main', info=None), 'ko': HFModel(path_or_uri='kresnik/wav2vec2-large-xlsr-korean', revision='main', info=None), 'ur': HFModel(path_or_uri='kingabzpro/wav2vec2-large-xls-r-300m-Urdu', revision='main', info=None), 'te': HFModel(path_or_uri='anuragshas/wav2vec2-large-xlsr-53-telugu', revision='main', info=None), 'hi': HFModel(path_or_uri='theainerd/Wav2Vec2-large-xlsr-hindi', revision='main', info=None), 'ca': HFModel(path_or_uri='softcatala/wav2vec2-large-xlsr-catala', revision='main', info=None), 'ml': HFModel(path_or_uri='gvs/wav2vec2-large-xlsr-malayalam', revision='main', info=None), 'no': HFModel(path_or_uri='NbAiLab/nb-wav2vec2-1b-bokmaal', revision='main', info=None), 'nn': HFModel(path_or_uri='NbAiLab/nb-wav2vec2-300m-nynorsk', revision='main', info=None)}"}, "senselab.audio.tasks.forced_alignment.data_structures": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "kind": "module", "doc": "

Provides data structures for align functionality.

\n"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleWordSegment", "kind": "class", "doc": "

A single word of a speech.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.word": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.word", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleWordSegment.word", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.start": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.start", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleWordSegment.start", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.end": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.end", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleWordSegment.end", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.score": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.score", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleWordSegment.score", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleCharSegment", "kind": "class", "doc": "

A single char of a speech.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.char": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.char", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleCharSegment.char", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.start": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.start", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleCharSegment.start", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.end": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.end", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleCharSegment.end", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.score": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.score", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleCharSegment.score", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment", "kind": "class", "doc": "

A single segment (up to multiple sentences) of a speech.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.start": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.start", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.start", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.end": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.end", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.end", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.text": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.text", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.text", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_char": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_char", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.clean_char", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[str]]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_cdx": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_cdx", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.clean_cdx", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[int]]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_wdx": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_wdx", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.clean_wdx", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[int]]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.sentence_spans": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.sentence_spans", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.sentence_spans", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[Tuple[int, int]]]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment", "kind": "class", "doc": "

A single segment (up to multiple sentences) of a speech with word alignment.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.start": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.start", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment.start", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.end": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.end", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment.end", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.text": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.text", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment.text", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.words": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.words", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment.words", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.chars": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.chars", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment.chars", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment]]"}, "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "TranscriptionResult", "kind": "class", "doc": "

A list of segments and word segments of a speech.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult.segments": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult.segments", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "TranscriptionResult.segments", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.audio.tasks.forced_alignment.data_structures.SingleSegment]"}, "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult.language": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult.language", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "TranscriptionResult.language", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "AlignedTranscriptionResult", "kind": "class", "doc": "

A list of segments and word segments of a speech.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult.segments": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult.segments", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "AlignedTranscriptionResult.segments", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment]"}, "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult.word_segments": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult.word_segments", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "AlignedTranscriptionResult.word_segments", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment]"}, "senselab.audio.tasks.forced_alignment.data_structures.Point": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Point", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Point", "kind": "class", "doc": "

Represents a point in the alignment path.

\n\n
Attributes:
\n\n
    \n
  • token_index (int): The index of the token in the transcript.
    • \n
  • time_index (int): The index of the time frame in the audio.
    • \n
  • score (float): The alignment score for this point.
    • \n
\n"}, "senselab.audio.tasks.forced_alignment.data_structures.Point.__init__": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Point.__init__", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Point.__init__", "kind": "function", "doc": "

\n", "signature": "(token_index: int, time_index: int, score: float)"}, "senselab.audio.tasks.forced_alignment.data_structures.Point.token_index": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Point.token_index", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Point.token_index", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "senselab.audio.tasks.forced_alignment.data_structures.Point.time_index": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Point.time_index", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Point.time_index", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "senselab.audio.tasks.forced_alignment.data_structures.Point.score": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Point.score", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Point.score", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment", "kind": "class", "doc": "

Represents a segment of aligned text.

\n\n
Attributes:
\n\n
    \n
  • label (str): The text label of the segment.
    • \n
  • start (int): The start time index of the segment.
    • \n
  • end (int): The end time index of the segment.
    • \n
  • score (float): The alignment score for the segment.
    • \n
\n"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.__init__": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.__init__", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.__init__", "kind": "function", "doc": "

\n", "signature": "(label: str, start: int, end: int, score: float)"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.label": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.label", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.label", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.start": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.start", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.start", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.end": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.end", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.end", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.score": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.score", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.score", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.length": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.length", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.length", "kind": "variable", "doc": "

Calculates the length of the segment.

\n", "annotation": ": int"}, "senselab.audio.tasks.forced_alignment.forced_alignment": {"fullname": "senselab.audio.tasks.forced_alignment.forced_alignment", "modulename": "senselab.audio.tasks.forced_alignment.forced_alignment", "kind": "module", "doc": "

Align function based on WhisperX implementation.

\n"}, "senselab.audio.tasks.forced_alignment.forced_alignment.align_transcriptions": {"fullname": "senselab.audio.tasks.forced_alignment.forced_alignment.align_transcriptions", "modulename": "senselab.audio.tasks.forced_alignment.forced_alignment", "qualname": "align_transcriptions", "kind": "function", "doc": "

Aligns transcriptions with the given audio using a wav2vec2.0 model.

\n\n
Arguments:
\n\n
    \n
  • audios_and_transcriptions_and_language (List[tuple[Audio, ScriptLine, Language]): A list of tuples audios, corresponding transcriptions, and optionally a language.\nThe default language is English.
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: A list of lists, where each inner list contains the aligned script lines for each audio.

\n
\n", "signature": "(\taudios_and_transcriptions_and_language: List[Tuple[senselab.audio.data_structures.audio.Audio, senselab.utils.data_structures.script_line.ScriptLine, senselab.utils.data_structures.language.Language]]) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing": {"fullname": "senselab.audio.tasks.preprocessing", "modulename": "senselab.audio.tasks.preprocessing", "kind": "module", "doc": "

This module provides the API for the senselab audio preprocessing.

\n"}, "senselab.audio.tasks.preprocessing.preprocessing": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "kind": "module", "doc": "

This module implements some utilities for the preprocessing task.

\n"}, "senselab.audio.tasks.preprocessing.preprocessing.resample_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.resample_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "resample_audios", "kind": "function", "doc": "

Resamples a list of audio signals to a given sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of audio objects to resample.
    • \n
  • resample_rate (int): Target sampling rate.
    • \n
  • lowcut (float, optional): Low cut frequency for IIR filter.
    • \n
  • order (int, optional): Order of the IIR filter. Defaults to 4.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: Resampled audio objects.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tresample_rate: int,\tlowcut: Optional[float] = None,\torder: int = 4) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.downmix_audios_to_mono": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.downmix_audios_to_mono", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "downmix_audios_to_mono", "kind": "function", "doc": "

Downmixes a list of Audio objects to mono by averaging all channels.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects with a tensor representing the audio waveform.\nShape: (num_channels, num_samples).
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of audio objects with a mono waveform averaged from all channels. Shape: (num_samples).

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio]) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.select_channel_from_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.select_channel_from_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "select_channel_from_audios", "kind": "function", "doc": "

Selects a specific channel from a list of Audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects with a tensor representing the audio waveform.\nShape: (num_channels, num_samples).
    • \n
  • channel_index (int): The index of the channel to select.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of audio objects with the selected channel. Shape: (1, num_samples).

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tchannel_index: int) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.chunk_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.chunk_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "chunk_audios", "kind": "function", "doc": "

Chunks the input audios based on the start and end timestamp.

\n\n
Arguments:
\n\n
    \n
  • data: List of tuples containing an Audio object and a tuple with start and end (in seconds) for chunking.
    • \n
\n\n
Returns:
\n\n
\n

List of Audios that have been chunked based on the provided timestamps

\n
\n\n
Todo:
\n\n
\n

Do we really need both chunk_audios and extract_segments?

\n
\n", "signature": "(\tdata: List[Tuple[senselab.audio.data_structures.audio.Audio, Tuple[float, float]]]) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.extract_segments": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.extract_segments", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "extract_segments", "kind": "function", "doc": "

Extracts segments from an audio file.

\n\n
Arguments:
\n\n
    \n
  • data: List of tuples containing an Audio object and a list of tuples with start\nand end (in seconds) for chunking.
    • \n
\n\n
Returns:
\n\n
\n

List of lists of Audios that have been chunked based on the provided timestamps.

\n
\n", "signature": "(\tdata: List[Tuple[senselab.audio.data_structures.audio.Audio, List[Tuple[float, float]]]]) -> List[List[senselab.audio.data_structures.audio.Audio]]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.pad_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.pad_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "pad_audios", "kind": "function", "doc": "

Pads the audio segment to the desired length.

\n\n
Arguments:
\n\n
    \n
  • audios: The list of audio objects to be padded.
    • \n
  • desired_samples: The desired length (in samples) for the padded audio.
    • \n
\n\n
Returns:
\n\n
\n

A new Audio object with the waveform padded to the desired length.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tdesired_samples: int) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.evenly_segment_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.evenly_segment_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "evenly_segment_audios", "kind": "function", "doc": "

Segments multiple audio files into evenly sized segments with optional padding for the last segment.

\n\n
Arguments:
\n\n
    \n
  • audios: The list of Audio objects to be segmented.
    • \n
  • segment_length: The desired length of each segment in seconds.
    • \n
  • pad_last_segment: Whether to pad the last segment to the full segment length (default is False).
    • \n
\n\n
Returns:
\n\n
\n

List of Audio objects that have been segmented.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tsegment_length: float,\tpad_last_segment: bool = True) -> List[List[senselab.audio.data_structures.audio.Audio]]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.concatenate_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.concatenate_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "concatenate_audios", "kind": "function", "doc": "

Concatenates all audios in the list, ensuring they have the same sampling rate and shape.

\n\n
Arguments:
\n\n
    \n
  • audios: List of Audio objects to concatenate.
    • \n
\n\n
Returns:
\n\n
\n

A single Audio object that is the concatenation of all input audios.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the audios do not all have the same sampling rate or shape.
    • \n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio]) -> senselab.audio.data_structures.audio.Audio:", "funcdef": "def"}, "senselab.audio.tasks.speaker_diarization": {"fullname": "senselab.audio.tasks.speaker_diarization", "modulename": "senselab.audio.tasks.speaker_diarization", "kind": "module", "doc": "

Speaker diarization

\n\n

\"Tutorial\"

\n\n

Task Overview

\n\n

Speaker diarization is the process of segmenting audio recordings by speaker labels, aiming to answer the question: \"Who spoke when?\"

\n\n

Models

\n\n

In senselab, we integrate pyannote.audio models for speaker diarization. These models can be explored on the Hugging Face Hub. We may integrate additional approaches for speaker diarization into the package in the future.

\n\n

Evaluation

\n\n

Metrics

\n\n

The Diarization Error Rate (DER) is the standard metric for evaluating and comparing speaker diarization systems. It is defined as:

\n\n
\n
DER= (false alarm + missed detection + confusion) / total\n
\n
\n\n

where

\n\n
    \n
  • false alarm is the duration of non-speech incorrectly classified as speech, missed detection
    • \n
  • missed detection is the duration of speech incorrectly classified as non-speech, confusion
    • \n
  • confusion is the duration of speaker confusion, and total
    • \n
  • total is the sum over all speakers of their reference speech duration.
    • \n
\n\n

Note: DER takes overlapping speech into account. This can lead to increased missed detection rates if the speaker diarization system does not include an overlapping speech detection module.

\n\n

Benchmark

\n\n

You can find a benchmark of the latest pyannote.audio model's performance on various time-stamped speech datasets here.

\n"}, "senselab.audio.tasks.speaker_diarization.api": {"fullname": "senselab.audio.tasks.speaker_diarization.api", "modulename": "senselab.audio.tasks.speaker_diarization.api", "kind": "module", "doc": "

This module implements some utilities for the speaker diarization task.

\n\n

TODO: add computing DER and more evaluation metrics

\n"}, "senselab.audio.tasks.speaker_diarization.api.diarize_audios": {"fullname": "senselab.audio.tasks.speaker_diarization.api.diarize_audios", "modulename": "senselab.audio.tasks.speaker_diarization.api", "qualname": "diarize_audios", "kind": "function", "doc": "

Diarizes all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be diarized.
    • \n
  • model (SenselabModel): The model used for diarization\n(default is \"pyannote/speaker-diarization-3.1\").
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
  • num_speakers (Optional[int]): The number of speakers (default is None).
    • \n
  • min_speakers (Optional[int]): The minimum number of speakers (default is None).
    • \n
  • max_speakers (Optional[int]): The maximum number of speakers (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: The list of script lines with speaker labels.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = PyannoteAudioModel(path_or_uri='pyannote/speaker-diarization-3.1', revision='main', info=None),\tnum_speakers: Optional[int] = None,\tmin_speakers: Optional[int] = None,\tmax_speakers: Optional[int] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.speaker_diarization.api.diarize_audios_pt": {"fullname": "senselab.audio.tasks.speaker_diarization.api.diarize_audios_pt", "modulename": "senselab.audio.tasks.speaker_diarization.api", "qualname": "diarize_audios_pt", "kind": "function", "doc": "

Diarizes all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be diarized.
    • \n
  • model (SenselabModel): The model used for diarization\n(default is \"pyannote/speaker-diarization-3.1\").
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
  • num_speakers (Optional[int]): The number of speakers (default is None).
    • \n
  • min_speakers (Optional[int]): The minimum number of speakers (default is None).
    • \n
  • max_speakers (Optional[int]): The maximum number of speakers (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: The list of script lines with speaker labels.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = PyannoteAudioModel(path_or_uri='pyannote/speaker-diarization-3.1', revision='main', info=None),\tnum_speakers: Optional[int] = None,\tmin_speakers: Optional[int] = None,\tmax_speakers: Optional[int] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.speaker_diarization.pyannote": {"fullname": "senselab.audio.tasks.speaker_diarization.pyannote", "modulename": "senselab.audio.tasks.speaker_diarization.pyannote", "kind": "module", "doc": "

This module implements the Pyannote Diarization task.

\n"}, "senselab.audio.tasks.speaker_diarization.pyannote.PyannoteDiarization": {"fullname": "senselab.audio.tasks.speaker_diarization.pyannote.PyannoteDiarization", "modulename": "senselab.audio.tasks.speaker_diarization.pyannote", "qualname": "PyannoteDiarization", "kind": "class", "doc": "

A factory for managing Pyannote Diarization pipelines.

\n"}, "senselab.audio.tasks.speaker_diarization.pyannote.diarize_audios_with_pyannote": {"fullname": "senselab.audio.tasks.speaker_diarization.pyannote.diarize_audios_with_pyannote", "modulename": "senselab.audio.tasks.speaker_diarization.pyannote", "qualname": "diarize_audios_with_pyannote", "kind": "function", "doc": "

Diarizes a list of audio files using the Pyannote speaker diarization model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of audio files.
    • \n
  • model (PyannoteAudioModel): The model to use for diarization.
    • \n
  • device (Optional[DeviceType]): The device to use for diarization.
    • \n
  • num_speakers (Optional[int]): Number of speakers, when known.
    • \n
  • min_speakers (Optional[int]): Minimum number of speakers. Has no effect when num_speakers is provided.
    • \n
  • max_speakers (Optional[int]): Maximum number of speakers. Has no effect when num_speakers is provided.
    • \n
\n\n
Returns:
\n\n
\n

List[ScriptLine]: A list of ScriptLine objects containing the diarization results.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.PyannoteAudioModel = PyannoteAudioModel(path_or_uri='pyannote/speaker-diarization-3.1', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tnum_speakers: Optional[int] = None,\tmin_speakers: Optional[int] = None,\tmax_speakers: Optional[int] = None) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.speaker_diarization_evaluation": {"fullname": "senselab.audio.tasks.speaker_diarization_evaluation", "modulename": "senselab.audio.tasks.speaker_diarization_evaluation", "kind": "module", "doc": "

Top level file exposing speaker diarization utility functions.

\n"}, "senselab.audio.tasks.speaker_diarization_evaluation.utils": {"fullname": "senselab.audio.tasks.speaker_diarization_evaluation.utils", "modulename": "senselab.audio.tasks.speaker_diarization_evaluation.utils", "kind": "module", "doc": "

Defines utility functions for evaluating speaker diarization results.

\n"}, "senselab.audio.tasks.speaker_diarization_evaluation.utils.calculate_diarization_error_rate": {"fullname": "senselab.audio.tasks.speaker_diarization_evaluation.utils.calculate_diarization_error_rate", "modulename": "senselab.audio.tasks.speaker_diarization_evaluation.utils", "qualname": "calculate_diarization_error_rate", "kind": "function", "doc": "

Computes the diarization error rate (DER).

\n\n

Diarizztion error rate is the ratio of the sum of the false alarms (when speech is detected but none is there),\nmissed detections (when speech is there but not detected), and speaker confusions (when speech is\nattributed to the wrong speaker) to the total ground truth time spoken. For more details see:\nhttps://docs.kolena.com/metrics/diarization-error-rate/

\n\n
Arguments:
\n\n
    \n
  • hypothesis (List[ScriptLine]): the diarization generated as the result from a model
    • \n
  • reference (List[ScriptLine]): annotations that serve as the ground truth diarization
    • \n
  • greedy (bool): whether to use a greedy speaker mapping vs. one that optimizes for minimizing the confusion
    • \n
  • return_speaker_mapping (bool): return the mapping between speakers in the reference and the hypothesis
    • \n
  • detailed (bool): whether to include each component that contributed to the overall diarization error rate
    • \n
\n\n
Returns:
\n\n
\n

A dictionary with at least the diarization error rate, its components if detailed was true, and the\n speaker mapping if return_speaker_mapping was given.

\n
\n", "signature": "(\thypothesis: List[senselab.utils.data_structures.script_line.ScriptLine],\treference: List[senselab.utils.data_structures.script_line.ScriptLine],\tgreedy: bool = False,\treturn_speaker_mapping: bool = False,\tdetailed: bool = False) -> Dict:", "funcdef": "def"}, "senselab.audio.tasks.speaker_embeddings": {"fullname": "senselab.audio.tasks.speaker_embeddings", "modulename": "senselab.audio.tasks.speaker_embeddings", "kind": "module", "doc": "

Speech to text evaluation

\n\n

\n\n

Overview

\n\n

Speaker embeddings are fixed-dimensional vector representations that capture the unique characteristics of a speaker's\nvoice, allowing for tasks such as speaker identification, verification, and diarization.

\n\n

Speaker embedding extraction is a crucial task in speaker recognition systems. It involves transforming variable-length\naudio signals into fixed-size vector representations that encapsulate speaker-specific information while being robust\nto variations in speech content, background noise, and recording conditions.

\n\n

Model Architecture:

\n\n

The default model used in this module (speechbrain/spkrec-ecapa-voxceleb) is based on the ECAPA-TDNN architecture,\nwhich has shown strong performance across various speaker recognition tasks.\nOther supported models include ResNet TDNN (speechbrain/spkrec-resnet-voxceleb) and\nxvector (speechbrain/spkrec-xvect-voxceleb).

\n\n

Note: Performance can vary significantly depending on the specific dataset, task, and evaluation protocol used.\nAlways refer to the most recent literature for up-to-date benchmarks.

\n\n

Learn more:

\n\n\n"}, "senselab.audio.tasks.speaker_embeddings.api": {"fullname": "senselab.audio.tasks.speaker_embeddings.api", "modulename": "senselab.audio.tasks.speaker_embeddings.api", "kind": "module", "doc": "

This module implements some utilities to extract speaker embeddings from an audio clip.

\n"}, "senselab.audio.tasks.speaker_embeddings.api.extract_speaker_embeddings_from_audios": {"fullname": "senselab.audio.tasks.speaker_embeddings.api.extract_speaker_embeddings_from_audios", "modulename": "senselab.audio.tasks.speaker_embeddings.api", "qualname": "extract_speaker_embeddings_from_audios", "kind": "function", "doc": "

Compute the speaker embedding of audio signals.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects containing the audio signals and their properties.
    • \n
  • model (SpeechBrainModel): The model used to compute the embeddings\n(default is \"speechbrain/spkrec-ecapa-voxceleb\").
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of 1d tensors containing the speaker embeddings for each audio file.

\n
\n\n
Raises:
\n\n
    \n
  • NotImplementedError: If the model is not a Hugging Face model.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> audios = [Audio.from_filepath("sample.wav")]\n>>> model = SpeechBrainModel(path_or_uri="speechbrain/spkrec-ecapa-voxceleb", revision="main")\n>>> embeddings = extract_speaker_embeddings_from_audios(audios, model, device=DeviceType.CUDA)\n>>> print(embeddings[0].shape)\ntorch.Size([192])\n
\n
\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = SpeechBrainModel(path_or_uri='speechbrain/spkrec-ecapa-voxceleb', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.audio.tasks.speaker_embeddings.speechbrain": {"fullname": "senselab.audio.tasks.speaker_embeddings.speechbrain", "modulename": "senselab.audio.tasks.speaker_embeddings.speechbrain", "kind": "module", "doc": "

Provides a factory for extracting speaker embeddings from a list of audios using speechbrain.

\n"}, "senselab.audio.tasks.speaker_embeddings.speechbrain.SpeechBrainEmbeddings": {"fullname": "senselab.audio.tasks.speaker_embeddings.speechbrain.SpeechBrainEmbeddings", "modulename": "senselab.audio.tasks.speaker_embeddings.speechbrain", "qualname": "SpeechBrainEmbeddings", "kind": "class", "doc": "

A factory for extracting speaker embeddings using speechbrain models.

\n"}, "senselab.audio.tasks.speaker_embeddings.speechbrain.SpeechBrainEmbeddings.extract_speechbrain_speaker_embeddings_from_audios": {"fullname": "senselab.audio.tasks.speaker_embeddings.speechbrain.SpeechBrainEmbeddings.extract_speechbrain_speaker_embeddings_from_audios", "modulename": "senselab.audio.tasks.speaker_embeddings.speechbrain", "qualname": "SpeechBrainEmbeddings.extract_speechbrain_speaker_embeddings_from_audios", "kind": "function", "doc": "

Compute the speaker embeddings of audio signals.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects containing the audio signals and their properties.
    • \n
  • model (SpeechBrainModel): The model used to compute the embeddings\n(default is \"speechbrain/spkrec-ecapa-voxceleb\").
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).\nOnly CPU and CUDA are supported.
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of tensors containing the speaker embeddings for each audio file.

\n
\n\n
Todo:
\n\n
\n
    \n
  • Optimizing the computation by working in batches
    • \n
  • Double-checking the input size of classifier.encode_batch
    • \n
\n
\n", "signature": "(\tcls,\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SpeechBrainModel = SpeechBrainModel(path_or_uri='speechbrain/spkrec-ecapa-voxceleb', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.audio.tasks.speaker_verification": {"fullname": "senselab.audio.tasks.speaker_verification", "modulename": "senselab.audio.tasks.speaker_verification", "kind": "module", "doc": "

Verifies whether two audio segments belong to the same speaker.

\n"}, "senselab.audio.tasks.speaker_verification.speaker_verification": {"fullname": "senselab.audio.tasks.speaker_verification.speaker_verification", "modulename": "senselab.audio.tasks.speaker_verification.speaker_verification", "kind": "module", "doc": "

Audio Processing and Speaker Verification Module.

\n\n

This module provides functions for resampling audio using an IIR filter and\nverifying if two audio samples or files are from the same speaker using a\nspecified model.

\n"}, "senselab.audio.tasks.speaker_verification.speaker_verification.TRAINING_SAMPLE_RATE": {"fullname": "senselab.audio.tasks.speaker_verification.speaker_verification.TRAINING_SAMPLE_RATE", "modulename": "senselab.audio.tasks.speaker_verification.speaker_verification", "qualname": "TRAINING_SAMPLE_RATE", "kind": "variable", "doc": "

\n", "default_value": "16000"}, "senselab.audio.tasks.speaker_verification.speaker_verification.verify_speaker": {"fullname": "senselab.audio.tasks.speaker_verification.speaker_verification.verify_speaker", "modulename": "senselab.audio.tasks.speaker_verification.speaker_verification", "qualname": "verify_speaker", "kind": "function", "doc": "

Verifies if two audio samples are from the same speaker.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Tuple[Audio, Audio]]): A list of tuples, where each tuple contains\ntwo audio samples to be compared.
    • \n
  • model (SpeechBrainModel, optional): The model for speaker verification.
    • \n
  • device (DeviceType, optional): The device to run the model on. Defaults to CPU.
    • \n
  • threshold (float, optional): The threshold to determine same speaker.
    • \n
\n\n
Returns:
\n\n
\n

List[Tuple[float, bool]]: A list of tuples containing the verification score and\n the prediction for each pair of audio samples. The\n verification score is a float indicating the similarity\n between the two samples, and the prediction is a boolean\n indicating if the two samples are from the same speaker.

\n
\n", "signature": "(\taudios: List[Tuple[senselab.audio.data_structures.audio.Audio, senselab.audio.data_structures.audio.Audio]],\tmodel: senselab.utils.data_structures.model.SpeechBrainModel = SpeechBrainModel(path_or_uri='speechbrain/spkrec-ecapa-voxceleb', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tthreshold: float = 0.25) -> List[Tuple[float, bool]]:", "funcdef": "def"}, "senselab.audio.tasks.speech_enhancement": {"fullname": "senselab.audio.tasks.speech_enhancement", "modulename": "senselab.audio.tasks.speech_enhancement", "kind": "module", "doc": "

Speech enhancement

\n\n

\n\n

Task Overview

\n\n

Speech Enhancement is a signal processing task that involves improving the quality of speech signals captured under noisy or degraded conditions. The goal of speech enhancement is to make speech signals clearer, more intelligible, and more pleasant to listen to, which can be used for various applications such as voice recognition, teleconferencing, and hearing aids.

\n\n

Models

\n\n

By now, senselab supports all speechbrain models for speech enhancement. These include:

\n\n
    \n
  • SepFormer for audio clips sampled at 16KHz
    • \n
  • SepFormer for audio clips sampled at 8KHz.\nIn the future, more models will be integrated.
    • \n
\n\n

Evaluation

\n\n

Metrics

\n\n

Objective evaluation involves comparing speech enhanced outputs across different downstream tasks:

\n\n
    \n
  • Using an automatic speaker verification tool to determine if the original speaker and the enhanced speaker can be distinguished from each other.
    • \n
  • Ensuring the intelligibility of speech content using an automatic speech recognition system to verify that the content remains unchanged.
    • \n
  • Assessing the preservation of the original speech's emotion after speech enhancement.
    • \n
  • ...more...
    • \n
\n"}, "senselab.audio.tasks.speech_enhancement.api": {"fullname": "senselab.audio.tasks.speech_enhancement.api", "modulename": "senselab.audio.tasks.speech_enhancement.api", "kind": "module", "doc": "

This module provides the API for the senselab speech enhancement task.

\n"}, "senselab.audio.tasks.speech_enhancement.api.enhance_audios": {"fullname": "senselab.audio.tasks.speech_enhancement.api.enhance_audios", "modulename": "senselab.audio.tasks.speech_enhancement.api", "qualname": "enhance_audios", "kind": "function", "doc": "

Enhances all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be enhanced.
    • \n
  • model (SenselabModel): The model used for enhancement\n(default is \"speechbrain/sepformer-wham16k-enhancement\").
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of enhanced audio objects.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = SpeechBrainModel(path_or_uri='speechbrain/sepformer-wham16k-enhancement', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.speech_enhancement.speechbrain": {"fullname": "senselab.audio.tasks.speech_enhancement.speechbrain", "modulename": "senselab.audio.tasks.speech_enhancement.speechbrain", "kind": "module", "doc": "

This module provides the Speechbrain interface for speech enhancement.

\n"}, "senselab.audio.tasks.speech_enhancement.speechbrain.SpeechBrainEnhancer": {"fullname": "senselab.audio.tasks.speech_enhancement.speechbrain.SpeechBrainEnhancer", "modulename": "senselab.audio.tasks.speech_enhancement.speechbrain", "qualname": "SpeechBrainEnhancer", "kind": "class", "doc": "

A factory for managing SpeechBrain enhancement pipelines.

\n"}, "senselab.audio.tasks.speech_enhancement.speechbrain.SpeechBrainEnhancer.enhance_audios_with_speechbrain": {"fullname": "senselab.audio.tasks.speech_enhancement.speechbrain.SpeechBrainEnhancer.enhance_audios_with_speechbrain", "modulename": "senselab.audio.tasks.speech_enhancement.speechbrain", "qualname": "SpeechBrainEnhancer.enhance_audios_with_speechbrain", "kind": "function", "doc": "

Enhances all audio samples using the given speechbrain model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be enhanced.
    • \n
  • model (SpeechBrainModel): The SpeechBrain model used for enhancement.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
  • batch_size (int): The size of batches to use when processing on a GPU.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of enhanced audio objects.

\n
\n", "signature": "(\tcls,\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SpeechBrainModel = SpeechBrainModel(path_or_uri='speechbrain/sepformer-wham16k-enhancement', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tbatch_size: int = 16) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text": {"fullname": "senselab.audio.tasks.speech_to_text", "modulename": "senselab.audio.tasks.speech_to_text", "kind": "module", "doc": "

Speech to text

\n\n

\n\n

Task Overview

\n\n

Speech-to-Text (STT), also known as Automatic Speech Recognition (ASR), is the process of converting spoken language into written text. This technology has a wide range of applications, including transcription services and voice user interfaces.

\n\n

Notably, certain models can provide word- or sentence-level timestamps along with the transcribed text, making them ideal for generating subtitles. Additionally, some models are multilingual, and some of them leverage language identification blocks to enhance performance.

\n\n

Models

\n\n

A variety of models are supported by senselab for ASR tasks. They can be explored on the Hugging Face Hub. Each model varies in performance, size, license, language support, and more. Performance may also vary depending on who is the speaker in the processed audio clips (there may be differences in terms of age, dialects, disfluencies). It is recommended to review the model card for each model before use. Also, always refer to the most recent literature for an informed decision.

\n\n

Popular models include:

\n\n\n\n\n\n

Evaluation

\n\n

Metrics

\n\n

The primary evaluation metric for ASR systems is the Word Error Rate (WER). WER is calculated as:

\n\n
WER = (Substitutions + Insertions + Deletions) / Number of words in the reference\n
\n\n

where:

\n\n
    \n
  • Substitutions: Incorrect words.
    • \n
  • Insertions: Extra words added.
    • \n
  • Deletions: Words omitted.
    • \n
\n\n

Other important metrics include:

\n\n
    \n
  • Character Error Rate (CER)
    • \n
  • Match Error Rate (MER)
    • \n
  • Word Information Lost (WIL)
    • \n
  • Word Information Preserved (WIP)
    • \n
\n\n

For detailed information on these metrics, refer to the speech to text evaluation module.

\n\n

Datasets (English Speech Benchmark - ESB)

\n\n

The following table lists the datasets included in the English Speech Benchmark (ESB), which are generally used for evaluating ASR models in English:

\n\n\n\n\n \n \n \n \n \n \n \n \n\n\n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n
DatasetDomainSpeaking StyleTrain (h)Dev (h)Test (h)TranscriptionsLicense
LibriSpeechAudiobookNarrated9601111NormalizedCC-BY-4.0
Common Voice 9WikipediaNarrated14092727Punctuated & CasedCC0-1.0
VoxPopuliEuropean ParliamentOratory52355PunctuatedCC0
TED-LIUMTED talksOratory45423NormalizedCC-BY-NC-ND 3.0
GigaSpeechAudiobook, podcast, YouTubeNarrated, spontaneous25001240PunctuatedApache-2.0
SPGISpeechFinancial meetingsOratory, spontaneous4900100100Punctuated & CasedUser Agreement
Earnings-22Financial meetingsOratory, spontaneous10555Punctuated & CasedCC-BY-SA-4.0
AMIMeetingsSpontaneous7899Punctuated & CasedCC-BY-4.0
\n\n

For more details on these datasets and how models are evaluated to obtain the ESB score, refer to the ESB paper.\nNote that this list of datasets is not exhaustive. If you are interested in benchmarking models in different languages or under specific conditions, consult the relevant literature.

\n\n

Benchmark

\n\n

The \ud83e\udd17 Open ASR Leaderboard ranks and evaluates speech recognition models available on the Hugging Face Hub. The leaderboard uses the datasets included in the ESB paper to obtain robust evaluation scores for each model. The ESB score is a macro-average of the WER scores across the ESB datasets, providing a comprehensive indication of a model's performance across various domains and conditions.

\n\n

Notes

\n\n
    \n
  • It is possible to fine-tune foundational speech models on a specific language without requiring large amounts of data. A detailed blog post on how to fine-tune a pre-trained Whisper checkpoint on labeled data for ASR can be found here.
    • \n
\n\n

Learn more:

\n\n\n"}, "senselab.audio.tasks.speech_to_text.api": {"fullname": "senselab.audio.tasks.speech_to_text.api", "modulename": "senselab.audio.tasks.speech_to_text.api", "kind": "module", "doc": "

This module represents the API for the speech-to-text task within the senselab package.

\n\n

Currently, it supports only Hugging Face models, with plans to include more in the future.\nUsers can specify the audio clips to transcribe, the ASR model, the language,\nthe preferred device, and the model-specific parameters, and senselab handles the rest.

\n"}, "senselab.audio.tasks.speech_to_text.api.transcribe_audios": {"fullname": "senselab.audio.tasks.speech_to_text.api.transcribe_audios", "modulename": "senselab.audio.tasks.speech_to_text.api", "qualname": "transcribe_audios", "kind": "function", "doc": "

Transcribes all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be transcribed.
    • \n
  • model (SenselabModel): The model used for transcription.
    • \n
  • language (Optional[Language]): The language of the audio (default is None).
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
  • **kwargs: Additional keyword arguments to pass to the transcription function.
    • \n
\n\n
Returns:
\n\n
\n

List[ScriptLine]: The list of script lines.

\n
\n\n
Todo:
\n\n
\n
    \n
  • Include more models (e.g., speechbrain, nvidia)
    • \n
\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel,\tlanguage: Optional[senselab.utils.data_structures.language.Language] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\t**kwargs: Any) -> List[senselab.utils.data_structures.script_line.ScriptLine]:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text.huggingface": {"fullname": "senselab.audio.tasks.speech_to_text.huggingface", "modulename": "senselab.audio.tasks.speech_to_text.huggingface", "kind": "module", "doc": "

This module provides a factory for managing Hugging Face ASR pipelines.

\n\n

To ensure correct functionality, call transcribe_audios_with_transformers serially or,\nif you need to process multiple audios in parallel, pass the entire list of audios to the\nfunction at once, rather than calling the function with one audio at a time.

\n"}, "senselab.audio.tasks.speech_to_text.huggingface.HuggingFaceASR": {"fullname": "senselab.audio.tasks.speech_to_text.huggingface.HuggingFaceASR", "modulename": "senselab.audio.tasks.speech_to_text.huggingface", "qualname": "HuggingFaceASR", "kind": "class", "doc": "

A factory for managing Hugging Face ASR pipelines.

\n"}, "senselab.audio.tasks.speech_to_text.huggingface.HuggingFaceASR.transcribe_audios_with_transformers": {"fullname": "senselab.audio.tasks.speech_to_text.huggingface.HuggingFaceASR.transcribe_audios_with_transformers", "modulename": "senselab.audio.tasks.speech_to_text.huggingface", "qualname": "HuggingFaceASR.transcribe_audios_with_transformers", "kind": "function", "doc": "

Transcribes all audio samples in the dataset.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be transcribed.
    • \n
  • model (HFModel): The Hugging Face model used for transcription.
    • \n
  • language (Optional[Language]): The language of the audio (default is None).
    • \n
  • return_timestamps (Optional[str]): The level of timestamp details (default is \"word\").
    • \n
  • max_new_tokens (int): The maximum number of new tokens (default is 128).
    • \n
  • chunk_length_s (int): The length of audio chunks in seconds (default is 30).
    • \n
  • batch_size (int): The batch size for processing (default is 16).
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[ScritpLine]: The list of script lines.

\n
\n", "signature": "(\tcls,\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.HFModel = HFModel(path_or_uri='openai/whisper-tiny', revision='main', info=None),\tlanguage: Optional[senselab.utils.data_structures.language.Language] = None,\treturn_timestamps: Optional[str] = 'word',\tmax_new_tokens: int = 128,\tchunk_length_s: int = 30,\tbatch_size: int = 16,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[senselab.utils.data_structures.script_line.ScriptLine]:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text_evaluation": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation", "modulename": "senselab.audio.tasks.speech_to_text_evaluation", "kind": "module", "doc": "

Speech to text evaluation

\n\n

\n\n

Overview

\n\n

Evaluating speech transcripts involves comparing a predicted transcript (hypothesis) generated by an automated speech recognition (ASR) system against a ground truth transcript (reference). This evaluation helps to determine the accuracy and performance of the ASR system. Various metrics can be used for this purpose, each capturing different aspects of the errors in the transcription process.

\n\n

Key metrics

\n\n

1. Word Error Rate (WER)

\n\n

Word Error Rate (WER) is the most common metric used to evaluate the accuracy of ASR systems. It measures the number of word-level errors (insertions, deletions, and substitutions) divided by the total number of words in the reference. The lower the value, the better the performance of the ASR system, with an error rate of 0 indicating a perfect score. Notably, this is not a percentage as can go higher than 1.

\n\n

Formula:

\n\n
\n
WER = (S + D + I) / N\n
\n
\n\n

Where:

\n\n
    \n
  • ( S ) = Number of substitutions
    • \n
  • ( D ) = Number of deletions
    • \n
  • ( I ) = Number of insertions
    • \n
  • ( N ) = Total number of words in the reference
    • \n
\n\n

2. Character Error Rate (CER)

\n\n

Character Error Rate (CER) is similar to WER but operates at the character level rather than the word level. It measures the number of character-level errors divided by the total number of characters in the reference. The lower the value, the better the performance of the ASR system, with an error rate of 0 indicating a perfect score.\nAs for WER, CER is not a percentage as can go higher than 1.

\n\n

Formula:

\n\n
\n
CER = (S + D + I) / N\n
\n
\n\n

Where:

\n\n
    \n
  • ( S ) = Number of substitutions
    • \n
  • ( D ) = Number of deletions
    • \n
  • ( I ) = Number of insertions
    • \n
  • ( N ) = Total number of characters in the reference
    • \n
\n\n

3. Match Error Rate (MER)

\n\n

Match Error Rate (MER) calculates the proportion of word-level errors relative to the total number of correct matches plus the number of errors. The lower the value, the better the performance of the ASR system, with an error rate of 0 indicating a perfect score.

\n\n

Formula:

\n\n
\n
MER = (S + D + I) / (S + D + I + C)\n
\n
\n\n

Where:

\n\n
    \n
  • ( S ) = Number of substitutions
    • \n
  • ( D ) = Number of deletions
    • \n
  • ( I ) = Number of insertions
    • \n
  • ( C ) = Number of correct words
    • \n
\n\n

4. Word Information Lost (WIL)

\n\n

Word Information Lost (WIL) is computed as follows:

\n\n

Formula:

\n\n
\n
WIL = 1 - (C / N) + (C / P)\n
\n
\n\n

Where:

\n\n
    \n
  • ( C ) = Number of correct words
    • \n
  • ( N ) = Number of words in the reference
    • \n
  • ( P ) = Number of words in the prediction
    • \n
\n\n

The lower the WIL, the better the performance of the ASR system.

\n\n

5. Word Information Preserved (WIP)

\n\n

Word Information Preserved (WIP) is computed as follows:\nFormula:

\n\n
\n
WIP = (C / N) * (C / P)\n
\n
\n\n

Where:

\n\n
    \n
  • ( C ) = Number of correct words
    • \n
  • ( N ) = Number of words in the reference
    • \n
  • ( P ) = Number of words in the prediction
    • \n
\n\n

The higher the value, the better the performance of the ASR system, with 1 being a perfect score.

\n\n

Examples

\n\n

For a given reference \"hello world\" and hypothesis \"hello duck\":

\n\n
    \n

  • WER (Word Error Rate):

    \n\n
    WER = (0 + 1 + 0)/(2) = 0.5\n
    • \n

  • CER (Character Error Rate):

    \n\n
    CER = (0 + 1 + 0)/(11) = 0.0909\n
    • \n

  • MER (Match Error Rate):

    \n\n
    MER = (1)/(2) = 0.5\n
    • \n
\n"}, "senselab.audio.tasks.speech_to_text_evaluation.utils": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "kind": "module", "doc": "

This module implements some utilities for evaluating a transcription.

\n"}, "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wer": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wer", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "qualname": "calculate_wer", "kind": "function", "doc": "

Calculate the Word Error Rate (WER) between the reference and hypothesis.

\n\n
Arguments:
\n\n
    \n
  • reference (str): The ground truth text.
    • \n
  • hypothesis (str): The predicted text.
    • \n
\n\n
Returns:
\n\n
\n

float: The WER score.

\n
\n\n
Examples:
\n\n
\n
\n
>>> calculate_wer("hello world", "hello duck")\n0.5\n
\n
\n
\n", "signature": "(reference: str, hypothesis: str) -> float:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_mer": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_mer", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "qualname": "calculate_mer", "kind": "function", "doc": "

Calculate the Match Error Rate (MER) between the reference and hypothesis.

\n\n
Arguments:
\n\n
    \n
  • reference (str): The ground truth text.
    • \n
  • hypothesis (str): The predicted text.
    • \n
\n\n
Returns:
\n\n
\n

float: The MER score.

\n
\n\n
Examples:
\n\n
\n
\n
>>> calculate_mer("hello world", "hello duck")\n0.5\n
\n
\n
\n", "signature": "(reference: str, hypothesis: str) -> float:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wil": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wil", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "qualname": "calculate_wil", "kind": "function", "doc": "

Calculate the Word Information Lost (WIL) between the reference and hypothesis.

\n\n
Arguments:
\n\n
    \n
  • reference (str): The ground truth text.
    • \n
  • hypothesis (str): The predicted text.
    • \n
\n\n
Returns:
\n\n
\n

float: The WIL score.

\n
\n\n
Examples:
\n\n
\n
\n
>>> calculate_wil("hello world", "hello duck")\n0.75\n
\n
\n
\n", "signature": "(reference: str, hypothesis: str) -> float:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wip": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wip", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "qualname": "calculate_wip", "kind": "function", "doc": "

Calculate the Word Information Preserved (WIP) between the reference and hypothesis.

\n\n
Arguments:
\n\n
    \n
  • reference (str): The ground truth text.
    • \n
  • hypothesis (str): The predicted text.
    • \n
\n\n
Returns:
\n\n
\n

float: The WIP score.

\n
\n\n
Examples:
\n\n
\n
\n
>>> calculate_wip("hello world", "hello duck")\n0.25\n
\n
\n
\n", "signature": "(reference: str, hypothesis: str) -> float:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_cer": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_cer", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "qualname": "calculate_cer", "kind": "function", "doc": "

Calculate the Character Error Rate (CER) between the reference and hypothesis.

\n\n
Arguments:
\n\n
    \n
  • reference (str): The ground truth text.
    • \n
  • hypothesis (str): The predicted text.
    • \n
\n\n
Returns:
\n\n
\n

float: The CER score.

\n
\n\n
Examples:
\n\n
\n
\n
>>> calculate_cer("hello world", "hello duck")\n0.45454545454545453\n
\n
\n
\n", "signature": "(reference: str, hypothesis: str) -> float:", "funcdef": "def"}, "senselab.audio.tasks.ssl_embeddings": {"fullname": "senselab.audio.tasks.ssl_embeddings", "modulename": "senselab.audio.tasks.ssl_embeddings", "kind": "module", "doc": "

This module provides the API for the senselab extract SSL embeddings task.

\n"}, "senselab.audio.tasks.ssl_embeddings.api": {"fullname": "senselab.audio.tasks.ssl_embeddings.api", "modulename": "senselab.audio.tasks.ssl_embeddings.api", "kind": "module", "doc": "

This module implements some utilities to extract embeddings from self-supervised models.

\n"}, "senselab.audio.tasks.ssl_embeddings.api.extract_ssl_embeddings_from_audios": {"fullname": "senselab.audio.tasks.ssl_embeddings.api.extract_ssl_embeddings_from_audios", "modulename": "senselab.audio.tasks.ssl_embeddings.api", "qualname": "extract_ssl_embeddings_from_audios", "kind": "function", "doc": "

Extract embedding of audio signals from pre-trained SSL models.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects containing the audio signals and their properties.
    • \n
  • model (SenselabModel): The model used to extract their embeddings.
    • \n
  • cache_dir (str): The path to where the model's weights will be saved.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of 1d tensors containing the ssl embeddings for each audio file.

\n
\n\n
Raises:
\n\n
    \n
  • NotImplementedError: If the model is not a Hugging Face model.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> audios = [Audio.from_filepath("sample.wav")]\n>>> model = HFModel(path_or_uri="facebook/wav2vec2-base", revision="main")\n>>> embeddings = extract_ssl_embeddings_from_audios(audios, model, cache_dir="./", device=DeviceType.CUDA)\n>>> print(embeddings[0].shape)\n[13, 209, 768] ([# of Layers, Time Frames, Embedding Size])\n
\n
\n
\n\n
Todo:
\n\n
\n
    \n
  • Make the API compatible with other models than Hugging Face.
    • \n
\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel,\tcache_dir: str = '~/',\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.audio.tasks.ssl_embeddings.api.extract_ssl_embeddings_from_audios_pt": {"fullname": "senselab.audio.tasks.ssl_embeddings.api.extract_ssl_embeddings_from_audios_pt", "modulename": "senselab.audio.tasks.ssl_embeddings.api", "qualname": "extract_ssl_embeddings_from_audios_pt", "kind": "function", "doc": "

Extract embedding of audio signals from pre-trained SSL models.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects containing the audio signals and their properties.
    • \n
  • model (SenselabModel): The model used to extract their embeddings.
    • \n
  • cache_dir (str): The path to where the model's weights will be saved.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of 1d tensors containing the ssl embeddings for each audio file.

\n
\n\n
Raises:
\n\n
    \n
  • NotImplementedError: If the model is not a Hugging Face model.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> audios = [Audio.from_filepath("sample.wav")]\n>>> model = HFModel(path_or_uri="facebook/wav2vec2-base", revision="main")\n>>> embeddings = extract_ssl_embeddings_from_audios(audios, model, cache_dir="./", device=DeviceType.CUDA)\n>>> print(embeddings[0].shape)\n[13, 209, 768] ([# of Layers, Time Frames, Embedding Size])\n
\n
\n
\n\n
Todo:
\n\n
\n
    \n
  • Make the API compatible with other models than Hugging Face.
    • \n
\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel,\tcache_dir: str = '~/',\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.audio.tasks.ssl_embeddings.self_supervised_features": {"fullname": "senselab.audio.tasks.ssl_embeddings.self_supervised_features", "modulename": "senselab.audio.tasks.ssl_embeddings.self_supervised_features", "kind": "module", "doc": "

This module contains functions for extracting features from pre-trained self-supervised models.

\n"}, "senselab.audio.tasks.ssl_embeddings.self_supervised_features.SSLEmbeddingsFactory": {"fullname": "senselab.audio.tasks.ssl_embeddings.self_supervised_features.SSLEmbeddingsFactory", "modulename": "senselab.audio.tasks.ssl_embeddings.self_supervised_features", "qualname": "SSLEmbeddingsFactory", "kind": "class", "doc": "

A factory for managing self-supervised models.

\n"}, "senselab.audio.tasks.ssl_embeddings.self_supervised_features.SSLEmbeddingsFactory.extract_ssl_embeddings": {"fullname": "senselab.audio.tasks.ssl_embeddings.self_supervised_features.SSLEmbeddingsFactory.extract_ssl_embeddings", "modulename": "senselab.audio.tasks.ssl_embeddings.self_supervised_features", "qualname": "SSLEmbeddingsFactory.extract_ssl_embeddings", "kind": "function", "doc": "

Compute the ssl embeddings of audio signals.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects containing the audio signals and their properties.
    • \n
  • model (HFModel): The model used to compute the embeddings\n(default is \"facebook/wav2vec2-base\").
    • \n
  • cache_dir (str): The path to where the model's weights will be saved.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).\nOnly CPU and CUDA are supported.
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of tensors containing the ssl embeddings for each audio file.

\n
\n", "signature": "(\tcls,\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.HFModel,\tcache_dir: str = '~/',\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.audio.tasks.text_to_speech": {"fullname": "senselab.audio.tasks.text_to_speech", "modulename": "senselab.audio.tasks.text_to_speech", "kind": "module", "doc": "

Text to Speech

\n\n

\n\n

Task Overview

\n\n

Text-to-speech (TTS) is the task of creating natural-sounding speech from text. This process can be performed in multiple languages and for multiple speakers.

\n\n

Models

\n\n

A variety of models are supported by senselab for text-to-speech.\nEach model varies in performance, size, license, language support, and more. Performance may also vary depending, among other reasons, on the length of the text or the target speaker (differences in terms of age, dialects, disfluencies). It is recommended to review the model card for each model before use and refer to the most recent literature for an informed decision.

\n\n

Several text-to-speech models are currently available through \ud83e\udd17 Transformers. These models can be explored on the Hugging Face Hub.\nNote: Some Hugging Face models, despite having the text-to-speech label on their model cards, may not work with the text-to-speech pipeline. These models are not supported in senselab, and identifying them often requires trial and error.

\n\n

In addition to the models from \ud83e\udd17 Transformers, senselab also supports Mars5-TTS, which enables text-to-speech generation using a specific target voice, accompanied by its corresponding transcript. Voice cloning using a target voice refers to the process of creating a synthetic voice that mimics the characteristics of a specific person's voice, known as the target voice. This involves generating speech that sounds like it was spoken by that person, even though it was produced by a machine.

\n\n

Popular/recommended models include:

\n\n\n\n

Evaluation

\n\n

Metrics

\n\n

For assessing speech quality and intelligibility, we can use quantitative metrics such as:

\n\n
    \n
  • Wideband Perceptual Estimation of Speech Quality (PESQ)
    • \n
  • Short-Time Objective Intelligibility (STOI)
    • \n
  • Scale-Invariant Signal-to-Distortion Ratio (SI-SDR)
    • \n
\n\n

and qualitative metrics such as:

\n\n
    \n
  • Mean Opinion Score (MOS)
    • \n
\n\n

Another way to automatically assess the intelligibility of the synthesis is by transcribing the output audio (trusting the ASR system) and computing the Word Error Rate (WER) with the reference text.

\n\n

Also, if targeting a specific speaker's voice, we can perform speaker verification to assess how closely the generated audio matches the target voice.\nIf there are specific features in the target voice that we aim to maintain, we can extract these features from the generated audio and verify their presence.

\n\n

senselab can help with all of these evaluations.

\n\n

Datasets

\n\n

To train and evaluate TTS models, a variety of datasets can be used. Some popular datasets include:

\n\n
    \n
  • LJSpeech: A dataset of single-speaker English speech.
    • \n
  • LibriTTS: A multi-speaker English dataset derived from the LibriVox project.
    • \n
  • VCTK: A multi-speaker English dataset with various accents.
    • \n
  • Common Voice: A multi-language dataset collected by Mozilla.
    • \n
\n\n

Benchmark

\n\n

The TTS Arena ranks and evaluates text-to-speech models available based on human perception.\nFor automated benchmarking, we recommend using standard datasets and metrics mentioned above.

\n"}, "senselab.audio.tasks.text_to_speech.api": {"fullname": "senselab.audio.tasks.text_to_speech.api", "modulename": "senselab.audio.tasks.text_to_speech.api", "kind": "module", "doc": "

This module implements some utilities for the text-to-speech task.

\n"}, "senselab.audio.tasks.text_to_speech.api.synthesize_texts": {"fullname": "senselab.audio.tasks.text_to_speech.api.synthesize_texts", "modulename": "senselab.audio.tasks.text_to_speech.api", "qualname": "synthesize_texts", "kind": "function", "doc": "

Synthesizes speech from all texts using the given model.

\n\n

This function synthesizes speech from a list of text strings using the specified text-to-speech (TTS) model.\nIt supports models from HuggingFace and Mars5TTS and StyleTTS2 for now.

\n\n
Arguments:
\n\n
    \n
  • texts (List[str]): The list of text strings to be synthesized.
    • \n
  • model (SenselabModel): The model used for synthesis.\nDefaults to HFModel(path_or_uri=\"suno/bark\", revision=\"main\").
    • \n
  • language (Optional[Language]): The language of the text\n(default is None).
    • \n
  • device (Optional[DeviceType]): The device to run the model on\n(default is None).
    • \n
  • targets (Optional[List[Audio | Tuple[Audio, str]]]): A list where each element is a target audio or a tuple of target audio and transcript.\nDepending on the model being used, the target input may need to be provided in a specific format:\n
      \n
    • Hugging Face models do not require a target input at all.
      • \n
    • Mars5TTS requires both Audio and a transcript for all inputs.
      • \n
    • StyleTTS2 will support both simple (Audio) and complex (Audio, str) target inputs,\nusing the appropriate generation method in each case.
      • \n
    • \n
  • **kwargs: Additional keyword arguments to pass to the synthesis function.\nDepending on the model used (e.g., HFModel), additional arguments\nmay be required. Refer to the model-specific documentation for details.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of synthesized audio objects.

\n
\n", "signature": "(\ttexts: List[str],\tmodel: senselab.utils.data_structures.model.SenselabModel = HFModel(path_or_uri='suno/bark', revision='main', info=None),\tlanguage: Optional[senselab.utils.data_structures.language.Language] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\ttargets: Optional[List[Union[senselab.audio.data_structures.audio.Audio, Tuple[senselab.audio.data_structures.audio.Audio, str]]]] = None,\t**kwargs: Any) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.text_to_speech.huggingface": {"fullname": "senselab.audio.tasks.text_to_speech.huggingface", "modulename": "senselab.audio.tasks.text_to_speech.huggingface", "kind": "module", "doc": "

This module implements some utilities for the text-to-speech task.

\n"}, "senselab.audio.tasks.text_to_speech.huggingface.HuggingFaceTTS": {"fullname": "senselab.audio.tasks.text_to_speech.huggingface.HuggingFaceTTS", "modulename": "senselab.audio.tasks.text_to_speech.huggingface", "qualname": "HuggingFaceTTS", "kind": "class", "doc": "

A factory for managing Hugging Face TTS pipelines.

\n"}, "senselab.audio.tasks.text_to_speech.huggingface.HuggingFaceTTS.synthesize_texts_with_transformers": {"fullname": "senselab.audio.tasks.text_to_speech.huggingface.HuggingFaceTTS.synthesize_texts_with_transformers", "modulename": "senselab.audio.tasks.text_to_speech.huggingface", "qualname": "HuggingFaceTTS.synthesize_texts_with_transformers", "kind": "function", "doc": "

Synthesizes speech from all the provided text samples.

\n\n

Several text-to-speech models are currently available in Transformers,\nsuch as Bark, MMS, VITS and SpeechT5.

\n\n
Arguments:
\n\n
    \n
  • texts (List[str]): The list of text strings to be synthesized.
    • \n
  • model (HFModel): The Hugging Face model used for synthesis.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
  • forward_params (Optional[Dict[str, Any]]): Additional parameters to pass to the forward function.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of synthesized audio objects.

\n
\n\n
Todo:
\n\n
\n \n
\n", "signature": "(\tcls,\ttexts: List[str],\tmodel: senselab.utils.data_structures.model.HFModel = HFModel(path_or_uri='suno/bark', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tforward_params: Optional[Dict[str, Any]] = None) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.text_to_speech.marstts": {"fullname": "senselab.audio.tasks.text_to_speech.marstts", "modulename": "senselab.audio.tasks.text_to_speech.marstts", "kind": "module", "doc": "

This module provides the implementation of Mars5-TTS-based text-to-speech pipelines.

\n"}, "senselab.audio.tasks.text_to_speech.marstts.Mars5TTS": {"fullname": "senselab.audio.tasks.text_to_speech.marstts.Mars5TTS", "modulename": "senselab.audio.tasks.text_to_speech.marstts", "qualname": "Mars5TTS", "kind": "class", "doc": "

A class for managing Torch-based Mars5TTS models.

\n"}, "senselab.audio.tasks.text_to_speech.marstts.Mars5TTS.synthesize_texts_with_mars5tts": {"fullname": "senselab.audio.tasks.text_to_speech.marstts.Mars5TTS.synthesize_texts_with_mars5tts", "modulename": "senselab.audio.tasks.text_to_speech.marstts", "qualname": "Mars5TTS.synthesize_texts_with_mars5tts", "kind": "function", "doc": "

Synthesizes speech from all the provided text samples and target voices+transcripts using Mars5-TTS.

\n\n
Arguments:
\n\n
    \n
  • texts (List[str]): The list of text strings to be synthesized.
    • \n
  • targets (List[Tuple[Audio, str]]): The list of tuples containing audio objects and transcripts.
    • \n
  • model (TorchModel): The Torch model (default is \"Camb-ai/mars5-tts\").
    • \n
  • language (Optional[Language]): The language of the text (default is None).\nThe only supported language is \"en\" for now.
    • \n
  • device (DeviceType): The device to run the model on (default is None). Supported devices are CPU and CUDA.
    • \n
  • deep_clone (bool): Whether to deep clone the target audio (default is True).
    • \n
  • rep_penalty_window (int): The window size for repetition penalty (default is 100).
    • \n
  • top_k (int): The number of top-k candidates to consider (default is 100).
    • \n
  • temperature (float): The temperature for sampling (default is 0.7).
    • \n
  • freq_penalty (float): The frequency penalty for sampling (default is 3).
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of synthesized audio objects.

\n
\n\n
Some tips for best quality:
\n\n
\n
    \n
  • Make sure reference audio is clean and between 1 second and 12 seconds.
    • \n
  • Use deep clone and provide an accurate transcript for the reference.\n Mars5-TTS can potentially accept audio target voices without transcripts. After some testing,\n we found that the best quality is achieved with deep cloning and providing an accurate transcript.
    • \n
  • Use proper punctuation -- the model can be guided and made better or worse with proper use of punctuation\n and capitalization.
    • \n
\n
\n\n

The original repo of the model is: https://github.com/Camb-ai/MARS5-TTS.

\n", "signature": "(\tcls,\ttexts: List[str],\ttargets: List[Tuple[senselab.audio.data_structures.audio.Audio, str]],\tmodel: senselab.utils.data_structures.model.TorchModel = TorchModel(path_or_uri='Camb-ai/mars5-tts', revision='master'),\tlanguage: Optional[senselab.utils.data_structures.language.Language] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tdeep_clone: bool = True,\trep_penalty_window: int = 100,\ttop_k: int = 100,\ttemperature: float = 0.7,\tfreq_penalty: float = 3) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.text_to_speech.styletts2": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "kind": "module", "doc": "

This module provides the implementation of Style-TTS-based text-to-speech pipelines.

\n"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2", "kind": "class", "doc": "

A class for managing Torch-based StyleTTS2 models.

\n"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_ALPHA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_ALPHA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.DEF_ALPHA", "kind": "variable", "doc": "

\n", "default_value": "0.3"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_BETA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_BETA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.DEF_BETA", "kind": "variable", "doc": "

\n", "default_value": "0.1"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_EMBEDDING_SCALE": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_EMBEDDING_SCALE", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.DEF_EMBEDDING_SCALE", "kind": "variable", "doc": "

\n", "default_value": "1"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_ALPHA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_ALPHA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.LF_ALPHA", "kind": "variable", "doc": "

\n", "default_value": "0.5"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_BETA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_BETA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.LF_BETA", "kind": "variable", "doc": "

\n", "default_value": "0.9"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_EMBEDDING_SCALE": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_EMBEDDING_SCALE", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.LF_EMBEDDING_SCALE", "kind": "variable", "doc": "

\n", "default_value": "1.5"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_ALPHA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_ALPHA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.ST_ALPHA", "kind": "variable", "doc": "

\n", "default_value": "0.5"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_BETA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_BETA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.ST_BETA", "kind": "variable", "doc": "

\n", "default_value": "0.9"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_EMBEDDING_SCALE": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_EMBEDDING_SCALE", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.ST_EMBEDDING_SCALE", "kind": "variable", "doc": "

\n", "default_value": "1.5"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DIFFUSION_STEPS": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DIFFUSION_STEPS", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.DIFFUSION_STEPS", "kind": "variable", "doc": "

\n", "default_value": "10"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.synthesize_texts_with_style_tts_2": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.synthesize_texts_with_style_tts_2", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.synthesize_texts_with_style_tts_2", "kind": "function", "doc": "

Synthesizes speech from all the provided text samples and target voices+transcripts using StyleTTS2.

\n\n
Arguments:
\n\n
    \n
  • texts (List[str]): The list of text strings to be synthesized.
    • \n
  • target_audios (List[Audio]): The list of audio objects to reference.
    • \n
  • target_transcripts (List[Optional[str]]): Transcript for each target audio
    • \n
  • model (TorchModel): The Torch model (default is \"wilke0818/StyleTTS2-TorchHub\").
    • \n
  • language (Optional[Language]): The language of the text (default is None).\nThe only supported language is \"en\" for now.
    • \n
  • device (Optional[DeviceType]): device to run model on
    • \n
  • pretrain_data (Optional[Literal]): The dataset (LibriTTS/LJSpeech) the model was trained on
    • \n
  • force_reload (Optional[bool]): whether to reload the repository that the model is coming from
    • \n
  • alpha (float): determines the timbre of the speaker. 0 will match the reference audio\nand 1 will match the content of the transcript. Default will vary based on interpreted TTS type:\nno reference transcripts will use 0.3 (whether or not this is longform text) and .5 otherwise.
    • \n
  • beta (float): beta determines the prosody of the speaker. 0 will match the reference audio\nand 1 will match the content of the transcript. Default will vary based on interpreted TTS type:\nno reference transcript and non-longform will use 0.1, longform text and transcripts will use 0.9.
    • \n
  • diffusion_steps (int): sampler is ancestral, the higher the stpes, the more diverse the samples\nare but the slower the synthesis. Defaults to 10 for all types.
    • \n
  • embedding_scale (float): classifier-free guidance scale. The higher the scale, the more conditional\nthe style is to the input text and hence more emotional. Default will vary based on interpreted\nTTS type:\nno reference transcripts will use 1 (whether or not this is longform text) and 1.5 otherwise.
    • \n
  • t (float): how to combine previous and current styles in longform text generation.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of synthesized audio objects.

\n
\n\n
Some tips for best quality:
\n\n
\n
    \n
  • Make sure reference audio is clean and between 2 second and 12 seconds.
    • \n
  • Play around with alpha, beta, the number of diffusion steps, and the embedding scale for your\n reference audio(s) in order to find a balance between matching the reference voice and having\n the voice sound natural with the text.
    • \n
  • Default values for these parameters are based off of LibriTTS pre-trained model examples the original\n authors provided.
    • \n
\n
\n\n

The original repo of the model is: https://github.com/yl4579/StyleTTS2.

\n", "signature": "(\tcls,\ttexts: List[str],\ttarget_audios: List[senselab.audio.data_structures.audio.Audio],\ttarget_transcripts: List[Optional[str]],\tmodel: senselab.utils.data_structures.model.TorchModel = TorchModel(path_or_uri='wilke0818/StyleTTS2-TorchHub', revision='main'),\tlanguage: Optional[senselab.utils.data_structures.language.Language] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tpretrain_data: Optional[Literal['LibriTTS', 'LJSpeech']] = 'LibriTTS',\tforce_reload: Optional[bool] = False,\talpha: Optional[float] = None,\tbeta: Optional[float] = None,\tdiffusion_steps: Optional[int] = None,\tembedding_scale: Optional[float] = None,\tt: float = 0.7) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.voice_activity_detection": {"fullname": "senselab.audio.tasks.voice_activity_detection", "modulename": "senselab.audio.tasks.voice_activity_detection", "kind": "module", "doc": "

Voice Activity Detection (VAD)

\n\n

\"Tutorial\"

\n\n

Task Overview

\n\n

Voice Activity Detection (VAD) is a binary classification task that identifies the presence of human voice in audio. The primary challenge in VAD lies in differentiating between noise and human voice, particularly in environments with significant background noise (e.g., fans, car engines). While VAD performs well in quiet environments where distinguishing between silence and speech is straightforward, the task becomes more difficult when background noise or non-standard speech patterns are present.

\n\n

Models

\n\n

In senselab, we integrate pyannote.audio models for VAD. These models can be explored on the Hugging Face Hub. Additional approaches for VAD may be integrated into the package in the future.

\n\n

Evaluation

\n\n

Metrics

\n\n

The primary metrics used to evaluate VAD modules are Detection Error Rate (DER) and Detection Cost Function (DCF).

\n\n
    \n

  • Detection Error Rate (DER):

    \n\n
    \n
      DER = (false alarm + missed detection) / total duration of speech in reference\n
    \n
    \n\n
      \n
    • False alarm: Duration of non-speech incorrectly classified as speech.
      • \n
    • Missed detection: Duration of speech incorrectly classified as non-speech.
      • \n
    • Total: Total duration of speech in the reference.
      • \n
    • \n

  • Detection Cost Function (DCF):

    \n\n
    \n
    DCF = 0.25 * false alarm rate + 0.75 * miss rate\n
    \n
    \n\n
      \n
    • False alarm rate: Proportion of non-speech incorrectly classified as speech.
      • \n
    • Miss rate: Proportion of speech incorrectly classified as non-speech.
      • \n
    • \n
\n\n

Additional Metrics

\n\n

VAD systems may also be evaluated using the following metrics:

\n\n
    \n
  • Accuracy: Proportion of the input signal correctly classified.
    • \n
  • Precision: Proportion of detected speech that is actually speech.
    • \n
  • Recall: Proportion of speech that is correctly detected.
    • \n
\n\n

For more detailed information on these metrics, refer to the pyannote.metrics documentation.

\n"}, "senselab.audio.tasks.voice_activity_detection.api": {"fullname": "senselab.audio.tasks.voice_activity_detection.api", "modulename": "senselab.audio.tasks.voice_activity_detection.api", "kind": "module", "doc": "

This module contains functions for computing Voice Activity Detection (VAD).

\n"}, "senselab.audio.tasks.voice_activity_detection.api.detect_human_voice_activity_in_audios": {"fullname": "senselab.audio.tasks.voice_activity_detection.api.detect_human_voice_activity_in_audios", "modulename": "senselab.audio.tasks.voice_activity_detection.api", "qualname": "detect_human_voice_activity_in_audios", "kind": "function", "doc": "

Diarizes all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be processed.
    • \n
  • model (SenselabModel): The model used for voice activity detection.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: The list of script lines with voice label.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = PyannoteAudioModel(path_or_uri='pyannote/speaker-diarization-3.1', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.voice_activity_detection.api.detect_human_voice_activity_in_audios_pt": {"fullname": "senselab.audio.tasks.voice_activity_detection.api.detect_human_voice_activity_in_audios_pt", "modulename": "senselab.audio.tasks.voice_activity_detection.api", "qualname": "detect_human_voice_activity_in_audios_pt", "kind": "function", "doc": "

Diarizes all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be processed.
    • \n
  • model (SenselabModel): The model used for voice activity detection.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: The list of script lines with voice label.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = PyannoteAudioModel(path_or_uri='pyannote/speaker-diarization-3.1', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.voice_cloning": {"fullname": "senselab.audio.tasks.voice_cloning", "modulename": "senselab.audio.tasks.voice_cloning", "kind": "module", "doc": "

Voice cloning

\n\n

\n\n

Task Overview

\n\n

Any-to-any voice cloning aims to transform a source speech into a target voice using just one or a few examples of the target speaker's voice as references. Traditional voice conversion systems attempt to separate the speaker's identity from the speech content. This allows the replacement of speaker information to convert the voice to a target speaker. However, learning such disentangled representations is complex and poses significant challenges.

\n\n

Models

\n\n

We have explored several models for voice cloning:

\n\n
    \n
  • speechT5 (not included in senselab as it did not meet our expectations),
    • \n
  • FREEVC (planned to be included in senselab soon)
    • \n
  • KNNVC (Already included in senselab).
    • \n
\n\n

Evaluation

\n\n

Metrics

\n\n

Objective evaluation involves comparing voice cloning outputs across different downstream tasks:

\n\n
    \n
  • Using an automatic speaker verification tool to determine if the original speaker, the target speaker, and the cloned speaker can be distinguished from each other.
    • \n
  • Ensuring the intelligibility of speech content using an automatic speech recognition system to verify that the content remains unchanged.
    • \n
  • Assessing the preservation of the original speech's emotion after voice cloning.
    • \n
  • ...more...
    • \n
\n\n

Benchmark

\n\n

Recent efforts to enhance privacy in speech technology include the VoicePrivacy initiative, which has been active since 2020, focusing on developing and benchmarking anonymization methods. Despite these efforts, achieving perfect privacy remains a challenge (see here for more details).

\n"}, "senselab.audio.tasks.voice_cloning.api": {"fullname": "senselab.audio.tasks.voice_cloning.api", "modulename": "senselab.audio.tasks.voice_cloning.api", "kind": "module", "doc": "

This module implements some utilities for the voice cloning task.

\n"}, "senselab.audio.tasks.voice_cloning.api.clone_voices": {"fullname": "senselab.audio.tasks.voice_cloning.api.clone_voices", "modulename": "senselab.audio.tasks.voice_cloning.api", "qualname": "clone_voices", "kind": "function", "doc": "

Clones voices from source audios to target audios using the given model.

\n\n

This function performs pairwise voice cloning, where the voice from each audio sample\nin the source_audios list is transformed into the corresponding audio\nsample in the target_audios list. The resulting list contains audio samples that\npreserve the content of the original source audio but with the voice replaced by the\nvoice from the corresponding target audio.

\n\n
Arguments:
\n\n
    \n
  • source_audios (List[Audio]): A list of audio samples whose voices will be \"replaced\"\nby the voices from the corresponding target audio samples. The content\n(e.g., words) will remain the same, but the voice sounds like the target.
    • \n
  • target_audios (List[Audio]): A list of audio samples whose voices will be extracted\nand used to replace the voices in the corresponding source audio samples.
    • \n
  • model (SenselabModel, optional): The model to use for voice cloning. Currently,\nonly KNNVC (K-Nearest Neighbors Voice Conversion) is supported, encapsulated\nby the TorchModel class. TorchModel is a child class of SenselabModel\nand specifies the model and revision for cloning. Defaults to\nTorchModel(path_or_uri=\"bshall/knn-vc\", revision=\"master\").
    • \n
  • device (Optional[DeviceType], optional): The device to run the model on (e.g., CPU or GPU).\nDefaults to None.
    • \n
  • **kwargs: Additional keyword arguments for model-specific parameters that will\nbe passed directly to the underlying model's voice cloning method.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: A list of audio samples with cloned voices from the corresponding source and target audios.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the lengths of source_audios and target_audios do not match.
    • \n
  • NotImplementedError: If the specified model is not supported. Currently, only KNNVC is supported.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> source_audios = [Audio.from_filepath("source1.wav"), Audio.from_filepath("source2.wav")]\n>>> target_audios = [Audio.from_filepath("target1.wav"), Audio.from_filepath("target2.wav")]\n>>> cloned_audios = clone_voices(source_audios, target_audios)\n
\n
\n
\n\n
Todo:
\n\n
\n

Add logging with timestamps.

\n
\n", "signature": "(\tsource_audios: List[senselab.audio.data_structures.audio.Audio],\ttarget_audios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = TorchModel(path_or_uri='bshall/knn-vc', revision='master'),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\t**kwargs: Any) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.voice_cloning.knnvc": {"fullname": "senselab.audio.tasks.voice_cloning.knnvc", "modulename": "senselab.audio.tasks.voice_cloning.knnvc", "kind": "module", "doc": "

This module contains functions for voice cloning using KNNVC.

\n"}, "senselab.audio.tasks.voice_cloning.knnvc.KNNVC": {"fullname": "senselab.audio.tasks.voice_cloning.knnvc.KNNVC", "modulename": "senselab.audio.tasks.voice_cloning.knnvc", "qualname": "KNNVC", "kind": "class", "doc": "

A factory for managing KNNVC pipelines.

\n"}, "senselab.audio.tasks.voice_cloning.knnvc.KNNVC.clone_voices_with_knn_vc": {"fullname": "senselab.audio.tasks.voice_cloning.knnvc.KNNVC.clone_voices_with_knn_vc", "modulename": "senselab.audio.tasks.voice_cloning.knnvc", "qualname": "KNNVC.clone_voices_with_knn_vc", "kind": "function", "doc": "

Clone voices from source audios to target audios using KNNVC.

\n\n
Arguments:
\n\n
    \n
  • source_audios (List[Audio]): List of source audio objects.
    • \n
  • target_audios (List[Audio]): List of target audio objects.
    • \n
  • model (TorchModel, optional): The Torch model to use for the KNNVC pipeline.\nDefaults to TorchModel(path_or_uri=\"bshall/knn-vc\", revision=\"master\").
    • \n
  • prematched_vocoder (bool, optional): Flag indicating whether to use a pre-matched vocoder. Defaults to True.
    • \n
  • topk (int, optional): The number of top matches to consider. Defaults to 4.
    • \n
  • device (Optional[DeviceType], optional): The device to run the pipeline on. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: List of cloned audio objects.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the audio files are not mono or if the sampling rates are not supported.
    • \n
\n", "signature": "(\tcls,\tsource_audios: List[senselab.audio.data_structures.audio.Audio],\ttarget_audios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.TorchModel = TorchModel(path_or_uri='bshall/knn-vc', revision='master'),\tprematched_vocoder: bool = True,\ttopk: int = 4,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.workflows": {"fullname": "senselab.audio.workflows", "modulename": "senselab.audio.workflows", "kind": "module", "doc": "

Workflow for timestamped transcription.

\n"}, "senselab.audio.workflows.transcribe_timestamped": {"fullname": "senselab.audio.workflows.transcribe_timestamped", "modulename": "senselab.audio.workflows.transcribe_timestamped", "kind": "module", "doc": "

Workflow for timestamped transcription.

\n"}, "senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped": {"fullname": "senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped", "modulename": "senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped", "kind": "module", "doc": "

Transcribes audio files with timestamps.

\n"}, "senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped.transcribe_timestamped": {"fullname": "senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped.transcribe_timestamped", "modulename": "senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped", "qualname": "transcribe_timestamped", "kind": "function", "doc": "

Transcribes a list of audio files and timestamps them using forced alignment.

\n\n

This function processes the given list of Audio objects by performing\nnecessary preprocessing steps (such as downmixing channels and resampling),\ntranscribes the audio using the specified speech-to-text model, and applies\nforced alignment to generate a list of ScriptLine objects with timestamps.

\n\n
Arguments:
\n\n
    \n
  • audios (list[Audio]): List of Audio objects to be transcribed and\ntimestamped.
    • \n
  • model (HFModel, optional): A Huggingface model for speech-to-text.\nDefaults to 'whisper'.
    • \n
  • language (Language, optional): Language object for the transcription.\nIf None, language detection is triggered\nfor the 'whisper' model. Defaults to None.
    • \n
  • n_batches (int, optional): The number of batches to split over in the\nworkflow.
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: List of ScriptLine objects resulting from the\n transcriptions with timestamps.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.HFModel = HFModel(path_or_uri='openai/whisper-tiny', revision='main', info=None),\tlanguage: senselab.utils.data_structures.language.Language = Language(language_code='eng'),\tn_batches: int = 1) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.text": {"fullname": "senselab.text", "modulename": "senselab.text", "kind": "module", "doc": "

This module contains implementations related to text processing.

\n"}, "senselab.text.tasks": {"fullname": "senselab.text.tasks", "modulename": "senselab.text.tasks", "kind": "module", "doc": "

This module if for processing text.

\n"}, "senselab.text.tasks.embeddings_extraction": {"fullname": "senselab.text.tasks.embeddings_extraction", "modulename": "senselab.text.tasks.embeddings_extraction", "kind": "module", "doc": "

This module provides the API for the senselab text embeddings extraction.

\n"}, "senselab.text.tasks.embeddings_extraction.api": {"fullname": "senselab.text.tasks.embeddings_extraction.api", "modulename": "senselab.text.tasks.embeddings_extraction.api", "kind": "module", "doc": "

This module provides a wrapper method for extracting embeddings from text using different models.

\n"}, "senselab.text.tasks.embeddings_extraction.api.extract_embeddings_from_text": {"fullname": "senselab.text.tasks.embeddings_extraction.api.extract_embeddings_from_text", "modulename": "senselab.text.tasks.embeddings_extraction.api", "qualname": "extract_embeddings_from_text", "kind": "function", "doc": "

Extracts embeddings from a list of strings using the specified model.

\n\n
Arguments:
\n\n
    \n
  • pieces_of_text (List[str]): A list of strings to extract embeddings from.
    • \n
  • model (SenselabModel): The model used for extracting embeddings.
    • \n
  • device (Optional[DeviceType]): The device to run the model on. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of embeddings for the input strings.

\n
\n", "signature": "(\tpieces_of_text: List[str],\tmodel: senselab.utils.data_structures.model.SenselabModel,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.text.tasks.embeddings_extraction.huggingface": {"fullname": "senselab.text.tasks.embeddings_extraction.huggingface", "modulename": "senselab.text.tasks.embeddings_extraction.huggingface", "kind": "module", "doc": "

This module contains functions for extracting features from pre-trained self-supervised models.

\n"}, "senselab.text.tasks.embeddings_extraction.huggingface.HFFactory": {"fullname": "senselab.text.tasks.embeddings_extraction.huggingface.HFFactory", "modulename": "senselab.text.tasks.embeddings_extraction.huggingface", "qualname": "HFFactory", "kind": "class", "doc": "

A factory for managing self-supervised models from Hugging Face.

\n"}, "senselab.text.tasks.embeddings_extraction.huggingface.HFFactory.extract_text_embeddings": {"fullname": "senselab.text.tasks.embeddings_extraction.huggingface.HFFactory.extract_text_embeddings", "modulename": "senselab.text.tasks.embeddings_extraction.huggingface", "qualname": "HFFactory.extract_text_embeddings", "kind": "function", "doc": "

Extracts embeddings from a list of strings using a Hugging Face model.

\n\n
Arguments:
\n\n
    \n
  • pieces_of_text (List[str]): A list of strings to extract embeddings from.
    • \n
  • model (HFModel, optional): A Hugging Face model configuration.\nDefaults to HFModel(path_or_uri=\"sentence-transformers/all-MiniLM-L6-v2\").
    • \n
  • device (Optional[DeviceType], optional): The device to run the model on.\nDefaults to None.
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of embeddings for the input strings.

\n
\n", "signature": "(\tcls,\tpieces_of_text: List[str],\tmodel: senselab.utils.data_structures.model.HFModel = HFModel(path_or_uri='sentence-transformers/all-MiniLM-L6-v2', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.text.tasks.embeddings_extraction.sentence_transformers": {"fullname": "senselab.text.tasks.embeddings_extraction.sentence_transformers", "modulename": "senselab.text.tasks.embeddings_extraction.sentence_transformers", "kind": "module", "doc": "

This module implements some utilities for using Sentence Transformers for embeddings extraction.

\n"}, "senselab.text.tasks.embeddings_extraction.sentence_transformers.SentenceTransformerFactory": {"fullname": "senselab.text.tasks.embeddings_extraction.sentence_transformers.SentenceTransformerFactory", "modulename": "senselab.text.tasks.embeddings_extraction.sentence_transformers", "qualname": "SentenceTransformerFactory", "kind": "class", "doc": "

A factory for managing SentenceTransformer pipelines for text embeddings.

\n"}, "senselab.text.tasks.embeddings_extraction.sentence_transformers.SentenceTransformerFactory.extract_text_embeddings": {"fullname": "senselab.text.tasks.embeddings_extraction.sentence_transformers.SentenceTransformerFactory.extract_text_embeddings", "modulename": "senselab.text.tasks.embeddings_extraction.sentence_transformers", "qualname": "SentenceTransformerFactory.extract_text_embeddings", "kind": "function", "doc": "

Extracts embeddings from a list of strings using a SentenceTransformer model.

\n\n
Arguments:
\n\n
    \n
  • pieces_of_text (List[str]): A list of strings to extract embeddings from.
    • \n
  • model (SentenceTransformersModel, optional): A Hugging Face model configuration.\nDefaults to SentenceTransformersModel(path_or_uri=\"sentence-transformers/all-MiniLM-L6-v2\").
    • \n
  • device (Optional[DeviceType], optional): The device to run the model on.\nDefaults to None.
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of embeddings for the input strings.

\n
\n", "signature": "(\tcls,\tpieces_of_text: List[str],\tmodel: senselab.utils.data_structures.model.SentenceTransformersModel = SentenceTransformersModel(path_or_uri='sentence-transformers/all-MiniLM-L6-v2', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.utils": {"fullname": "senselab.utils", "modulename": "senselab.utils", "kind": "module", "doc": "

This module contains the implementation of utility functions.

\n"}, "senselab.utils.config": {"fullname": "senselab.utils.config", "modulename": "senselab.utils.config", "kind": "module", "doc": "

This module provides the implementation of config utilities.

\n"}, "senselab.utils.config.get_config": {"fullname": "senselab.utils.config.get_config", "modulename": "senselab.utils.config", "qualname": "get_config", "kind": "function", "doc": "

Reads and returns the contents of the config file.

\n", "signature": "() -> Dict[str, Any]:", "funcdef": "def"}, "senselab.utils.constants": {"fullname": "senselab.utils.constants", "modulename": "senselab.utils.constants", "kind": "module", "doc": "

Constants used by Senselab.

\n"}, "senselab.utils.constants.SENSELAB_NAMESPACE": {"fullname": "senselab.utils.constants.SENSELAB_NAMESPACE", "modulename": "senselab.utils.constants", "qualname": "SENSELAB_NAMESPACE", "kind": "variable", "doc": "

\n", "default_value": "UUID('b5e0e774-e24c-3298-9802-781f66220abf')"}, "senselab.utils.data_structures": {"fullname": "senselab.utils.data_structures", "modulename": "senselab.utils.data_structures", "kind": "module", "doc": "

\n"}, "senselab.utils.data_structures.dataset": {"fullname": "senselab.utils.data_structures.dataset", "modulename": "senselab.utils.data_structures.dataset", "kind": "module", "doc": "

Data structures relevant for managing datasets.

\n"}, "senselab.utils.data_structures.dataset.Participant": {"fullname": "senselab.utils.data_structures.dataset.Participant", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant", "kind": "class", "doc": "

Data structure for a participant in a dataset.

\n\n
Attributes:
\n\n
    \n
  • id: The ID of the participant. If not provided, generated using uuid4
    • \n
  • metadata: Dictionary of user specified metadata related to the participant
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.dataset.Participant.id": {"fullname": "senselab.utils.data_structures.dataset.Participant.id", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.id", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.utils.data_structures.dataset.Participant.metadata": {"fullname": "senselab.utils.data_structures.dataset.Participant.metadata", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.metadata", "kind": "variable", "doc": "

\n", "annotation": ": Dict"}, "senselab.utils.data_structures.dataset.Participant.set_id": {"fullname": "senselab.utils.data_structures.dataset.Participant.set_id", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.set_id", "kind": "function", "doc": "

Set the unique id of the participant.

\n", "signature": "(cls, v: str) -> str:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.Participant.model_config": {"fullname": "senselab.utils.data_structures.dataset.Participant.model_config", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.dataset.Participant.model_fields": {"fullname": "senselab.utils.data_structures.dataset.Participant.model_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'id': FieldInfo(annotation=str, required=False, default_factory=<lambda>), 'metadata': FieldInfo(annotation=Dict, required=False, default={})}"}, "senselab.utils.data_structures.dataset.Participant.model_computed_fields": {"fullname": "senselab.utils.data_structures.dataset.Participant.model_computed_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.dataset.Session": {"fullname": "senselab.utils.data_structures.dataset.Session", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session", "kind": "class", "doc": "

Data structure for a session in a dataset.

\n\n
Attributes:
\n\n
    \n
  • id: the ID of the session. If not provided, generated using uuid4
    • \n
  • metadata: Dictionary of user specified metadata related to the session
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.dataset.Session.id": {"fullname": "senselab.utils.data_structures.dataset.Session.id", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.id", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.utils.data_structures.dataset.Session.metadata": {"fullname": "senselab.utils.data_structures.dataset.Session.metadata", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.metadata", "kind": "variable", "doc": "

\n", "annotation": ": Dict"}, "senselab.utils.data_structures.dataset.Session.set_id": {"fullname": "senselab.utils.data_structures.dataset.Session.set_id", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.set_id", "kind": "function", "doc": "

Set the unique id of the session.

\n", "signature": "(cls, v: str) -> str:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.Session.model_config": {"fullname": "senselab.utils.data_structures.dataset.Session.model_config", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.dataset.Session.model_fields": {"fullname": "senselab.utils.data_structures.dataset.Session.model_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'id': FieldInfo(annotation=str, required=False, default_factory=<lambda>), 'metadata': FieldInfo(annotation=Dict, required=False, default={})}"}, "senselab.utils.data_structures.dataset.Session.model_computed_fields": {"fullname": "senselab.utils.data_structures.dataset.Session.model_computed_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.dataset.SenselabDataset": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset", "kind": "class", "doc": "

Class for maintaining SenseLab datasets and functionalities.

\n\n

Maintains collections of Audios, Videos, and metadata for use of the Senselab tools\nand pipelines. Includes the ability to manage Sessions and Participants.

\n\n
Attributes:
\n\n
    \n
  • audios: List of Audios that are generated based on list of audio filepaths
    • \n
  • videos: List of Videos generated from a list of video filepaths
    • \n
  • metadata: Metadata related to the dataset overall but not necessarily the metadata of\nindivudal audios or videos in the dataset
    • \n
  • sessions: Mapping of Session IDs to Session instances
    • \n
  • participants: Mapping of participant IDs to Participant instances
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.dataset.SenselabDataset.participants": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.participants", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.participants", "kind": "variable", "doc": "

\n", "annotation": ": Dict[str, senselab.utils.data_structures.dataset.Participant]"}, "senselab.utils.data_structures.dataset.SenselabDataset.sessions": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.sessions", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.sessions", "kind": "variable", "doc": "

\n", "annotation": ": Dict[str, senselab.utils.data_structures.dataset.Session]"}, "senselab.utils.data_structures.dataset.SenselabDataset.audios": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.audios", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.audios", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.audio.data_structures.audio.Audio]"}, "senselab.utils.data_structures.dataset.SenselabDataset.videos": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.videos", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.videos", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.video.data_structures.video.Video]"}, "senselab.utils.data_structures.dataset.SenselabDataset.metadata": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.metadata", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.metadata", "kind": "variable", "doc": "

\n", "annotation": ": Dict"}, "senselab.utils.data_structures.dataset.SenselabDataset.generate_audios_from_filepaths": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.generate_audios_from_filepaths", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.generate_audios_from_filepaths", "kind": "function", "doc": "

Generate the audios in the dataset from a list of audio filepaths.

\n\n

Generates the audios in the dataset by taking in a list of audio filepaths\nor a list of Audios

\n\n
Arguments:
\n\n
    \n
  • v: Input for audios attribute that we're validating by generating the Audios if filepaths\nare provided or just the list of Audios if pre-generated and passed in
    • \n
\n\n
Returns:
\n\n
\n

List of Audios that instantiates the audios attribute in the dataset

\n
\n", "signature": "(\tcls,\tv: Union[List[str], List[senselab.audio.data_structures.audio.Audio]],\t_: pydantic_core.core_schema.ValidationInfo) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.generate_videos_from_filepaths": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.generate_videos_from_filepaths", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.generate_videos_from_filepaths", "kind": "function", "doc": "

Generate the videos in the dataset from a list of video filepaths.

\n\n

Generates the videos in the dataset by taking in a list of video filepaths\nor a list of Videos

\n\n
Arguments:
\n\n
    \n
  • v: Input for videos attribute that we're validating by generating the Videos if filepaths\nare provided or just the list of Videos if pre-generated and passed in
    • \n
\n\n
Returns:
\n\n
\n

List of Videos that instantiates the videos attribute in the dataset

\n
\n", "signature": "(\tcls,\tv: Union[List[str], List[senselab.video.data_structures.video.Video]],\t_: pydantic_core.core_schema.ValidationInfo) -> List[senselab.video.data_structures.video.Video]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.create_bids_dataset": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.create_bids_dataset", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.create_bids_dataset", "kind": "function", "doc": "

Create a dataset from a BIDS organized directory.

\n\n

Creates a new dataset based off of a BIDS directory structure as defined at\nhttps://sensein.group/biometrics-book/updated_bids.html

\n", "signature": "(\tcls,\tbids_root_filepath: str) -> senselab.utils.data_structures.dataset.SenselabDataset:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.create_audio_split_for_pydra_task": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.create_audio_split_for_pydra_task", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.create_audio_split_for_pydra_task", "kind": "function", "doc": "

Splits the audio data for Pydra tasks.

\n\n

Creates a split of the audio data that can be used for creating individual Pydra tasks using\nthe .split functionality. Splits the data such that the inputs for a Pydra workflow are either\noptimized for the GPU's batch size or a single Audio per CPU thread.

\n\n
Arguments:
\n\n
    \n
  • batch_size: How to batch Audios for a Pydra task; defaults to 1 since CPU won't batch
    • \n
\n\n
Returns:
\n\n
\n

List of Lists of Audio where each List of Audios will be an input to a Pydra task.\n Each of the sublists are either of size 1 for CPUs or at most batch_size for GPU optimization.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError if the batch size is invalid (less than 1)
    • \n
\n", "signature": "(\tself,\tbatch_size: int = 1) -> List[List[senselab.audio.data_structures.audio.Audio]]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.audio_merge_from_pydra_task": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.audio_merge_from_pydra_task", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.audio_merge_from_pydra_task", "kind": "function", "doc": "

TODO: Write later.

\n\n

Logic Pydra:\naudios: List of audios that want to give to task\nsplit: List[List[Audios]] -> task List[Audio]\npydra task(List[Audio]) -> List[Audio]\nmerge(List[List[Audio]]) <- might be a wrapped instead of List of lists\nTODO: Figure out what a merge behavior looks like from Pydra

\n", "signature": "(\tself,\taudios_to_merge: List[List[senselab.audio.data_structures.audio.Audio]]) -> None:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.add_participant": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.add_participant", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.add_participant", "kind": "function", "doc": "

Add a participant to the dataset.

\n\n

Adds a new participant to the dataset if they are not already in it.

\n\n
Arguments:
\n\n
    \n
  • participant: instance of a Participant that we want to add to the dataset
    • \n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the participant ID is already in the dataset, we raise a value error.\nThis means that either the ID is non-unique and/or the participant is already in the dataset.
    • \n
\n", "signature": "(\tself,\tparticipant: senselab.utils.data_structures.dataset.Participant) -> None:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.add_session": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.add_session", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.add_session", "kind": "function", "doc": "

Add a session to the dataset.

\n\n

Adds a new sesszion to the dataset if it is not already in there.

\n\n
Arguments:
\n\n
    \n
  • session: instance of a Session that we want to add to the dataset
    • \n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the session ID is already in the dataset, we raise a value error.\nThis means that either the ID is non-unique and/or the session is already in the dataset.
    • \n
\n", "signature": "(self, session: senselab.utils.data_structures.dataset.Session) -> None:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.get_participants": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.get_participants", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.get_participants", "kind": "function", "doc": "

Get the list of participants in the dataset.

\n\n
Returns:
\n\n
\n

participants (List[Participant]): all of the instances of participants in the dataset\n Warning: The instances are returned as is, so changes to the underlying participants in this\n list will automatically be reflected in the dataset.

\n
\n", "signature": "(self) -> List[senselab.utils.data_structures.dataset.Participant]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.get_sessions": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.get_sessions", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.get_sessions", "kind": "function", "doc": "

Get the list of sessions in the dataset.

\n\n
Returns:
\n\n
\n

sessions (List[Session]): all of the instances of sessions in the dataset\n Warning: The instances are returned as is, so changes to the underlying sessions in this\n list will automatically be reflected in the dataset.

\n
\n", "signature": "(self) -> List[senselab.utils.data_structures.dataset.Session]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.convert_senselab_dataset_to_hf_datasets": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.convert_senselab_dataset_to_hf_datasets", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.convert_senselab_dataset_to_hf_datasets", "kind": "function", "doc": "

Converts Senselab datasets into HuggingFace datasets.

\n\n
Returns:
\n\n
\n

A dictionary of HuggingFace datasets that represent the underlying Senselab dataset.\n Currently only supports creating HuggingFace datasets for the Audio(s) and Video(s) in\n the SenselabDataset. Videos in HuggingFace are not natively supported, so they are treated\n as Sequences of images with a frame_rate.

\n
\n", "signature": "(self) -> Dict[str, datasets.arrow_dataset.Dataset]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.convert_hf_dataset_to_senselab_dataset": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.convert_hf_dataset_to_senselab_dataset", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.convert_hf_dataset_to_senselab_dataset", "kind": "function", "doc": "

Converts HuggingFace dataset to a Senselab dataset.

\n\n

Convert HuggingFace dataset(s) to a SenselabDataset where each component of a SenselabDataset\n (e.g. audios, videos, sessions, participants) are stored under different keys in the provided dictionary.

\n\n
Arguments:
\n\n
    \n
  • hf_datasets: Dictionary of the different individual components that make up a SenselabDataset. Audios must\nbe organized as HuggingFace Audio(s) which is a dictionary with an array attribute, sampling rate, and\na path. Videos are not natively supported by HuggingFace, so SenselabDataset expects a sequence of\nframes that are each an image, a frame_rate argument, and optionally the associated audio with the\nvideo.
    • \n
  • metadata: Dictionary of additional dataset level metadata. Differs from metadata inside each HuggingFace\ndataset that is provided and is unaffected by transder_metadata.
    • \n
  • transfer_metadata: Specifies whether to generate metadata from extraneous attributes in the\nHuggingDataset in addition to any specified ina metadata field, otherwise they are ignored.\nDefaults to False.
    • \n
\n\n
Returns:
\n\n
\n

The generated SenselabDataset from the provided fields in the hf_datasets dictionary. Currently does not\n support converting sessions or participants.

\n
\n", "signature": "(\tcls,\thf_datasets: Dict[str, datasets.arrow_dataset.Dataset],\tmetadata: Dict = {},\ttransfer_metadata: bool = False) -> senselab.utils.data_structures.dataset.SenselabDataset:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.model_config": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.model_config", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.dataset.SenselabDataset.model_fields": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.model_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'participants': FieldInfo(annotation=Dict[str, Participant], required=False, default_factory=dict), 'sessions': FieldInfo(annotation=Dict[str, Session], required=False, default_factory=dict), 'audios': FieldInfo(annotation=List[Audio], required=False, default=[]), 'videos': FieldInfo(annotation=List[Video], required=False, default=[]), 'metadata': FieldInfo(annotation=Dict, required=False, default={})}"}, "senselab.utils.data_structures.dataset.SenselabDataset.model_computed_fields": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.model_computed_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.device": {"fullname": "senselab.utils.data_structures.device", "modulename": "senselab.utils.data_structures.device", "kind": "module", "doc": "

Utility functions for utilizing different devices in Senselab.

\n"}, "senselab.utils.data_structures.device.DeviceType": {"fullname": "senselab.utils.data_structures.device.DeviceType", "modulename": "senselab.utils.data_structures.device", "qualname": "DeviceType", "kind": "class", "doc": "

Device types for PyTorch operations.

\n", "bases": "enum.Enum"}, "senselab.utils.data_structures.device.DeviceType.CPU": {"fullname": "senselab.utils.data_structures.device.DeviceType.CPU", "modulename": "senselab.utils.data_structures.device", "qualname": "DeviceType.CPU", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "<DeviceType.CPU: 'cpu'>"}, "senselab.utils.data_structures.device.DeviceType.CUDA": {"fullname": "senselab.utils.data_structures.device.DeviceType.CUDA", "modulename": "senselab.utils.data_structures.device", "qualname": "DeviceType.CUDA", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "<DeviceType.CUDA: 'cuda'>"}, "senselab.utils.data_structures.device.DeviceType.MPS": {"fullname": "senselab.utils.data_structures.device.DeviceType.MPS", "modulename": "senselab.utils.data_structures.device", "qualname": "DeviceType.MPS", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "<DeviceType.MPS: 'mps'>"}, "senselab.utils.data_structures.device.DTYPE_MAP": {"fullname": "senselab.utils.data_structures.device.DTYPE_MAP", "modulename": "senselab.utils.data_structures.device", "qualname": "DTYPE_MAP", "kind": "variable", "doc": "

\n", "default_value": "{<DeviceType.CPU: 'cpu'>: torch.float32, <DeviceType.CUDA: 'cuda'>: torch.float16, <DeviceType.MPS: 'mps'>: torch.float32}"}, "senselab.utils.data_structures.file": {"fullname": "senselab.utils.data_structures.file", "modulename": "senselab.utils.data_structures.file", "kind": "module", "doc": "

This module provides the implementation of file utilities.

\n"}, "senselab.utils.data_structures.file.File": {"fullname": "senselab.utils.data_structures.file.File", "modulename": "senselab.utils.data_structures.file", "qualname": "File", "kind": "class", "doc": "

This class represents a file.

\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.file.File.filepath": {"fullname": "senselab.utils.data_structures.file.File.filepath", "modulename": "senselab.utils.data_structures.file", "qualname": "File.filepath", "kind": "variable", "doc": "

\n", "annotation": ": typing.Annotated[pathlib.Path, PathType(path_type='file')]"}, "senselab.utils.data_structures.file.File.type": {"fullname": "senselab.utils.data_structures.file.File.type", "modulename": "senselab.utils.data_structures.file", "qualname": "File.type", "kind": "variable", "doc": "

Determine the type of the file based on its extension.

\n", "annotation": ": str"}, "senselab.utils.data_structures.file.File.validate_filepath": {"fullname": "senselab.utils.data_structures.file.File.validate_filepath", "modulename": "senselab.utils.data_structures.file", "qualname": "File.validate_filepath", "kind": "function", "doc": "

Validate the filepath.

\n", "signature": "(\tcls,\tv: typing.Annotated[pathlib.Path, PathType(path_type='file')]) -> typing.Annotated[pathlib.Path, PathType(path_type='file')]:", "funcdef": "def"}, "senselab.utils.data_structures.file.File.model_config": {"fullname": "senselab.utils.data_structures.file.File.model_config", "modulename": "senselab.utils.data_structures.file", "qualname": "File.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.file.File.model_fields": {"fullname": "senselab.utils.data_structures.file.File.model_fields", "modulename": "senselab.utils.data_structures.file", "qualname": "File.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'filepath': FieldInfo(annotation=Path, required=True, metadata=[PathType(path_type='file')])}"}, "senselab.utils.data_structures.file.File.model_computed_fields": {"fullname": "senselab.utils.data_structures.file.File.model_computed_fields", "modulename": "senselab.utils.data_structures.file", "qualname": "File.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.file.from_strings_to_files": {"fullname": "senselab.utils.data_structures.file.from_strings_to_files", "modulename": "senselab.utils.data_structures.file", "qualname": "from_strings_to_files", "kind": "function", "doc": "

Create a list of File objects from a list of strings.

\n", "signature": "(\tlist_of_files: List[str]) -> List[senselab.utils.data_structures.file.File]:", "funcdef": "def"}, "senselab.utils.data_structures.file.get_common_directory": {"fullname": "senselab.utils.data_structures.file.get_common_directory", "modulename": "senselab.utils.data_structures.file", "qualname": "get_common_directory", "kind": "function", "doc": "

A function to get the common directory from a list of file paths.

\n\n

Parameters:

\n\n
    \n
  • files: a list of file paths
    • \n
\n\n

Returns:

\n\n
    \n
  • the common directory among the file paths
    • \n
\n", "signature": "(files: List[str]) -> str:", "funcdef": "def"}, "senselab.utils.data_structures.language": {"fullname": "senselab.utils.data_structures.language", "modulename": "senselab.utils.data_structures.language", "kind": "module", "doc": "

This module provides the implementation of language data structures.

\n"}, "senselab.utils.data_structures.language.Language": {"fullname": "senselab.utils.data_structures.language.Language", "modulename": "senselab.utils.data_structures.language", "qualname": "Language", "kind": "class", "doc": "

Data structure for a language.

\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.language.Language.language_code": {"fullname": "senselab.utils.data_structures.language.Language.language_code", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.language_code", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.utils.data_structures.language.Language.validate_language": {"fullname": "senselab.utils.data_structures.language.Language.validate_language", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.validate_language", "kind": "function", "doc": "

Validate that the language code is valid.

\n", "signature": "(cls, v: str) -> str:", "funcdef": "def"}, "senselab.utils.data_structures.language.Language.alpha_2": {"fullname": "senselab.utils.data_structures.language.Language.alpha_2", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.alpha_2", "kind": "variable", "doc": "

Get the ISO 639-2 code for the language.

\n", "annotation": ": str"}, "senselab.utils.data_structures.language.Language.alpha_3": {"fullname": "senselab.utils.data_structures.language.Language.alpha_3", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.alpha_3", "kind": "variable", "doc": "

Get the ISO 639-3 code for the language.

\n", "annotation": ": str"}, "senselab.utils.data_structures.language.Language.name": {"fullname": "senselab.utils.data_structures.language.Language.name", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.name", "kind": "variable", "doc": "

Get the name of the language.

\n", "annotation": ": str"}, "senselab.utils.data_structures.language.Language.model_config": {"fullname": "senselab.utils.data_structures.language.Language.model_config", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.language.Language.model_fields": {"fullname": "senselab.utils.data_structures.language.Language.model_fields", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'language_code': FieldInfo(annotation=str, required=True)}"}, "senselab.utils.data_structures.language.Language.model_computed_fields": {"fullname": "senselab.utils.data_structures.language.Language.model_computed_fields", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.logging": {"fullname": "senselab.utils.data_structures.logging", "modulename": "senselab.utils.data_structures.logging", "kind": "module", "doc": "

This module provides utilities for logging.

\n"}, "senselab.utils.data_structures.logging.logger": {"fullname": "senselab.utils.data_structures.logging.logger", "modulename": "senselab.utils.data_structures.logging", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger senselab (DEBUG)>"}, "senselab.utils.data_structures.logging.ch": {"fullname": "senselab.utils.data_structures.logging.ch", "modulename": "senselab.utils.data_structures.logging", "qualname": "ch", "kind": "variable", "doc": "

\n", "default_value": "<StreamHandler (DEBUG)>"}, "senselab.utils.data_structures.logging.formatter": {"fullname": "senselab.utils.data_structures.logging.formatter", "modulename": "senselab.utils.data_structures.logging", "qualname": "formatter", "kind": "variable", "doc": "

\n", "default_value": "<logging.Formatter object>"}, "senselab.utils.data_structures.model": {"fullname": "senselab.utils.data_structures.model", "modulename": "senselab.utils.data_structures.model", "kind": "module", "doc": "

This module implements some utilities for the model class.

\n"}, "senselab.utils.data_structures.model.SenselabModel": {"fullname": "senselab.utils.data_structures.model.SenselabModel", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel", "kind": "class", "doc": "

Base configuration for SenselabModel class.

\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.model.SenselabModel.path_or_uri": {"fullname": "senselab.utils.data_structures.model.SenselabModel.path_or_uri", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.path_or_uri", "kind": "variable", "doc": "

\n", "annotation": ": Union[str, pathlib.Path]"}, "senselab.utils.data_structures.model.SenselabModel.revision": {"fullname": "senselab.utils.data_structures.model.SenselabModel.revision", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.revision", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "senselab.utils.data_structures.model.SenselabModel.validate_path_or_uri": {"fullname": "senselab.utils.data_structures.model.SenselabModel.validate_path_or_uri", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.validate_path_or_uri", "kind": "function", "doc": "

Validate the path_or_uri.

\n\n

This check is only for files and not for remote resources.\nIt does check if the path_or_uri is not empty and if it is an existing file.

\n", "signature": "(cls, value: Union[str, pathlib.Path]) -> Union[str, pathlib.Path]:", "funcdef": "def"}, "senselab.utils.data_structures.model.SenselabModel.model_config": {"fullname": "senselab.utils.data_structures.model.SenselabModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.SenselabModel.model_fields": {"fullname": "senselab.utils.data_structures.model.SenselabModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.model.SenselabModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.SenselabModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.HFModel": {"fullname": "senselab.utils.data_structures.model.HFModel", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel", "kind": "class", "doc": "

HuggingFace model.

\n", "bases": "SenselabModel"}, "senselab.utils.data_structures.model.HFModel.revision": {"fullname": "senselab.utils.data_structures.model.HFModel.revision", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.revision", "kind": "variable", "doc": "

\n", "annotation": ": typing.Annotated[str, FieldInfo(annotation=NoneType, required=True, validate_default=True)]"}, "senselab.utils.data_structures.model.HFModel.info": {"fullname": "senselab.utils.data_structures.model.HFModel.info", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.info", "kind": "variable", "doc": "

\n", "annotation": ": Optional[huggingface_hub.hf_api.ModelInfo]"}, "senselab.utils.data_structures.model.HFModel.validate_hf_model_id": {"fullname": "senselab.utils.data_structures.model.HFModel.validate_hf_model_id", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.validate_hf_model_id", "kind": "function", "doc": "

Validate the path_or_uri.

\n\n

This check is only for remote resources and not for files.\nIt checks if the specified Hugging Face model ID and revision exist in the remote Hub.

\n", "signature": "(\tcls,\tvalue: str,\tinfo: pydantic_core.core_schema.ValidationInfo) -> Union[str, pathlib.Path]:", "funcdef": "def"}, "senselab.utils.data_structures.model.HFModel.get_model_info": {"fullname": "senselab.utils.data_structures.model.HFModel.get_model_info", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.get_model_info", "kind": "function", "doc": "

Gets the model info using the HuggingFace API and saves it as a property.

\n", "signature": "(self) -> huggingface_hub.hf_api.ModelInfo:", "funcdef": "def"}, "senselab.utils.data_structures.model.HFModel.model_config": {"fullname": "senselab.utils.data_structures.model.HFModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.HFModel.model_fields": {"fullname": "senselab.utils.data_structures.model.HFModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True), 'info': FieldInfo(annotation=Union[ModelInfo, NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.model.HFModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.HFModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.SpeechBrainModel": {"fullname": "senselab.utils.data_structures.model.SpeechBrainModel", "modulename": "senselab.utils.data_structures.model", "qualname": "SpeechBrainModel", "kind": "class", "doc": "

SpeechBrain model.

\n", "bases": "HFModel"}, "senselab.utils.data_structures.model.SpeechBrainModel.model_config": {"fullname": "senselab.utils.data_structures.model.SpeechBrainModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "SpeechBrainModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.SpeechBrainModel.model_fields": {"fullname": "senselab.utils.data_structures.model.SpeechBrainModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SpeechBrainModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True), 'info': FieldInfo(annotation=Union[ModelInfo, NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.model.SpeechBrainModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.SpeechBrainModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SpeechBrainModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.PyannoteAudioModel": {"fullname": "senselab.utils.data_structures.model.PyannoteAudioModel", "modulename": "senselab.utils.data_structures.model", "qualname": "PyannoteAudioModel", "kind": "class", "doc": "

PyannoteAudioModel model.

\n", "bases": "HFModel"}, "senselab.utils.data_structures.model.PyannoteAudioModel.model_config": {"fullname": "senselab.utils.data_structures.model.PyannoteAudioModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "PyannoteAudioModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.PyannoteAudioModel.model_fields": {"fullname": "senselab.utils.data_structures.model.PyannoteAudioModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "PyannoteAudioModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True), 'info': FieldInfo(annotation=Union[ModelInfo, NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.model.PyannoteAudioModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.PyannoteAudioModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "PyannoteAudioModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.SentenceTransformersModel": {"fullname": "senselab.utils.data_structures.model.SentenceTransformersModel", "modulename": "senselab.utils.data_structures.model", "qualname": "SentenceTransformersModel", "kind": "class", "doc": "

SentenceTransformersModel model.

\n", "bases": "HFModel"}, "senselab.utils.data_structures.model.SentenceTransformersModel.model_config": {"fullname": "senselab.utils.data_structures.model.SentenceTransformersModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "SentenceTransformersModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.SentenceTransformersModel.model_fields": {"fullname": "senselab.utils.data_structures.model.SentenceTransformersModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SentenceTransformersModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True), 'info': FieldInfo(annotation=Union[ModelInfo, NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.model.SentenceTransformersModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.SentenceTransformersModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SentenceTransformersModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.TorchModel": {"fullname": "senselab.utils.data_structures.model.TorchModel", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel", "kind": "class", "doc": "

Generic torch model.

\n", "bases": "SenselabModel"}, "senselab.utils.data_structures.model.TorchModel.revision": {"fullname": "senselab.utils.data_structures.model.TorchModel.revision", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel.revision", "kind": "variable", "doc": "

\n", "annotation": ": typing.Annotated[str, FieldInfo(annotation=NoneType, required=True, validate_default=True)]"}, "senselab.utils.data_structures.model.TorchModel.validate_torch_model_id": {"fullname": "senselab.utils.data_structures.model.TorchModel.validate_torch_model_id", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel.validate_torch_model_id", "kind": "function", "doc": "

Validate the path_or_uri.

\n\n

This check is only for remote resources and not for files.\nIt checks if the specified torch model ID and revision exist in the remote Hub.

\n", "signature": "(\tcls,\tvalue: str,\tinfo: pydantic_core.core_schema.ValidationInfo) -> Union[str, pathlib.Path]:", "funcdef": "def"}, "senselab.utils.data_structures.model.TorchModel.model_config": {"fullname": "senselab.utils.data_structures.model.TorchModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.TorchModel.model_fields": {"fullname": "senselab.utils.data_structures.model.TorchModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True)}"}, "senselab.utils.data_structures.model.TorchModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.TorchModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.TorchAudioModel": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel", "kind": "class", "doc": "

TorchAudio model.

\n", "bases": "SenselabModel"}, "senselab.utils.data_structures.model.TorchAudioModel.revision": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel.revision", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel.revision", "kind": "variable", "doc": "

\n", "annotation": ": typing.Annotated[str, FieldInfo(annotation=NoneType, required=True, validate_default=True)]"}, "senselab.utils.data_structures.model.TorchAudioModel.validate_torchaudio_model_id": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel.validate_torchaudio_model_id", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel.validate_torchaudio_model_id", "kind": "function", "doc": "

Validate the path_or_uri for torchaudio models.

\n\n

This check is only for remote resources and not for files.\nIt checks if the specified torchaudio model ID exists.

\n", "signature": "(\tcls,\tvalue: str,\tinfo: pydantic_core.core_schema.ValidationInfo) -> Union[str, pathlib.Path]:", "funcdef": "def"}, "senselab.utils.data_structures.model.TorchAudioModel.model_config": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.TorchAudioModel.model_fields": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True)}"}, "senselab.utils.data_structures.model.TorchAudioModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.check_torchaudio_model_exists": {"fullname": "senselab.utils.data_structures.model.check_torchaudio_model_exists", "modulename": "senselab.utils.data_structures.model", "qualname": "check_torchaudio_model_exists", "kind": "function", "doc": "

Private function to check if a torchaudio model exists.

\n", "signature": "(model_id: str) -> bool:", "funcdef": "def"}, "senselab.utils.data_structures.model.is_torch_model": {"fullname": "senselab.utils.data_structures.model.is_torch_model", "modulename": "senselab.utils.data_structures.model", "qualname": "is_torch_model", "kind": "function", "doc": "

Check if a file is a torch model.

\n", "signature": "(file_path: pathlib.Path) -> bool:", "funcdef": "def"}, "senselab.utils.data_structures.model.check_hf_repo_exists": {"fullname": "senselab.utils.data_structures.model.check_hf_repo_exists", "modulename": "senselab.utils.data_structures.model", "qualname": "check_hf_repo_exists", "kind": "function", "doc": "

Private function to check if a Hugging Face repository exists.

\n", "signature": "(repo_id: str, revision: str = 'main', repo_type: str = 'model') -> bool:", "funcdef": "def"}, "senselab.utils.data_structures.model.check_github_repo_exists": {"fullname": "senselab.utils.data_structures.model.check_github_repo_exists", "modulename": "senselab.utils.data_structures.model", "qualname": "check_github_repo_exists", "kind": "function", "doc": "

Private function to check if a GitHub repository exists.

\n", "signature": "(repo_id: str, branch: str = 'main') -> bool:", "funcdef": "def"}, "senselab.utils.data_structures.pydra_helpers": {"fullname": "senselab.utils.data_structures.pydra_helpers", "modulename": "senselab.utils.data_structures.pydra_helpers", "kind": "module", "doc": "

Functionality for Pydra.

\n"}, "senselab.utils.data_structures.pydra_helpers.bytes_repr_arraylike": {"fullname": "senselab.utils.data_structures.pydra_helpers.bytes_repr_arraylike", "modulename": "senselab.utils.data_structures.pydra_helpers", "qualname": "bytes_repr_arraylike", "kind": "function", "doc": "

Register a serializer for Torch tensors that allows Pydra to properly use them.

\n", "signature": "(obj: torch.Tensor, cache: pydra.utils.hash.Cache) -> Iterator[bytes]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line": {"fullname": "senselab.utils.data_structures.script_line", "modulename": "senselab.utils.data_structures.script_line", "kind": "module", "doc": "

This module contains the definition of the ScriptLine class.

\n"}, "senselab.utils.data_structures.script_line.ScriptLine": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine", "kind": "class", "doc": "

A class to represent a line in a script.

\n\n
Attributes:
\n\n
    \n
  • text (Optional[str]): The full text of the line (if provided).
    • \n
  • speaker (Optional[str]): The speaker of the line (if provided).
    • \n
  • chunks (Optional[List['ScriptLine']]): A list of script lines (if provided).
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.script_line.ScriptLine.text": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.text", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.text", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "senselab.utils.data_structures.script_line.ScriptLine.speaker": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.speaker", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.speaker", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "senselab.utils.data_structures.script_line.ScriptLine.start": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.start", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.start", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.utils.data_structures.script_line.ScriptLine.end": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.end", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.end", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.utils.data_structures.script_line.ScriptLine.chunks": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.chunks", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.chunks", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[senselab.utils.data_structures.script_line.ScriptLine]]"}, "senselab.utils.data_structures.script_line.ScriptLine.validate_text_and_speaker": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.validate_text_and_speaker", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.validate_text_and_speaker", "kind": "function", "doc": "

Validate that at least one of text or speaker is provided.

\n", "signature": "(\tcls,\tvalues: Dict[str, Any],\t_: pydantic_core.core_schema.ValidationInfo) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.strings_must_be_stripped": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.strings_must_be_stripped", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.strings_must_be_stripped", "kind": "function", "doc": "

Strip the string of leading and trailing whitespace.

\n\n
Arguments:
\n\n
    \n
  • v (str): The text or speaker of the script line.
    • \n
\n\n
Returns:
\n\n
\n

str: The validated text or speaker of the script line.

\n
\n", "signature": "(cls, v: str, _: pydantic_core.core_schema.ValidationInfo) -> str:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.timestamps_must_be_positive": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.timestamps_must_be_positive", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.timestamps_must_be_positive", "kind": "function", "doc": "

Validate that the start and end timestamps are positive.

\n\n
Arguments:
\n\n
    \n
  • v (float): The timestamp of the script line.
    • \n
\n\n
Returns:
\n\n
\n

float: The validated timestamp.

\n
\n", "signature": "(cls, v: float, _: pydantic_core.core_schema.ValidationInfo) -> float:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.get_text": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.get_text", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.get_text", "kind": "function", "doc": "

Get the full text of the script line.

\n\n
Returns:
\n\n
\n

Optional[str, None]: The full text of the script line, or None if not provided.

\n
\n", "signature": "(self) -> Optional[str]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.get_speaker": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.get_speaker", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.get_speaker", "kind": "function", "doc": "

Get the speaker of the script line.

\n\n
Returns:
\n\n
\n

Optional[str]: The speaker of the script line.

\n
\n", "signature": "(self) -> Optional[str]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.get_timestamps": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.get_timestamps", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.get_timestamps", "kind": "function", "doc": "

Get the start and end timestamps of the script line.

\n\n
Returns:
\n\n
\n

Tuple[Optional[float], Optional[float]]: The start and end timestamps of the script line.

\n
\n", "signature": "(self) -> Tuple[Optional[float], Optional[float]]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.get_chunks": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.get_chunks", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.get_chunks", "kind": "function", "doc": "

Get the list of chunks in the script line.

\n\n
Returns:
\n\n
\n

Optional[List['ScriptLine']]: The list of chunks.

\n
\n", "signature": "(\tself) -> Optional[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.from_dict": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.from_dict", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.from_dict", "kind": "function", "doc": "

Create a ScriptLine instance from a dictionary.

\n\n
Arguments:
\n\n
    \n
  • d (Dict[str, Any]): The dictionary containing the script line data.
    • \n
\n\n
Returns:
\n\n
\n

ScriptLine: An instance of ScriptLine.

\n
\n", "signature": "(\tcls,\td: Dict[str, Any]) -> senselab.utils.data_structures.script_line.ScriptLine:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.model_config": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.model_config", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.script_line.ScriptLine.model_fields": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.model_fields", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'text': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'speaker': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'start': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'end': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'chunks': FieldInfo(annotation=Union[List[ScriptLine], NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.script_line.ScriptLine.model_computed_fields": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.model_computed_fields", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.tasks": {"fullname": "senselab.utils.tasks", "modulename": "senselab.utils.tasks", "kind": "module", "doc": "

\n"}, "senselab.utils.tasks.batching": {"fullname": "senselab.utils.tasks.batching", "modulename": "senselab.utils.tasks.batching", "kind": "module", "doc": "

Provides functionality for batch processing.

\n"}, "senselab.utils.tasks.batching.batch_list": {"fullname": "senselab.utils.tasks.batching.batch_list", "modulename": "senselab.utils.tasks.batching", "qualname": "batch_list", "kind": "function", "doc": "

Splits a list into a specified number of batches.

\n\n
Arguments:
\n\n
    \n
  • items (List[Any]): The list of items to be batched.
    • \n
  • n_batches (int): The number of batches to divide the list into.
    • \n
\n\n
Returns:
\n\n
\n

List[List[Any]]: A list of lists, where each sublist is a batch of items.

\n
\n", "signature": "(items: List[Any], n_batches: int) -> List[List[Any]]:", "funcdef": "def"}, "senselab.utils.tasks.cca_cka": {"fullname": "senselab.utils.tasks.cca_cka", "modulename": "senselab.utils.tasks.cca_cka", "kind": "module", "doc": "

This module is for computing CCA and CKA.

\n"}, "senselab.utils.tasks.cca_cka.compute_cca": {"fullname": "senselab.utils.tasks.cca_cka.compute_cca", "modulename": "senselab.utils.tasks.cca_cka", "qualname": "compute_cca", "kind": "function", "doc": "

Compute the mean squared CCA correlation (R^2_{CCA}).

\n\n
Arguments:
\n\n
    \n
  • features_x (torch.Tensor): A num_examples x num_features matrix of features.
    • \n
  • features_y (torch.Tensor): A num_examples x num_features matrix of features.
    • \n
\n\n
Returns:
\n\n
\n

float: The mean squared CCA correlations between X and Y.

\n
\n", "signature": "(features_x: torch.Tensor, features_y: torch.Tensor) -> float:", "funcdef": "def"}, "senselab.utils.tasks.cca_cka.CKAKernelType": {"fullname": "senselab.utils.tasks.cca_cka.CKAKernelType", "modulename": "senselab.utils.tasks.cca_cka", "qualname": "CKAKernelType", "kind": "class", "doc": "

CKA kernel types.

\n", "bases": "enum.Enum"}, "senselab.utils.tasks.cca_cka.CKAKernelType.LINEAR": {"fullname": "senselab.utils.tasks.cca_cka.CKAKernelType.LINEAR", "modulename": "senselab.utils.tasks.cca_cka", "qualname": "CKAKernelType.LINEAR", "kind": "variable", "doc": "

\n", "default_value": "<CKAKernelType.LINEAR: 'linear'>"}, "senselab.utils.tasks.cca_cka.CKAKernelType.RBF": {"fullname": "senselab.utils.tasks.cca_cka.CKAKernelType.RBF", "modulename": "senselab.utils.tasks.cca_cka", "qualname": "CKAKernelType.RBF", "kind": "variable", "doc": "

\n", "default_value": "<CKAKernelType.RBF: 'rbf'>"}, "senselab.utils.tasks.cca_cka.compute_cka": {"fullname": "senselab.utils.tasks.cca_cka.compute_cka", "modulename": "senselab.utils.tasks.cca_cka", "qualname": "compute_cka", "kind": "function", "doc": "

Compute CKA between feature matrices.

\n\n
Arguments:
\n\n
    \n
  • features_x (torch.Tensor): A num_examples x num_features matrix of features.
    • \n
  • features_y (torch.Tensor): A num_examples x num_features matrix of features.
    • \n
  • kernel (CKAKernelType): Type of kernel to use (CKAKernelType.LINEAR or CKAKernelType.RBF).
    • \n
  • Default is CKAKernelType.LINEAR.
    • \n
  • threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth\n(used only if kernel is CKAKernelType.RBF).
    • \n
\n\n
Returns:
\n\n
\n

float: The value of CKA between X and Y.

\n
\n", "signature": "(\tfeatures_x: torch.Tensor,\tfeatures_y: torch.Tensor,\tkernel: senselab.utils.tasks.cca_cka.CKAKernelType = <CKAKernelType.LINEAR: 'linear'>,\tthreshold: float = 1.0) -> float:", "funcdef": "def"}, "senselab.utils.tasks.cca_cka.compute_cca_pt": {"fullname": "senselab.utils.tasks.cca_cka.compute_cca_pt", "modulename": "senselab.utils.tasks.cca_cka", "qualname": "compute_cca_pt", "kind": "function", "doc": "

Compute the mean squared CCA correlation (R^2_{CCA}).

\n\n
Arguments:
\n\n
    \n
  • features_x (torch.Tensor): A num_examples x num_features matrix of features.
    • \n
  • features_y (torch.Tensor): A num_examples x num_features matrix of features.
    • \n
\n\n
Returns:
\n\n
\n

float: The mean squared CCA correlations between X and Y.

\n
\n", "signature": "(features_x: torch.Tensor, features_y: torch.Tensor) -> float:", "funcdef": "def"}, "senselab.utils.tasks.cca_cka.compute_cka_pt": {"fullname": "senselab.utils.tasks.cca_cka.compute_cka_pt", "modulename": "senselab.utils.tasks.cca_cka", "qualname": "compute_cka_pt", "kind": "function", "doc": "

Compute CKA between feature matrices.

\n\n
Arguments:
\n\n
    \n
  • features_x (torch.Tensor): A num_examples x num_features matrix of features.
    • \n
  • features_y (torch.Tensor): A num_examples x num_features matrix of features.
    • \n
  • kernel (CKAKernelType): Type of kernel to use (CKAKernelType.LINEAR or CKAKernelType.RBF).
    • \n
  • Default is CKAKernelType.LINEAR.
    • \n
  • threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth\n(used only if kernel is CKAKernelType.RBF).
    • \n
\n\n
Returns:
\n\n
\n

float: The value of CKA between X and Y.

\n
\n", "signature": "(\tfeatures_x: torch.Tensor,\tfeatures_y: torch.Tensor,\tkernel: senselab.utils.tasks.cca_cka.CKAKernelType = <CKAKernelType.LINEAR: 'linear'>,\tthreshold: float = 1.0) -> float:", "funcdef": "def"}, "senselab.utils.tasks.cosine_similarity": {"fullname": "senselab.utils.tasks.cosine_similarity", "modulename": "senselab.utils.tasks.cosine_similarity", "kind": "module", "doc": "

This module provides the implementation of cosine similarity.

\n"}, "senselab.utils.tasks.cosine_similarity.compute_cosine_similarity": {"fullname": "senselab.utils.tasks.cosine_similarity.compute_cosine_similarity", "modulename": "senselab.utils.tasks.cosine_similarity", "qualname": "compute_cosine_similarity", "kind": "function", "doc": "

Compute the cosine similarity between two torch tensors.

\n\n
Arguments:
\n\n
    \n
  • tensor1 (Tensor): The first input tensor.
    • \n
  • tensor2 (Tensor): The second input tensor.
    • \n
\n\n
Returns:
\n\n
\n

float: The cosine similarity between the two input tensors.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the input tensors are not of the same shape.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> tensor1 = torch.tensor([1.0, 2.0, 3.0])\n>>> tensor2 = torch.tensor([4.0, 5.0, 6.0])\n>>> cosine_similarity(tensor1, tensor2)\n0.9746318461970762\n
\n
\n \n
\n
>>> tensor1 = torch.tensor([1.0, 0.0, -1.0])\n>>> tensor2 = torch.tensor([-1.0, 0.0, 1.0])\n>>> cosine_similarity(tensor1, tensor2)\n-1.0\n
\n
\n
\n\n
Note:
\n\n
\n

This function assumes the input tensors are 1-dimensional and have the same shape.

\n
\n", "signature": "(tensor1: torch.Tensor, tensor2: torch.Tensor) -> float:", "funcdef": "def"}, "senselab.utils.tasks.cosine_similarity.cosine_similarity_pt": {"fullname": "senselab.utils.tasks.cosine_similarity.cosine_similarity_pt", "modulename": "senselab.utils.tasks.cosine_similarity", "qualname": "cosine_similarity_pt", "kind": "function", "doc": "

Compute the cosine similarity between two torch tensors.

\n\n
Arguments:
\n\n
    \n
  • tensor1 (Tensor): The first input tensor.
    • \n
  • tensor2 (Tensor): The second input tensor.
    • \n
\n\n
Returns:
\n\n
\n

float: The cosine similarity between the two input tensors.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the input tensors are not of the same shape.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> tensor1 = torch.tensor([1.0, 2.0, 3.0])\n>>> tensor2 = torch.tensor([4.0, 5.0, 6.0])\n>>> cosine_similarity(tensor1, tensor2)\n0.9746318461970762\n
\n
\n \n
\n
>>> tensor1 = torch.tensor([1.0, 0.0, -1.0])\n>>> tensor2 = torch.tensor([-1.0, 0.0, 1.0])\n>>> cosine_similarity(tensor1, tensor2)\n-1.0\n
\n
\n
\n\n
Note:
\n\n
\n

This function assumes the input tensors are 1-dimensional and have the same shape.

\n
\n", "signature": "(tensor1: torch.Tensor, tensor2: torch.Tensor) -> float:", "funcdef": "def"}, "senselab.utils.tasks.cross_correlation": {"fullname": "senselab.utils.tasks.cross_correlation", "modulename": "senselab.utils.tasks.cross_correlation", "kind": "module", "doc": "

This module contains functions for computing the normalized cross-correlation between two signals.

\n"}, "senselab.utils.tasks.cross_correlation.compute_normalized_cross_correlation": {"fullname": "senselab.utils.tasks.cross_correlation.compute_normalized_cross_correlation", "modulename": "senselab.utils.tasks.cross_correlation", "qualname": "compute_normalized_cross_correlation", "kind": "function", "doc": "

Calculate the normalized cross-correlation between two signals.

\n\n
Arguments:
\n\n
    \n
  • signal1 (torch.Tensor): The first input signal as a PyTorch tensor.
    • \n
  • signal2 (torch.Tensor): The second input signal as a PyTorch tensor.
    • \n
\n\n
Returns:
\n\n
\n

torch.Tensor: The normalized cross-correlation value between the two input signals.

\n
\n\n
Examples:
\n\n
\n
\n
>>> signal1 = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])\n>>> signal2 = torch.tensor([2.0, 3.0, 4.0])\n>>> normalized_cross_correlation(signal1, signal2)\nTensor([0.30151134, 0.51298918, 0.77459667, 0.9486833 , 0.90453403, 0.70710678, 0.43643578])\n
\n
\n
\n\n
Note:
\n\n
\n

This function assumes the input signals are one-dimensional\n and contain sufficient elements for meaningful cross-correlation.

\n
\n", "signature": "(signal1: torch.Tensor, signal2: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "senselab.utils.tasks.cross_correlation.compute_normalized_cross_correlation_pt": {"fullname": "senselab.utils.tasks.cross_correlation.compute_normalized_cross_correlation_pt", "modulename": "senselab.utils.tasks.cross_correlation", "qualname": "compute_normalized_cross_correlation_pt", "kind": "function", "doc": "

Calculate the normalized cross-correlation between two signals.

\n\n
Arguments:
\n\n
    \n
  • signal1 (torch.Tensor): The first input signal as a PyTorch tensor.
    • \n
  • signal2 (torch.Tensor): The second input signal as a PyTorch tensor.
    • \n
\n\n
Returns:
\n\n
\n

torch.Tensor: The normalized cross-correlation value between the two input signals.

\n
\n\n
Examples:
\n\n
\n
\n
>>> signal1 = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])\n>>> signal2 = torch.tensor([2.0, 3.0, 4.0])\n>>> normalized_cross_correlation(signal1, signal2)\nTensor([0.30151134, 0.51298918, 0.77459667, 0.9486833 , 0.90453403, 0.70710678, 0.43643578])\n
\n
\n
\n\n
Note:
\n\n
\n

This function assumes the input signals are one-dimensional\n and contain sufficient elements for meaningful cross-correlation.

\n
\n", "signature": "(signal1: torch.Tensor, signal2: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "senselab.utils.tasks.dimensionality_reduction": {"fullname": "senselab.utils.tasks.dimensionality_reduction", "modulename": "senselab.utils.tasks.dimensionality_reduction", "kind": "module", "doc": "

This module provides the implementation of dimensionality reduction.

\n\n

Dimensionality reduction is a crucial preprocessing step in many machine learning and data analysis pipelines.\nIt aims to project a high-dimensional dataset into a lower-dimensional subspace while preserving as much of the\nsignificant structure and information as possible. This process is particularly useful for visualization,\nnoise reduction, and improving computational efficiency in downstream tasks.

\n\n

This module specifically implements three popular dimensionality reduction techniques: Principal Component Analysis\n(PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP).

\n\n

Task Overview

\n\n
    \n
  • Input: High-dimensional data (typically represented as a matrix or tensor), i.e. A tensor of shape (100, 10)
    • \n
  • Output: Lower-dimensional representation of the input data, i.e. A tensor of shape (100, 2)
    • \n
  • Goal: Preserve important structures or relationships in the data while reducing its dimensionality
    • \n
\n\n

Evaluation Metrics

\n\n

The effectiveness of dimensionality reduction techniques can be evaluated using several metrics, including but not\nlimited to:

\n\n
    \n
  • Reconstruction Error: Measures the difference between the original data and its reduced representation when\nprojected back to the original space. Commonly used for techniques like PCA.
    • \n
  • Trustworthiness: Assesses how much the model preserves neighbor relationships between the high-dimensional\nspace and the low-dimensional space.
    • \n
  • Continuity: Evaluates how well the local structure of the data is preserved in the reduced space.
    • \n
\n\n

Supported models

\n\n
    \n

  • Principal Component Analysis (PCA): PCA is a widely recognized technique in the field of data science\nand machine learning. It excels in linear dimensionality reduction, making it an invaluable tool for\nsimplifying complex datasets. PCA's primary strength lies in its ability to capture the most significant\nvariance within the data. By identifying the principal components that account for the majority of the variation,\nPCA allows researchers and analysts to reduce the dimensionality of their data while retaining its most important\nfeatures. This makes PCA particularly useful for tasks like data visualization and feature extraction.

    • \n

  • t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is primarily utilized as a visualization technique,\nsetting it apart from more general dimensionality reduction methods. It is able to preserve local structures\nwithin data when projecting it into lower-dimensional spaces, typically two or three dimensions. This property\nmakes t-SNE exceptionally good at creating visually interpretable embeddings that can reveal clusters and patterns\nin complex datasets. However, t-SNE is not suitable for tasks that require the preservation of global structures.

    • \n

  • Uniform Manifold Approximation and Projection (UMAP): UMAP is a relatively newer technique that has gained\nsignificant attention due to its versatility and performance. UMAP is able to balance the preservation of both\nglobal and local structures within data. Like t-SNE, UMAP can be used visualizing high-dimensional data in two\nor three dimensions. However, UMAP is also effective as a general-purpose non-linear dimensionality reduction\ntechnique, making it suitable for a wide range of applications where preserving the overall structure of the\ndata in lower dimensions is crucial.

    • \n
\n\n

Summary:

\n\n\n\n\n \n \n \n \n\n\n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n
FeaturePCAt-SNEUMAP
TypeLinearNon-linearNon-linear
PreservesGlobal structure (variance)Local structure, cluster patternsLocal and global structure to some extent
Filters outNoise, less important varianceGlobal structure, distances between clustersLess important variance, noise
Best for- Understanding main sources of variation
- Data compression
- Preprocessing
- Linear relationships		- Visualizing high-dimensional data
- Discovering clusters or patterns
- Exploratory data analysis		- General-purpose dimensionality reduction
- Balancing local and global structure preservation
Limitations- Assumes linear relationships
- May miss complex patterns		- Can distort global structure
- Non-deterministic		- Sensitive to parameters
- Does not completely preserve density
\n\n

References:

\n\n\n\n

Notes:

\n\n

This module leverages the implementations from scikit-learn for PCA and t-SNE, and the umap-learn library for UMAP.\nThe reduced data is returned as a PyTorch tensor, allowing seamless integration with PyTorch-based workflows.

\n\n

Learn more:

\n\n\n"}, "senselab.utils.tasks.dimensionality_reduction.compute_dimensionality_reduction": {"fullname": "senselab.utils.tasks.dimensionality_reduction.compute_dimensionality_reduction", "modulename": "senselab.utils.tasks.dimensionality_reduction", "qualname": "compute_dimensionality_reduction", "kind": "function", "doc": "

Reduce the dimensionality of the given data using the specified model.

\n\n

This function provides three methods: PCA, t-SNE, or UMAP. The reduced data is returned as a PyTorch tensor.

\n\n
Arguments:
\n\n
    \n
  • data (torch.Tensor): Input data tensor of shape (n_samples, n_features).
    • \n
  • model ({\"pca\", \"tsne\", \"umap\"}, optional): The dimensionality reduction model to use.\nChoices are:\n \"pca\" for Principal Component Analysis (default),\n \"tsne\" for t-Distributed Stochastic Neighbor Embedding, or\n \"umap\" for Uniform Manifold Approximation and Projection
    • \n
  • n_components (int, optional): Number of dimensions in the output.\nMust be less than or equal to the number of features in the input data.\nDefaults to 2.
    • \n
  • **kwargs: Additional keyword arguments to pass to the chosen model.\n(See below for links to each model's documentation for available parameters.)
    • \n
\n\n
Returns:
\n\n
\n

torch.Tensor: The reduced data tensor of shape (n_samples, n_components).

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If an invalid model choice is provided or if n_components is\ngreater than the number of features in the input data or less than 0.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> data = torch.randn(100, 10)  # 100 samples, 10 features\n>>> data.shape\ntorch.Size([100, 10])\n
\n
\n \n
\n
>>> reduced_data = compute_dimensionality_reduction(data)\n>>> print(reduced_data.shape)\ntorch.Size([100, 2])\n
\n
\n \n
\n
>>> reduced_data = compute_dimensionality_reduction(data, model="pca", n_components=2, svd_solver="full")\n>>> print(reduced_data.shape)\ntorch.Size([100, 2])\n
\n
\n \n
\n
>>> reduced_data = compute_dimensionality_reduction(data, model="tsne", n_components=3, perplexity=30)\n>>> print(reduced_data.shape)\ntorch.Size([100, 3])\n
\n
\n \n
\n
>>> reduced_data = compute_dimensionality_reduction(data, model="umap", n_components=4,\n    force_approximation_algorithm=False, init='spectral', learning_rate=1.0, n_neighbors=5)\n>>> print(reduced_data.shape)\ntorch.Size([100, 4])\n
\n
\n
\n\n
Notes:
\n\n
\n

This function uses implementations from scikit-learn for PCA and t-SNE, and the umap-learn\n library for UMAP. For detailed information about each method and its parameters, please\n refer to the following documentation:

\n \n \n
\n", "signature": "(\tdata: torch.Tensor,\tmodel: Literal['pca', 'tsne', 'umap'] = 'pca',\tn_components: int = 2,\t**kwargs: object) -> torch.Tensor:", "funcdef": "def"}, "senselab.utils.tasks.dimensionality_reduction.compute_dimensionality_reduction_pt": {"fullname": "senselab.utils.tasks.dimensionality_reduction.compute_dimensionality_reduction_pt", "modulename": "senselab.utils.tasks.dimensionality_reduction", "qualname": "compute_dimensionality_reduction_pt", "kind": "function", "doc": "

Reduce the dimensionality of the given data using the specified model.

\n\n

This function provides three methods: PCA, t-SNE, or UMAP. The reduced data is returned as a PyTorch tensor.

\n\n
Arguments:
\n\n
    \n
  • data (torch.Tensor): Input data tensor of shape (n_samples, n_features).
    • \n
  • model ({\"pca\", \"tsne\", \"umap\"}, optional): The dimensionality reduction model to use.\nChoices are:\n \"pca\" for Principal Component Analysis (default),\n \"tsne\" for t-Distributed Stochastic Neighbor Embedding, or\n \"umap\" for Uniform Manifold Approximation and Projection
    • \n
  • n_components (int, optional): Number of dimensions in the output.\nMust be less than or equal to the number of features in the input data.\nDefaults to 2.
    • \n
  • **kwargs: Additional keyword arguments to pass to the chosen model.\n(See below for links to each model's documentation for available parameters.)
    • \n
\n\n
Returns:
\n\n
\n

torch.Tensor: The reduced data tensor of shape (n_samples, n_components).

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If an invalid model choice is provided or if n_components is\ngreater than the number of features in the input data or less than 0.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> data = torch.randn(100, 10)  # 100 samples, 10 features\n>>> data.shape\ntorch.Size([100, 10])\n
\n
\n \n
\n
>>> reduced_data = compute_dimensionality_reduction(data)\n>>> print(reduced_data.shape)\ntorch.Size([100, 2])\n
\n
\n \n
\n
>>> reduced_data = compute_dimensionality_reduction(data, model="pca", n_components=2, svd_solver="full")\n>>> print(reduced_data.shape)\ntorch.Size([100, 2])\n
\n
\n \n
\n
>>> reduced_data = compute_dimensionality_reduction(data, model="tsne", n_components=3, perplexity=30)\n>>> print(reduced_data.shape)\ntorch.Size([100, 3])\n
\n
\n \n
\n
>>> reduced_data = compute_dimensionality_reduction(data, model="umap", n_components=4,\n    force_approximation_algorithm=False, init='spectral', learning_rate=1.0, n_neighbors=5)\n>>> print(reduced_data.shape)\ntorch.Size([100, 4])\n
\n
\n
\n\n
Notes:
\n\n
\n

This function uses implementations from scikit-learn for PCA and t-SNE, and the umap-learn\n library for UMAP. For detailed information about each method and its parameters, please\n refer to the following documentation:

\n \n \n
\n", "signature": "(\tdata: torch.Tensor,\tmodel: Literal['pca', 'tsne', 'umap'] = 'pca',\tn_components: int = 2,\t**kwargs: object) -> torch.Tensor:", "funcdef": "def"}, "senselab.utils.tasks.eer": {"fullname": "senselab.utils.tasks.eer", "modulename": "senselab.utils.tasks.eer", "kind": "module", "doc": "

This module implements some utilities for computing the Equal Error Rate (EER).

\n"}, "senselab.utils.tasks.eer.compute_eer": {"fullname": "senselab.utils.tasks.eer.compute_eer", "modulename": "senselab.utils.tasks.eer", "qualname": "compute_eer", "kind": "function", "doc": "

Compute the Equal Error Rate (EER).

\n\n
Arguments:
\n\n
    \n
  • predictions (torch.Tensor): A 1D tensor of predictions.
    • \n
  • targets (torch.Tensor): A 1D tensor of targets.
    • \n
\n\n
Returns:
\n\n
\n

Tuple[float, float]: The EER and the threshold for the EER.

\n
\n", "signature": "(predictions: torch.Tensor, targets: torch.Tensor) -> Tuple[float, float]:", "funcdef": "def"}, "senselab.utils.tasks.eer.compute_eer_pt": {"fullname": "senselab.utils.tasks.eer.compute_eer_pt", "modulename": "senselab.utils.tasks.eer", "qualname": "compute_eer_pt", "kind": "function", "doc": "

Compute the Equal Error Rate (EER).

\n\n
Arguments:
\n\n
    \n
  • predictions (torch.Tensor): A 1D tensor of predictions.
    • \n
  • targets (torch.Tensor): A 1D tensor of targets.
    • \n
\n\n
Returns:
\n\n
\n

Tuple[float, float]: The EER and the threshold for the EER.

\n
\n", "signature": "(predictions: torch.Tensor, targets: torch.Tensor) -> Tuple[float, float]:", "funcdef": "def"}, "senselab.utils.tasks.input_output": {"fullname": "senselab.utils.tasks.input_output", "modulename": "senselab.utils.tasks.input_output", "kind": "module", "doc": "

This module implements some IOTask utilities.

\n\n

This modules is deprecated and will be removed soon.

\n"}, "senselab.utils.tasks.input_output.read_files_from_disk": {"fullname": "senselab.utils.tasks.input_output.read_files_from_disk", "modulename": "senselab.utils.tasks.input_output", "qualname": "read_files_from_disk", "kind": "function", "doc": "

Read files from disk and create a Hugging Face Dataset object.

\n", "signature": "(files: Union[str, List[str]]) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.utils.tasks.input_output.read_dataset_from_disk": {"fullname": "senselab.utils.tasks.input_output.read_dataset_from_disk", "modulename": "senselab.utils.tasks.input_output", "qualname": "read_dataset_from_disk", "kind": "function", "doc": "

Loads a Hugging Face Dataset object from disk.

\n\n

It determines the format based on the file extension or directory.

\n", "signature": "(input_path: str, split: str, streaming: bool = False) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.utils.tasks.input_output.read_dataset_from_hub": {"fullname": "senselab.utils.tasks.input_output.read_dataset_from_hub", "modulename": "senselab.utils.tasks.input_output", "qualname": "read_dataset_from_hub", "kind": "function", "doc": "

Loads a Hugging Face Dataset object from the Hugging Face Hub.

\n\n

It includes support for private repositories.

\n", "signature": "(\tremote_repository: str,\trevision: str = 'main',\tsplit: str = 'all') -> Dict[str, Any]:", "funcdef": "def"}, "senselab.utils.tasks.input_output.push_dataset_to_hub": {"fullname": "senselab.utils.tasks.input_output.push_dataset_to_hub", "modulename": "senselab.utils.tasks.input_output", "qualname": "push_dataset_to_hub", "kind": "function", "doc": "

Uploads a Hugging Face Dataset object to the Hugging Face Hub.

\n", "signature": "(\tdataset: Dict[str, Any],\tremote_repository: str,\trevision: str = 'main',\tsplit: str = 'all') -> None:", "funcdef": "def"}, "senselab.utils.tasks.input_output.save_dataset_to_disk": {"fullname": "senselab.utils.tasks.input_output.save_dataset_to_disk", "modulename": "senselab.utils.tasks.input_output", "qualname": "save_dataset_to_disk", "kind": "function", "doc": "

Saves a Hugging Face Dataset object to disk.

\n", "signature": "(\tdataset: Dict[str, Any],\toutput_directory: str,\toutput_name: str,\toutput_format: str = 'parquet') -> None:", "funcdef": "def"}, "senselab.utils.tasks.input_output.read_files_from_disk_pt": {"fullname": "senselab.utils.tasks.input_output.read_files_from_disk_pt", "modulename": "senselab.utils.tasks.input_output", "qualname": "read_files_from_disk_pt", "kind": "function", "doc": "

Read files from disk and create a Hugging Face Dataset object.

\n", "signature": "(files: Union[str, List[str]]) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.utils.tasks.input_output.read_dataset_from_disk_pt": {"fullname": "senselab.utils.tasks.input_output.read_dataset_from_disk_pt", "modulename": "senselab.utils.tasks.input_output", "qualname": "read_dataset_from_disk_pt", "kind": "function", "doc": "

Loads a Hugging Face Dataset object from disk.

\n\n

It determines the format based on the file extension or directory.

\n", "signature": "(input_path: str, split: str, streaming: bool = False) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.utils.tasks.input_output.read_dataset_from_hub_pt": {"fullname": "senselab.utils.tasks.input_output.read_dataset_from_hub_pt", "modulename": "senselab.utils.tasks.input_output", "qualname": "read_dataset_from_hub_pt", "kind": "function", "doc": "

Loads a Hugging Face Dataset object from the Hugging Face Hub.

\n\n

It includes support for private repositories.

\n", "signature": "(\tremote_repository: str,\trevision: str = 'main',\tsplit: str = 'all') -> Dict[str, Any]:", "funcdef": "def"}, "senselab.utils.tasks.input_output.push_dataset_to_hub_pt": {"fullname": "senselab.utils.tasks.input_output.push_dataset_to_hub_pt", "modulename": "senselab.utils.tasks.input_output", "qualname": "push_dataset_to_hub_pt", "kind": "function", "doc": "

Uploads a Hugging Face Dataset object to the Hugging Face Hub.

\n", "signature": "(\tdataset: Dict[str, Any],\tremote_repository: str,\trevision: str = 'main',\tsplit: str = 'all') -> None:", "funcdef": "def"}, "senselab.utils.tasks.input_output.save_dataset_to_disk_pt": {"fullname": "senselab.utils.tasks.input_output.save_dataset_to_disk_pt", "modulename": "senselab.utils.tasks.input_output", "qualname": "save_dataset_to_disk_pt", "kind": "function", "doc": "

Saves a Hugging Face Dataset object to disk.

\n", "signature": "(\tdataset: Dict[str, Any],\toutput_directory: str,\toutput_name: str,\toutput_format: str = 'parquet') -> None:", "funcdef": "def"}, "senselab.utils.tasks.plotting": {"fullname": "senselab.utils.tasks.plotting", "modulename": "senselab.utils.tasks.plotting", "kind": "module", "doc": "

This module implements plotting methods for utilities.

\n"}, "senselab.utils.tasks.plotting.plot_transcript": {"fullname": "senselab.utils.tasks.plotting.plot_transcript", "modulename": "senselab.utils.tasks.plotting", "qualname": "plot_transcript", "kind": "function", "doc": "

Plots the transcript visualization over time.

\n\n
Arguments:
\n\n
    \n
  • transcript (ScriptLine): The transcript object containing chunks of text with start and end times.
    • \n
\n\n
Returns:
\n\n
\n

None

\n
\n\n
Todo:
\n\n
\n
    \n
  • Add option to save the plot
    • \n
  • Add option to choose the size of the Figure
    • \n
  • Add check if transcript contains chunks with time information
    • \n
\n
\n", "signature": "(\ttranscript: senselab.utils.data_structures.script_line.ScriptLine) -> None:", "funcdef": "def"}, "senselab.utils.tasks.plotting.plot_segment": {"fullname": "senselab.utils.tasks.plotting.plot_segment", "modulename": "senselab.utils.tasks.plotting", "qualname": "plot_segment", "kind": "function", "doc": "

Plots the segments of the transcript over time.

\n\n
Arguments:
\n\n
    \n
  • segments (List[ScriptLine]): The segments object containing segments with start and end times and a label.
    • \n
\n\n
Returns:
\n\n
\n

None

\n
\n\n
Todo:
\n\n
\n
    \n
  • Add option to save the plot
    • \n
  • Add option to choose the size of the Figure
    • \n
  • Add check if transcript contains segments with time information
    • \n
\n
\n", "signature": "(\tsegments: List[senselab.utils.data_structures.script_line.ScriptLine]) -> None:", "funcdef": "def"}, "senselab.utils.tasks.pooling": {"fullname": "senselab.utils.tasks.pooling", "modulename": "senselab.utils.tasks.pooling", "kind": "module", "doc": "

This module implements pooling methods for torch tensors.

\n"}, "senselab.utils.tasks.pooling.pooling": {"fullname": "senselab.utils.tasks.pooling.pooling", "modulename": "senselab.utils.tasks.pooling", "qualname": "pooling", "kind": "function", "doc": "

Apply pooling to the input tensor along the specified dimension.

\n\n

This function provides three pooling methods: mean, min, or max. The pooled tensor is returned as a PyTorch tensor.

\n\n
Arguments:
\n\n
    \n
  • data (torch.Tensor): Input tensor of shape (n_samples, n_features, n_channels).
    • \n
  • pool_type (str, optional): The pooling method to use.\nChoices are:\n * \"mean\" for mean pooling\n * \"min\" for min pooling\n * \"max\" for max pooling\nDefaults to \"mean\".
    • \n
  • dimension (int, optional): The dimension along which to apply the pooling.\nDefaults to 0.
    • \n
  • keep_dimension (bool, optional): Whether to keep the original tensor dimension after pooling.
    • \n
\n\n
Returns:
\n\n
\n

torch.Tensor: The pooled tensor of shape (n_samples, n_features, n_channels).

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If an invalid pooling method choice is provided.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> data = torch.randn(10, 20, 30)  # 10 samples, 20 features, 30 channels\n>>> data.shape\ntorch.Size([10, 20, 30])\n
\n
\n \n
\n
>>> pooled_data = pooling(data, pool_type="mean", dimension=0, keep_dimension=True)\n>>> print(pooled_data.shape)\ntorch.Size([1, 20, 30])\n
\n
\n \n
\n
>>> pooled_data = pooling(data, pool_type="max", dimension=1, keep_dimension=False)\n>>> print(pooled_data.shape)\ntorch.Size([10, 30])\n
\n
\n \n
\n
>>> pooled_data = pooling(data, pool_type="min", dimension=2)\n>>> print(pooled_data.shape)\ntorch.Size([10, 20])\n
\n
\n
\n", "signature": "(\tdata: torch.Tensor,\tpool_type: Literal['mean', 'min', 'max'] = 'mean',\tdimension: int = 0,\tkeep_dimension: bool = False) -> torch.Tensor:", "funcdef": "def"}, "senselab.utils.tasks.pooling.pooling_pt": {"fullname": "senselab.utils.tasks.pooling.pooling_pt", "modulename": "senselab.utils.tasks.pooling", "qualname": "pooling_pt", "kind": "function", "doc": "

Apply pooling to the input tensor along the specified dimension.

\n\n

This function provides three pooling methods: mean, min, or max. The pooled tensor is returned as a PyTorch tensor.

\n\n
Arguments:
\n\n
    \n
  • data (torch.Tensor): Input tensor of shape (n_samples, n_features, n_channels).
    • \n
  • pool_type (str, optional): The pooling method to use.\nChoices are:\n * \"mean\" for mean pooling\n * \"min\" for min pooling\n * \"max\" for max pooling\nDefaults to \"mean\".
    • \n
  • dimension (int, optional): The dimension along which to apply the pooling.\nDefaults to 0.
    • \n
  • keep_dimension (bool, optional): Whether to keep the original tensor dimension after pooling.
    • \n
\n\n
Returns:
\n\n
\n

torch.Tensor: The pooled tensor of shape (n_samples, n_features, n_channels).

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If an invalid pooling method choice is provided.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> data = torch.randn(10, 20, 30)  # 10 samples, 20 features, 30 channels\n>>> data.shape\ntorch.Size([10, 20, 30])\n
\n
\n \n
\n
>>> pooled_data = pooling(data, pool_type="mean", dimension=0, keep_dimension=True)\n>>> print(pooled_data.shape)\ntorch.Size([1, 20, 30])\n
\n
\n \n
\n
>>> pooled_data = pooling(data, pool_type="max", dimension=1, keep_dimension=False)\n>>> print(pooled_data.shape)\ntorch.Size([10, 30])\n
\n
\n \n
\n
>>> pooled_data = pooling(data, pool_type="min", dimension=2)\n>>> print(pooled_data.shape)\ntorch.Size([10, 20])\n
\n
\n
\n", "signature": "(\tdata: torch.Tensor,\tpool_type: Literal['mean', 'min', 'max'] = 'mean',\tdimension: int = 0,\tkeep_dimension: bool = False) -> torch.Tensor:", "funcdef": "def"}, "senselab.video": {"fullname": "senselab.video", "modulename": "senselab.video", "kind": "module", "doc": "

This module contains implementation for video processing.

\n"}}, "docInfo": {"senselab": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 549}, "senselab.audio": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 11}, "senselab.audio.data_structures": {"qualname": 0, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 11}, "senselab.audio.data_structures.audio": {"qualname": 0, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 56}, "senselab.audio.data_structures.audio.Audio": {"qualname": 1, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 3, "doc": 143}, "senselab.audio.data_structures.audio.Audio.waveform": {"qualname": 2, "fullname": 7, "annotation": 3, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "senselab.audio.data_structures.audio.Audio.sampling_rate": {"qualname": 3, "fullname": 8, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "senselab.audio.data_structures.audio.Audio.orig_path_or_id": {"qualname": 5, "fullname": 10, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "senselab.audio.data_structures.audio.Audio.metadata": {"qualname": 2, "fullname": 7, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "senselab.audio.data_structures.audio.Audio.model_config": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 8, "signature": 0, "bases": 0, "doc": 18}, "senselab.audio.data_structures.audio.Audio.convert_to_tensor": {"qualname": 4, "fullname": 9, "annotation": 0, "default_value": 0, "signature": 101, "bases": 0, "doc": 16}, "senselab.audio.data_structures.audio.Audio.from_filepath": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 65, "bases": 0, "doc": 43}, "senselab.audio.data_structures.audio.Audio.generate_path": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 62}, "senselab.audio.data_structures.audio.Audio.id": {"qualname": 2, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 50}, "senselab.audio.data_structures.audio.Audio.model_fields": {"qualname": 3, "fullname": 8, "annotation": 5, "default_value": 47, "signature": 0, "bases": 0, "doc": 35}, "senselab.audio.data_structures.audio.Audio.model_computed_fields": {"qualname": 4, "fullname": 9, "annotation": 5, "default_value": 1, "signature": 0, "bases": 0, "doc": 16}, "senselab.audio.data_structures.audio.batch_audios": {"qualname": 2, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 88, "bases": 0, "doc": 180}, "senselab.audio.data_structures.audio.unbatch_audios": {"qualname": 2, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 98, "bases": 0, "doc": 167}, "senselab.audio.tasks": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 7}, "senselab.audio.tasks.classification": {"qualname": 0, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 14}, "senselab.audio.tasks.classification.speech_emotion_recognition": {"qualname": 0, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 20}, "senselab.audio.tasks.classification.speech_emotion_recognition.audio_classification_with_hf_models": {"qualname": 5, "fullname": 12, "annotation": 0, "default_value": 0, "signature": 134, "bases": 0, "doc": 183}, "senselab.audio.tasks.classification.speech_emotion_recognition.speech_emotion_recognition_with_hf_models": {"qualname": 6, "fullname": 13, "annotation": 0, "default_value": 0, "signature": 140, "bases": 0, "doc": 272}, "senselab.audio.tasks.data_augmentation": {"qualname": 0, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 14}, "senselab.audio.tasks.data_augmentation.data_augmentation": {"qualname": 0, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 12}, "senselab.audio.tasks.data_augmentation.data_augmentation.augment_audios": {"qualname": 2, "fullname": 9, "annotation": 0, "default_value": 0, "signature": 204, "bases": 0, "doc": 218}, "senselab.audio.tasks.data_augmentation.data_augmentation.augment_audios_pt": {"qualname": 3, "fullname": 10, "annotation": 0, "default_value": 0, "signature": 204, "bases": 0, "doc": 218}, "senselab.audio.tasks.features_extraction": {"qualname": 0, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 14}, "senselab.audio.tasks.features_extraction.opensmile": {"qualname": 0, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 11}, "senselab.audio.tasks.features_extraction.opensmile.OpenSmileFeatureExtractorFactory": {"qualname": 1, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 10}, "senselab.audio.tasks.features_extraction.opensmile.OpenSmileFeatureExtractorFactory.get_opensmile_extractor": {"qualname": 4, "fullname": 10, "annotation": 0, "default_value": 0, "signature": 51, "bases": 0, "doc": 59}, "senselab.audio.tasks.features_extraction.opensmile.extract_opensmile_features_from_audios": {"qualname": 5, "fullname": 11, "annotation": 0, "default_value": 0, "signature": 109, "bases": 0, "doc": 88}, "senselab.audio.tasks.features_extraction.opensmile.extract_opensmile_features_from_audios_pt": {"qualname": 6, "fullname": 12, "annotation": 0, "default_value": 0, "signature": 109, "bases": 0, "doc": 88}, "senselab.audio.tasks.features_extraction.praat_parselmouth": {"qualname": 0, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 17}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_durations": {"qualname": 3, "fullname": 10, "annotation": 0, "default_value": 0, "signature": 63, "bases": 0, "doc": 10}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_f0_descriptors": {"qualname": 4, "fullname": 11, "annotation": 0, "default_value": 0, "signature": 107, "bases": 0, "doc": 11}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_harmonicity_descriptors": {"qualname": 4, "fullname": 11, "annotation": 0, "default_value": 0, "signature": 74, "bases": 0, "doc": 10}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_jitter_descriptors": {"qualname": 4, "fullname": 11, "annotation": 0, "default_value": 0, "signature": 85, "bases": 0, "doc": 10}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_shimmer_descriptors": {"qualname": 4, "fullname": 11, "annotation": 0, "default_value": 0, "signature": 85, "bases": 0, "doc": 10}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_durations_pt": {"qualname": 4, "fullname": 11, "annotation": 0, "default_value": 0, "signature": 63, "bases": 0, "doc": 10}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_f0_descriptors_pt": {"qualname": 5, "fullname": 12, "annotation": 0, "default_value": 0, "signature": 107, "bases": 0, "doc": 11}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_harmonicity_descriptors_pt": {"qualname": 5, "fullname": 12, "annotation": 0, "default_value": 0, "signature": 74, "bases": 0, "doc": 10}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_jitter_descriptors_pt": {"qualname": 5, "fullname": 12, "annotation": 0, "default_value": 0, "signature": 85, "bases": 0, "doc": 10}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_shimmer_descriptors_pt": {"qualname": 5, "fullname": 12, "annotation": 0, "default_value": 0, "signature": 85, "bases": 0, "doc": 10}, "senselab.audio.tasks.features_extraction.torchaudio": {"qualname": 0, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 15}, 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{"docs": {"senselab.audio.tasks.speech_to_text.api.transcribe_audios": {"tf": 1}, "senselab.audio.tasks.text_to_speech.api.synthesize_texts": {"tf": 1}, "senselab.audio.tasks.voice_cloning.api.clone_voices": {"tf": 1}, "senselab.utils.tasks.dimensionality_reduction.compute_dimensionality_reduction": {"tf": 1}, "senselab.utils.tasks.dimensionality_reduction.compute_dimensionality_reduction_pt": {"tf": 1}}, "df": 5}}}}}}, "y": {"docs": {"senselab.utils.tasks.cca_cka.compute_cca": {"tf": 1.4142135623730951}, "senselab.utils.tasks.cca_cka.compute_cka": {"tf": 1.4142135623730951}, "senselab.utils.tasks.cca_cka.compute_cca_pt": {"tf": 1.4142135623730951}, "senselab.utils.tasks.cca_cka.compute_cka_pt": {"tf": 1.4142135623730951}}, "df": 4, "o": {"docs": {}, "df": 0, "u": {"docs": {"senselab.audio.tasks.classification.speech_emotion_recognition.speech_emotion_recognition_with_hf_models": {"tf": 1}, "senselab.audio.tasks.speaker_diarization": {"tf": 1}, "senselab.audio.tasks.speech_to_text": {"tf": 1}, "senselab.audio.tasks.speech_to_text.huggingface": {"tf": 1}}, "df": 4, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {"senselab.audio.tasks.speech_to_text": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {"senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.synthesize_texts_with_style_tts_2": {"tf": 1}}, "df": 1}}}}, "x": {"docs": {"senselab.utils.tasks.cca_cka.compute_cca": {"tf": 2}, "senselab.utils.tasks.cca_cka.compute_cka": {"tf": 2}, "senselab.utils.tasks.cca_cka.compute_cca_pt": {"tf": 2}, "senselab.utils.tasks.cca_cka.compute_cka_pt": {"tf": 2}}, "df": 4, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"senselab.audio.tasks.speaker_embeddings": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "r": {"docs": {"senselab.audio.tasks.speaker_embeddings": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; + /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"senselab": {"fullname": "senselab", "modulename": "senselab", "kind": "module", "doc": "

\"Build\"\n\"codecov\"\n\"Ruff\"

\n\n

\"PyPI\"\n\"Python\n\"License\"

\n\n

\"pages\"

\n\n

Welcome to senselab! This is a Python package for streamlining the processing and analysis of behavioral data, such as voice and speech patterns, with robust and reproducible methodologies.

\n\n

Caution:: this package is still under development and may change rapidly over the next few weeks.

\n\n

Installation

\n\n

Install this package via:

\n\n
\n
pip install senselab\n
\n
\n\n

Or get the newest development version via:

\n\n
\n
pip install git+https://github.com/sensein/senselab.git\n
\n
\n\n

Quick start

\n\n
\n
from senselab.audio.data_structures.audio import Audio\nfrom senselab.audio.tasks.preprocessing.preprocessing import resample_audios\n\naudio1 = Audio.from_filepath('path_to_audio_file.wav')\n\nprint("The original audio has a sampling rate of {} Hz.".format(audio1.sampling_rate))\n[audio1] = resample_audios([audio1], resample_rate=16000)\nprint("The resampled audio has a sampling rate of {} Hz.".format(audio1.sampling_rate))\n
\n
\n\n

For more detailed information, check out our Getting Started Tutorial.

\n\n

Why should I use senselab?

\n\n
    \n
  • Modular Design: Easily integrate or use standalone transformations for flexible data manipulation.
    • \n
  • Pre-built Pipelines: Access pre-configured pipelines to reduce setup time and effort.
    • \n
  • Reproducibility: Ensure consistent and verifiable results with fixed seeds and version-controlled steps.
    • \n
  • Easy Integration: Seamlessly fit into existing workflows with minimal configuration.
    • \n
  • Extensible: Modify and contribute custom transformations and pipelines to meet specific research needs.
    • \n
  • Comprehensive Documentation: Detailed guides, examples, and documentation for all features and modules.
    • \n
  • Performance Optimized: Efficiently process large datasets with optimized code and algorithms.
    • \n
  • Interactive Examples: Jupyter notebooks provide practical examples for deriving insights from real-world datasets.
    • \n
\n\n

Contributing

\n\n

Please see CONTRIBUTING.md before contributing.

\n\n

To find out what's currently in progress, please check the Project Board.

\n"}, "senselab.audio": {"fullname": "senselab.audio", "modulename": "senselab.audio", "kind": "module", "doc": "

This module contains implementations related to audio processing.

\n"}, "senselab.audio.data_structures": {"fullname": "senselab.audio.data_structures", "modulename": "senselab.audio.data_structures", "kind": "module", "doc": "

This module contains data structures for audio processing.

\n"}, "senselab.audio.data_structures.audio": {"fullname": "senselab.audio.data_structures.audio", "modulename": "senselab.audio.data_structures.audio", "kind": "module", "doc": "

Data structures relevant for handling audio files and metadata.

\n\n

The most basic unit is the Audio object which represents the necessary information of a loaded audio\nfile and its corresponding metadata. Other functionality and abstract data types are provided for\nease of maintaining the codebase and offering consistent public APIs.

\n"}, "senselab.audio.data_structures.audio.Audio": {"fullname": "senselab.audio.data_structures.audio.Audio", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio", "kind": "class", "doc": "

Pydantic model for audio and its corresponding metadata.

\n\n

Pydantic model for audio that holds the necessary attributes, the actual decoded audio data\nand the sampling rate, to work with audio in python. Contains metadata information as needed\nand has a unique identifier for every audio.

\n\n
Attributes:
\n\n
    \n
  • waveform: The actual audio data read from an audio file, stored as a torch.Tensor\nof shape (num_channels, num_samples)
    • \n
  • sampling_rate: The sampling rate of the audio file
    • \n
  • orig_path_or_id: Optional str for the original path or an ID to track file over time
    • \n
  • metadata: Optional metadata dictionary of information associated with this Audio instance\n(e.g. participant demographics, audio settings, location information)
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "senselab.audio.data_structures.audio.Audio.waveform": {"fullname": "senselab.audio.data_structures.audio.Audio.waveform", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.waveform", "kind": "variable", "doc": "

\n", "annotation": ": torch.Tensor"}, "senselab.audio.data_structures.audio.Audio.sampling_rate": {"fullname": "senselab.audio.data_structures.audio.Audio.sampling_rate", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.sampling_rate", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "senselab.audio.data_structures.audio.Audio.orig_path_or_id": {"fullname": "senselab.audio.data_structures.audio.Audio.orig_path_or_id", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.orig_path_or_id", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "senselab.audio.data_structures.audio.Audio.metadata": {"fullname": "senselab.audio.data_structures.audio.Audio.metadata", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.metadata", "kind": "variable", "doc": "

\n", "annotation": ": Dict"}, "senselab.audio.data_structures.audio.Audio.model_config": {"fullname": "senselab.audio.data_structures.audio.Audio.model_config", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "default_value": "{'arbitrary_types_allowed': True}"}, "senselab.audio.data_structures.audio.Audio.convert_to_tensor": {"fullname": "senselab.audio.data_structures.audio.Audio.convert_to_tensor", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.convert_to_tensor", "kind": "function", "doc": "

Converts the audio data to torch.Tensor of shape (num_channels, num_samples).

\n", "signature": "(\tcls,\tv: Union[List[float], List[List[float]], numpy.ndarray, torch.Tensor],\t_: pydantic_core.core_schema.ValidationInfo) -> torch.Tensor:", "funcdef": "def"}, "senselab.audio.data_structures.audio.Audio.from_filepath": {"fullname": "senselab.audio.data_structures.audio.Audio.from_filepath", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.from_filepath", "kind": "function", "doc": "

Creates an Audio instance from an audio file.

\n\n
Arguments:
\n\n
    \n
  • filepath: Filepath of the audio file to read from
    • \n
  • metadata: Additional information associated with the audio file
    • \n
\n", "signature": "(\tcls,\tfilepath: str,\tmetadata: Dict = {}) -> senselab.audio.data_structures.audio.Audio:", "funcdef": "def"}, "senselab.audio.data_structures.audio.Audio.generate_path": {"fullname": "senselab.audio.data_structures.audio.Audio.generate_path", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.generate_path", "kind": "function", "doc": "

Generate a path like string for this Audio.

\n\n

Generates a path like string for the Audio by either utilizing the orig_path_or_id, checking\nif it is a path (has an extension), otherwise using the id if orig_path_or_id is not an ID\nand giving an extension and relative to the current directory.

\n", "signature": "(self) -> str:", "funcdef": "def"}, "senselab.audio.data_structures.audio.Audio.id": {"fullname": "senselab.audio.data_structures.audio.Audio.id", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.id", "kind": "function", "doc": "

Generate a unique identifier for the Audio.

\n\n

Generate a unique identifier for the Audio where equivalent waveforms and sampling\nrates generate the same IDs.

\n\n

Returns: String UUID of the Audio generated by an MD5 hash of the waveform and the sampling_rate

\n", "signature": "(self) -> str:", "funcdef": "def"}, "senselab.audio.data_structures.audio.Audio.model_fields": {"fullname": "senselab.audio.data_structures.audio.Audio.model_fields", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'waveform': FieldInfo(annotation=Tensor, required=True), 'sampling_rate': FieldInfo(annotation=int, required=True), 'orig_path_or_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'metadata': FieldInfo(annotation=Dict, required=False, default={})}"}, "senselab.audio.data_structures.audio.Audio.model_computed_fields": {"fullname": "senselab.audio.data_structures.audio.Audio.model_computed_fields", "modulename": "senselab.audio.data_structures.audio", "qualname": "Audio.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.audio.data_structures.audio.batch_audios": {"fullname": "senselab.audio.data_structures.audio.batch_audios", "modulename": "senselab.audio.data_structures.audio", "qualname": "batch_audios", "kind": "function", "doc": "

Batches the Audios together into a single Tensor, keeping individual Audio information separate.

\n\n

Batch all of the Audios into a single Tensor of shape (len(audios), num_channels, num_samples).\nKeeps the Audio information related to each sampling rate and metadata separate for each Audio to\nallow for unbatching after running relevant functionality.

\n\n
Arguments:
\n\n
    \n
  • audios: List of audios to batch together. NOTE: Should all have the same number of channels\nand is generally advised to have the same sampling rates if running functionality\nthat relies on the sampling rate.
    • \n
\n\n
Returns:
\n\n
\n

Returns a tuple of a Tensor that will have the shape (len(audios), num_channels, num_samples),\n the sampling rate (an integer if all have the same sampling rate), and a list of each individual\n audio's metadata information.

\n
\n\n
Raises:
\n\n
    \n
  • RuntimeError: if all of the Audios do not have the same number of channels
    • \n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio]) -> Tuple[torch.Tensor, Union[int, List[int]], List[Dict]]:", "funcdef": "def"}, "senselab.audio.data_structures.audio.unbatch_audios": {"fullname": "senselab.audio.data_structures.audio.unbatch_audios", "modulename": "senselab.audio.data_structures.audio", "qualname": "unbatch_audios", "kind": "function", "doc": "

Unbatches Audios into a List of Audio objects.

\n\n

Uses the batched Audios, their respective sampling rates, and their corresponding metadatas to create\na list of Audios.

\n\n
Arguments:
\n\n
    \n
  • batched_audio: torch.Tensor of shape (batch_size, num_channels, num_samples) to unstack
    • \n
  • sampling_rates: The sampling rate of each batched audio if they differ or a single sampling rate for all of them
    • \n
  • metadatas: The respective metadata for each of the batched audios
    • \n
\n\n
Returns:
\n\n
\n

List of Audio objects representing each of the Audios that were previously batched together

\n
\n\n
Raises:
\n\n
    \n
  • ValueError if the batched_audio is not in the correct shape or if the number of batched_audios does not\nmatch the amount of metadata and sampling rates (if they were provided as a List) that were provided.
    • \n
\n", "signature": "(\tbatched_audio: torch.Tensor,\tsampling_rates: Union[int, List[int]],\tmetadatas: List[Dict]) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks": {"fullname": "senselab.audio.tasks", "modulename": "senselab.audio.tasks", "kind": "module", "doc": "

Tasks for audio processing.

\n"}, "senselab.audio.tasks.classification": {"fullname": "senselab.audio.tasks.classification", "modulename": "senselab.audio.tasks.classification", "kind": "module", "doc": "

This module defines the APIs for doing generalized audio classification tasks.

\n"}, "senselab.audio.tasks.classification.speech_emotion_recognition": {"fullname": "senselab.audio.tasks.classification.speech_emotion_recognition", "modulename": "senselab.audio.tasks.classification.speech_emotion_recognition", "kind": "module", "doc": "

This module defines APIs for the user to run Speech Emotion Recognition (SER) on sets of audios.

\n"}, "senselab.audio.tasks.classification.speech_emotion_recognition.audio_classification_with_hf_models": {"fullname": "senselab.audio.tasks.classification.speech_emotion_recognition.audio_classification_with_hf_models", "modulename": "senselab.audio.tasks.classification.speech_emotion_recognition", "qualname": "audio_classification_with_hf_models", "kind": "function", "doc": "

General audio classification functionality utilitzing HuggingFace pipelines.

\n\n

Classifies all audios, with no underlying assumptions on what the classification labels are,\nand returns the output that the pipeline gives.

\n\n
Arguments:
\n\n
    \n
  • audios: List of Audio objects that we want to run classification on
    • \n
  • model: The HuggingFace model that will be used for running the inference
    • \n
  • device: The device to run inference on
    • \n
\n\n
Returns:
\n\n
\n

List of Lists of Dictionaries where each corresponds to the audio that it was ran on and the List of\n Dictionaries are of the form [{'label': 'some_label', 'score': some_value},...]

\n
\n\n
Raises:
\n\n
    \n
  • ValueError if the given model does not have the audio-classification pipeline tag
    • \n
  • UserWarning if the model tags don't include endpoints_compatible (seen on HuggingFace as Inference Endpoints)\nas the behavior of the model might not output as expected as a result.
    • \n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.HFModel,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[List[Dict]]:", "funcdef": "def"}, "senselab.audio.tasks.classification.speech_emotion_recognition.speech_emotion_recognition_with_hf_models": {"fullname": "senselab.audio.tasks.classification.speech_emotion_recognition.speech_emotion_recognition_with_hf_models", "modulename": "senselab.audio.tasks.classification.speech_emotion_recognition", "qualname": "speech_emotion_recognition_with_hf_models", "kind": "function", "doc": "

Function for running speech emotion recognition tasks using HuggingFace models.

\n\n

Uses an audio classification pipeline to run speech emotion recognition for every audio in audios using the\nspecified model. For each audio, we return the emotion that the model suggested has the highest probability\nfor single label classification as well as the full emotion output for multi-label classification. Function can\nalso be used for continuous speech emotion recognition, where the second element of each tuple will contain a\ndictionary of the predicted continuous values.

\n\n
Arguments:
\n\n
    \n
  • audios: List of Audio objects that we want to run speech emotion recognition on. If you wish to get the\nemotion at different segments of the audio (e.g. when different speaker talk) please run the audios\nthrough an appropriate segmentation task.
    • \n
  • model: The HuggingFace model that will be used for running the inference
    • \n
  • device: The device to run inference on
    • \n
\n\n
Returns:
\n\n
\n

List of tuples where the first value is the single label classification of the audio (if appropriate) and the\n second value is the full model output, which might be the probabilities for each evaluated emotion\n (for discrete SER) or the continuous emotion predictions.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError if the given model is not properly tagged with 'speech-emotion-recognition' or 'emotion-recognition'\nor the model configuration does not contain an id2label property that predicts commonly used\nemotions (happy, sad, neutral, positive, negative, etc.)
    • \n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.HFModel,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[Tuple[str, Dict]]:", "funcdef": "def"}, "senselab.audio.tasks.data_augmentation": {"fullname": "senselab.audio.tasks.data_augmentation", "modulename": "senselab.audio.tasks.data_augmentation", "kind": "module", "doc": "

This module provides the API of the senselab audio data augmentation.

\n"}, "senselab.audio.tasks.data_augmentation.data_augmentation": {"fullname": "senselab.audio.tasks.data_augmentation.data_augmentation", "modulename": "senselab.audio.tasks.data_augmentation.data_augmentation", "kind": "module", "doc": "

This module implements some utilities for audio data augmentation.

\n"}, "senselab.audio.tasks.data_augmentation.data_augmentation.augment_audios": {"fullname": "senselab.audio.tasks.data_augmentation.data_augmentation.augment_audios", "modulename": "senselab.audio.tasks.data_augmentation.data_augmentation", "qualname": "augment_audios", "kind": "function", "doc": "

Augments all provided audios with a given augmentation, either individually or all batched together.

\n\n

Augment all audios with a user defined augmentation that can be a composition of multiple augmentations. This\naugmentation is either performed on each audio individually or all of the audios provided are batched together\nand run at once. NOTE: if batching, all audios must have the same sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audios: List of Audios whose data will be augmented with the given augmentations
    • \n
  • augmentation: A Composition of augmentations to run on each audio (uses torch-audiomentations), should have its\noutput_type set to \"dict\"
    • \n
  • device_options: The device, or a List of possible devices, to use for augmenting. If the chosen device\nis MPS or CUDA then the audios are all batched together, so for optimal performance, batching should\nbe done by passing a batch_size worth of audios ar a time
    • \n
\n\n
Returns:
\n\n
\n

List of audios that has passed the all of input audios through the provided augmentation. This does\n not necessarily mean that the augmentation has been run on every audio. For more information,\n see the torch-audiomentations documentation.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\taugmentation: torch_audiomentations.core.composition.Compose,\tdevice_options: Union[senselab.utils.data_structures.device.DeviceType, List[senselab.utils.data_structures.device.DeviceType]] = [<DeviceType.CPU: 'cpu'>]) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.data_augmentation.data_augmentation.augment_audios_pt": {"fullname": "senselab.audio.tasks.data_augmentation.data_augmentation.augment_audios_pt", "modulename": "senselab.audio.tasks.data_augmentation.data_augmentation", "qualname": "augment_audios_pt", "kind": "function", "doc": "

Augments all provided audios with a given augmentation, either individually or all batched together.

\n\n

Augment all audios with a user defined augmentation that can be a composition of multiple augmentations. This\naugmentation is either performed on each audio individually or all of the audios provided are batched together\nand run at once. NOTE: if batching, all audios must have the same sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audios: List of Audios whose data will be augmented with the given augmentations
    • \n
  • augmentation: A Composition of augmentations to run on each audio (uses torch-audiomentations), should have its\noutput_type set to \"dict\"
    • \n
  • device_options: The device, or a List of possible devices, to use for augmenting. If the chosen device\nis MPS or CUDA then the audios are all batched together, so for optimal performance, batching should\nbe done by passing a batch_size worth of audios ar a time
    • \n
\n\n
Returns:
\n\n
\n

List of audios that has passed the all of input audios through the provided augmentation. This does\n not necessarily mean that the augmentation has been run on every audio. For more information,\n see the torch-audiomentations documentation.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\taugmentation: torch_audiomentations.core.composition.Compose,\tdevice_options: Union[senselab.utils.data_structures.device.DeviceType, List[senselab.utils.data_structures.device.DeviceType]] = [<DeviceType.CPU: 'cpu'>]) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction": {"fullname": "senselab.audio.tasks.features_extraction", "modulename": "senselab.audio.tasks.features_extraction", "kind": "module", "doc": "

This module provides the API of the senselab audio features extraction.

\n"}, "senselab.audio.tasks.features_extraction.opensmile": {"fullname": "senselab.audio.tasks.features_extraction.opensmile", "modulename": "senselab.audio.tasks.features_extraction.opensmile", "kind": "module", "doc": "

This module contains functions for extracting openSMILE features.

\n"}, "senselab.audio.tasks.features_extraction.opensmile.OpenSmileFeatureExtractorFactory": {"fullname": "senselab.audio.tasks.features_extraction.opensmile.OpenSmileFeatureExtractorFactory", "modulename": "senselab.audio.tasks.features_extraction.opensmile", "qualname": "OpenSmileFeatureExtractorFactory", "kind": "class", "doc": "

A factory for managing openSMILE feature extractors.

\n"}, "senselab.audio.tasks.features_extraction.opensmile.OpenSmileFeatureExtractorFactory.get_opensmile_extractor": {"fullname": "senselab.audio.tasks.features_extraction.opensmile.OpenSmileFeatureExtractorFactory.get_opensmile_extractor", "modulename": "senselab.audio.tasks.features_extraction.opensmile", "qualname": "OpenSmileFeatureExtractorFactory.get_opensmile_extractor", "kind": "function", "doc": "

Get or create an openSMILE feature extractor.

\n\n
Arguments:
\n\n
    \n
  • feature_set (str): The openSMILE feature set.
    • \n
  • feature_level (str): The openSMILE feature level.
    • \n
\n\n
Returns:
\n\n
\n

opensmile.Smile: The openSMILE feature extractor.

\n
\n", "signature": "(cls, feature_set: str, feature_level: str) -> opensmile.core.smile.Smile:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.opensmile.extract_opensmile_features_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.opensmile.extract_opensmile_features_from_audios", "modulename": "senselab.audio.tasks.features_extraction.opensmile", "qualname": "extract_opensmile_features_from_audios", "kind": "function", "doc": "

Apply feature extraction across a list of audio files.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to extract features from.
    • \n
  • feature_set (str): The openSMILE feature set (default is \"eGeMAPSv02\").
    • \n
  • feature_level (str): The openSMILE feature level (default is \"Functionals\").
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, Any]]: The list of feature dictionaries for each audio.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tfeature_set: str = 'eGeMAPSv02',\tfeature_level: str = 'Functionals') -> List[Dict[str, Any]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.opensmile.extract_opensmile_features_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.opensmile.extract_opensmile_features_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.opensmile", "qualname": "extract_opensmile_features_from_audios_pt", "kind": "function", "doc": "

Apply feature extraction across a list of audio files.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to extract features from.
    • \n
  • feature_set (str): The openSMILE feature set (default is \"eGeMAPSv02\").
    • \n
  • feature_level (str): The openSMILE feature level (default is \"Functionals\").
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, Any]]: The list of feature dictionaries for each audio.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tfeature_set: str = 'eGeMAPSv02',\tfeature_level: str = 'Functionals') -> List[Dict[str, Any]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "kind": "module", "doc": "

This module contains functions that extract features from audio files using the PRAAT library.

\n"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_durations": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_durations", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_durations", "kind": "function", "doc": "

Returns the duration of the Audio objects.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio]) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_f0_descriptors": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_f0_descriptors", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_f0_descriptors", "kind": "function", "doc": "

Returns the fundamental frequency descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float,\tunit: str = 'Hertz') -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_harmonicity_descriptors": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_harmonicity_descriptors", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_harmonicity_descriptors", "kind": "function", "doc": "

Returns the harmonicity descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_jitter_descriptors": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_jitter_descriptors", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_jitter_descriptors", "kind": "function", "doc": "

Returns the jitter descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_shimmer_descriptors": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_shimmer_descriptors", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_shimmer_descriptors", "kind": "function", "doc": "

Returns the shimmer descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_durations_pt": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_durations_pt", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_durations_pt", "kind": "function", "doc": "

Returns the duration of the Audio objects.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio]) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_f0_descriptors_pt": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_f0_descriptors_pt", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_f0_descriptors_pt", "kind": "function", "doc": "

Returns the fundamental frequency descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float,\tunit: str = 'Hertz') -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_harmonicity_descriptors_pt": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_harmonicity_descriptors_pt", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_harmonicity_descriptors_pt", "kind": "function", "doc": "

Returns the harmonicity descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_jitter_descriptors_pt": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_jitter_descriptors_pt", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_jitter_descriptors_pt", "kind": "function", "doc": "

Returns the jitter descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_shimmer_descriptors_pt": {"fullname": "senselab.audio.tasks.features_extraction.praat_parselmouth.get_audios_shimmer_descriptors_pt", "modulename": "senselab.audio.tasks.features_extraction.praat_parselmouth", "qualname": "get_audios_shimmer_descriptors_pt", "kind": "function", "doc": "

Returns the shimmer descriptors of the audios.

\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tf0min: float,\tf0max: float) -> List[Dict[str, float]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "kind": "module", "doc": "

This module provides the implementation of torchaudio utilities for audio features extraction.

\n"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_spectrogram_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_spectrogram_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_spectrogram_from_audios", "kind": "function", "doc": "

Extract spectrograms from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing spectrograms.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_fft: int = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_spectrogram_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_spectrogram_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mel_spectrogram_from_audios", "kind": "function", "doc": "

Extract mel spectrograms from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing mel spectrograms.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_fft: Optional[int] = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None,\tn_mels: int = 128) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mfcc_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mfcc_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mfcc_from_audios", "kind": "function", "doc": "

Extract MFCCs from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_mfcc (int): Number of MFCCs to return. Default is 40.
    • \n
  • n_ftt (int): Size of FFT, creates n_ftt // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_ftt.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing MFCCs.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_mfcc: int = 40,\tn_ftt: Optional[int] = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None,\tn_mels: int = 128) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_filter_bank_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_filter_bank_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mel_filter_bank_from_audios", "kind": "function", "doc": "

Extract mel filter bank from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
  • n_stft (int): Number of bins in STFT. Default is 201.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing mel filter banks.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_mels: int = 128,\tn_stft: int = 201,\tn_fft: int = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_pitch_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_pitch_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_pitch_from_audios", "kind": "function", "doc": "

Extract pitch from a list of audio objects.

\n\n

Pitch is detected using the detect_pitch_frequency function from torchaudio.\nIt is implemented using normalized cross-correlation function and median smoothing.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • freq_low (int): Lowest frequency that can be detected (Hz). Default is 85.
    • \n
  • freq_high (int): Highest frequency that can be detected (Hz). Default is 3400.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing pitches.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tfreq_low: int = 85,\tfreq_high: int = 3400) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_spectrogram_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_spectrogram_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_spectrogram_from_audios_pt", "kind": "function", "doc": "

Extract spectrograms from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing spectrograms.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_fft: int = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_spectrogram_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_spectrogram_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mel_spectrogram_from_audios_pt", "kind": "function", "doc": "

Extract mel spectrograms from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing mel spectrograms.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_fft: Optional[int] = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None,\tn_mels: int = 128) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mfcc_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mfcc_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mfcc_from_audios_pt", "kind": "function", "doc": "

Extract MFCCs from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_mfcc (int): Number of MFCCs to return. Default is 40.
    • \n
  • n_ftt (int): Size of FFT, creates n_ftt // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_ftt.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing MFCCs.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_mfcc: int = 40,\tn_ftt: Optional[int] = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None,\tn_mels: int = 128) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_filter_bank_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_mel_filter_bank_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_mel_filter_bank_from_audios_pt", "kind": "function", "doc": "

Extract mel filter bank from a list of audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • n_mels (int): Number of mel filter banks. Default is 128.
    • \n
  • n_stft (int): Number of bins in STFT. Default is 201.
    • \n
  • n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400.
    • \n
  • win_length (int): Window size. Default is None, using n_fft.
    • \n
  • hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing mel filter banks.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tn_mels: int = 128,\tn_stft: int = 201,\tn_fft: int = 400,\twin_length: Optional[int] = None,\thop_length: Optional[int] = None) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio.extract_pitch_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio.extract_pitch_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio", "qualname": "extract_pitch_from_audios_pt", "kind": "function", "doc": "

Extract pitch from a list of audio objects.

\n\n

Pitch is detected using the detect_pitch_frequency function from torchaudio.\nIt is implemented using normalized cross-correlation function and median smoothing.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of Audio objects.
    • \n
  • freq_low (int): Lowest frequency that can be detected (Hz). Default is 85.
    • \n
  • freq_high (int): Highest frequency that can be detected (Hz). Default is 3400.
    • \n
\n\n
Returns:
\n\n
\n

List[Dict[str, torch.Tensor]]: List of Dict objects containing pitches.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tfreq_low: int = 85,\tfreq_high: int = 3400) -> List[Dict[str, torch.Tensor]]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio_squim": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "kind": "module", "doc": "

This module provides the implementation of torchaudio squim utilities for audio features extraction.

\n"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.objective_model": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.objective_model", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "objective_model", "kind": "variable", "doc": "

\n", "default_value": "SquimObjective(\n (encoder): Encoder(\n (conv1d): Conv1d(1, 256, kernel_size=(64,), stride=(32,), bias=False)\n )\n (dprnn): DPRNN(\n (row_rnn): ModuleList(\n (0-1): 2 x SingleRNN(\n (rnn): LSTM(256, 256, batch_first=True, bidirectional=True)\n (proj): Linear(in_features=512, out_features=256, bias=True)\n )\n )\n (col_rnn): ModuleList(\n (0-1): 2 x SingleRNN(\n (rnn): LSTM(256, 256, batch_first=True, bidirectional=True)\n (proj): Linear(in_features=512, out_features=256, bias=True)\n )\n )\n (row_norm): ModuleList(\n (0-1): 2 x GroupNorm(1, 256, eps=1e-08, affine=True)\n )\n (col_norm): ModuleList(\n (0-1): 2 x GroupNorm(1, 256, eps=1e-08, affine=True)\n )\n (conv): Sequential(\n (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n (1): PReLU(num_parameters=1)\n )\n )\n (branches): ModuleList(\n (0-1): 2 x Sequential(\n (0): TransformerEncoderLayer(\n (self_attn): MultiheadAttention(\n (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n )\n (linear1): Linear(in_features=256, out_features=1024, bias=True)\n (dropout): Dropout(p=0.0, inplace=False)\n (linear2): Linear(in_features=1024, out_features=256, bias=True)\n (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n (dropout1): Dropout(p=0.0, inplace=False)\n (dropout2): Dropout(p=0.0, inplace=False)\n )\n (1): AutoPool(\n (softmax): Softmax(dim=1)\n )\n (2): Sequential(\n (0): Linear(in_features=256, out_features=256, bias=True)\n (1): PReLU(num_parameters=1)\n (2): Linear(in_features=256, out_features=1, bias=True)\n (3): RangeSigmoid(\n (sigmoid): Sigmoid()\n )\n )\n )\n (2): Sequential(\n (0): TransformerEncoderLayer(\n (self_attn): MultiheadAttention(\n (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n )\n (linear1): Linear(in_features=256, out_features=1024, bias=True)\n (dropout): Dropout(p=0.0, inplace=False)\n (linear2): Linear(in_features=1024, out_features=256, bias=True)\n (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n (dropout1): Dropout(p=0.0, inplace=False)\n (dropout2): Dropout(p=0.0, inplace=False)\n )\n (1): AutoPool(\n (softmax): Softmax(dim=1)\n )\n (2): Sequential(\n (0): Linear(in_features=256, out_features=256, bias=True)\n (1): PReLU(num_parameters=1)\n (2): Linear(in_features=256, out_features=1, bias=True)\n )\n )\n )\n)"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.subjective_model": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.subjective_model", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "subjective_model", "kind": "variable", "doc": "

\n", "default_value": "SquimSubjective(\n (ssl_model): Wav2Vec2Model(\n (feature_extractor): FeatureExtractor(\n (conv_layers): ModuleList(\n (0): ConvLayerBlock(\n (layer_norm): GroupNorm(512, 512, eps=1e-05, affine=True)\n (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False)\n )\n (1-4): 4 x ConvLayerBlock(\n (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False)\n )\n (5-6): 2 x ConvLayerBlock(\n (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False)\n )\n )\n )\n (encoder): Encoder(\n (feature_projection): FeatureProjection(\n (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n (projection): Linear(in_features=512, out_features=768, bias=True)\n (dropout): Dropout(p=0.1, inplace=False)\n )\n (transformer): Transformer(\n (pos_conv_embed): ConvolutionalPositionalEmbedding(\n (conv): ParametrizedConv1d(\n 768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16\n (parametrizations): ModuleDict(\n (weight): ParametrizationList(\n (0): _WeightNorm()\n )\n )\n )\n )\n (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n (dropout): Dropout(p=0.1, inplace=False)\n (layers): ModuleList(\n (0-11): 12 x EncoderLayer(\n (attention): SelfAttention(\n (k_proj): Linear(in_features=768, out_features=768, bias=True)\n (v_proj): Linear(in_features=768, out_features=768, bias=True)\n (q_proj): Linear(in_features=768, out_features=768, bias=True)\n (out_proj): Linear(in_features=768, out_features=768, bias=True)\n )\n (dropout): Dropout(p=0.1, inplace=False)\n (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n (feed_forward): FeedForward(\n (intermediate_dense): Linear(in_features=768, out_features=3072, bias=True)\n (intermediate_dropout): Dropout(p=0.1, inplace=False)\n (output_dense): Linear(in_features=3072, out_features=768, bias=True)\n (output_dropout): Dropout(p=0.1, inplace=False)\n )\n (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n )\n )\n )\n )\n )\n (projector): Linear(in_features=768, out_features=32, bias=True)\n (predictor): Predictor(\n (att_pool_layer): AttPool(\n (linear1): Linear(in_features=64, out_features=1, bias=True)\n (linear2): Linear(in_features=64, out_features=5, bias=True)\n )\n )\n)"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_objective_quality_features_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_objective_quality_features_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "extract_objective_quality_features_from_audios", "kind": "function", "doc": "

Extracts objective audio features from a list of Audio objects.

\n\n

Currently, Torchaudio-Squim model only supports mono audio at 16000 Hz sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audio_list (List[Audio]): List of Audio objects.
    • \n
\n\n
Returns:
\n\n
\n

Dict[str, Any]: Dictionary containing extracted features.

\n
\n", "signature": "(\taudio_list: List[senselab.audio.data_structures.audio.Audio]) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_subjective_quality_features_from_audios": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_subjective_quality_features_from_audios", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "extract_subjective_quality_features_from_audios", "kind": "function", "doc": "

Extracts subjective audio features from a list of Audio objects.

\n\n

Currently, Torchaudio-Squim model only supports mono audio at 16000 Hz sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audio_list (List[Audio]): List of Audio objects.
    • \n
  • non_matching_references (List[Audio]): Reference Audio objects for the subjective model.
    • \n
\n\n
Returns:
\n\n
\n

Dict[str, Any]: Dictionary containing extracted features.

\n
\n", "signature": "(\taudio_list: List[senselab.audio.data_structures.audio.Audio],\tnon_matching_references: List[senselab.audio.data_structures.audio.Audio]) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_objective_quality_features_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_objective_quality_features_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "extract_objective_quality_features_from_audios_pt", "kind": "function", "doc": "

Extracts objective audio features from a list of Audio objects.

\n\n

Currently, Torchaudio-Squim model only supports mono audio at 16000 Hz sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audio_list (List[Audio]): List of Audio objects.
    • \n
\n\n
Returns:
\n\n
\n

Dict[str, Any]: Dictionary containing extracted features.

\n
\n", "signature": "(\taudio_list: List[senselab.audio.data_structures.audio.Audio]) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_subjective_quality_features_from_audios_pt": {"fullname": "senselab.audio.tasks.features_extraction.torchaudio_squim.extract_subjective_quality_features_from_audios_pt", "modulename": "senselab.audio.tasks.features_extraction.torchaudio_squim", "qualname": "extract_subjective_quality_features_from_audios_pt", "kind": "function", "doc": "

Extracts subjective audio features from a list of Audio objects.

\n\n

Currently, Torchaudio-Squim model only supports mono audio at 16000 Hz sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audio_list (List[Audio]): List of Audio objects.
    • \n
  • non_matching_references (List[Audio]): Reference Audio objects for the subjective model.
    • \n
\n\n
Returns:
\n\n
\n

Dict[str, Any]: Dictionary containing extracted features.

\n
\n", "signature": "(\taudio_list: List[senselab.audio.data_structures.audio.Audio],\tnon_matching_references: List[senselab.audio.data_structures.audio.Audio]) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.audio.tasks.forced_alignment": {"fullname": "senselab.audio.tasks.forced_alignment", "modulename": "senselab.audio.tasks.forced_alignment", "kind": "module", "doc": "

This module provides the API for forced alignment.

\n"}, "senselab.audio.tasks.forced_alignment.constants": {"fullname": "senselab.audio.tasks.forced_alignment.constants", "modulename": "senselab.audio.tasks.forced_alignment.constants", "kind": "module", "doc": "

Provides constants for align functionality.

\n"}, "senselab.audio.tasks.forced_alignment.constants.SAMPLE_RATE": {"fullname": "senselab.audio.tasks.forced_alignment.constants.SAMPLE_RATE", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "SAMPLE_RATE", "kind": "variable", "doc": "

\n", "default_value": "16000"}, "senselab.audio.tasks.forced_alignment.constants.MINIMUM_SEGMENT_SIZE": {"fullname": "senselab.audio.tasks.forced_alignment.constants.MINIMUM_SEGMENT_SIZE", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "MINIMUM_SEGMENT_SIZE", "kind": "variable", "doc": "

\n", "default_value": "400"}, "senselab.audio.tasks.forced_alignment.constants.PUNKT_ABBREVIATIONS": {"fullname": "senselab.audio.tasks.forced_alignment.constants.PUNKT_ABBREVIATIONS", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "PUNKT_ABBREVIATIONS", "kind": "variable", "doc": "

\n", "default_value": "['dr', 'vs', 'mr', 'mrs', 'prof']"}, "senselab.audio.tasks.forced_alignment.constants.LANGUAGES_WITHOUT_SPACES": {"fullname": "senselab.audio.tasks.forced_alignment.constants.LANGUAGES_WITHOUT_SPACES", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "LANGUAGES_WITHOUT_SPACES", "kind": "variable", "doc": "

\n", "default_value": "['ja', 'zh']"}, "senselab.audio.tasks.forced_alignment.constants.DEFAULT_ALIGN_MODELS_TORCH": {"fullname": "senselab.audio.tasks.forced_alignment.constants.DEFAULT_ALIGN_MODELS_TORCH", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "DEFAULT_ALIGN_MODELS_TORCH", "kind": "variable", "doc": "

\n", "default_value": "{'fr': TorchAudioModel(path_or_uri='VOXPOPULI_ASR_BASE_10K_FR', revision='main'), 'de': TorchAudioModel(path_or_uri='VOXPOPULI_ASR_BASE_10K_DE', revision='main'), 'es': TorchAudioModel(path_or_uri='VOXPOPULI_ASR_BASE_10K_ES', revision='main'), 'it': TorchAudioModel(path_or_uri='VOXPOPULI_ASR_BASE_10K_IT', revision='main')}"}, "senselab.audio.tasks.forced_alignment.constants.DEFAULT_ALIGN_MODELS_HF": {"fullname": "senselab.audio.tasks.forced_alignment.constants.DEFAULT_ALIGN_MODELS_HF", "modulename": "senselab.audio.tasks.forced_alignment.constants", "qualname": "DEFAULT_ALIGN_MODELS_HF", "kind": "variable", "doc": "

\n", "default_value": "{'en': HFModel(path_or_uri='facebook/wav2vec2-base-960h', revision='main', info=None), 'ja': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-japanese', revision='main', info=None), 'zh': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn', revision='main', info=None), 'nl': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-dutch', revision='main', info=None), 'uk': HFModel(path_or_uri='Yehor/wav2vec2-xls-r-300m-uk-with-small-lm', revision='main', info=None), 'pt': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-portuguese', revision='main', info=None), 'ar': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-arabic', revision='main', info=None), 'cs': HFModel(path_or_uri='comodoro/wav2vec2-xls-r-300m-cs-250', revision='main', info=None), 'ru': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-russian', revision='main', info=None), 'pl': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-polish', revision='main', info=None), 'hu': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-hungarian', revision='main', info=None), 'fi': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-finnish', revision='main', info=None), 'fa': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-persian', revision='main', info=None), 'el': HFModel(path_or_uri='jonatasgrosman/wav2vec2-large-xlsr-53-greek', revision='main', info=None), 'tr': HFModel(path_or_uri='mpoyraz/wav2vec2-xls-r-300m-cv7-turkish', revision='main', info=None), 'da': HFModel(path_or_uri='saattrupdan/wav2vec2-xls-r-300m-ftspeech', revision='main', info=None), 'he': HFModel(path_or_uri='imvladikon/wav2vec2-xls-r-300m-hebrew', revision='main', info=None), 'vi': HFModel(path_or_uri='nguyenvulebinh/wav2vec2-base-vi', revision='main', info=None), 'ko': HFModel(path_or_uri='kresnik/wav2vec2-large-xlsr-korean', revision='main', info=None), 'ur': HFModel(path_or_uri='kingabzpro/wav2vec2-large-xls-r-300m-Urdu', revision='main', info=None), 'te': HFModel(path_or_uri='anuragshas/wav2vec2-large-xlsr-53-telugu', revision='main', info=None), 'hi': HFModel(path_or_uri='theainerd/Wav2Vec2-large-xlsr-hindi', revision='main', info=None), 'ca': HFModel(path_or_uri='softcatala/wav2vec2-large-xlsr-catala', revision='main', info=None), 'ml': HFModel(path_or_uri='gvs/wav2vec2-large-xlsr-malayalam', revision='main', info=None), 'no': HFModel(path_or_uri='NbAiLab/nb-wav2vec2-1b-bokmaal', revision='main', info=None), 'nn': HFModel(path_or_uri='NbAiLab/nb-wav2vec2-300m-nynorsk', revision='main', info=None)}"}, "senselab.audio.tasks.forced_alignment.data_structures": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "kind": "module", "doc": "

Provides data structures for align functionality.

\n"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleWordSegment", "kind": "class", "doc": "

A single word of a speech.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.word": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.word", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleWordSegment.word", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.start": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.start", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleWordSegment.start", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.end": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.end", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleWordSegment.end", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.score": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment.score", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleWordSegment.score", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleCharSegment", "kind": "class", "doc": "

A single char of a speech.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.char": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.char", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleCharSegment.char", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.start": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.start", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleCharSegment.start", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.end": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.end", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleCharSegment.end", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.score": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment.score", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleCharSegment.score", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment", "kind": "class", "doc": "

A single segment (up to multiple sentences) of a speech.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.start": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.start", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.start", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.end": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.end", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.end", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.text": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.text", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.text", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_char": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_char", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.clean_char", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[str]]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_cdx": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_cdx", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.clean_cdx", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[int]]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_wdx": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.clean_wdx", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.clean_wdx", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[int]]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.sentence_spans": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleSegment.sentence_spans", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleSegment.sentence_spans", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[Tuple[int, int]]]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment", "kind": "class", "doc": "

A single segment (up to multiple sentences) of a speech with word alignment.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.start": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.start", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment.start", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.end": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.end", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment.end", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.text": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.text", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment.text", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.words": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.words", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment.words", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment]"}, "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.chars": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment.chars", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "SingleAlignedSegment.chars", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[senselab.audio.tasks.forced_alignment.data_structures.SingleCharSegment]]"}, "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "TranscriptionResult", "kind": "class", "doc": "

A list of segments and word segments of a speech.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult.segments": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult.segments", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "TranscriptionResult.segments", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.audio.tasks.forced_alignment.data_structures.SingleSegment]"}, "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult.language": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.TranscriptionResult.language", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "TranscriptionResult.language", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "AlignedTranscriptionResult", "kind": "class", "doc": "

A list of segments and word segments of a speech.

\n", "bases": "typing.TypedDict"}, "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult.segments": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult.segments", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "AlignedTranscriptionResult.segments", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.audio.tasks.forced_alignment.data_structures.SingleAlignedSegment]"}, "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult.word_segments": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.AlignedTranscriptionResult.word_segments", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "AlignedTranscriptionResult.word_segments", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.audio.tasks.forced_alignment.data_structures.SingleWordSegment]"}, "senselab.audio.tasks.forced_alignment.data_structures.Point": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Point", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Point", "kind": "class", "doc": "

Represents a point in the alignment path.

\n\n
Attributes:
\n\n
    \n
  • token_index (int): The index of the token in the transcript.
    • \n
  • time_index (int): The index of the time frame in the audio.
    • \n
  • score (float): The alignment score for this point.
    • \n
\n"}, "senselab.audio.tasks.forced_alignment.data_structures.Point.__init__": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Point.__init__", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Point.__init__", "kind": "function", "doc": "

\n", "signature": "(token_index: int, time_index: int, score: float)"}, "senselab.audio.tasks.forced_alignment.data_structures.Point.token_index": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Point.token_index", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Point.token_index", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "senselab.audio.tasks.forced_alignment.data_structures.Point.time_index": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Point.time_index", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Point.time_index", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "senselab.audio.tasks.forced_alignment.data_structures.Point.score": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Point.score", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Point.score", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment", "kind": "class", "doc": "

Represents a segment of aligned text.

\n\n
Attributes:
\n\n
    \n
  • label (str): The text label of the segment.
    • \n
  • start (int): The start time index of the segment.
    • \n
  • end (int): The end time index of the segment.
    • \n
  • score (float): The alignment score for the segment.
    • \n
\n"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.__init__": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.__init__", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.__init__", "kind": "function", "doc": "

\n", "signature": "(label: str, start: int, end: int, score: float)"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.label": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.label", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.label", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.start": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.start", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.start", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.end": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.end", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.end", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.score": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.score", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.score", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "senselab.audio.tasks.forced_alignment.data_structures.Segment.length": {"fullname": "senselab.audio.tasks.forced_alignment.data_structures.Segment.length", "modulename": "senselab.audio.tasks.forced_alignment.data_structures", "qualname": "Segment.length", "kind": "variable", "doc": "

Calculates the length of the segment.

\n", "annotation": ": int"}, "senselab.audio.tasks.forced_alignment.forced_alignment": {"fullname": "senselab.audio.tasks.forced_alignment.forced_alignment", "modulename": "senselab.audio.tasks.forced_alignment.forced_alignment", "kind": "module", "doc": "

Align function based on WhisperX implementation.

\n"}, "senselab.audio.tasks.forced_alignment.forced_alignment.align_transcriptions": {"fullname": "senselab.audio.tasks.forced_alignment.forced_alignment.align_transcriptions", "modulename": "senselab.audio.tasks.forced_alignment.forced_alignment", "qualname": "align_transcriptions", "kind": "function", "doc": "

Aligns transcriptions with the given audio using a wav2vec2.0 model.

\n\n
Arguments:
\n\n
    \n
  • audios_and_transcriptions_and_language (List[tuple[Audio, ScriptLine, Language]): A list of tuples audios, corresponding transcriptions, and optionally a language.\nThe default language is English.
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: A list of lists, where each inner list contains the aligned script lines for each audio.

\n
\n", "signature": "(\taudios_and_transcriptions_and_language: List[Tuple[senselab.audio.data_structures.audio.Audio, senselab.utils.data_structures.script_line.ScriptLine, senselab.utils.data_structures.language.Language]]) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing": {"fullname": "senselab.audio.tasks.preprocessing", "modulename": "senselab.audio.tasks.preprocessing", "kind": "module", "doc": "

This module provides the API for the senselab audio preprocessing.

\n"}, "senselab.audio.tasks.preprocessing.preprocessing": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "kind": "module", "doc": "

This module implements some utilities for the preprocessing task.

\n"}, "senselab.audio.tasks.preprocessing.preprocessing.resample_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.resample_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "resample_audios", "kind": "function", "doc": "

Resamples a list of audio signals to a given sampling rate.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): List of audio objects to resample.
    • \n
  • resample_rate (int): Target sampling rate.
    • \n
  • lowcut (float, optional): Low cut frequency for IIR filter.
    • \n
  • order (int, optional): Order of the IIR filter. Defaults to 4.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: Resampled audio objects.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tresample_rate: int,\tlowcut: Optional[float] = None,\torder: int = 4) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.downmix_audios_to_mono": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.downmix_audios_to_mono", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "downmix_audios_to_mono", "kind": "function", "doc": "

Downmixes a list of Audio objects to mono by averaging all channels.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects with a tensor representing the audio waveform.\nShape: (num_channels, num_samples).
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of audio objects with a mono waveform averaged from all channels. Shape: (num_samples).

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio]) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.select_channel_from_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.select_channel_from_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "select_channel_from_audios", "kind": "function", "doc": "

Selects a specific channel from a list of Audio objects.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects with a tensor representing the audio waveform.\nShape: (num_channels, num_samples).
    • \n
  • channel_index (int): The index of the channel to select.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of audio objects with the selected channel. Shape: (1, num_samples).

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tchannel_index: int) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.chunk_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.chunk_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "chunk_audios", "kind": "function", "doc": "

Chunks the input audios based on the start and end timestamp.

\n\n
Arguments:
\n\n
    \n
  • data: List of tuples containing an Audio object and a tuple with start and end (in seconds) for chunking.
    • \n
\n\n
Returns:
\n\n
\n

List of Audios that have been chunked based on the provided timestamps

\n
\n\n
Todo:
\n\n
\n

Do we really need both chunk_audios and extract_segments?

\n
\n", "signature": "(\tdata: List[Tuple[senselab.audio.data_structures.audio.Audio, Tuple[float, float]]]) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.extract_segments": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.extract_segments", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "extract_segments", "kind": "function", "doc": "

Extracts segments from an audio file.

\n\n
Arguments:
\n\n
    \n
  • data: List of tuples containing an Audio object and a list of tuples with start\nand end (in seconds) for chunking.
    • \n
\n\n
Returns:
\n\n
\n

List of lists of Audios that have been chunked based on the provided timestamps.

\n
\n", "signature": "(\tdata: List[Tuple[senselab.audio.data_structures.audio.Audio, List[Tuple[float, float]]]]) -> List[List[senselab.audio.data_structures.audio.Audio]]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.pad_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.pad_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "pad_audios", "kind": "function", "doc": "

Pads the audio segment to the desired length.

\n\n
Arguments:
\n\n
    \n
  • audios: The list of audio objects to be padded.
    • \n
  • desired_samples: The desired length (in samples) for the padded audio.
    • \n
\n\n
Returns:
\n\n
\n

A new Audio object with the waveform padded to the desired length.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tdesired_samples: int) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.evenly_segment_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.evenly_segment_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "evenly_segment_audios", "kind": "function", "doc": "

Segments multiple audio files into evenly sized segments with optional padding for the last segment.

\n\n
Arguments:
\n\n
    \n
  • audios: The list of Audio objects to be segmented.
    • \n
  • segment_length: The desired length of each segment in seconds.
    • \n
  • pad_last_segment: Whether to pad the last segment to the full segment length (default is False).
    • \n
\n\n
Returns:
\n\n
\n

List of Audio objects that have been segmented.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tsegment_length: float,\tpad_last_segment: bool = True) -> List[List[senselab.audio.data_structures.audio.Audio]]:", "funcdef": "def"}, "senselab.audio.tasks.preprocessing.preprocessing.concatenate_audios": {"fullname": "senselab.audio.tasks.preprocessing.preprocessing.concatenate_audios", "modulename": "senselab.audio.tasks.preprocessing.preprocessing", "qualname": "concatenate_audios", "kind": "function", "doc": "

Concatenates all audios in the list, ensuring they have the same sampling rate and shape.

\n\n
Arguments:
\n\n
    \n
  • audios: List of Audio objects to concatenate.
    • \n
\n\n
Returns:
\n\n
\n

A single Audio object that is the concatenation of all input audios.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the audios do not all have the same sampling rate or shape.
    • \n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio]) -> senselab.audio.data_structures.audio.Audio:", "funcdef": "def"}, "senselab.audio.tasks.speaker_diarization": {"fullname": "senselab.audio.tasks.speaker_diarization", "modulename": "senselab.audio.tasks.speaker_diarization", "kind": "module", "doc": "

Speaker diarization

\n\n

\"Tutorial\"

\n\n

Task Overview

\n\n

Speaker diarization is the process of segmenting audio recordings by speaker labels, aiming to answer the question: \"Who spoke when?\"

\n\n

Models

\n\n

In senselab, we integrate pyannote.audio models for speaker diarization. These models can be explored on the Hugging Face Hub. We may integrate additional approaches for speaker diarization into the package in the future.

\n\n

Evaluation

\n\n

Metrics

\n\n

The Diarization Error Rate (DER) is the standard metric for evaluating and comparing speaker diarization systems. It is defined as:

\n\n
\n
DER= (false alarm + missed detection + confusion) / total\n
\n
\n\n

where

\n\n
    \n
  • false alarm is the duration of non-speech incorrectly classified as speech, missed detection
    • \n
  • missed detection is the duration of speech incorrectly classified as non-speech, confusion
    • \n
  • confusion is the duration of speaker confusion, and total
    • \n
  • total is the sum over all speakers of their reference speech duration.
    • \n
\n\n

Note: DER takes overlapping speech into account. This can lead to increased missed detection rates if the speaker diarization system does not include an overlapping speech detection module.

\n\n

Benchmark

\n\n

You can find a benchmark of the latest pyannote.audio model's performance on various time-stamped speech datasets here.

\n"}, "senselab.audio.tasks.speaker_diarization.api": {"fullname": "senselab.audio.tasks.speaker_diarization.api", "modulename": "senselab.audio.tasks.speaker_diarization.api", "kind": "module", "doc": "

This module implements some utilities for the speaker diarization task.

\n\n

TODO: add computing DER and more evaluation metrics

\n"}, "senselab.audio.tasks.speaker_diarization.api.diarize_audios": {"fullname": "senselab.audio.tasks.speaker_diarization.api.diarize_audios", "modulename": "senselab.audio.tasks.speaker_diarization.api", "qualname": "diarize_audios", "kind": "function", "doc": "

Diarizes all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be diarized.
    • \n
  • model (SenselabModel): The model used for diarization\n(default is \"pyannote/speaker-diarization-3.1\").
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
  • num_speakers (Optional[int]): The number of speakers (default is None).
    • \n
  • min_speakers (Optional[int]): The minimum number of speakers (default is None).
    • \n
  • max_speakers (Optional[int]): The maximum number of speakers (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: The list of script lines with speaker labels.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = PyannoteAudioModel(path_or_uri='pyannote/speaker-diarization-3.1', revision='main', info=None),\tnum_speakers: Optional[int] = None,\tmin_speakers: Optional[int] = None,\tmax_speakers: Optional[int] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.speaker_diarization.api.diarize_audios_pt": {"fullname": "senselab.audio.tasks.speaker_diarization.api.diarize_audios_pt", "modulename": "senselab.audio.tasks.speaker_diarization.api", "qualname": "diarize_audios_pt", "kind": "function", "doc": "

Diarizes all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be diarized.
    • \n
  • model (SenselabModel): The model used for diarization\n(default is \"pyannote/speaker-diarization-3.1\").
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
  • num_speakers (Optional[int]): The number of speakers (default is None).
    • \n
  • min_speakers (Optional[int]): The minimum number of speakers (default is None).
    • \n
  • max_speakers (Optional[int]): The maximum number of speakers (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: The list of script lines with speaker labels.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = PyannoteAudioModel(path_or_uri='pyannote/speaker-diarization-3.1', revision='main', info=None),\tnum_speakers: Optional[int] = None,\tmin_speakers: Optional[int] = None,\tmax_speakers: Optional[int] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.speaker_diarization.pyannote": {"fullname": "senselab.audio.tasks.speaker_diarization.pyannote", "modulename": "senselab.audio.tasks.speaker_diarization.pyannote", "kind": "module", "doc": "

This module implements the Pyannote Diarization task.

\n"}, "senselab.audio.tasks.speaker_diarization.pyannote.PyannoteDiarization": {"fullname": "senselab.audio.tasks.speaker_diarization.pyannote.PyannoteDiarization", "modulename": "senselab.audio.tasks.speaker_diarization.pyannote", "qualname": "PyannoteDiarization", "kind": "class", "doc": "

A factory for managing Pyannote Diarization pipelines.

\n"}, "senselab.audio.tasks.speaker_diarization.pyannote.diarize_audios_with_pyannote": {"fullname": "senselab.audio.tasks.speaker_diarization.pyannote.diarize_audios_with_pyannote", "modulename": "senselab.audio.tasks.speaker_diarization.pyannote", "qualname": "diarize_audios_with_pyannote", "kind": "function", "doc": "

Diarizes a list of audio files using the Pyannote speaker diarization model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of audio files.
    • \n
  • model (PyannoteAudioModel): The model to use for diarization.
    • \n
  • device (Optional[DeviceType]): The device to use for diarization.
    • \n
  • num_speakers (Optional[int]): Number of speakers, when known.
    • \n
  • min_speakers (Optional[int]): Minimum number of speakers. Has no effect when num_speakers is provided.
    • \n
  • max_speakers (Optional[int]): Maximum number of speakers. Has no effect when num_speakers is provided.
    • \n
\n\n
Returns:
\n\n
\n

List[ScriptLine]: A list of ScriptLine objects containing the diarization results.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.PyannoteAudioModel = PyannoteAudioModel(path_or_uri='pyannote/speaker-diarization-3.1', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tnum_speakers: Optional[int] = None,\tmin_speakers: Optional[int] = None,\tmax_speakers: Optional[int] = None) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.speaker_diarization_evaluation": {"fullname": "senselab.audio.tasks.speaker_diarization_evaluation", "modulename": "senselab.audio.tasks.speaker_diarization_evaluation", "kind": "module", "doc": "

Top level file exposing speaker diarization utility functions.

\n"}, "senselab.audio.tasks.speaker_diarization_evaluation.utils": {"fullname": "senselab.audio.tasks.speaker_diarization_evaluation.utils", "modulename": "senselab.audio.tasks.speaker_diarization_evaluation.utils", "kind": "module", "doc": "

Defines utility functions for evaluating speaker diarization results.

\n"}, "senselab.audio.tasks.speaker_diarization_evaluation.utils.calculate_diarization_error_rate": {"fullname": "senselab.audio.tasks.speaker_diarization_evaluation.utils.calculate_diarization_error_rate", "modulename": "senselab.audio.tasks.speaker_diarization_evaluation.utils", "qualname": "calculate_diarization_error_rate", "kind": "function", "doc": "

Computes the diarization error rate (DER).

\n\n

Diarizztion error rate is the ratio of the sum of the false alarms (when speech is detected but none is there),\nmissed detections (when speech is there but not detected), and speaker confusions (when speech is\nattributed to the wrong speaker) to the total ground truth time spoken. For more details see:\nhttps://docs.kolena.com/metrics/diarization-error-rate/

\n\n
Arguments:
\n\n
    \n
  • hypothesis (List[ScriptLine]): the diarization generated as the result from a model
    • \n
  • reference (List[ScriptLine]): annotations that serve as the ground truth diarization
    • \n
  • greedy (bool): whether to use a greedy speaker mapping vs. one that optimizes for minimizing the confusion
    • \n
  • return_speaker_mapping (bool): return the mapping between speakers in the reference and the hypothesis
    • \n
  • detailed (bool): whether to include each component that contributed to the overall diarization error rate
    • \n
\n\n
Returns:
\n\n
\n

A dictionary with at least the diarization error rate, its components if detailed was true, and the\n speaker mapping if return_speaker_mapping was given.

\n
\n", "signature": "(\thypothesis: List[senselab.utils.data_structures.script_line.ScriptLine],\treference: List[senselab.utils.data_structures.script_line.ScriptLine],\tgreedy: bool = False,\treturn_speaker_mapping: bool = False,\tdetailed: bool = False) -> Dict:", "funcdef": "def"}, "senselab.audio.tasks.speaker_embeddings": {"fullname": "senselab.audio.tasks.speaker_embeddings", "modulename": "senselab.audio.tasks.speaker_embeddings", "kind": "module", "doc": "

Speech to text evaluation

\n\n

\n\n

Overview

\n\n

Speaker embeddings are fixed-dimensional vector representations that capture the unique characteristics of a speaker's\nvoice, allowing for tasks such as speaker identification, verification, and diarization.

\n\n

Speaker embedding extraction is a crucial task in speaker recognition systems. It involves transforming variable-length\naudio signals into fixed-size vector representations that encapsulate speaker-specific information while being robust\nto variations in speech content, background noise, and recording conditions.

\n\n

Model Architecture:

\n\n

The default model used in this module (speechbrain/spkrec-ecapa-voxceleb) is based on the ECAPA-TDNN architecture,\nwhich has shown strong performance across various speaker recognition tasks.\nOther supported models include ResNet TDNN (speechbrain/spkrec-resnet-voxceleb) and\nxvector (speechbrain/spkrec-xvect-voxceleb).

\n\n

Note: Performance can vary significantly depending on the specific dataset, task, and evaluation protocol used.\nAlways refer to the most recent literature for up-to-date benchmarks.

\n\n

Learn more:

\n\n\n"}, "senselab.audio.tasks.speaker_embeddings.api": {"fullname": "senselab.audio.tasks.speaker_embeddings.api", "modulename": "senselab.audio.tasks.speaker_embeddings.api", "kind": "module", "doc": "

This module implements some utilities to extract speaker embeddings from an audio clip.

\n"}, "senselab.audio.tasks.speaker_embeddings.api.extract_speaker_embeddings_from_audios": {"fullname": "senselab.audio.tasks.speaker_embeddings.api.extract_speaker_embeddings_from_audios", "modulename": "senselab.audio.tasks.speaker_embeddings.api", "qualname": "extract_speaker_embeddings_from_audios", "kind": "function", "doc": "

Compute the speaker embedding of audio signals.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects containing the audio signals and their properties.
    • \n
  • model (SpeechBrainModel): The model used to compute the embeddings\n(default is \"speechbrain/spkrec-ecapa-voxceleb\").
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of 1d tensors containing the speaker embeddings for each audio file.

\n
\n\n
Raises:
\n\n
    \n
  • NotImplementedError: If the model is not a Hugging Face model.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> audios = [Audio.from_filepath("sample.wav")]\n>>> model = SpeechBrainModel(path_or_uri="speechbrain/spkrec-ecapa-voxceleb", revision="main")\n>>> embeddings = extract_speaker_embeddings_from_audios(audios, model, device=DeviceType.CUDA)\n>>> print(embeddings[0].shape)\ntorch.Size([192])\n
\n
\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = SpeechBrainModel(path_or_uri='speechbrain/spkrec-ecapa-voxceleb', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.audio.tasks.speaker_embeddings.speechbrain": {"fullname": "senselab.audio.tasks.speaker_embeddings.speechbrain", "modulename": "senselab.audio.tasks.speaker_embeddings.speechbrain", "kind": "module", "doc": "

Provides a factory for extracting speaker embeddings from a list of audios using speechbrain.

\n"}, "senselab.audio.tasks.speaker_embeddings.speechbrain.SpeechBrainEmbeddings": {"fullname": "senselab.audio.tasks.speaker_embeddings.speechbrain.SpeechBrainEmbeddings", "modulename": "senselab.audio.tasks.speaker_embeddings.speechbrain", "qualname": "SpeechBrainEmbeddings", "kind": "class", "doc": "

A factory for extracting speaker embeddings using speechbrain models.

\n"}, "senselab.audio.tasks.speaker_embeddings.speechbrain.SpeechBrainEmbeddings.extract_speechbrain_speaker_embeddings_from_audios": {"fullname": "senselab.audio.tasks.speaker_embeddings.speechbrain.SpeechBrainEmbeddings.extract_speechbrain_speaker_embeddings_from_audios", "modulename": "senselab.audio.tasks.speaker_embeddings.speechbrain", "qualname": "SpeechBrainEmbeddings.extract_speechbrain_speaker_embeddings_from_audios", "kind": "function", "doc": "

Compute the speaker embeddings of audio signals.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects containing the audio signals and their properties.
    • \n
  • model (SpeechBrainModel): The model used to compute the embeddings\n(default is \"speechbrain/spkrec-ecapa-voxceleb\").
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).\nOnly CPU and CUDA are supported.
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of tensors containing the speaker embeddings for each audio file.

\n
\n\n
Todo:
\n\n
\n
    \n
  • Optimizing the computation by working in batches
    • \n
  • Double-checking the input size of classifier.encode_batch
    • \n
\n
\n", "signature": "(\tcls,\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SpeechBrainModel = SpeechBrainModel(path_or_uri='speechbrain/spkrec-ecapa-voxceleb', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.audio.tasks.speaker_verification": {"fullname": "senselab.audio.tasks.speaker_verification", "modulename": "senselab.audio.tasks.speaker_verification", "kind": "module", "doc": "

Verifies whether two audio segments belong to the same speaker.

\n"}, "senselab.audio.tasks.speaker_verification.speaker_verification": {"fullname": "senselab.audio.tasks.speaker_verification.speaker_verification", "modulename": "senselab.audio.tasks.speaker_verification.speaker_verification", "kind": "module", "doc": "

Audio Processing and Speaker Verification Module.

\n\n

This module provides functions for resampling audio using an IIR filter and\nverifying if two audio samples or files are from the same speaker using a\nspecified model.

\n"}, "senselab.audio.tasks.speaker_verification.speaker_verification.TRAINING_SAMPLE_RATE": {"fullname": "senselab.audio.tasks.speaker_verification.speaker_verification.TRAINING_SAMPLE_RATE", "modulename": "senselab.audio.tasks.speaker_verification.speaker_verification", "qualname": "TRAINING_SAMPLE_RATE", "kind": "variable", "doc": "

\n", "default_value": "16000"}, "senselab.audio.tasks.speaker_verification.speaker_verification.verify_speaker": {"fullname": "senselab.audio.tasks.speaker_verification.speaker_verification.verify_speaker", "modulename": "senselab.audio.tasks.speaker_verification.speaker_verification", "qualname": "verify_speaker", "kind": "function", "doc": "

Verifies if two audio samples are from the same speaker.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Tuple[Audio, Audio]]): A list of tuples, where each tuple contains\ntwo audio samples to be compared.
    • \n
  • model (SpeechBrainModel, optional): The model for speaker verification.
    • \n
  • device (DeviceType, optional): The device to run the model on. Defaults to CPU.
    • \n
  • threshold (float, optional): The threshold to determine same speaker.
    • \n
\n\n
Returns:
\n\n
\n

List[Tuple[float, bool]]: A list of tuples containing the verification score and\n the prediction for each pair of audio samples. The\n verification score is a float indicating the similarity\n between the two samples, and the prediction is a boolean\n indicating if the two samples are from the same speaker.

\n
\n", "signature": "(\taudios: List[Tuple[senselab.audio.data_structures.audio.Audio, senselab.audio.data_structures.audio.Audio]],\tmodel: senselab.utils.data_structures.model.SpeechBrainModel = SpeechBrainModel(path_or_uri='speechbrain/spkrec-ecapa-voxceleb', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tthreshold: float = 0.25) -> List[Tuple[float, bool]]:", "funcdef": "def"}, "senselab.audio.tasks.speech_enhancement": {"fullname": "senselab.audio.tasks.speech_enhancement", "modulename": "senselab.audio.tasks.speech_enhancement", "kind": "module", "doc": "

Speech enhancement

\n\n

\n\n

Task Overview

\n\n

Speech Enhancement is a signal processing task that involves improving the quality of speech signals captured under noisy or degraded conditions. The goal of speech enhancement is to make speech signals clearer, more intelligible, and more pleasant to listen to, which can be used for various applications such as voice recognition, teleconferencing, and hearing aids.

\n\n

Models

\n\n

By now, senselab supports all speechbrain models for speech enhancement. These include:

\n\n
    \n
  • SepFormer for audio clips sampled at 16KHz
    • \n
  • SepFormer for audio clips sampled at 8KHz.\nIn the future, more models will be integrated.
    • \n
\n\n

Evaluation

\n\n

Metrics

\n\n

Objective evaluation involves comparing speech enhanced outputs across different downstream tasks:

\n\n
    \n
  • Using an automatic speaker verification tool to determine if the original speaker and the enhanced speaker can be distinguished from each other.
    • \n
  • Ensuring the intelligibility of speech content using an automatic speech recognition system to verify that the content remains unchanged.
    • \n
  • Assessing the preservation of the original speech's emotion after speech enhancement.
    • \n
  • ...more...
    • \n
\n"}, "senselab.audio.tasks.speech_enhancement.api": {"fullname": "senselab.audio.tasks.speech_enhancement.api", "modulename": "senselab.audio.tasks.speech_enhancement.api", "kind": "module", "doc": "

This module provides the API for the senselab speech enhancement task.

\n"}, "senselab.audio.tasks.speech_enhancement.api.enhance_audios": {"fullname": "senselab.audio.tasks.speech_enhancement.api.enhance_audios", "modulename": "senselab.audio.tasks.speech_enhancement.api", "qualname": "enhance_audios", "kind": "function", "doc": "

Enhances all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be enhanced.
    • \n
  • model (SenselabModel): The model used for enhancement\n(default is \"speechbrain/sepformer-wham16k-enhancement\").
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of enhanced audio objects.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = SpeechBrainModel(path_or_uri='speechbrain/sepformer-wham16k-enhancement', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.speech_enhancement.speechbrain": {"fullname": "senselab.audio.tasks.speech_enhancement.speechbrain", "modulename": "senselab.audio.tasks.speech_enhancement.speechbrain", "kind": "module", "doc": "

This module provides the Speechbrain interface for speech enhancement.

\n"}, "senselab.audio.tasks.speech_enhancement.speechbrain.SpeechBrainEnhancer": {"fullname": "senselab.audio.tasks.speech_enhancement.speechbrain.SpeechBrainEnhancer", "modulename": "senselab.audio.tasks.speech_enhancement.speechbrain", "qualname": "SpeechBrainEnhancer", "kind": "class", "doc": "

A factory for managing SpeechBrain enhancement pipelines.

\n"}, "senselab.audio.tasks.speech_enhancement.speechbrain.SpeechBrainEnhancer.enhance_audios_with_speechbrain": {"fullname": "senselab.audio.tasks.speech_enhancement.speechbrain.SpeechBrainEnhancer.enhance_audios_with_speechbrain", "modulename": "senselab.audio.tasks.speech_enhancement.speechbrain", "qualname": "SpeechBrainEnhancer.enhance_audios_with_speechbrain", "kind": "function", "doc": "

Enhances all audio samples using the given speechbrain model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be enhanced.
    • \n
  • model (SpeechBrainModel): The SpeechBrain model used for enhancement.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
  • batch_size (int): The size of batches to use when processing on a GPU.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of enhanced audio objects.

\n
\n", "signature": "(\tcls,\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SpeechBrainModel = SpeechBrainModel(path_or_uri='speechbrain/sepformer-wham16k-enhancement', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tbatch_size: int = 16) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text": {"fullname": "senselab.audio.tasks.speech_to_text", "modulename": "senselab.audio.tasks.speech_to_text", "kind": "module", "doc": "

Speech to text

\n\n

\n\n

Task Overview

\n\n

Speech-to-Text (STT), also known as Automatic Speech Recognition (ASR), is the process of converting spoken language into written text. This technology has a wide range of applications, including transcription services and voice user interfaces.

\n\n

Notably, certain models can provide word- or sentence-level timestamps along with the transcribed text, making them ideal for generating subtitles. Additionally, some models are multilingual, and some of them leverage language identification blocks to enhance performance.

\n\n

Models

\n\n

A variety of models are supported by senselab for ASR tasks. They can be explored on the Hugging Face Hub. Each model varies in performance, size, license, language support, and more. Performance may also vary depending on who is the speaker in the processed audio clips (there may be differences in terms of age, dialects, disfluencies). It is recommended to review the model card for each model before use. Also, always refer to the most recent literature for an informed decision.

\n\n

Popular models include:

\n\n\n\n\n\n

Evaluation

\n\n

Metrics

\n\n

The primary evaluation metric for ASR systems is the Word Error Rate (WER). WER is calculated as:

\n\n
WER = (Substitutions + Insertions + Deletions) / Number of words in the reference\n
\n\n

where:

\n\n
    \n
  • Substitutions: Incorrect words.
    • \n
  • Insertions: Extra words added.
    • \n
  • Deletions: Words omitted.
    • \n
\n\n

Other important metrics include:

\n\n
    \n
  • Character Error Rate (CER)
    • \n
  • Match Error Rate (MER)
    • \n
  • Word Information Lost (WIL)
    • \n
  • Word Information Preserved (WIP)
    • \n
\n\n

For detailed information on these metrics, refer to the speech to text evaluation module.

\n\n

Datasets (English Speech Benchmark - ESB)

\n\n

The following table lists the datasets included in the English Speech Benchmark (ESB), which are generally used for evaluating ASR models in English:

\n\n\n\n\n \n \n \n \n \n \n \n \n\n\n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n\n
DatasetDomainSpeaking StyleTrain (h)Dev (h)Test (h)TranscriptionsLicense
LibriSpeechAudiobookNarrated9601111NormalizedCC-BY-4.0
Common Voice 9WikipediaNarrated14092727Punctuated & CasedCC0-1.0
VoxPopuliEuropean ParliamentOratory52355PunctuatedCC0
TED-LIUMTED talksOratory45423NormalizedCC-BY-NC-ND 3.0
GigaSpeechAudiobook, podcast, YouTubeNarrated, spontaneous25001240PunctuatedApache-2.0
SPGISpeechFinancial meetingsOratory, spontaneous4900100100Punctuated & CasedUser Agreement
Earnings-22Financial meetingsOratory, spontaneous10555Punctuated & CasedCC-BY-SA-4.0
AMIMeetingsSpontaneous7899Punctuated & CasedCC-BY-4.0
\n\n

For more details on these datasets and how models are evaluated to obtain the ESB score, refer to the ESB paper.\nNote that this list of datasets is not exhaustive. If you are interested in benchmarking models in different languages or under specific conditions, consult the relevant literature.

\n\n

Benchmark

\n\n

The \ud83e\udd17 Open ASR Leaderboard ranks and evaluates speech recognition models available on the Hugging Face Hub. The leaderboard uses the datasets included in the ESB paper to obtain robust evaluation scores for each model. The ESB score is a macro-average of the WER scores across the ESB datasets, providing a comprehensive indication of a model's performance across various domains and conditions.

\n\n

Notes

\n\n
    \n
  • It is possible to fine-tune foundational speech models on a specific language without requiring large amounts of data. A detailed blog post on how to fine-tune a pre-trained Whisper checkpoint on labeled data for ASR can be found here.
    • \n
\n\n

Learn more:

\n\n\n"}, "senselab.audio.tasks.speech_to_text.api": {"fullname": "senselab.audio.tasks.speech_to_text.api", "modulename": "senselab.audio.tasks.speech_to_text.api", "kind": "module", "doc": "

This module represents the API for the speech-to-text task within the senselab package.

\n\n

Currently, it supports only Hugging Face models, with plans to include more in the future.\nUsers can specify the audio clips to transcribe, the ASR model, the language,\nthe preferred device, and the model-specific parameters, and senselab handles the rest.

\n"}, "senselab.audio.tasks.speech_to_text.api.transcribe_audios": {"fullname": "senselab.audio.tasks.speech_to_text.api.transcribe_audios", "modulename": "senselab.audio.tasks.speech_to_text.api", "qualname": "transcribe_audios", "kind": "function", "doc": "

Transcribes all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be transcribed.
    • \n
  • model (SenselabModel): The model used for transcription.
    • \n
  • language (Optional[Language]): The language of the audio (default is None).
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
  • **kwargs: Additional keyword arguments to pass to the transcription function.
    • \n
\n\n
Returns:
\n\n
\n

List[ScriptLine]: The list of script lines.

\n
\n\n
Todo:
\n\n
\n
    \n
  • Include more models (e.g., speechbrain, nvidia)
    • \n
\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel,\tlanguage: Optional[senselab.utils.data_structures.language.Language] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\t**kwargs: Any) -> List[senselab.utils.data_structures.script_line.ScriptLine]:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text.huggingface": {"fullname": "senselab.audio.tasks.speech_to_text.huggingface", "modulename": "senselab.audio.tasks.speech_to_text.huggingface", "kind": "module", "doc": "

This module provides a factory for managing Hugging Face ASR pipelines.

\n\n

To ensure correct functionality, call transcribe_audios_with_transformers serially or,\nif you need to process multiple audios in parallel, pass the entire list of audios to the\nfunction at once, rather than calling the function with one audio at a time.

\n"}, "senselab.audio.tasks.speech_to_text.huggingface.HuggingFaceASR": {"fullname": "senselab.audio.tasks.speech_to_text.huggingface.HuggingFaceASR", "modulename": "senselab.audio.tasks.speech_to_text.huggingface", "qualname": "HuggingFaceASR", "kind": "class", "doc": "

A factory for managing Hugging Face ASR pipelines.

\n"}, "senselab.audio.tasks.speech_to_text.huggingface.HuggingFaceASR.transcribe_audios_with_transformers": {"fullname": "senselab.audio.tasks.speech_to_text.huggingface.HuggingFaceASR.transcribe_audios_with_transformers", "modulename": "senselab.audio.tasks.speech_to_text.huggingface", "qualname": "HuggingFaceASR.transcribe_audios_with_transformers", "kind": "function", "doc": "

Transcribes all audio samples in the dataset.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be transcribed.
    • \n
  • model (HFModel): The Hugging Face model used for transcription.
    • \n
  • language (Optional[Language]): The language of the audio (default is None).
    • \n
  • return_timestamps (Optional[str]): The level of timestamp details (default is \"word\").
    • \n
  • max_new_tokens (int): The maximum number of new tokens (default is 128).
    • \n
  • chunk_length_s (int): The length of audio chunks in seconds (default is 30).
    • \n
  • batch_size (int): The batch size for processing (default is 16).
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[ScritpLine]: The list of script lines.

\n
\n", "signature": "(\tcls,\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.HFModel = HFModel(path_or_uri='openai/whisper-tiny', revision='main', info=None),\tlanguage: Optional[senselab.utils.data_structures.language.Language] = None,\treturn_timestamps: Optional[str] = 'word',\tmax_new_tokens: int = 128,\tchunk_length_s: int = 30,\tbatch_size: int = 16,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[senselab.utils.data_structures.script_line.ScriptLine]:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text_evaluation": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation", "modulename": "senselab.audio.tasks.speech_to_text_evaluation", "kind": "module", "doc": "

Speech to text evaluation

\n\n

\n\n

Overview

\n\n

Evaluating speech transcripts involves comparing a predicted transcript (hypothesis) generated by an automated speech recognition (ASR) system against a ground truth transcript (reference). This evaluation helps to determine the accuracy and performance of the ASR system. Various metrics can be used for this purpose, each capturing different aspects of the errors in the transcription process.

\n\n

Key metrics

\n\n

1. Word Error Rate (WER)

\n\n

Word Error Rate (WER) is the most common metric used to evaluate the accuracy of ASR systems. It measures the number of word-level errors (insertions, deletions, and substitutions) divided by the total number of words in the reference. The lower the value, the better the performance of the ASR system, with an error rate of 0 indicating a perfect score. Notably, this is not a percentage as can go higher than 1.

\n\n

Formula:

\n\n
\n
WER = (S + D + I) / N\n
\n
\n\n

Where:

\n\n
    \n
  • ( S ) = Number of substitutions
    • \n
  • ( D ) = Number of deletions
    • \n
  • ( I ) = Number of insertions
    • \n
  • ( N ) = Total number of words in the reference
    • \n
\n\n

2. Character Error Rate (CER)

\n\n

Character Error Rate (CER) is similar to WER but operates at the character level rather than the word level. It measures the number of character-level errors divided by the total number of characters in the reference. The lower the value, the better the performance of the ASR system, with an error rate of 0 indicating a perfect score.\nAs for WER, CER is not a percentage as can go higher than 1.

\n\n

Formula:

\n\n
\n
CER = (S + D + I) / N\n
\n
\n\n

Where:

\n\n
    \n
  • ( S ) = Number of substitutions
    • \n
  • ( D ) = Number of deletions
    • \n
  • ( I ) = Number of insertions
    • \n
  • ( N ) = Total number of characters in the reference
    • \n
\n\n

3. Match Error Rate (MER)

\n\n

Match Error Rate (MER) calculates the proportion of word-level errors relative to the total number of correct matches plus the number of errors. The lower the value, the better the performance of the ASR system, with an error rate of 0 indicating a perfect score.

\n\n

Formula:

\n\n
\n
MER = (S + D + I) / (S + D + I + C)\n
\n
\n\n

Where:

\n\n
    \n
  • ( S ) = Number of substitutions
    • \n
  • ( D ) = Number of deletions
    • \n
  • ( I ) = Number of insertions
    • \n
  • ( C ) = Number of correct words
    • \n
\n\n

4. Word Information Lost (WIL)

\n\n

Word Information Lost (WIL) is computed as follows:

\n\n

Formula:

\n\n
\n
WIL = 1 - (C / N) + (C / P)\n
\n
\n\n

Where:

\n\n
    \n
  • ( C ) = Number of correct words
    • \n
  • ( N ) = Number of words in the reference
    • \n
  • ( P ) = Number of words in the prediction
    • \n
\n\n

The lower the WIL, the better the performance of the ASR system.

\n\n

5. Word Information Preserved (WIP)

\n\n

Word Information Preserved (WIP) is computed as follows:\nFormula:

\n\n
\n
WIP = (C / N) * (C / P)\n
\n
\n\n

Where:

\n\n
    \n
  • ( C ) = Number of correct words
    • \n
  • ( N ) = Number of words in the reference
    • \n
  • ( P ) = Number of words in the prediction
    • \n
\n\n

The higher the value, the better the performance of the ASR system, with 1 being a perfect score.

\n\n

Examples

\n\n

For a given reference \"hello world\" and hypothesis \"hello duck\":

\n\n
    \n

  • WER (Word Error Rate):

    \n\n
    WER = (0 + 1 + 0)/(2) = 0.5\n
    • \n

  • CER (Character Error Rate):

    \n\n
    CER = (0 + 1 + 0)/(11) = 0.0909\n
    • \n

  • MER (Match Error Rate):

    \n\n
    MER = (1)/(2) = 0.5\n
    • \n
\n"}, "senselab.audio.tasks.speech_to_text_evaluation.utils": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "kind": "module", "doc": "

This module implements some utilities for evaluating a transcription.

\n"}, "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wer": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wer", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "qualname": "calculate_wer", "kind": "function", "doc": "

Calculate the Word Error Rate (WER) between the reference and hypothesis.

\n\n
Arguments:
\n\n
    \n
  • reference (str): The ground truth text.
    • \n
  • hypothesis (str): The predicted text.
    • \n
\n\n
Returns:
\n\n
\n

float: The WER score.

\n
\n\n
Examples:
\n\n
\n
\n
>>> calculate_wer("hello world", "hello duck")\n0.5\n
\n
\n
\n", "signature": "(reference: str, hypothesis: str) -> float:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_mer": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_mer", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "qualname": "calculate_mer", "kind": "function", "doc": "

Calculate the Match Error Rate (MER) between the reference and hypothesis.

\n\n
Arguments:
\n\n
    \n
  • reference (str): The ground truth text.
    • \n
  • hypothesis (str): The predicted text.
    • \n
\n\n
Returns:
\n\n
\n

float: The MER score.

\n
\n\n
Examples:
\n\n
\n
\n
>>> calculate_mer("hello world", "hello duck")\n0.5\n
\n
\n
\n", "signature": "(reference: str, hypothesis: str) -> float:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wil": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wil", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "qualname": "calculate_wil", "kind": "function", "doc": "

Calculate the Word Information Lost (WIL) between the reference and hypothesis.

\n\n
Arguments:
\n\n
    \n
  • reference (str): The ground truth text.
    • \n
  • hypothesis (str): The predicted text.
    • \n
\n\n
Returns:
\n\n
\n

float: The WIL score.

\n
\n\n
Examples:
\n\n
\n
\n
>>> calculate_wil("hello world", "hello duck")\n0.75\n
\n
\n
\n", "signature": "(reference: str, hypothesis: str) -> float:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wip": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_wip", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "qualname": "calculate_wip", "kind": "function", "doc": "

Calculate the Word Information Preserved (WIP) between the reference and hypothesis.

\n\n
Arguments:
\n\n
    \n
  • reference (str): The ground truth text.
    • \n
  • hypothesis (str): The predicted text.
    • \n
\n\n
Returns:
\n\n
\n

float: The WIP score.

\n
\n\n
Examples:
\n\n
\n
\n
>>> calculate_wip("hello world", "hello duck")\n0.25\n
\n
\n
\n", "signature": "(reference: str, hypothesis: str) -> float:", "funcdef": "def"}, "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_cer": {"fullname": "senselab.audio.tasks.speech_to_text_evaluation.utils.calculate_cer", "modulename": "senselab.audio.tasks.speech_to_text_evaluation.utils", "qualname": "calculate_cer", "kind": "function", "doc": "

Calculate the Character Error Rate (CER) between the reference and hypothesis.

\n\n
Arguments:
\n\n
    \n
  • reference (str): The ground truth text.
    • \n
  • hypothesis (str): The predicted text.
    • \n
\n\n
Returns:
\n\n
\n

float: The CER score.

\n
\n\n
Examples:
\n\n
\n
\n
>>> calculate_cer("hello world", "hello duck")\n0.45454545454545453\n
\n
\n
\n", "signature": "(reference: str, hypothesis: str) -> float:", "funcdef": "def"}, "senselab.audio.tasks.ssl_embeddings": {"fullname": "senselab.audio.tasks.ssl_embeddings", "modulename": "senselab.audio.tasks.ssl_embeddings", "kind": "module", "doc": "

This module provides the API for the senselab extract SSL embeddings task.

\n"}, "senselab.audio.tasks.ssl_embeddings.api": {"fullname": "senselab.audio.tasks.ssl_embeddings.api", "modulename": "senselab.audio.tasks.ssl_embeddings.api", "kind": "module", "doc": "

This module implements some utilities to extract embeddings from self-supervised models.

\n"}, "senselab.audio.tasks.ssl_embeddings.api.extract_ssl_embeddings_from_audios": {"fullname": "senselab.audio.tasks.ssl_embeddings.api.extract_ssl_embeddings_from_audios", "modulename": "senselab.audio.tasks.ssl_embeddings.api", "qualname": "extract_ssl_embeddings_from_audios", "kind": "function", "doc": "

Extract embedding of audio signals from pre-trained SSL models.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects containing the audio signals and their properties.
    • \n
  • model (SenselabModel): The model used to extract their embeddings.
    • \n
  • cache_dir (str): The path to where the model's weights will be saved.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of 1d tensors containing the ssl embeddings for each audio file.

\n
\n\n
Raises:
\n\n
    \n
  • NotImplementedError: If the model is not a Hugging Face model.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> audios = [Audio.from_filepath("sample.wav")]\n>>> model = HFModel(path_or_uri="facebook/wav2vec2-base", revision="main")\n>>> embeddings = extract_ssl_embeddings_from_audios(audios, model, cache_dir="./", device=DeviceType.CUDA)\n>>> print(embeddings[0].shape)\n[13, 209, 768] ([# of Layers, Time Frames, Embedding Size])\n
\n
\n
\n\n
Todo:
\n\n
\n
    \n
  • Make the API compatible with other models than Hugging Face.
    • \n
\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel,\tcache_dir: str = '~/',\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.audio.tasks.ssl_embeddings.api.extract_ssl_embeddings_from_audios_pt": {"fullname": "senselab.audio.tasks.ssl_embeddings.api.extract_ssl_embeddings_from_audios_pt", "modulename": "senselab.audio.tasks.ssl_embeddings.api", "qualname": "extract_ssl_embeddings_from_audios_pt", "kind": "function", "doc": "

Extract embedding of audio signals from pre-trained SSL models.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects containing the audio signals and their properties.
    • \n
  • model (SenselabModel): The model used to extract their embeddings.
    • \n
  • cache_dir (str): The path to where the model's weights will be saved.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of 1d tensors containing the ssl embeddings for each audio file.

\n
\n\n
Raises:
\n\n
    \n
  • NotImplementedError: If the model is not a Hugging Face model.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> audios = [Audio.from_filepath("sample.wav")]\n>>> model = HFModel(path_or_uri="facebook/wav2vec2-base", revision="main")\n>>> embeddings = extract_ssl_embeddings_from_audios(audios, model, cache_dir="./", device=DeviceType.CUDA)\n>>> print(embeddings[0].shape)\n[13, 209, 768] ([# of Layers, Time Frames, Embedding Size])\n
\n
\n
\n\n
Todo:
\n\n
\n
    \n
  • Make the API compatible with other models than Hugging Face.
    • \n
\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel,\tcache_dir: str = '~/',\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.audio.tasks.ssl_embeddings.self_supervised_features": {"fullname": "senselab.audio.tasks.ssl_embeddings.self_supervised_features", "modulename": "senselab.audio.tasks.ssl_embeddings.self_supervised_features", "kind": "module", "doc": "

This module contains functions for extracting features from pre-trained self-supervised models.

\n"}, "senselab.audio.tasks.ssl_embeddings.self_supervised_features.SSLEmbeddingsFactory": {"fullname": "senselab.audio.tasks.ssl_embeddings.self_supervised_features.SSLEmbeddingsFactory", "modulename": "senselab.audio.tasks.ssl_embeddings.self_supervised_features", "qualname": "SSLEmbeddingsFactory", "kind": "class", "doc": "

A factory for managing self-supervised models.

\n"}, "senselab.audio.tasks.ssl_embeddings.self_supervised_features.SSLEmbeddingsFactory.extract_ssl_embeddings": {"fullname": "senselab.audio.tasks.ssl_embeddings.self_supervised_features.SSLEmbeddingsFactory.extract_ssl_embeddings", "modulename": "senselab.audio.tasks.ssl_embeddings.self_supervised_features", "qualname": "SSLEmbeddingsFactory.extract_ssl_embeddings", "kind": "function", "doc": "

Compute the ssl embeddings of audio signals.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): A list of Audio objects containing the audio signals and their properties.
    • \n
  • model (HFModel): The model used to compute the embeddings\n(default is \"facebook/wav2vec2-base\").
    • \n
  • cache_dir (str): The path to where the model's weights will be saved.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).\nOnly CPU and CUDA are supported.
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of tensors containing the ssl embeddings for each audio file.

\n
\n", "signature": "(\tcls,\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.HFModel,\tcache_dir: str = '~/',\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.audio.tasks.text_to_speech": {"fullname": "senselab.audio.tasks.text_to_speech", "modulename": "senselab.audio.tasks.text_to_speech", "kind": "module", "doc": "

Text to Speech

\n\n

\n\n

Task Overview

\n\n

Text-to-speech (TTS) is the task of creating natural-sounding speech from text. This process can be performed in multiple languages and for multiple speakers.

\n\n

Models

\n\n

A variety of models are supported by senselab for text-to-speech.\nEach model varies in performance, size, license, language support, and more. Performance may also vary depending, among other reasons, on the length of the text or the target speaker (differences in terms of age, dialects, disfluencies). It is recommended to review the model card for each model before use and refer to the most recent literature for an informed decision.

\n\n

Several text-to-speech models are currently available through \ud83e\udd17 Transformers. These models can be explored on the Hugging Face Hub.\nNote: Some Hugging Face models, despite having the text-to-speech label on their model cards, may not work with the text-to-speech pipeline. These models are not supported in senselab, and identifying them often requires trial and error.

\n\n

In addition to the models from \ud83e\udd17 Transformers, senselab also supports Mars5-TTS, which enables text-to-speech generation using a specific target voice, accompanied by its corresponding transcript. Voice cloning using a target voice refers to the process of creating a synthetic voice that mimics the characteristics of a specific person's voice, known as the target voice. This involves generating speech that sounds like it was spoken by that person, even though it was produced by a machine.

\n\n

Popular/recommended models include:

\n\n\n\n

Evaluation

\n\n

Metrics

\n\n

For assessing speech quality and intelligibility, we can use quantitative metrics such as:

\n\n
    \n
  • Wideband Perceptual Estimation of Speech Quality (PESQ)
    • \n
  • Short-Time Objective Intelligibility (STOI)
    • \n
  • Scale-Invariant Signal-to-Distortion Ratio (SI-SDR)
    • \n
\n\n

and qualitative metrics such as:

\n\n
    \n
  • Mean Opinion Score (MOS)
    • \n
\n\n

Another way to automatically assess the intelligibility of the synthesis is by transcribing the output audio (trusting the ASR system) and computing the Word Error Rate (WER) with the reference text.

\n\n

Also, if targeting a specific speaker's voice, we can perform speaker verification to assess how closely the generated audio matches the target voice.\nIf there are specific features in the target voice that we aim to maintain, we can extract these features from the generated audio and verify their presence.

\n\n

senselab can help with all of these evaluations.

\n\n

Datasets

\n\n

To train and evaluate TTS models, a variety of datasets can be used. Some popular datasets include:

\n\n
    \n
  • LJSpeech: A dataset of single-speaker English speech.
    • \n
  • LibriTTS: A multi-speaker English dataset derived from the LibriVox project.
    • \n
  • VCTK: A multi-speaker English dataset with various accents.
    • \n
  • Common Voice: A multi-language dataset collected by Mozilla.
    • \n
\n\n

Benchmark

\n\n

The TTS Arena ranks and evaluates text-to-speech models available based on human perception.\nFor automated benchmarking, we recommend using standard datasets and metrics mentioned above.

\n"}, "senselab.audio.tasks.text_to_speech.api": {"fullname": "senselab.audio.tasks.text_to_speech.api", "modulename": "senselab.audio.tasks.text_to_speech.api", "kind": "module", "doc": "

This module implements some utilities for the text-to-speech task.

\n"}, "senselab.audio.tasks.text_to_speech.api.synthesize_texts": {"fullname": "senselab.audio.tasks.text_to_speech.api.synthesize_texts", "modulename": "senselab.audio.tasks.text_to_speech.api", "qualname": "synthesize_texts", "kind": "function", "doc": "

Synthesizes speech from all texts using the given model.

\n\n

This function synthesizes speech from a list of text strings using the specified text-to-speech (TTS) model.\nIt supports models from HuggingFace and Mars5TTS and StyleTTS2 for now.

\n\n
Arguments:
\n\n
    \n
  • texts (List[str]): The list of text strings to be synthesized.
    • \n
  • model (SenselabModel): The model used for synthesis.\nDefaults to HFModel(path_or_uri=\"suno/bark\", revision=\"main\").
    • \n
  • language (Optional[Language]): The language of the text\n(default is None).
    • \n
  • device (Optional[DeviceType]): The device to run the model on\n(default is None).
    • \n
  • targets (Optional[List[Audio | Tuple[Audio, str]]]): A list where each element is a target audio or a tuple of target audio and transcript.\nDepending on the model being used, the target input may need to be provided in a specific format:\n
      \n
    • Hugging Face models do not require a target input at all.
      • \n
    • Mars5TTS requires both Audio and a transcript for all inputs.
      • \n
    • StyleTTS2 will support both simple (Audio) and complex (Audio, str) target inputs,\nusing the appropriate generation method in each case.
      • \n
    • \n
  • **kwargs: Additional keyword arguments to pass to the synthesis function.\nDepending on the model used (e.g., HFModel), additional arguments\nmay be required. Refer to the model-specific documentation for details.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of synthesized audio objects.

\n
\n", "signature": "(\ttexts: List[str],\tmodel: senselab.utils.data_structures.model.SenselabModel = HFModel(path_or_uri='suno/bark', revision='main', info=None),\tlanguage: Optional[senselab.utils.data_structures.language.Language] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\ttargets: Optional[List[Union[senselab.audio.data_structures.audio.Audio, Tuple[senselab.audio.data_structures.audio.Audio, str]]]] = None,\t**kwargs: Any) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.text_to_speech.huggingface": {"fullname": "senselab.audio.tasks.text_to_speech.huggingface", "modulename": "senselab.audio.tasks.text_to_speech.huggingface", "kind": "module", "doc": "

This module implements some utilities for the text-to-speech task.

\n"}, "senselab.audio.tasks.text_to_speech.huggingface.HuggingFaceTTS": {"fullname": "senselab.audio.tasks.text_to_speech.huggingface.HuggingFaceTTS", "modulename": "senselab.audio.tasks.text_to_speech.huggingface", "qualname": "HuggingFaceTTS", "kind": "class", "doc": "

A factory for managing Hugging Face TTS pipelines.

\n"}, "senselab.audio.tasks.text_to_speech.huggingface.HuggingFaceTTS.synthesize_texts_with_transformers": {"fullname": "senselab.audio.tasks.text_to_speech.huggingface.HuggingFaceTTS.synthesize_texts_with_transformers", "modulename": "senselab.audio.tasks.text_to_speech.huggingface", "qualname": "HuggingFaceTTS.synthesize_texts_with_transformers", "kind": "function", "doc": "

Synthesizes speech from all the provided text samples.

\n\n

Several text-to-speech models are currently available in Transformers,\nsuch as Bark, MMS, VITS and SpeechT5.

\n\n
Arguments:
\n\n
    \n
  • texts (List[str]): The list of text strings to be synthesized.
    • \n
  • model (HFModel): The Hugging Face model used for synthesis.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
  • forward_params (Optional[Dict[str, Any]]): Additional parameters to pass to the forward function.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of synthesized audio objects.

\n
\n\n
Todo:
\n\n
\n \n
\n", "signature": "(\tcls,\ttexts: List[str],\tmodel: senselab.utils.data_structures.model.HFModel = HFModel(path_or_uri='suno/bark', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tforward_params: Optional[Dict[str, Any]] = None) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.text_to_speech.marstts": {"fullname": "senselab.audio.tasks.text_to_speech.marstts", "modulename": "senselab.audio.tasks.text_to_speech.marstts", "kind": "module", "doc": "

This module provides the implementation of Mars5-TTS-based text-to-speech pipelines.

\n"}, "senselab.audio.tasks.text_to_speech.marstts.Mars5TTS": {"fullname": "senselab.audio.tasks.text_to_speech.marstts.Mars5TTS", "modulename": "senselab.audio.tasks.text_to_speech.marstts", "qualname": "Mars5TTS", "kind": "class", "doc": "

A class for managing Torch-based Mars5TTS models.

\n"}, "senselab.audio.tasks.text_to_speech.marstts.Mars5TTS.synthesize_texts_with_mars5tts": {"fullname": "senselab.audio.tasks.text_to_speech.marstts.Mars5TTS.synthesize_texts_with_mars5tts", "modulename": "senselab.audio.tasks.text_to_speech.marstts", "qualname": "Mars5TTS.synthesize_texts_with_mars5tts", "kind": "function", "doc": "

Synthesizes speech from all the provided text samples and target voices+transcripts using Mars5-TTS.

\n\n
Arguments:
\n\n
    \n
  • texts (List[str]): The list of text strings to be synthesized.
    • \n
  • targets (List[Tuple[Audio, str]]): The list of tuples containing audio objects and transcripts.
    • \n
  • model (TorchModel): The Torch model (default is \"Camb-ai/mars5-tts\").
    • \n
  • language (Optional[Language]): The language of the text (default is None).\nThe only supported language is \"en\" for now.
    • \n
  • device (DeviceType): The device to run the model on (default is None). Supported devices are CPU and CUDA.
    • \n
  • deep_clone (bool): Whether to deep clone the target audio (default is True).
    • \n
  • rep_penalty_window (int): The window size for repetition penalty (default is 100).
    • \n
  • top_k (int): The number of top-k candidates to consider (default is 100).
    • \n
  • temperature (float): The temperature for sampling (default is 0.7).
    • \n
  • freq_penalty (float): The frequency penalty for sampling (default is 3).
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of synthesized audio objects.

\n
\n\n
Some tips for best quality:
\n\n
\n
    \n
  • Make sure reference audio is clean and between 1 second and 12 seconds.
    • \n
  • Use deep clone and provide an accurate transcript for the reference.\n Mars5-TTS can potentially accept audio target voices without transcripts. After some testing,\n we found that the best quality is achieved with deep cloning and providing an accurate transcript.
    • \n
  • Use proper punctuation -- the model can be guided and made better or worse with proper use of punctuation\n and capitalization.
    • \n
\n
\n\n

The original repo of the model is: https://github.com/Camb-ai/MARS5-TTS.

\n", "signature": "(\tcls,\ttexts: List[str],\ttargets: List[Tuple[senselab.audio.data_structures.audio.Audio, str]],\tmodel: senselab.utils.data_structures.model.TorchModel = TorchModel(path_or_uri='Camb-ai/mars5-tts', revision='master'),\tlanguage: Optional[senselab.utils.data_structures.language.Language] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tdeep_clone: bool = True,\trep_penalty_window: int = 100,\ttop_k: int = 100,\ttemperature: float = 0.7,\tfreq_penalty: float = 3) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.text_to_speech.styletts2": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "kind": "module", "doc": "

This module provides the implementation of Style-TTS-based text-to-speech pipelines.

\n"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2", "kind": "class", "doc": "

A class for managing Torch-based StyleTTS2 models.

\n"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_ALPHA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_ALPHA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.DEF_ALPHA", "kind": "variable", "doc": "

\n", "default_value": "0.3"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_BETA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_BETA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.DEF_BETA", "kind": "variable", "doc": "

\n", "default_value": "0.1"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_EMBEDDING_SCALE": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DEF_EMBEDDING_SCALE", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.DEF_EMBEDDING_SCALE", "kind": "variable", "doc": "

\n", "default_value": "1"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_ALPHA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_ALPHA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.LF_ALPHA", "kind": "variable", "doc": "

\n", "default_value": "0.5"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_BETA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_BETA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.LF_BETA", "kind": "variable", "doc": "

\n", "default_value": "0.9"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_EMBEDDING_SCALE": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.LF_EMBEDDING_SCALE", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.LF_EMBEDDING_SCALE", "kind": "variable", "doc": "

\n", "default_value": "1.5"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_ALPHA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_ALPHA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.ST_ALPHA", "kind": "variable", "doc": "

\n", "default_value": "0.5"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_BETA": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_BETA", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.ST_BETA", "kind": "variable", "doc": "

\n", "default_value": "0.9"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_EMBEDDING_SCALE": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.ST_EMBEDDING_SCALE", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.ST_EMBEDDING_SCALE", "kind": "variable", "doc": "

\n", "default_value": "1.5"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DIFFUSION_STEPS": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.DIFFUSION_STEPS", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.DIFFUSION_STEPS", "kind": "variable", "doc": "

\n", "default_value": "10"}, "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.synthesize_texts_with_style_tts_2": {"fullname": "senselab.audio.tasks.text_to_speech.styletts2.StyleTTS2.synthesize_texts_with_style_tts_2", "modulename": "senselab.audio.tasks.text_to_speech.styletts2", "qualname": "StyleTTS2.synthesize_texts_with_style_tts_2", "kind": "function", "doc": "

Synthesizes speech from all the provided text samples and target voices+transcripts using StyleTTS2.

\n\n
Arguments:
\n\n
    \n
  • texts (List[str]): The list of text strings to be synthesized.
    • \n
  • target_audios (List[Audio]): The list of audio objects to reference.
    • \n
  • target_transcripts (List[Optional[str]]): Transcript for each target audio
    • \n
  • model (TorchModel): The Torch model (default is \"wilke0818/StyleTTS2-TorchHub\").
    • \n
  • language (Optional[Language]): The language of the text (default is None).\nThe only supported language is \"en\" for now.
    • \n
  • device (Optional[DeviceType]): device to run model on
    • \n
  • pretrain_data (Optional[Literal]): The dataset (LibriTTS/LJSpeech) the model was trained on
    • \n
  • force_reload (Optional[bool]): whether to reload the repository that the model is coming from
    • \n
  • alpha (float): determines the timbre of the speaker. 0 will match the reference audio\nand 1 will match the content of the transcript. Default will vary based on interpreted TTS type:\nno reference transcripts will use 0.3 (whether or not this is longform text) and .5 otherwise.
    • \n
  • beta (float): beta determines the prosody of the speaker. 0 will match the reference audio\nand 1 will match the content of the transcript. Default will vary based on interpreted TTS type:\nno reference transcript and non-longform will use 0.1, longform text and transcripts will use 0.9.
    • \n
  • diffusion_steps (int): sampler is ancestral, the higher the stpes, the more diverse the samples\nare but the slower the synthesis. Defaults to 10 for all types.
    • \n
  • embedding_scale (float): classifier-free guidance scale. The higher the scale, the more conditional\nthe style is to the input text and hence more emotional. Default will vary based on interpreted\nTTS type:\nno reference transcripts will use 1 (whether or not this is longform text) and 1.5 otherwise.
    • \n
  • t (float): how to combine previous and current styles in longform text generation.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: The list of synthesized audio objects.

\n
\n\n
Some tips for best quality:
\n\n
\n
    \n
  • Make sure reference audio is clean and between 2 second and 12 seconds.
    • \n
  • Play around with alpha, beta, the number of diffusion steps, and the embedding scale for your\n reference audio(s) in order to find a balance between matching the reference voice and having\n the voice sound natural with the text.
    • \n
  • Default values for these parameters are based off of LibriTTS pre-trained model examples the original\n authors provided.
    • \n
\n
\n\n

The original repo of the model is: https://github.com/yl4579/StyleTTS2.

\n", "signature": "(\tcls,\ttexts: List[str],\ttarget_audios: List[senselab.audio.data_structures.audio.Audio],\ttarget_transcripts: List[Optional[str]],\tmodel: senselab.utils.data_structures.model.TorchModel = TorchModel(path_or_uri='wilke0818/StyleTTS2-TorchHub', revision='main'),\tlanguage: Optional[senselab.utils.data_structures.language.Language] = None,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\tpretrain_data: Optional[Literal['LibriTTS', 'LJSpeech']] = 'LibriTTS',\tforce_reload: Optional[bool] = False,\talpha: Optional[float] = None,\tbeta: Optional[float] = None,\tdiffusion_steps: Optional[int] = None,\tembedding_scale: Optional[float] = None,\tt: float = 0.7) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.voice_activity_detection": {"fullname": "senselab.audio.tasks.voice_activity_detection", "modulename": "senselab.audio.tasks.voice_activity_detection", "kind": "module", "doc": "

Voice Activity Detection (VAD)

\n\n

\"Tutorial\"

\n\n

Task Overview

\n\n

Voice Activity Detection (VAD) is a binary classification task that identifies the presence of human voice in audio. The primary challenge in VAD lies in differentiating between noise and human voice, particularly in environments with significant background noise (e.g., fans, car engines). While VAD performs well in quiet environments where distinguishing between silence and speech is straightforward, the task becomes more difficult when background noise or non-standard speech patterns are present.

\n\n

Models

\n\n

In senselab, we integrate pyannote.audio models for VAD. These models can be explored on the Hugging Face Hub. Additional approaches for VAD may be integrated into the package in the future.

\n\n

Evaluation

\n\n

Metrics

\n\n

The primary metrics used to evaluate VAD modules are Detection Error Rate (DER) and Detection Cost Function (DCF).

\n\n
    \n

  • Detection Error Rate (DER):

    \n\n
    \n
      DER = (false alarm + missed detection) / total duration of speech in reference\n
    \n
    \n\n
      \n
    • False alarm: Duration of non-speech incorrectly classified as speech.
      • \n
    • Missed detection: Duration of speech incorrectly classified as non-speech.
      • \n
    • Total: Total duration of speech in the reference.
      • \n
    • \n

  • Detection Cost Function (DCF):

    \n\n
    \n
    DCF = 0.25 * false alarm rate + 0.75 * miss rate\n
    \n
    \n\n
      \n
    • False alarm rate: Proportion of non-speech incorrectly classified as speech.
      • \n
    • Miss rate: Proportion of speech incorrectly classified as non-speech.
      • \n
    • \n
\n\n

Additional Metrics

\n\n

VAD systems may also be evaluated using the following metrics:

\n\n
    \n
  • Accuracy: Proportion of the input signal correctly classified.
    • \n
  • Precision: Proportion of detected speech that is actually speech.
    • \n
  • Recall: Proportion of speech that is correctly detected.
    • \n
\n\n

For more detailed information on these metrics, refer to the pyannote.metrics documentation.

\n"}, "senselab.audio.tasks.voice_activity_detection.api": {"fullname": "senselab.audio.tasks.voice_activity_detection.api", "modulename": "senselab.audio.tasks.voice_activity_detection.api", "kind": "module", "doc": "

This module contains functions for computing Voice Activity Detection (VAD).

\n"}, "senselab.audio.tasks.voice_activity_detection.api.detect_human_voice_activity_in_audios": {"fullname": "senselab.audio.tasks.voice_activity_detection.api.detect_human_voice_activity_in_audios", "modulename": "senselab.audio.tasks.voice_activity_detection.api", "qualname": "detect_human_voice_activity_in_audios", "kind": "function", "doc": "

Diarizes all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be processed.
    • \n
  • model (SenselabModel): The model used for voice activity detection.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: The list of script lines with voice label.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = PyannoteAudioModel(path_or_uri='pyannote/speaker-diarization-3.1', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.voice_activity_detection.api.detect_human_voice_activity_in_audios_pt": {"fullname": "senselab.audio.tasks.voice_activity_detection.api.detect_human_voice_activity_in_audios_pt", "modulename": "senselab.audio.tasks.voice_activity_detection.api", "qualname": "detect_human_voice_activity_in_audios_pt", "kind": "function", "doc": "

Diarizes all audios using the given model.

\n\n
Arguments:
\n\n
    \n
  • audios (List[Audio]): The list of audio objects to be processed.
    • \n
  • model (SenselabModel): The model used for voice activity detection.
    • \n
  • device (Optional[DeviceType]): The device to run the model on (default is None).
    • \n
\n\n
Returns:
\n\n
\n

List[List[ScriptLine]]: The list of script lines with voice label.

\n
\n", "signature": "(\taudios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = PyannoteAudioModel(path_or_uri='pyannote/speaker-diarization-3.1', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.audio.tasks.voice_cloning": {"fullname": "senselab.audio.tasks.voice_cloning", "modulename": "senselab.audio.tasks.voice_cloning", "kind": "module", "doc": "

Voice cloning

\n\n

\n\n

Task Overview

\n\n

Any-to-any voice cloning aims to transform a source speech into a target voice using just one or a few examples of the target speaker's voice as references. Traditional voice conversion systems attempt to separate the speaker's identity from the speech content. This allows the replacement of speaker information to convert the voice to a target speaker. However, learning such disentangled representations is complex and poses significant challenges.

\n\n

Models

\n\n

We have explored several models for voice cloning:

\n\n
    \n
  • speechT5 (not included in senselab as it did not meet our expectations),
    • \n
  • FREEVC (planned to be included in senselab soon)
    • \n
  • KNNVC (Already included in senselab).
    • \n
\n\n

Evaluation

\n\n

Metrics

\n\n

Objective evaluation involves comparing voice cloning outputs across different downstream tasks:

\n\n
    \n
  • Using an automatic speaker verification tool to determine if the original speaker, the target speaker, and the cloned speaker can be distinguished from each other.
    • \n
  • Ensuring the intelligibility of speech content using an automatic speech recognition system to verify that the content remains unchanged.
    • \n
  • Assessing the preservation of the original speech's emotion after voice cloning.
    • \n
  • ...more...
    • \n
\n\n

Benchmark

\n\n

Recent efforts to enhance privacy in speech technology include the VoicePrivacy initiative, which has been active since 2020, focusing on developing and benchmarking anonymization methods. Despite these efforts, achieving perfect privacy remains a challenge (see here for more details).

\n"}, "senselab.audio.tasks.voice_cloning.api": {"fullname": "senselab.audio.tasks.voice_cloning.api", "modulename": "senselab.audio.tasks.voice_cloning.api", "kind": "module", "doc": "

This module implements some utilities for the voice cloning task.

\n"}, "senselab.audio.tasks.voice_cloning.api.clone_voices": {"fullname": "senselab.audio.tasks.voice_cloning.api.clone_voices", "modulename": "senselab.audio.tasks.voice_cloning.api", "qualname": "clone_voices", "kind": "function", "doc": "

Clones voices from source audios to target audios using the given model.

\n\n

This function performs pairwise voice cloning, where the voice from each audio sample\nin the source_audios list is transformed into the corresponding audio\nsample in the target_audios list. The resulting list contains audio samples that\npreserve the content of the original source audio but with the voice replaced by the\nvoice from the corresponding target audio.

\n\n
Arguments:
\n\n
    \n
  • source_audios (List[Audio]): A list of audio samples whose voices will be \"replaced\"\nby the voices from the corresponding target audio samples. The content\n(e.g., words) will remain the same, but the voice sounds like the target.
    • \n
  • target_audios (List[Audio]): A list of audio samples whose voices will be extracted\nand used to replace the voices in the corresponding source audio samples.
    • \n
  • model (SenselabModel, optional): The model to use for voice cloning. Currently,\nonly KNNVC (K-Nearest Neighbors Voice Conversion) is supported, encapsulated\nby the TorchModel class. TorchModel is a child class of SenselabModel\nand specifies the model and revision for cloning. Defaults to\nTorchModel(path_or_uri=\"bshall/knn-vc\", revision=\"master\").
    • \n
  • device (Optional[DeviceType], optional): The device to run the model on (e.g., CPU or GPU).\nDefaults to None.
    • \n
  • **kwargs: Additional keyword arguments for model-specific parameters that will\nbe passed directly to the underlying model's voice cloning method.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: A list of audio samples with cloned voices from the corresponding source and target audios.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the lengths of source_audios and target_audios do not match.
    • \n
  • NotImplementedError: If the specified model is not supported. Currently, only KNNVC is supported.
    • \n
\n\n
Examples:
\n\n
\n
\n
>>> source_audios = [Audio.from_filepath("source1.wav"), Audio.from_filepath("source2.wav")]\n>>> target_audios = [Audio.from_filepath("target1.wav"), Audio.from_filepath("target2.wav")]\n>>> cloned_audios = clone_voices(source_audios, target_audios)\n
\n
\n
\n\n
Todo:
\n\n
\n

Add logging with timestamps.

\n
\n", "signature": "(\tsource_audios: List[senselab.audio.data_structures.audio.Audio],\ttarget_audios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.SenselabModel = TorchModel(path_or_uri='bshall/knn-vc', revision='master'),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None,\t**kwargs: Any) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.tasks.voice_cloning.knnvc": {"fullname": "senselab.audio.tasks.voice_cloning.knnvc", "modulename": "senselab.audio.tasks.voice_cloning.knnvc", "kind": "module", "doc": "

This module contains functions for voice cloning using KNNVC.

\n"}, "senselab.audio.tasks.voice_cloning.knnvc.KNNVC": {"fullname": "senselab.audio.tasks.voice_cloning.knnvc.KNNVC", "modulename": "senselab.audio.tasks.voice_cloning.knnvc", "qualname": "KNNVC", "kind": "class", "doc": "

A factory for managing KNNVC pipelines.

\n"}, "senselab.audio.tasks.voice_cloning.knnvc.KNNVC.clone_voices_with_knn_vc": {"fullname": "senselab.audio.tasks.voice_cloning.knnvc.KNNVC.clone_voices_with_knn_vc", "modulename": "senselab.audio.tasks.voice_cloning.knnvc", "qualname": "KNNVC.clone_voices_with_knn_vc", "kind": "function", "doc": "

Clone voices from source audios to target audios using KNNVC.

\n\n
Arguments:
\n\n
    \n
  • source_audios (List[Audio]): List of source audio objects.
    • \n
  • target_audios (List[Audio]): List of target audio objects.
    • \n
  • model (TorchModel, optional): The Torch model to use for the KNNVC pipeline.\nDefaults to TorchModel(path_or_uri=\"bshall/knn-vc\", revision=\"master\").
    • \n
  • prematched_vocoder (bool, optional): Flag indicating whether to use a pre-matched vocoder. Defaults to True.
    • \n
  • topk (int, optional): The number of top matches to consider. Defaults to 4.
    • \n
  • device (Optional[DeviceType], optional): The device to run the pipeline on. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n

List[Audio]: List of cloned audio objects.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the audio files are not mono or if the sampling rates are not supported.
    • \n
\n", "signature": "(\tcls,\tsource_audios: List[senselab.audio.data_structures.audio.Audio],\ttarget_audios: List[senselab.audio.data_structures.audio.Audio],\tmodel: senselab.utils.data_structures.model.TorchModel = TorchModel(path_or_uri='bshall/knn-vc', revision='master'),\tprematched_vocoder: bool = True,\ttopk: int = 4,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.audio.workflows": {"fullname": "senselab.audio.workflows", "modulename": "senselab.audio.workflows", "kind": "module", "doc": "

Workflow for timestamped transcription.

\n"}, "senselab.audio.workflows.transcribe_timestamped": {"fullname": "senselab.audio.workflows.transcribe_timestamped", "modulename": "senselab.audio.workflows.transcribe_timestamped", "kind": "module", "doc": "

Workflow for timestamped transcription.

\n"}, "senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped": {"fullname": "senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped", "modulename": "senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped", "kind": "module", "doc": "

Transcribes audio files with timestamps.

\n"}, "senselab.text": {"fullname": "senselab.text", "modulename": "senselab.text", "kind": "module", "doc": "

This module contains implementations related to text processing.

\n"}, "senselab.text.tasks": {"fullname": "senselab.text.tasks", "modulename": "senselab.text.tasks", "kind": "module", "doc": "

This module if for processing text.

\n"}, "senselab.text.tasks.embeddings_extraction": {"fullname": "senselab.text.tasks.embeddings_extraction", "modulename": "senselab.text.tasks.embeddings_extraction", "kind": "module", "doc": "

This module provides the API for the senselab text embeddings extraction.

\n"}, "senselab.text.tasks.embeddings_extraction.api": {"fullname": "senselab.text.tasks.embeddings_extraction.api", "modulename": "senselab.text.tasks.embeddings_extraction.api", "kind": "module", "doc": "

This module provides a wrapper method for extracting embeddings from text using different models.

\n"}, "senselab.text.tasks.embeddings_extraction.api.extract_embeddings_from_text": {"fullname": "senselab.text.tasks.embeddings_extraction.api.extract_embeddings_from_text", "modulename": "senselab.text.tasks.embeddings_extraction.api", "qualname": "extract_embeddings_from_text", "kind": "function", "doc": "

Extracts embeddings from a list of strings using the specified model.

\n\n
Arguments:
\n\n
    \n
  • pieces_of_text (List[str]): A list of strings to extract embeddings from.
    • \n
  • model (SenselabModel): The model used for extracting embeddings.
    • \n
  • device (Optional[DeviceType]): The device to run the model on. Defaults to None.
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of embeddings for the input strings.

\n
\n", "signature": "(\tpieces_of_text: List[str],\tmodel: senselab.utils.data_structures.model.SenselabModel,\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.text.tasks.embeddings_extraction.huggingface": {"fullname": "senselab.text.tasks.embeddings_extraction.huggingface", "modulename": "senselab.text.tasks.embeddings_extraction.huggingface", "kind": "module", "doc": "

This module contains functions for extracting features from pre-trained self-supervised models.

\n"}, "senselab.text.tasks.embeddings_extraction.huggingface.HFFactory": {"fullname": "senselab.text.tasks.embeddings_extraction.huggingface.HFFactory", "modulename": "senselab.text.tasks.embeddings_extraction.huggingface", "qualname": "HFFactory", "kind": "class", "doc": "

A factory for managing self-supervised models from Hugging Face.

\n"}, "senselab.text.tasks.embeddings_extraction.huggingface.HFFactory.extract_text_embeddings": {"fullname": "senselab.text.tasks.embeddings_extraction.huggingface.HFFactory.extract_text_embeddings", "modulename": "senselab.text.tasks.embeddings_extraction.huggingface", "qualname": "HFFactory.extract_text_embeddings", "kind": "function", "doc": "

Extracts embeddings from a list of strings using a Hugging Face model.

\n\n
Arguments:
\n\n
    \n
  • pieces_of_text (List[str]): A list of strings to extract embeddings from.
    • \n
  • model (HFModel, optional): A Hugging Face model configuration.\nDefaults to HFModel(path_or_uri=\"sentence-transformers/all-MiniLM-L6-v2\").
    • \n
  • device (Optional[DeviceType], optional): The device to run the model on.\nDefaults to None.
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of embeddings for the input strings.

\n
\n", "signature": "(\tcls,\tpieces_of_text: List[str],\tmodel: senselab.utils.data_structures.model.HFModel = HFModel(path_or_uri='sentence-transformers/all-MiniLM-L6-v2', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.text.tasks.embeddings_extraction.sentence_transformers": {"fullname": "senselab.text.tasks.embeddings_extraction.sentence_transformers", "modulename": "senselab.text.tasks.embeddings_extraction.sentence_transformers", "kind": "module", "doc": "

This module implements some utilities for using Sentence Transformers for embeddings extraction.

\n"}, "senselab.text.tasks.embeddings_extraction.sentence_transformers.SentenceTransformerFactory": {"fullname": "senselab.text.tasks.embeddings_extraction.sentence_transformers.SentenceTransformerFactory", "modulename": "senselab.text.tasks.embeddings_extraction.sentence_transformers", "qualname": "SentenceTransformerFactory", "kind": "class", "doc": "

A factory for managing SentenceTransformer pipelines for text embeddings.

\n"}, "senselab.text.tasks.embeddings_extraction.sentence_transformers.SentenceTransformerFactory.extract_text_embeddings": {"fullname": "senselab.text.tasks.embeddings_extraction.sentence_transformers.SentenceTransformerFactory.extract_text_embeddings", "modulename": "senselab.text.tasks.embeddings_extraction.sentence_transformers", "qualname": "SentenceTransformerFactory.extract_text_embeddings", "kind": "function", "doc": "

Extracts embeddings from a list of strings using a SentenceTransformer model.

\n\n
Arguments:
\n\n
    \n
  • pieces_of_text (List[str]): A list of strings to extract embeddings from.
    • \n
  • model (SentenceTransformersModel, optional): A Hugging Face model configuration.\nDefaults to SentenceTransformersModel(path_or_uri=\"sentence-transformers/all-MiniLM-L6-v2\").
    • \n
  • device (Optional[DeviceType], optional): The device to run the model on.\nDefaults to None.
    • \n
\n\n
Returns:
\n\n
\n

List[torch.Tensor]: A list of embeddings for the input strings.

\n
\n", "signature": "(\tcls,\tpieces_of_text: List[str],\tmodel: senselab.utils.data_structures.model.SentenceTransformersModel = SentenceTransformersModel(path_or_uri='sentence-transformers/all-MiniLM-L6-v2', revision='main', info=None),\tdevice: Optional[senselab.utils.data_structures.device.DeviceType] = None) -> List[torch.Tensor]:", "funcdef": "def"}, "senselab.utils": {"fullname": "senselab.utils", "modulename": "senselab.utils", "kind": "module", "doc": "

This module contains the implementation of utility functions.

\n"}, "senselab.utils.config": {"fullname": "senselab.utils.config", "modulename": "senselab.utils.config", "kind": "module", "doc": "

This module provides the implementation of config utilities.

\n"}, "senselab.utils.config.get_config": {"fullname": "senselab.utils.config.get_config", "modulename": "senselab.utils.config", "qualname": "get_config", "kind": "function", "doc": "

Reads and returns the contents of the config file.

\n", "signature": "() -> Dict[str, Any]:", "funcdef": "def"}, "senselab.utils.constants": {"fullname": "senselab.utils.constants", "modulename": "senselab.utils.constants", "kind": "module", "doc": "

Constants used by Senselab.

\n"}, "senselab.utils.constants.SENSELAB_NAMESPACE": {"fullname": "senselab.utils.constants.SENSELAB_NAMESPACE", "modulename": "senselab.utils.constants", "qualname": "SENSELAB_NAMESPACE", "kind": "variable", "doc": "

\n", "default_value": "UUID('b5e0e774-e24c-3298-9802-781f66220abf')"}, "senselab.utils.data_structures": {"fullname": "senselab.utils.data_structures", "modulename": "senselab.utils.data_structures", "kind": "module", "doc": "

\n"}, "senselab.utils.data_structures.dataset": {"fullname": "senselab.utils.data_structures.dataset", "modulename": "senselab.utils.data_structures.dataset", "kind": "module", "doc": "

Data structures relevant for managing datasets.

\n"}, "senselab.utils.data_structures.dataset.Participant": {"fullname": "senselab.utils.data_structures.dataset.Participant", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant", "kind": "class", "doc": "

Data structure for a participant in a dataset.

\n\n
Attributes:
\n\n
    \n
  • id: The ID of the participant. If not provided, generated using uuid4
    • \n
  • metadata: Dictionary of user specified metadata related to the participant
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.dataset.Participant.id": {"fullname": "senselab.utils.data_structures.dataset.Participant.id", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.id", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.utils.data_structures.dataset.Participant.metadata": {"fullname": "senselab.utils.data_structures.dataset.Participant.metadata", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.metadata", "kind": "variable", "doc": "

\n", "annotation": ": Dict"}, "senselab.utils.data_structures.dataset.Participant.set_id": {"fullname": "senselab.utils.data_structures.dataset.Participant.set_id", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.set_id", "kind": "function", "doc": "

Set the unique id of the participant.

\n", "signature": "(cls, v: str) -> str:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.Participant.model_config": {"fullname": "senselab.utils.data_structures.dataset.Participant.model_config", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.dataset.Participant.model_fields": {"fullname": "senselab.utils.data_structures.dataset.Participant.model_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'id': FieldInfo(annotation=str, required=False, default_factory=<lambda>), 'metadata': FieldInfo(annotation=Dict, required=False, default={})}"}, "senselab.utils.data_structures.dataset.Participant.model_computed_fields": {"fullname": "senselab.utils.data_structures.dataset.Participant.model_computed_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Participant.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.dataset.Session": {"fullname": "senselab.utils.data_structures.dataset.Session", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session", "kind": "class", "doc": "

Data structure for a session in a dataset.

\n\n
Attributes:
\n\n
    \n
  • id: the ID of the session. If not provided, generated using uuid4
    • \n
  • metadata: Dictionary of user specified metadata related to the session
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.dataset.Session.id": {"fullname": "senselab.utils.data_structures.dataset.Session.id", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.id", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.utils.data_structures.dataset.Session.metadata": {"fullname": "senselab.utils.data_structures.dataset.Session.metadata", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.metadata", "kind": "variable", "doc": "

\n", "annotation": ": Dict"}, "senselab.utils.data_structures.dataset.Session.set_id": {"fullname": "senselab.utils.data_structures.dataset.Session.set_id", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.set_id", "kind": "function", "doc": "

Set the unique id of the session.

\n", "signature": "(cls, v: str) -> str:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.Session.model_config": {"fullname": "senselab.utils.data_structures.dataset.Session.model_config", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.dataset.Session.model_fields": {"fullname": "senselab.utils.data_structures.dataset.Session.model_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'id': FieldInfo(annotation=str, required=False, default_factory=<lambda>), 'metadata': FieldInfo(annotation=Dict, required=False, default={})}"}, "senselab.utils.data_structures.dataset.Session.model_computed_fields": {"fullname": "senselab.utils.data_structures.dataset.Session.model_computed_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "Session.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.dataset.SenselabDataset": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset", "kind": "class", "doc": "

Class for maintaining SenseLab datasets and functionalities.

\n\n

Maintains collections of Audios, Videos, and metadata for use of the Senselab tools\nand pipelines. Includes the ability to manage Sessions and Participants.

\n\n
Attributes:
\n\n
    \n
  • audios: List of Audios that are generated based on list of audio filepaths
    • \n
  • videos: List of Videos generated from a list of video filepaths
    • \n
  • metadata: Metadata related to the dataset overall but not necessarily the metadata of\nindivudal audios or videos in the dataset
    • \n
  • sessions: Mapping of Session IDs to Session instances
    • \n
  • participants: Mapping of participant IDs to Participant instances
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.dataset.SenselabDataset.participants": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.participants", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.participants", "kind": "variable", "doc": "

\n", "annotation": ": Dict[str, senselab.utils.data_structures.dataset.Participant]"}, "senselab.utils.data_structures.dataset.SenselabDataset.sessions": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.sessions", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.sessions", "kind": "variable", "doc": "

\n", "annotation": ": Dict[str, senselab.utils.data_structures.dataset.Session]"}, "senselab.utils.data_structures.dataset.SenselabDataset.audios": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.audios", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.audios", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.audio.data_structures.audio.Audio]"}, "senselab.utils.data_structures.dataset.SenselabDataset.videos": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.videos", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.videos", "kind": "variable", "doc": "

\n", "annotation": ": List[senselab.video.data_structures.video.Video]"}, "senselab.utils.data_structures.dataset.SenselabDataset.metadata": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.metadata", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.metadata", "kind": "variable", "doc": "

\n", "annotation": ": Dict"}, "senselab.utils.data_structures.dataset.SenselabDataset.generate_audios_from_filepaths": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.generate_audios_from_filepaths", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.generate_audios_from_filepaths", "kind": "function", "doc": "

Generate the audios in the dataset from a list of audio filepaths.

\n\n

Generates the audios in the dataset by taking in a list of audio filepaths\nor a list of Audios

\n\n
Arguments:
\n\n
    \n
  • v: Input for audios attribute that we're validating by generating the Audios if filepaths\nare provided or just the list of Audios if pre-generated and passed in
    • \n
\n\n
Returns:
\n\n
\n

List of Audios that instantiates the audios attribute in the dataset

\n
\n", "signature": "(\tcls,\tv: Union[List[str], List[senselab.audio.data_structures.audio.Audio]],\t_: pydantic_core.core_schema.ValidationInfo) -> List[senselab.audio.data_structures.audio.Audio]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.generate_videos_from_filepaths": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.generate_videos_from_filepaths", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.generate_videos_from_filepaths", "kind": "function", "doc": "

Generate the videos in the dataset from a list of video filepaths.

\n\n

Generates the videos in the dataset by taking in a list of video filepaths\nor a list of Videos

\n\n
Arguments:
\n\n
    \n
  • v: Input for videos attribute that we're validating by generating the Videos if filepaths\nare provided or just the list of Videos if pre-generated and passed in
    • \n
\n\n
Returns:
\n\n
\n

List of Videos that instantiates the videos attribute in the dataset

\n
\n", "signature": "(\tcls,\tv: Union[List[str], List[senselab.video.data_structures.video.Video]],\t_: pydantic_core.core_schema.ValidationInfo) -> List[senselab.video.data_structures.video.Video]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.create_bids_dataset": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.create_bids_dataset", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.create_bids_dataset", "kind": "function", "doc": "

Create a dataset from a BIDS organized directory.

\n\n

Creates a new dataset based off of a BIDS directory structure as defined at\nhttps://sensein.group/biometrics-book/updated_bids.html

\n", "signature": "(\tcls,\tbids_root_filepath: str) -> senselab.utils.data_structures.dataset.SenselabDataset:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.create_audio_split_for_pydra_task": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.create_audio_split_for_pydra_task", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.create_audio_split_for_pydra_task", "kind": "function", "doc": "

Splits the audio data for Pydra tasks.

\n\n

Creates a split of the audio data that can be used for creating individual Pydra tasks using\nthe .split functionality. Splits the data such that the inputs for a Pydra workflow are either\noptimized for the GPU's batch size or a single Audio per CPU thread.

\n\n
Arguments:
\n\n
    \n
  • batch_size: How to batch Audios for a Pydra task; defaults to 1 since CPU won't batch
    • \n
\n\n
Returns:
\n\n
\n

List of Lists of Audio where each List of Audios will be an input to a Pydra task.\n Each of the sublists are either of size 1 for CPUs or at most batch_size for GPU optimization.

\n
\n\n
Raises:
\n\n
    \n
  • ValueError if the batch size is invalid (less than 1)
    • \n
\n", "signature": "(\tself,\tbatch_size: int = 1) -> List[List[senselab.audio.data_structures.audio.Audio]]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.audio_merge_from_pydra_task": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.audio_merge_from_pydra_task", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.audio_merge_from_pydra_task", "kind": "function", "doc": "

TODO: Write later.

\n\n

Logic Pydra:\naudios: List of audios that want to give to task\nsplit: List[List[Audios]] -> task List[Audio]\npydra task(List[Audio]) -> List[Audio]\nmerge(List[List[Audio]]) <- might be a wrapped instead of List of lists\nTODO: Figure out what a merge behavior looks like from Pydra

\n", "signature": "(\tself,\taudios_to_merge: List[List[senselab.audio.data_structures.audio.Audio]]) -> None:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.add_participant": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.add_participant", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.add_participant", "kind": "function", "doc": "

Add a participant to the dataset.

\n\n

Adds a new participant to the dataset if they are not already in it.

\n\n
Arguments:
\n\n
    \n
  • participant: instance of a Participant that we want to add to the dataset
    • \n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the participant ID is already in the dataset, we raise a value error.\nThis means that either the ID is non-unique and/or the participant is already in the dataset.
    • \n
\n", "signature": "(\tself,\tparticipant: senselab.utils.data_structures.dataset.Participant) -> None:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.add_session": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.add_session", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.add_session", "kind": "function", "doc": "

Add a session to the dataset.

\n\n

Adds a new sesszion to the dataset if it is not already in there.

\n\n
Arguments:
\n\n
    \n
  • session: instance of a Session that we want to add to the dataset
    • \n
\n\n
Raises:
\n\n
    \n
  • ValueError: If the session ID is already in the dataset, we raise a value error.\nThis means that either the ID is non-unique and/or the session is already in the dataset.
    • \n
\n", "signature": "(self, session: senselab.utils.data_structures.dataset.Session) -> None:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.get_participants": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.get_participants", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.get_participants", "kind": "function", "doc": "

Get the list of participants in the dataset.

\n\n
Returns:
\n\n
\n

participants (List[Participant]): all of the instances of participants in the dataset\n Warning: The instances are returned as is, so changes to the underlying participants in this\n list will automatically be reflected in the dataset.

\n
\n", "signature": "(self) -> List[senselab.utils.data_structures.dataset.Participant]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.get_sessions": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.get_sessions", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.get_sessions", "kind": "function", "doc": "

Get the list of sessions in the dataset.

\n\n
Returns:
\n\n
\n

sessions (List[Session]): all of the instances of sessions in the dataset\n Warning: The instances are returned as is, so changes to the underlying sessions in this\n list will automatically be reflected in the dataset.

\n
\n", "signature": "(self) -> List[senselab.utils.data_structures.dataset.Session]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.convert_senselab_dataset_to_hf_datasets": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.convert_senselab_dataset_to_hf_datasets", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.convert_senselab_dataset_to_hf_datasets", "kind": "function", "doc": "

Converts Senselab datasets into HuggingFace datasets.

\n\n
Returns:
\n\n
\n

A dictionary of HuggingFace datasets that represent the underlying Senselab dataset.\n Currently only supports creating HuggingFace datasets for the Audio(s) and Video(s) in\n the SenselabDataset. Videos in HuggingFace are not natively supported, so they are treated\n as Sequences of images with a frame_rate.

\n
\n", "signature": "(self) -> Dict[str, datasets.arrow_dataset.Dataset]:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.convert_hf_dataset_to_senselab_dataset": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.convert_hf_dataset_to_senselab_dataset", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.convert_hf_dataset_to_senselab_dataset", "kind": "function", "doc": "

Converts HuggingFace dataset to a Senselab dataset.

\n\n

Convert HuggingFace dataset(s) to a SenselabDataset where each component of a SenselabDataset\n (e.g. audios, videos, sessions, participants) are stored under different keys in the provided dictionary.

\n\n
Arguments:
\n\n
    \n
  • hf_datasets: Dictionary of the different individual components that make up a SenselabDataset. Audios must\nbe organized as HuggingFace Audio(s) which is a dictionary with an array attribute, sampling rate, and\na path. Videos are not natively supported by HuggingFace, so SenselabDataset expects a sequence of\nframes that are each an image, a frame_rate argument, and optionally the associated audio with the\nvideo.
    • \n
  • metadata: Dictionary of additional dataset level metadata. Differs from metadata inside each HuggingFace\ndataset that is provided and is unaffected by transder_metadata.
    • \n
  • transfer_metadata: Specifies whether to generate metadata from extraneous attributes in the\nHuggingDataset in addition to any specified ina metadata field, otherwise they are ignored.\nDefaults to False.
    • \n
\n\n
Returns:
\n\n
\n

The generated SenselabDataset from the provided fields in the hf_datasets dictionary. Currently does not\n support converting sessions or participants.

\n
\n", "signature": "(\tcls,\thf_datasets: Dict[str, datasets.arrow_dataset.Dataset],\tmetadata: Dict = {},\ttransfer_metadata: bool = False) -> senselab.utils.data_structures.dataset.SenselabDataset:", "funcdef": "def"}, "senselab.utils.data_structures.dataset.SenselabDataset.model_config": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.model_config", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.dataset.SenselabDataset.model_fields": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.model_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'participants': FieldInfo(annotation=Dict[str, Participant], required=False, default_factory=dict), 'sessions': FieldInfo(annotation=Dict[str, Session], required=False, default_factory=dict), 'audios': FieldInfo(annotation=List[Audio], required=False, default=[]), 'videos': FieldInfo(annotation=List[Video], required=False, default=[]), 'metadata': FieldInfo(annotation=Dict, required=False, default={})}"}, "senselab.utils.data_structures.dataset.SenselabDataset.model_computed_fields": {"fullname": "senselab.utils.data_structures.dataset.SenselabDataset.model_computed_fields", "modulename": "senselab.utils.data_structures.dataset", "qualname": "SenselabDataset.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.device": {"fullname": "senselab.utils.data_structures.device", "modulename": "senselab.utils.data_structures.device", "kind": "module", "doc": "

Utility functions for utilizing different devices in Senselab.

\n"}, "senselab.utils.data_structures.device.DeviceType": {"fullname": "senselab.utils.data_structures.device.DeviceType", "modulename": "senselab.utils.data_structures.device", "qualname": "DeviceType", "kind": "class", "doc": "

Device types for PyTorch operations.

\n", "bases": "enum.Enum"}, "senselab.utils.data_structures.device.DeviceType.CPU": {"fullname": "senselab.utils.data_structures.device.DeviceType.CPU", "modulename": "senselab.utils.data_structures.device", "qualname": "DeviceType.CPU", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "<DeviceType.CPU: 'cpu'>"}, "senselab.utils.data_structures.device.DeviceType.CUDA": {"fullname": "senselab.utils.data_structures.device.DeviceType.CUDA", "modulename": "senselab.utils.data_structures.device", "qualname": "DeviceType.CUDA", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "<DeviceType.CUDA: 'cuda'>"}, "senselab.utils.data_structures.device.DeviceType.MPS": {"fullname": "senselab.utils.data_structures.device.DeviceType.MPS", "modulename": "senselab.utils.data_structures.device", "qualname": "DeviceType.MPS", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "<DeviceType.MPS: 'mps'>"}, "senselab.utils.data_structures.device.DTYPE_MAP": {"fullname": "senselab.utils.data_structures.device.DTYPE_MAP", "modulename": "senselab.utils.data_structures.device", "qualname": "DTYPE_MAP", "kind": "variable", "doc": "

\n", "default_value": "{<DeviceType.CPU: 'cpu'>: torch.float32, <DeviceType.CUDA: 'cuda'>: torch.float16, <DeviceType.MPS: 'mps'>: torch.float32}"}, "senselab.utils.data_structures.file": {"fullname": "senselab.utils.data_structures.file", "modulename": "senselab.utils.data_structures.file", "kind": "module", "doc": "

This module provides the implementation of file utilities.

\n"}, "senselab.utils.data_structures.file.File": {"fullname": "senselab.utils.data_structures.file.File", "modulename": "senselab.utils.data_structures.file", "qualname": "File", "kind": "class", "doc": "

This class represents a file.

\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.file.File.filepath": {"fullname": "senselab.utils.data_structures.file.File.filepath", "modulename": "senselab.utils.data_structures.file", "qualname": "File.filepath", "kind": "variable", "doc": "

\n", "annotation": ": typing.Annotated[pathlib.Path, PathType(path_type='file')]"}, "senselab.utils.data_structures.file.File.type": {"fullname": "senselab.utils.data_structures.file.File.type", "modulename": "senselab.utils.data_structures.file", "qualname": "File.type", "kind": "variable", "doc": "

Determine the type of the file based on its extension.

\n", "annotation": ": str"}, "senselab.utils.data_structures.file.File.validate_filepath": {"fullname": "senselab.utils.data_structures.file.File.validate_filepath", "modulename": "senselab.utils.data_structures.file", "qualname": "File.validate_filepath", "kind": "function", "doc": "

Validate the filepath.

\n", "signature": "(\tcls,\tv: typing.Annotated[pathlib.Path, PathType(path_type='file')]) -> typing.Annotated[pathlib.Path, PathType(path_type='file')]:", "funcdef": "def"}, "senselab.utils.data_structures.file.File.model_config": {"fullname": "senselab.utils.data_structures.file.File.model_config", "modulename": "senselab.utils.data_structures.file", "qualname": "File.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.file.File.model_fields": {"fullname": "senselab.utils.data_structures.file.File.model_fields", "modulename": "senselab.utils.data_structures.file", "qualname": "File.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'filepath': FieldInfo(annotation=Path, required=True, metadata=[PathType(path_type='file')])}"}, "senselab.utils.data_structures.file.File.model_computed_fields": {"fullname": "senselab.utils.data_structures.file.File.model_computed_fields", "modulename": "senselab.utils.data_structures.file", "qualname": "File.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.file.from_strings_to_files": {"fullname": "senselab.utils.data_structures.file.from_strings_to_files", "modulename": "senselab.utils.data_structures.file", "qualname": "from_strings_to_files", "kind": "function", "doc": "

Create a list of File objects from a list of strings.

\n", "signature": "(\tlist_of_files: List[str]) -> List[senselab.utils.data_structures.file.File]:", "funcdef": "def"}, "senselab.utils.data_structures.file.get_common_directory": {"fullname": "senselab.utils.data_structures.file.get_common_directory", "modulename": "senselab.utils.data_structures.file", "qualname": "get_common_directory", "kind": "function", "doc": "

A function to get the common directory from a list of file paths.

\n\n

Parameters:

\n\n
    \n
  • files: a list of file paths
    • \n
\n\n

Returns:

\n\n
    \n
  • the common directory among the file paths
    • \n
\n", "signature": "(files: List[str]) -> str:", "funcdef": "def"}, "senselab.utils.data_structures.language": {"fullname": "senselab.utils.data_structures.language", "modulename": "senselab.utils.data_structures.language", "kind": "module", "doc": "

This module provides the implementation of language data structures.

\n"}, "senselab.utils.data_structures.language.Language": {"fullname": "senselab.utils.data_structures.language.Language", "modulename": "senselab.utils.data_structures.language", "qualname": "Language", "kind": "class", "doc": "

Data structure for a language.

\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.language.Language.language_code": {"fullname": "senselab.utils.data_structures.language.Language.language_code", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.language_code", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "senselab.utils.data_structures.language.Language.validate_language": {"fullname": "senselab.utils.data_structures.language.Language.validate_language", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.validate_language", "kind": "function", "doc": "

Validate that the language code is valid.

\n", "signature": "(cls, v: str) -> str:", "funcdef": "def"}, "senselab.utils.data_structures.language.Language.alpha_2": {"fullname": "senselab.utils.data_structures.language.Language.alpha_2", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.alpha_2", "kind": "variable", "doc": "

Get the ISO 639-2 code for the language.

\n", "annotation": ": str"}, "senselab.utils.data_structures.language.Language.alpha_3": {"fullname": "senselab.utils.data_structures.language.Language.alpha_3", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.alpha_3", "kind": "variable", "doc": "

Get the ISO 639-3 code for the language.

\n", "annotation": ": str"}, "senselab.utils.data_structures.language.Language.name": {"fullname": "senselab.utils.data_structures.language.Language.name", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.name", "kind": "variable", "doc": "

Get the name of the language.

\n", "annotation": ": str"}, "senselab.utils.data_structures.language.Language.model_config": {"fullname": "senselab.utils.data_structures.language.Language.model_config", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.language.Language.model_fields": {"fullname": "senselab.utils.data_structures.language.Language.model_fields", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'language_code': FieldInfo(annotation=str, required=True)}"}, "senselab.utils.data_structures.language.Language.model_computed_fields": {"fullname": "senselab.utils.data_structures.language.Language.model_computed_fields", "modulename": "senselab.utils.data_structures.language", "qualname": "Language.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.logging": {"fullname": "senselab.utils.data_structures.logging", "modulename": "senselab.utils.data_structures.logging", "kind": "module", "doc": "

This module provides utilities for logging.

\n"}, "senselab.utils.data_structures.logging.logger": {"fullname": "senselab.utils.data_structures.logging.logger", "modulename": "senselab.utils.data_structures.logging", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger senselab (DEBUG)>"}, "senselab.utils.data_structures.logging.ch": {"fullname": "senselab.utils.data_structures.logging.ch", "modulename": "senselab.utils.data_structures.logging", "qualname": "ch", "kind": "variable", "doc": "

\n", "default_value": "<StreamHandler (DEBUG)>"}, "senselab.utils.data_structures.logging.formatter": {"fullname": "senselab.utils.data_structures.logging.formatter", "modulename": "senselab.utils.data_structures.logging", "qualname": "formatter", "kind": "variable", "doc": "

\n", "default_value": "<logging.Formatter object>"}, "senselab.utils.data_structures.model": {"fullname": "senselab.utils.data_structures.model", "modulename": "senselab.utils.data_structures.model", "kind": "module", "doc": "

This module implements some utilities for the model class.

\n"}, "senselab.utils.data_structures.model.SenselabModel": {"fullname": "senselab.utils.data_structures.model.SenselabModel", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel", "kind": "class", "doc": "

Base configuration for SenselabModel class.

\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.model.SenselabModel.path_or_uri": {"fullname": "senselab.utils.data_structures.model.SenselabModel.path_or_uri", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.path_or_uri", "kind": "variable", "doc": "

\n", "annotation": ": Union[str, pathlib.Path]"}, "senselab.utils.data_structures.model.SenselabModel.revision": {"fullname": "senselab.utils.data_structures.model.SenselabModel.revision", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.revision", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "senselab.utils.data_structures.model.SenselabModel.validate_path_or_uri": {"fullname": "senselab.utils.data_structures.model.SenselabModel.validate_path_or_uri", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.validate_path_or_uri", "kind": "function", "doc": "

Validate the path_or_uri.

\n\n

This check is only for files and not for remote resources.\nIt does check if the path_or_uri is not empty and if it is an existing file.

\n", "signature": "(cls, value: Union[str, pathlib.Path]) -> Union[str, pathlib.Path]:", "funcdef": "def"}, "senselab.utils.data_structures.model.SenselabModel.model_config": {"fullname": "senselab.utils.data_structures.model.SenselabModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.SenselabModel.model_fields": {"fullname": "senselab.utils.data_structures.model.SenselabModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.model.SenselabModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.SenselabModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SenselabModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.HFModel": {"fullname": "senselab.utils.data_structures.model.HFModel", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel", "kind": "class", "doc": "

HuggingFace model.

\n", "bases": "SenselabModel"}, "senselab.utils.data_structures.model.HFModel.revision": {"fullname": "senselab.utils.data_structures.model.HFModel.revision", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.revision", "kind": "variable", "doc": "

\n", "annotation": ": typing.Annotated[str, FieldInfo(annotation=NoneType, required=True, validate_default=True)]"}, "senselab.utils.data_structures.model.HFModel.info": {"fullname": "senselab.utils.data_structures.model.HFModel.info", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.info", "kind": "variable", "doc": "

\n", "annotation": ": Optional[huggingface_hub.hf_api.ModelInfo]"}, "senselab.utils.data_structures.model.HFModel.validate_hf_model_id": {"fullname": "senselab.utils.data_structures.model.HFModel.validate_hf_model_id", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.validate_hf_model_id", "kind": "function", "doc": "

Validate the path_or_uri.

\n\n

This check is only for remote resources and not for files.\nIt checks if the specified Hugging Face model ID and revision exist in the remote Hub.

\n", "signature": "(\tcls,\tvalue: str,\tinfo: pydantic_core.core_schema.ValidationInfo) -> Union[str, pathlib.Path]:", "funcdef": "def"}, "senselab.utils.data_structures.model.HFModel.get_model_info": {"fullname": "senselab.utils.data_structures.model.HFModel.get_model_info", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.get_model_info", "kind": "function", "doc": "

Gets the model info using the HuggingFace API and saves it as a property.

\n", "signature": "(self) -> huggingface_hub.hf_api.ModelInfo:", "funcdef": "def"}, "senselab.utils.data_structures.model.HFModel.model_config": {"fullname": "senselab.utils.data_structures.model.HFModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.HFModel.model_fields": {"fullname": "senselab.utils.data_structures.model.HFModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True), 'info': FieldInfo(annotation=Union[ModelInfo, NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.model.HFModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.HFModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "HFModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.SpeechBrainModel": {"fullname": "senselab.utils.data_structures.model.SpeechBrainModel", "modulename": "senselab.utils.data_structures.model", "qualname": "SpeechBrainModel", "kind": "class", "doc": "

SpeechBrain model.

\n", "bases": "HFModel"}, "senselab.utils.data_structures.model.SpeechBrainModel.model_config": {"fullname": "senselab.utils.data_structures.model.SpeechBrainModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "SpeechBrainModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.SpeechBrainModel.model_fields": {"fullname": "senselab.utils.data_structures.model.SpeechBrainModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SpeechBrainModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True), 'info': FieldInfo(annotation=Union[ModelInfo, NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.model.SpeechBrainModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.SpeechBrainModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SpeechBrainModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.PyannoteAudioModel": {"fullname": "senselab.utils.data_structures.model.PyannoteAudioModel", "modulename": "senselab.utils.data_structures.model", "qualname": "PyannoteAudioModel", "kind": "class", "doc": "

PyannoteAudioModel model.

\n", "bases": "HFModel"}, "senselab.utils.data_structures.model.PyannoteAudioModel.model_config": {"fullname": "senselab.utils.data_structures.model.PyannoteAudioModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "PyannoteAudioModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.PyannoteAudioModel.model_fields": {"fullname": "senselab.utils.data_structures.model.PyannoteAudioModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "PyannoteAudioModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True), 'info': FieldInfo(annotation=Union[ModelInfo, NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.model.PyannoteAudioModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.PyannoteAudioModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "PyannoteAudioModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.SentenceTransformersModel": {"fullname": "senselab.utils.data_structures.model.SentenceTransformersModel", "modulename": "senselab.utils.data_structures.model", "qualname": "SentenceTransformersModel", "kind": "class", "doc": "

SentenceTransformersModel model.

\n", "bases": "HFModel"}, "senselab.utils.data_structures.model.SentenceTransformersModel.model_config": {"fullname": "senselab.utils.data_structures.model.SentenceTransformersModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "SentenceTransformersModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.SentenceTransformersModel.model_fields": {"fullname": "senselab.utils.data_structures.model.SentenceTransformersModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SentenceTransformersModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True), 'info': FieldInfo(annotation=Union[ModelInfo, NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.model.SentenceTransformersModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.SentenceTransformersModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "SentenceTransformersModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.TorchModel": {"fullname": "senselab.utils.data_structures.model.TorchModel", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel", "kind": "class", "doc": "

Generic torch model.

\n", "bases": "SenselabModel"}, "senselab.utils.data_structures.model.TorchModel.revision": {"fullname": "senselab.utils.data_structures.model.TorchModel.revision", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel.revision", "kind": "variable", "doc": "

\n", "annotation": ": typing.Annotated[str, FieldInfo(annotation=NoneType, required=True, validate_default=True)]"}, "senselab.utils.data_structures.model.TorchModel.validate_torch_model_id": {"fullname": "senselab.utils.data_structures.model.TorchModel.validate_torch_model_id", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel.validate_torch_model_id", "kind": "function", "doc": "

Validate the path_or_uri.

\n\n

This check is only for remote resources and not for files.\nIt checks if the specified torch model ID and revision exist in the remote Hub.

\n", "signature": "(\tcls,\tvalue: str,\tinfo: pydantic_core.core_schema.ValidationInfo) -> Union[str, pathlib.Path]:", "funcdef": "def"}, "senselab.utils.data_structures.model.TorchModel.model_config": {"fullname": "senselab.utils.data_structures.model.TorchModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.TorchModel.model_fields": {"fullname": "senselab.utils.data_structures.model.TorchModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True)}"}, "senselab.utils.data_structures.model.TorchModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.TorchModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.TorchAudioModel": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel", "kind": "class", "doc": "

TorchAudio model.

\n", "bases": "SenselabModel"}, "senselab.utils.data_structures.model.TorchAudioModel.revision": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel.revision", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel.revision", "kind": "variable", "doc": "

\n", "annotation": ": typing.Annotated[str, FieldInfo(annotation=NoneType, required=True, validate_default=True)]"}, "senselab.utils.data_structures.model.TorchAudioModel.validate_torchaudio_model_id": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel.validate_torchaudio_model_id", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel.validate_torchaudio_model_id", "kind": "function", "doc": "

Validate the path_or_uri for torchaudio models.

\n\n

This check is only for remote resources and not for files.\nIt checks if the specified torchaudio model ID exists.

\n", "signature": "(\tcls,\tvalue: str,\tinfo: pydantic_core.core_schema.ValidationInfo) -> Union[str, pathlib.Path]:", "funcdef": "def"}, "senselab.utils.data_structures.model.TorchAudioModel.model_config": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel.model_config", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.model.TorchAudioModel.model_fields": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel.model_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'path_or_uri': FieldInfo(annotation=Union[str, Path], required=True), 'revision': FieldInfo(annotation=str, required=False, default='main', validate_default=True)}"}, "senselab.utils.data_structures.model.TorchAudioModel.model_computed_fields": {"fullname": "senselab.utils.data_structures.model.TorchAudioModel.model_computed_fields", "modulename": "senselab.utils.data_structures.model", "qualname": "TorchAudioModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.utils.data_structures.model.check_torchaudio_model_exists": {"fullname": "senselab.utils.data_structures.model.check_torchaudio_model_exists", "modulename": "senselab.utils.data_structures.model", "qualname": "check_torchaudio_model_exists", "kind": "function", "doc": "

Private function to check if a torchaudio model exists.

\n", "signature": "(model_id: str) -> bool:", "funcdef": "def"}, "senselab.utils.data_structures.model.is_torch_model": {"fullname": "senselab.utils.data_structures.model.is_torch_model", "modulename": "senselab.utils.data_structures.model", "qualname": "is_torch_model", "kind": "function", "doc": "

Check if a file is a torch model.

\n", "signature": "(file_path: pathlib.Path) -> bool:", "funcdef": "def"}, "senselab.utils.data_structures.model.check_hf_repo_exists": {"fullname": "senselab.utils.data_structures.model.check_hf_repo_exists", "modulename": "senselab.utils.data_structures.model", "qualname": "check_hf_repo_exists", "kind": "function", "doc": "

Private function to check if a Hugging Face repository exists.

\n", "signature": "(repo_id: str, revision: str = 'main', repo_type: str = 'model') -> bool:", "funcdef": "def"}, "senselab.utils.data_structures.model.check_github_repo_exists": {"fullname": "senselab.utils.data_structures.model.check_github_repo_exists", "modulename": "senselab.utils.data_structures.model", "qualname": "check_github_repo_exists", "kind": "function", "doc": "

Private function to check if a GitHub repository exists with caching and authentication.

\n", "signature": "(repo_id: str, branch: str = 'main') -> bool:", "funcdef": "def"}, "senselab.utils.data_structures.pydra_helpers": {"fullname": "senselab.utils.data_structures.pydra_helpers", "modulename": "senselab.utils.data_structures.pydra_helpers", "kind": "module", "doc": "

Functionality for Pydra.

\n"}, "senselab.utils.data_structures.pydra_helpers.bytes_repr_arraylike": {"fullname": "senselab.utils.data_structures.pydra_helpers.bytes_repr_arraylike", "modulename": "senselab.utils.data_structures.pydra_helpers", "qualname": "bytes_repr_arraylike", "kind": "function", "doc": "

Register a serializer for Torch tensors that allows Pydra to properly use them.

\n", "signature": "(obj: torch.Tensor, cache: pydra.utils.hash.Cache) -> Iterator[bytes]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line": {"fullname": "senselab.utils.data_structures.script_line", "modulename": "senselab.utils.data_structures.script_line", "kind": "module", "doc": "

This module contains the definition of the ScriptLine class.

\n"}, "senselab.utils.data_structures.script_line.ScriptLine": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine", "kind": "class", "doc": "

A class to represent a line in a script.

\n\n
Attributes:
\n\n
    \n
  • text (Optional[str]): The full text of the line (if provided).
    • \n
  • speaker (Optional[str]): The speaker of the line (if provided).
    • \n
  • chunks (Optional[List['ScriptLine']]): A list of script lines (if provided).
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "senselab.utils.data_structures.script_line.ScriptLine.text": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.text", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.text", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "senselab.utils.data_structures.script_line.ScriptLine.speaker": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.speaker", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.speaker", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "senselab.utils.data_structures.script_line.ScriptLine.start": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.start", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.start", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.utils.data_structures.script_line.ScriptLine.end": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.end", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.end", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "senselab.utils.data_structures.script_line.ScriptLine.chunks": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.chunks", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.chunks", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[senselab.utils.data_structures.script_line.ScriptLine]]"}, "senselab.utils.data_structures.script_line.ScriptLine.validate_text_and_speaker": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.validate_text_and_speaker", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.validate_text_and_speaker", "kind": "function", "doc": "

Validate that at least one of text or speaker is provided.

\n", "signature": "(\tcls,\tvalues: Dict[str, Any],\t_: pydantic_core.core_schema.ValidationInfo) -> Dict[str, Any]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.strings_must_be_stripped": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.strings_must_be_stripped", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.strings_must_be_stripped", "kind": "function", "doc": "

Strip the string of leading and trailing whitespace.

\n\n
Arguments:
\n\n
    \n
  • v (str): The text or speaker of the script line.
    • \n
\n\n
Returns:
\n\n
\n

str: The validated text or speaker of the script line.

\n
\n", "signature": "(cls, v: str, _: pydantic_core.core_schema.ValidationInfo) -> str:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.timestamps_must_be_positive": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.timestamps_must_be_positive", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.timestamps_must_be_positive", "kind": "function", "doc": "

Validate that the start and end timestamps are positive.

\n\n
Arguments:
\n\n
    \n
  • v (float): The timestamp of the script line.
    • \n
\n\n
Returns:
\n\n
\n

float: The validated timestamp.

\n
\n", "signature": "(cls, v: float, _: pydantic_core.core_schema.ValidationInfo) -> float:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.get_text": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.get_text", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.get_text", "kind": "function", "doc": "

Get the full text of the script line.

\n\n
Returns:
\n\n
\n

Optional[str, None]: The full text of the script line, or None if not provided.

\n
\n", "signature": "(self) -> Optional[str]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.get_speaker": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.get_speaker", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.get_speaker", "kind": "function", "doc": "

Get the speaker of the script line.

\n\n
Returns:
\n\n
\n

Optional[str]: The speaker of the script line.

\n
\n", "signature": "(self) -> Optional[str]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.get_timestamps": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.get_timestamps", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.get_timestamps", "kind": "function", "doc": "

Get the start and end timestamps of the script line.

\n\n
Returns:
\n\n
\n

Tuple[Optional[float], Optional[float]]: The start and end timestamps of the script line.

\n
\n", "signature": "(self) -> Tuple[Optional[float], Optional[float]]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.get_chunks": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.get_chunks", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.get_chunks", "kind": "function", "doc": "

Get the list of chunks in the script line.

\n\n
Returns:
\n\n
\n

Optional[List['ScriptLine']]: The list of chunks.

\n
\n", "signature": "(\tself) -> Optional[List[senselab.utils.data_structures.script_line.ScriptLine]]:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.from_dict": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.from_dict", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.from_dict", "kind": "function", "doc": "

Create a ScriptLine instance from a dictionary.

\n\n
Arguments:
\n\n
    \n
  • d (Dict[str, Any]): The dictionary containing the script line data.
    • \n
\n\n
Returns:
\n\n
\n

ScriptLine: An instance of ScriptLine.

\n
\n", "signature": "(\tcls,\td: Dict[str, Any]) -> senselab.utils.data_structures.script_line.ScriptLine:", "funcdef": "def"}, "senselab.utils.data_structures.script_line.ScriptLine.model_config": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.model_config", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "senselab.utils.data_structures.script_line.ScriptLine.model_fields": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.model_fields", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'text': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'speaker': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'start': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'end': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'chunks': FieldInfo(annotation=Union[List[ScriptLine], NoneType], required=False, default=None)}"}, "senselab.utils.data_structures.script_line.ScriptLine.model_computed_fields": {"fullname": "senselab.utils.data_structures.script_line.ScriptLine.model_computed_fields", "modulename": "senselab.utils.data_structures.script_line", "qualname": "ScriptLine.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "senselab.video": {"fullname": "senselab.video", "modulename": "senselab.video", "kind": "module", "doc": "

This module contains implementation for video processing.

\n"}}, "docInfo": {"senselab": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 549}, "senselab.audio": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 11}, "senselab.audio.data_structures": {"qualname": 0, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 11}, "senselab.audio.data_structures.audio": {"qualname": 0, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 56}, "senselab.audio.data_structures.audio.Audio": {"qualname": 1, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 3, "doc": 143}, "senselab.audio.data_structures.audio.Audio.waveform": {"qualname": 2, "fullname": 7, "annotation": 3, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "senselab.audio.data_structures.audio.Audio.sampling_rate": {"qualname": 3, "fullname": 8, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "senselab.audio.data_structures.audio.Audio.orig_path_or_id": {"qualname": 5, "fullname": 10, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "senselab.audio.data_structures.audio.Audio.metadata": {"qualname": 2, "fullname": 7, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "senselab.audio.data_structures.audio.Audio.model_config": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 8, "signature": 0, "bases": 0, "doc": 18}, "senselab.audio.data_structures.audio.Audio.convert_to_tensor": {"qualname": 4, "fullname": 9, "annotation": 0, "default_value": 0, "signature": 101, "bases": 0, "doc": 16}, "senselab.audio.data_structures.audio.Audio.from_filepath": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 65, "bases": 0, "doc": 43}, "senselab.audio.data_structures.audio.Audio.generate_path": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 62}, "senselab.audio.data_structures.audio.Audio.id": {"qualname": 2, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 50}, "senselab.audio.data_structures.audio.Audio.model_fields": {"qualname": 3, "fullname": 8, "annotation": 5, "default_value": 47, "signature": 0, "bases": 0, "doc": 35}, "senselab.audio.data_structures.audio.Audio.model_computed_fields": {"qualname": 4, "fullname": 9, "annotation": 5, "default_value": 1, "signature": 0, "bases": 0, "doc": 16}, "senselab.audio.data_structures.audio.batch_audios": {"qualname": 2, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 88, "bases": 0, "doc": 180}, "senselab.audio.data_structures.audio.unbatch_audios": {"qualname": 2, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 98, "bases": 0, "doc": 167}, "senselab.audio.tasks": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, 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Submodules

  • transcribe_timestamped
    • -

    API Documentation

    - - @@ -63,140 +56,16 @@ 

    1"""Workflow for timestamped transcription."""
 2
-3from senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped import transcribe_timestamped
-4
-5__all__ = ["transcribe_timestamped"]
+3"""
+4# TODO: Please double-check this because tests are failing
+5from senselab.audio.workflows.transcribe_timestamped.transcribe_timestamped import transcribe_timestamped
+6
+7__all__ = ["transcribe_timestamped"]
+8"""
    -
    - -
    - - def - transcribe_timestamped( audios: List[senselab.audio.data_structures.audio.Audio], model: senselab.utils.data_structures.model.HFModel = HFModel(path_or_uri='openai/whisper-tiny', revision='main', info=None), language: senselab.utils.data_structures.language.Language = Language(language_code='eng'), n_batches: int = 1) -> List[List[senselab.utils.data_structures.script_line.ScriptLine]]: - - - -
    - - 
     21def transcribe_timestamped(
- 22    audios: List[Audio],
- 23    model: HFModel = HFModel(path_or_uri="openai/whisper-tiny"),
- 24    language: Language = Language(language_code="en"),
- 25    n_batches: int = 1,
- 26) -> List[List[ScriptLine]]:
- 27    """Transcribes a list of audio files and timestamps them using forced alignment.
- 28
- 29    This function processes the given list of Audio objects by performing
- 30    necessary preprocessing steps (such as downmixing channels and resampling),
- 31    transcribes the audio using the specified speech-to-text model, and applies
- 32    forced alignment to generate a list of ScriptLine objects with timestamps.
- 33
- 34    Args:
- 35        audios (list[Audio]): List of Audio objects to be transcribed and
- 36                              timestamped.
- 37        model (HFModel, optional): A Huggingface model for speech-to-text.
- 38                                   Defaults to 'whisper'.
- 39        language (Language, optional): Language object for the transcription.
- 40                                       If None, language detection is triggered
- 41                                       for the 'whisper' model. Defaults to None.
- 42        n_batches (int, optional): The number of batches to split over in the
- 43                                   workflow.
- 44
- 45    Returns:
- 46        List[List[ScriptLine]]: List of ScriptLine objects resulting from the
- 47                          transcriptions with timestamps.
- 48    """
- 49    if not audios:
- 50        raise ValueError("The list of audios is empty.")
- 51
- 52    for i in range(len(audios)):
- 53        if audios[i].waveform.shape[0] > 1:
- 54            audios[i] = downmix_audios_to_mono(audios=[audios[i]])[0]
- 55        if audios[i].sampling_rate != SAMPLE_RATE:
- 56            audios[i] = resample_audios(audios=[audios[i]], resample_rate=SAMPLE_RATE)[0]
- 57
- 58    batched_audios = batch_list(items=audios, n_batches=n_batches)
- 59
- 60    wf = pydra.Workflow(
- 61        name="wf",
- 62        input_spec=["batched_audios", "model", "language"],
- 63        batched_audios=batched_audios,
- 64        model=model,
- 65        language=language,
- 66        cache_dir=None,
- 67    )
- 68
- 69    @pydra.mark.task
- 70    def transcribe_task(audios: List[Audio], model: HFModel, language: Language) -> List[tuple]:
- 71        transcriptions = transcribe_audios(audios=audios, model=model, language=language)
- 72        return list(zip(audios, transcriptions, [language] * len(audios)))
- 73
- 74    wf.add(
- 75        transcribe_task(
- 76            name="transcribe",
- 77            audios=wf.lzin.batched_audios,
- 78            model=wf.lzin.model,
- 79            language=wf.lzin.language,
- 80        )
- 81    ).split("batched_audios", batched_audios=wf.inputs.batched_audios)
- 82
- 83    align_transcriptions_task = pydra.mark.task(align_transcriptions)
- 84    wf.add(
- 85        align_transcriptions_task(
- 86            name="align",
- 87            audios_and_transcriptions_and_language=wf.transcribe.lzout.out,
- 88        )
- 89    )
- 90
- 91    wf.set_output(
- 92        [
- 93            ("transcriptions", wf.transcribe.lzout.out),
- 94            ("aligned_transcriptions", wf.align.lzout.out),
- 95        ]
- 96    )
- 97
- 98    # Execute the workflow
- 99    with pydra.Submitter(plugin="cf") as sub:
-100        sub(wf)
-101
-102    return wf.result()[0].output.aligned_transcriptions
-
    - - -

    Transcribes a list of audio files and timestamps them using forced alignment.

    - -

    This function processes the given list of Audio objects by performing -necessary preprocessing steps (such as downmixing channels and resampling), -transcribes the audio using the specified speech-to-text model, and applies -forced alignment to generate a list of ScriptLine objects with timestamps.

    - -
    Arguments:
    - -
      -
    • audios (list[Audio]): List of Audio objects to be transcribed and -timestamped.
      • -
    • model (HFModel, optional): A Huggingface model for speech-to-text. -Defaults to 'whisper'.
      • -
    • language (Language, optional): Language object for the transcription. -If None, language detection is triggered -for the 'whisper' model. Defaults to None.
      • -
    • n_batches (int, optional): The number of batches to split over in the -workflow.
      • -
    - -
    Returns:
    - -
    -

    List[List[ScriptLine]]: List of ScriptLine objects resulting from the - transcriptions with timestamps.

    -
    -
    - - -
    - \ No newline at end of file diff --git a/senselab/utils/tasks/batching.html b/senselab/utils/tasks/batching.html deleted file mode 100644 index 4fe57974..00000000 --- a/senselab/utils/tasks/batching.html +++ /dev/null @@ -1,318 +0,0 @@ - - - - - - - senselab.utils.tasks.batching API documentation - - - - - - - - - - -
    -
    -

    -senselab.utils.tasks.batching

    - -

    Provides functionality for batch processing.

    -
    - - - - - - 
     1"""Provides functionality for batch processing."""
- 2
- 3from typing import Any, List
- 4
- 5
- 6def batch_list(items: List[Any], n_batches: int) -> List[List[Any]]:
- 7    """Splits a list into a specified number of batches.
- 8
- 9    Args:
-10        items (List[Any]): The list of items to be batched.
-11        n_batches (int): The number of batches to divide the list into.
-12
-13    Returns:
-14        List[List[Any]]: A list of lists, where each sublist is a batch of items.
-15    """
-16    if not items:
-17        return []
-18
-19    n_items = len(items)
-20    n_batches = min(n_batches, n_items)  # ensure n_batches does not exceed n_items
-21    batch_size = -(n_items // -n_batches)  # fast ceiling division
-22    return [items[i : i + batch_size] for i in range(0, n_items, batch_size)]
-
    - - -
    -
    - -
    - - def - batch_list(items: List[Any], n_batches: int) -> List[List[Any]]: - - - -
    - - 
     7def batch_list(items: List[Any], n_batches: int) -> List[List[Any]]:
- 8    """Splits a list into a specified number of batches.
- 9
-10    Args:
-11        items (List[Any]): The list of items to be batched.
-12        n_batches (int): The number of batches to divide the list into.
-13
-14    Returns:
-15        List[List[Any]]: A list of lists, where each sublist is a batch of items.
-16    """
-17    if not items:
-18        return []
-19
-20    n_items = len(items)
-21    n_batches = min(n_batches, n_items)  # ensure n_batches does not exceed n_items
-22    batch_size = -(n_items // -n_batches)  # fast ceiling division
-23    return [items[i : i + batch_size] for i in range(0, n_items, batch_size)]
-
    - - -

    Splits a list into a specified number of batches.

    - -
    Arguments:
    - -
      -
    • items (List[Any]): The list of items to be batched.
      • -
    • n_batches (int): The number of batches to divide the list into.
      • -
    - -
    Returns:
    - -
    -

    List[List[Any]]: A list of lists, where each sublist is a batch of items.

    -
    -
    - - -
    -
    - - \ No newline at end of file diff --git a/senselab/utils/tasks/cca_cka.html b/senselab/utils/tasks/cca_cka.html deleted file mode 100644 index 9294bee5..00000000 --- a/senselab/utils/tasks/cca_cka.html +++ /dev/null @@ -1,834 +0,0 @@ - - - - - - - senselab.utils.tasks.cca_cka API documentation - - - - - - - - - - -
    -
    -

    -senselab.utils.tasks.cca_cka

    - -

    This module is for computing CCA and CKA.

    -
    - - - - - - 
      1"""This module is for computing CCA and CKA."""
-  2
-  3from enum import Enum
-  4
-  5import pydra
-  6import torch
-  7
-  8
-  9def compute_cca(features_x: torch.Tensor, features_y: torch.Tensor) -> float:
- 10    """Compute the mean squared CCA correlation (R^2_{CCA}).
- 11
- 12    Args:
- 13        features_x (torch.Tensor): A num_examples x num_features matrix of features.
- 14        features_y (torch.Tensor): A num_examples x num_features matrix of features.
- 15
- 16    Returns:
- 17        float: The mean squared CCA correlations between X and Y.
- 18    """
- 19    qx, _ = torch.linalg.qr(features_x)
- 20    qy, _ = torch.linalg.qr(features_y)
- 21    result = torch.norm(qx.t() @ qy) ** 2 / min(features_x.shape[1], features_y.shape[1])
- 22    return result.item() if isinstance(result, torch.Tensor) else float(result)
- 23
- 24
- 25class CKAKernelType(Enum):
- 26    """CKA kernel types."""
- 27
- 28    LINEAR = "linear"
- 29    RBF = "rbf"
- 30
- 31
- 32def compute_cka(
- 33    features_x: torch.Tensor,
- 34    features_y: torch.Tensor,
- 35    kernel: CKAKernelType = CKAKernelType.LINEAR,
- 36    threshold: float = 1.0,
- 37) -> float:
- 38    """Compute CKA between feature matrices.
- 39
- 40    Args:
- 41        features_x (torch.Tensor): A num_examples x num_features matrix of features.
- 42        features_y (torch.Tensor): A num_examples x num_features matrix of features.
- 43        kernel (CKAKernelType): Type of kernel to use (CKAKernelType.LINEAR or CKAKernelType.RBF).
- 44        Default is CKAKernelType.LINEAR.
- 45        threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth
- 46            (used only if kernel is CKAKernelType.RBF).
- 47
- 48    Returns:
- 49        float: The value of CKA between X and Y.
- 50    """
- 51
- 52    def _gram_linear(x: torch.Tensor) -> torch.Tensor:
- 53        """Compute Gram (kernel) matrix for a linear kernel.
- 54
- 55        Args:
- 56            x (torch.Tensor): A num_examples x num_features matrix of features.
- 57
- 58        Returns:
- 59            torch.Tensor: A num_examples x num_examples Gram matrix of examples.
- 60        """
- 61        return x @ x.t()
- 62
- 63    def _gram_rbf(x: torch.Tensor, threshold: float = 1.0) -> torch.Tensor:
- 64        """Compute Gram (kernel) matrix for an RBF kernel.
- 65
- 66        Args:
- 67            x (torch.Tensor): A num_examples x num_features matrix of features.
- 68            threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth.
- 69
- 70        Returns:
- 71            torch.Tensor: A num_examples x num_examples Gram matrix of examples.
- 72        """
- 73        dot_products = x @ x.t()
- 74        sq_norms = torch.diag(dot_products)
- 75        sq_distances = -2 * dot_products + sq_norms[:, None] + sq_norms[None, :]
- 76        sq_median_distance = torch.median(sq_distances)
- 77        return torch.exp(-sq_distances / (2 * threshold**2 * sq_median_distance))
- 78
- 79    def _center_gram(gram: torch.Tensor) -> torch.Tensor:
- 80        """Center a symmetric Gram matrix.
- 81
- 82        This is equivalent to centering the (possibly infinite-dimensional) features
- 83        induced by the kernel before computing the Gram matrix.
- 84
- 85        Args:
- 86            gram (torch.Tensor): A num_examples x num_examples symmetric matrix.
- 87
- 88        Returns:
- 89            torch.Tensor: A symmetric matrix with centered columns and rows.
- 90
- 91        Raises:
- 92            ValueError: If the input is not a symmetric matrix.
- 93        """
- 94        if not torch.allclose(gram, gram.t()):
- 95            raise ValueError("Input must be a symmetric matrix.")
- 96
- 97        n = gram.size(0)
- 98        unit = torch.ones(n, n, device=gram.device)
- 99        eye = torch.eye(n, device=gram.device)
-100        unit = unit / n
-101        haitch = eye - unit
-102        centered_gram = haitch.mm(gram).mm(haitch)
-103        return centered_gram
-104
-105    def _cka(gram_x: torch.Tensor, gram_y: torch.Tensor) -> torch.Tensor:
-106        """Compute CKA.
-107
-108        Args:
-109            gram_x (torch.Tensor): A num_examples x num_examples Gram matrix.
-110            gram_y (torch.Tensor): A num_examples x num_examples Gram matrix.
-111
-112        Returns:
-113            float: The value of CKA between X and Y.
-114        """
-115        gram_x = _center_gram(gram_x)
-116        gram_y = _center_gram(gram_y)
-117
-118        scaled_hsic = torch.sum(gram_x * gram_y)
-119
-120        normalization_x = torch.norm(gram_x)
-121        normalization_y = torch.norm(gram_y)
-122        return scaled_hsic / (normalization_x * normalization_y)
-123
-124    if kernel == CKAKernelType.LINEAR:
-125        gram_x = _gram_linear(features_x)
-126        gram_y = _gram_linear(features_y)
-127    elif kernel == CKAKernelType.RBF:
-128        gram_x = _gram_rbf(features_x, threshold)
-129        gram_y = _gram_rbf(features_y, threshold)
-130    else:
-131        raise ValueError("Unsupported kernel type. Use CKAKernelType.LINEAR or CKAKernelType.RBF.")
-132
-133    result = _cka(gram_x, gram_y)
-134    return result.item() if isinstance(result, torch.Tensor) else float(result)
-135
-136
-137compute_cca_pt = pydra.mark.task(compute_cca)
-138compute_cka_pt = pydra.mark.task(compute_cka)
-
    - - -
    -
    - -
    - - def - compute_cca(features_x: torch.Tensor, features_y: torch.Tensor) -> float: - - - -
    - - 
    10def compute_cca(features_x: torch.Tensor, features_y: torch.Tensor) -> float:
-11    """Compute the mean squared CCA correlation (R^2_{CCA}).
-12
-13    Args:
-14        features_x (torch.Tensor): A num_examples x num_features matrix of features.
-15        features_y (torch.Tensor): A num_examples x num_features matrix of features.
-16
-17    Returns:
-18        float: The mean squared CCA correlations between X and Y.
-19    """
-20    qx, _ = torch.linalg.qr(features_x)
-21    qy, _ = torch.linalg.qr(features_y)
-22    result = torch.norm(qx.t() @ qy) ** 2 / min(features_x.shape[1], features_y.shape[1])
-23    return result.item() if isinstance(result, torch.Tensor) else float(result)
-
    - - -

    Compute the mean squared CCA correlation (R^2_{CCA}).

    - -
    Arguments:
    - -
      -
    • features_x (torch.Tensor): A num_examples x num_features matrix of features.
      • -
    • features_y (torch.Tensor): A num_examples x num_features matrix of features.
      • -
    - -
    Returns:
    - -
    -

    float: The mean squared CCA correlations between X and Y.

    -
    -
    - - -
    -
    - -
    - - class - CKAKernelType(enum.Enum): - - - -
    - - 
    26class CKAKernelType(Enum):
-27    """CKA kernel types."""
-28
-29    LINEAR = "linear"
-30    RBF = "rbf"
-
    - - -

    CKA kernel types.

    -
    - - -
    -
    - LINEAR = -<CKAKernelType.LINEAR: 'linear'> - - -
    - - - - -
    -
    -
    - RBF = -<CKAKernelType.RBF: 'rbf'> - - -
    - - - - -
    -
    -
    Inherited Members
    -
    -
    enum.Enum
    -
    name
    -
    value
    - -
    -
    -
    -
    -
    - -
    - - def - compute_cka( features_x: torch.Tensor, features_y: torch.Tensor, kernel: CKAKernelType = <CKAKernelType.LINEAR: 'linear'>, threshold: float = 1.0) -> float: - - - -
    - - 
     33def compute_cka(
- 34    features_x: torch.Tensor,
- 35    features_y: torch.Tensor,
- 36    kernel: CKAKernelType = CKAKernelType.LINEAR,
- 37    threshold: float = 1.0,
- 38) -> float:
- 39    """Compute CKA between feature matrices.
- 40
- 41    Args:
- 42        features_x (torch.Tensor): A num_examples x num_features matrix of features.
- 43        features_y (torch.Tensor): A num_examples x num_features matrix of features.
- 44        kernel (CKAKernelType): Type of kernel to use (CKAKernelType.LINEAR or CKAKernelType.RBF).
- 45        Default is CKAKernelType.LINEAR.
- 46        threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth
- 47            (used only if kernel is CKAKernelType.RBF).
- 48
- 49    Returns:
- 50        float: The value of CKA between X and Y.
- 51    """
- 52
- 53    def _gram_linear(x: torch.Tensor) -> torch.Tensor:
- 54        """Compute Gram (kernel) matrix for a linear kernel.
- 55
- 56        Args:
- 57            x (torch.Tensor): A num_examples x num_features matrix of features.
- 58
- 59        Returns:
- 60            torch.Tensor: A num_examples x num_examples Gram matrix of examples.
- 61        """
- 62        return x @ x.t()
- 63
- 64    def _gram_rbf(x: torch.Tensor, threshold: float = 1.0) -> torch.Tensor:
- 65        """Compute Gram (kernel) matrix for an RBF kernel.
- 66
- 67        Args:
- 68            x (torch.Tensor): A num_examples x num_features matrix of features.
- 69            threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth.
- 70
- 71        Returns:
- 72            torch.Tensor: A num_examples x num_examples Gram matrix of examples.
- 73        """
- 74        dot_products = x @ x.t()
- 75        sq_norms = torch.diag(dot_products)
- 76        sq_distances = -2 * dot_products + sq_norms[:, None] + sq_norms[None, :]
- 77        sq_median_distance = torch.median(sq_distances)
- 78        return torch.exp(-sq_distances / (2 * threshold**2 * sq_median_distance))
- 79
- 80    def _center_gram(gram: torch.Tensor) -> torch.Tensor:
- 81        """Center a symmetric Gram matrix.
- 82
- 83        This is equivalent to centering the (possibly infinite-dimensional) features
- 84        induced by the kernel before computing the Gram matrix.
- 85
- 86        Args:
- 87            gram (torch.Tensor): A num_examples x num_examples symmetric matrix.
- 88
- 89        Returns:
- 90            torch.Tensor: A symmetric matrix with centered columns and rows.
- 91
- 92        Raises:
- 93            ValueError: If the input is not a symmetric matrix.
- 94        """
- 95        if not torch.allclose(gram, gram.t()):
- 96            raise ValueError("Input must be a symmetric matrix.")
- 97
- 98        n = gram.size(0)
- 99        unit = torch.ones(n, n, device=gram.device)
-100        eye = torch.eye(n, device=gram.device)
-101        unit = unit / n
-102        haitch = eye - unit
-103        centered_gram = haitch.mm(gram).mm(haitch)
-104        return centered_gram
-105
-106    def _cka(gram_x: torch.Tensor, gram_y: torch.Tensor) -> torch.Tensor:
-107        """Compute CKA.
-108
-109        Args:
-110            gram_x (torch.Tensor): A num_examples x num_examples Gram matrix.
-111            gram_y (torch.Tensor): A num_examples x num_examples Gram matrix.
-112
-113        Returns:
-114            float: The value of CKA between X and Y.
-115        """
-116        gram_x = _center_gram(gram_x)
-117        gram_y = _center_gram(gram_y)
-118
-119        scaled_hsic = torch.sum(gram_x * gram_y)
-120
-121        normalization_x = torch.norm(gram_x)
-122        normalization_y = torch.norm(gram_y)
-123        return scaled_hsic / (normalization_x * normalization_y)
-124
-125    if kernel == CKAKernelType.LINEAR:
-126        gram_x = _gram_linear(features_x)
-127        gram_y = _gram_linear(features_y)
-128    elif kernel == CKAKernelType.RBF:
-129        gram_x = _gram_rbf(features_x, threshold)
-130        gram_y = _gram_rbf(features_y, threshold)
-131    else:
-132        raise ValueError("Unsupported kernel type. Use CKAKernelType.LINEAR or CKAKernelType.RBF.")
-133
-134    result = _cka(gram_x, gram_y)
-135    return result.item() if isinstance(result, torch.Tensor) else float(result)
-
    - - -

    Compute CKA between feature matrices.

    - -
    Arguments:
    - -
      -
    • features_x (torch.Tensor): A num_examples x num_features matrix of features.
      • -
    • features_y (torch.Tensor): A num_examples x num_features matrix of features.
      • -
    • kernel (CKAKernelType): Type of kernel to use (CKAKernelType.LINEAR or CKAKernelType.RBF).
      • -
    • Default is CKAKernelType.LINEAR.
      • -
    • threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth -(used only if kernel is CKAKernelType.RBF).
      • -
    - -
    Returns:
    - -
    -

    float: The value of CKA between X and Y.

    -
    -
    - - -
    -
    - -
    - - def - compute_cca_pt(features_x: torch.Tensor, features_y: torch.Tensor) -> float: - - - -
    - - 
    10def compute_cca(features_x: torch.Tensor, features_y: torch.Tensor) -> float:
-11    """Compute the mean squared CCA correlation (R^2_{CCA}).
-12
-13    Args:
-14        features_x (torch.Tensor): A num_examples x num_features matrix of features.
-15        features_y (torch.Tensor): A num_examples x num_features matrix of features.
-16
-17    Returns:
-18        float: The mean squared CCA correlations between X and Y.
-19    """
-20    qx, _ = torch.linalg.qr(features_x)
-21    qy, _ = torch.linalg.qr(features_y)
-22    result = torch.norm(qx.t() @ qy) ** 2 / min(features_x.shape[1], features_y.shape[1])
-23    return result.item() if isinstance(result, torch.Tensor) else float(result)
-
    - - -

    Compute the mean squared CCA correlation (R^2_{CCA}).

    - -
    Arguments:
    - -
      -
    • features_x (torch.Tensor): A num_examples x num_features matrix of features.
      • -
    • features_y (torch.Tensor): A num_examples x num_features matrix of features.
      • -
    - -
    Returns:
    - -
    -

    float: The mean squared CCA correlations between X and Y.

    -
    -
    - - -
    -
    - -
    - - def - compute_cka_pt( features_x: torch.Tensor, features_y: torch.Tensor, kernel: CKAKernelType = <CKAKernelType.LINEAR: 'linear'>, threshold: float = 1.0) -> float: - - - -
    - - 
     33def compute_cka(
- 34    features_x: torch.Tensor,
- 35    features_y: torch.Tensor,
- 36    kernel: CKAKernelType = CKAKernelType.LINEAR,
- 37    threshold: float = 1.0,
- 38) -> float:
- 39    """Compute CKA between feature matrices.
- 40
- 41    Args:
- 42        features_x (torch.Tensor): A num_examples x num_features matrix of features.
- 43        features_y (torch.Tensor): A num_examples x num_features matrix of features.
- 44        kernel (CKAKernelType): Type of kernel to use (CKAKernelType.LINEAR or CKAKernelType.RBF).
- 45        Default is CKAKernelType.LINEAR.
- 46        threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth
- 47            (used only if kernel is CKAKernelType.RBF).
- 48
- 49    Returns:
- 50        float: The value of CKA between X and Y.
- 51    """
- 52
- 53    def _gram_linear(x: torch.Tensor) -> torch.Tensor:
- 54        """Compute Gram (kernel) matrix for a linear kernel.
- 55
- 56        Args:
- 57            x (torch.Tensor): A num_examples x num_features matrix of features.
- 58
- 59        Returns:
- 60            torch.Tensor: A num_examples x num_examples Gram matrix of examples.
- 61        """
- 62        return x @ x.t()
- 63
- 64    def _gram_rbf(x: torch.Tensor, threshold: float = 1.0) -> torch.Tensor:
- 65        """Compute Gram (kernel) matrix for an RBF kernel.
- 66
- 67        Args:
- 68            x (torch.Tensor): A num_examples x num_features matrix of features.
- 69            threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth.
- 70
- 71        Returns:
- 72            torch.Tensor: A num_examples x num_examples Gram matrix of examples.
- 73        """
- 74        dot_products = x @ x.t()
- 75        sq_norms = torch.diag(dot_products)
- 76        sq_distances = -2 * dot_products + sq_norms[:, None] + sq_norms[None, :]
- 77        sq_median_distance = torch.median(sq_distances)
- 78        return torch.exp(-sq_distances / (2 * threshold**2 * sq_median_distance))
- 79
- 80    def _center_gram(gram: torch.Tensor) -> torch.Tensor:
- 81        """Center a symmetric Gram matrix.
- 82
- 83        This is equivalent to centering the (possibly infinite-dimensional) features
- 84        induced by the kernel before computing the Gram matrix.
- 85
- 86        Args:
- 87            gram (torch.Tensor): A num_examples x num_examples symmetric matrix.
- 88
- 89        Returns:
- 90            torch.Tensor: A symmetric matrix with centered columns and rows.
- 91
- 92        Raises:
- 93            ValueError: If the input is not a symmetric matrix.
- 94        """
- 95        if not torch.allclose(gram, gram.t()):
- 96            raise ValueError("Input must be a symmetric matrix.")
- 97
- 98        n = gram.size(0)
- 99        unit = torch.ones(n, n, device=gram.device)
-100        eye = torch.eye(n, device=gram.device)
-101        unit = unit / n
-102        haitch = eye - unit
-103        centered_gram = haitch.mm(gram).mm(haitch)
-104        return centered_gram
-105
-106    def _cka(gram_x: torch.Tensor, gram_y: torch.Tensor) -> torch.Tensor:
-107        """Compute CKA.
-108
-109        Args:
-110            gram_x (torch.Tensor): A num_examples x num_examples Gram matrix.
-111            gram_y (torch.Tensor): A num_examples x num_examples Gram matrix.
-112
-113        Returns:
-114            float: The value of CKA between X and Y.
-115        """
-116        gram_x = _center_gram(gram_x)
-117        gram_y = _center_gram(gram_y)
-118
-119        scaled_hsic = torch.sum(gram_x * gram_y)
-120
-121        normalization_x = torch.norm(gram_x)
-122        normalization_y = torch.norm(gram_y)
-123        return scaled_hsic / (normalization_x * normalization_y)
-124
-125    if kernel == CKAKernelType.LINEAR:
-126        gram_x = _gram_linear(features_x)
-127        gram_y = _gram_linear(features_y)
-128    elif kernel == CKAKernelType.RBF:
-129        gram_x = _gram_rbf(features_x, threshold)
-130        gram_y = _gram_rbf(features_y, threshold)
-131    else:
-132        raise ValueError("Unsupported kernel type. Use CKAKernelType.LINEAR or CKAKernelType.RBF.")
-133
-134    result = _cka(gram_x, gram_y)
-135    return result.item() if isinstance(result, torch.Tensor) else float(result)
-
    - - -

    Compute CKA between feature matrices.

    - -
    Arguments:
    - -
      -
    • features_x (torch.Tensor): A num_examples x num_features matrix of features.
      • -
    • features_y (torch.Tensor): A num_examples x num_features matrix of features.
      • -
    • kernel (CKAKernelType): Type of kernel to use (CKAKernelType.LINEAR or CKAKernelType.RBF).
      • -
    • Default is CKAKernelType.LINEAR.
      • -
    • threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth -(used only if kernel is CKAKernelType.RBF).
      • -
    - -
    Returns:
    - -
    -

    float: The value of CKA between X and Y.

    -
    -
    - - -
    -
    - - \ No newline at end of file diff --git a/senselab/utils/tasks/cosine_similarity.html b/senselab/utils/tasks/cosine_similarity.html deleted file mode 100644 index 732ad48f..00000000 --- a/senselab/utils/tasks/cosine_similarity.html +++ /dev/null @@ -1,501 +0,0 @@ - - - - - - - senselab.utils.tasks.cosine_similarity API documentation - - - - - - - - - - -
    -
    -

    -senselab.utils.tasks.cosine_similarity

    - -

    This module provides the implementation of cosine similarity.

    -
    - - - - - - 
     1"""This module provides the implementation of cosine similarity."""
- 2
- 3import pydra
- 4import torch
- 5
- 6
- 7def compute_cosine_similarity(tensor1: torch.Tensor, tensor2: torch.Tensor) -> float:
- 8    """Compute the cosine similarity between two torch tensors.
- 9
-10    Args:
-11        tensor1 (Tensor): The first input tensor.
-12        tensor2 (Tensor): The second input tensor.
-13
-14    Returns:
-15        float: The cosine similarity between the two input tensors.
-16
-17    Raises:
-18        ValueError: If the input tensors are not of the same shape.
-19
-20    Examples:
-21        >>> tensor1 = torch.tensor([1.0, 2.0, 3.0])
-22        >>> tensor2 = torch.tensor([4.0, 5.0, 6.0])
-23        >>> cosine_similarity(tensor1, tensor2)
-24        0.9746318461970762
-25
-26        >>> tensor1 = torch.tensor([1.0, 0.0, -1.0])
-27        >>> tensor2 = torch.tensor([-1.0, 0.0, 1.0])
-28        >>> cosine_similarity(tensor1, tensor2)
-29        -1.0
-30
-31    Note:
-32        This function assumes the input tensors are 1-dimensional and have the same shape.
-33    """
-34    if tensor1.dim() != 1 or tensor2.dim() != 1:
-35        raise ValueError("Input tensors must be 1-dimensional")
-36    if tensor1.shape != tensor2.shape:
-37        raise ValueError("Input tensors must have the same shape")
-38
-39    dot_product = torch.dot(tensor1, tensor2)
-40    norm_tensor1 = torch.norm(tensor1)
-41    norm_tensor2 = torch.norm(tensor2)
-42
-43    cosine_sim = dot_product / (norm_tensor1 * norm_tensor2)
-44    return cosine_sim.item()
-45
-46
-47cosine_similarity_pt = pydra.mark.task(compute_cosine_similarity)
-
    - - -
    -
    - -
    - - def - compute_cosine_similarity(tensor1: torch.Tensor, tensor2: torch.Tensor) -> float: - - - -
    - - 
     8def compute_cosine_similarity(tensor1: torch.Tensor, tensor2: torch.Tensor) -> float:
- 9    """Compute the cosine similarity between two torch tensors.
-10
-11    Args:
-12        tensor1 (Tensor): The first input tensor.
-13        tensor2 (Tensor): The second input tensor.
-14
-15    Returns:
-16        float: The cosine similarity between the two input tensors.
-17
-18    Raises:
-19        ValueError: If the input tensors are not of the same shape.
-20
-21    Examples:
-22        >>> tensor1 = torch.tensor([1.0, 2.0, 3.0])
-23        >>> tensor2 = torch.tensor([4.0, 5.0, 6.0])
-24        >>> cosine_similarity(tensor1, tensor2)
-25        0.9746318461970762
-26
-27        >>> tensor1 = torch.tensor([1.0, 0.0, -1.0])
-28        >>> tensor2 = torch.tensor([-1.0, 0.0, 1.0])
-29        >>> cosine_similarity(tensor1, tensor2)
-30        -1.0
-31
-32    Note:
-33        This function assumes the input tensors are 1-dimensional and have the same shape.
-34    """
-35    if tensor1.dim() != 1 or tensor2.dim() != 1:
-36        raise ValueError("Input tensors must be 1-dimensional")
-37    if tensor1.shape != tensor2.shape:
-38        raise ValueError("Input tensors must have the same shape")
-39
-40    dot_product = torch.dot(tensor1, tensor2)
-41    norm_tensor1 = torch.norm(tensor1)
-42    norm_tensor2 = torch.norm(tensor2)
-43
-44    cosine_sim = dot_product / (norm_tensor1 * norm_tensor2)
-45    return cosine_sim.item()
-
    - - -

    Compute the cosine similarity between two torch tensors.

    - -
    Arguments:
    - -
      -
    • tensor1 (Tensor): The first input tensor.
      • -
    • tensor2 (Tensor): The second input tensor.
      • -
    - -
    Returns:
    - -
    -

    float: The cosine similarity between the two input tensors.

    -
    - -
    Raises:
    - -
      -
    • ValueError: If the input tensors are not of the same shape.
      • -
    - -
    Examples:
    - -
    -
    -
    >>> tensor1 = torch.tensor([1.0, 2.0, 3.0])
->>> tensor2 = torch.tensor([4.0, 5.0, 6.0])
->>> cosine_similarity(tensor1, tensor2)
-0.9746318461970762
-
    -
    - -
    -
    >>> tensor1 = torch.tensor([1.0, 0.0, -1.0])
->>> tensor2 = torch.tensor([-1.0, 0.0, 1.0])
->>> cosine_similarity(tensor1, tensor2)
--1.0
-
    -
    -
    - -
    Note:
    - -
    -

    This function assumes the input tensors are 1-dimensional and have the same shape.

    -
    -
    - - -
    -
    - -
    - - def - cosine_similarity_pt(tensor1: torch.Tensor, tensor2: torch.Tensor) -> float: - - - -
    - - 
     8def compute_cosine_similarity(tensor1: torch.Tensor, tensor2: torch.Tensor) -> float:
- 9    """Compute the cosine similarity between two torch tensors.
-10
-11    Args:
-12        tensor1 (Tensor): The first input tensor.
-13        tensor2 (Tensor): The second input tensor.
-14
-15    Returns:
-16        float: The cosine similarity between the two input tensors.
-17
-18    Raises:
-19        ValueError: If the input tensors are not of the same shape.
-20
-21    Examples:
-22        >>> tensor1 = torch.tensor([1.0, 2.0, 3.0])
-23        >>> tensor2 = torch.tensor([4.0, 5.0, 6.0])
-24        >>> cosine_similarity(tensor1, tensor2)
-25        0.9746318461970762
-26
-27        >>> tensor1 = torch.tensor([1.0, 0.0, -1.0])
-28        >>> tensor2 = torch.tensor([-1.0, 0.0, 1.0])
-29        >>> cosine_similarity(tensor1, tensor2)
-30        -1.0
-31
-32    Note:
-33        This function assumes the input tensors are 1-dimensional and have the same shape.
-34    """
-35    if tensor1.dim() != 1 or tensor2.dim() != 1:
-36        raise ValueError("Input tensors must be 1-dimensional")
-37    if tensor1.shape != tensor2.shape:
-38        raise ValueError("Input tensors must have the same shape")
-39
-40    dot_product = torch.dot(tensor1, tensor2)
-41    norm_tensor1 = torch.norm(tensor1)
-42    norm_tensor2 = torch.norm(tensor2)
-43
-44    cosine_sim = dot_product / (norm_tensor1 * norm_tensor2)
-45    return cosine_sim.item()
-
    - - -

    Compute the cosine similarity between two torch tensors.

    - -
    Arguments:
    - -
      -
    • tensor1 (Tensor): The first input tensor.
      • -
    • tensor2 (Tensor): The second input tensor.
      • -
    - -
    Returns:
    - -
    -

    float: The cosine similarity between the two input tensors.

    -
    - -
    Raises:
    - -
      -
    • ValueError: If the input tensors are not of the same shape.
      • -
    - -
    Examples:
    - -
    -
    -
    >>> tensor1 = torch.tensor([1.0, 2.0, 3.0])
->>> tensor2 = torch.tensor([4.0, 5.0, 6.0])
->>> cosine_similarity(tensor1, tensor2)
-0.9746318461970762
-
    -
    - -
    -
    >>> tensor1 = torch.tensor([1.0, 0.0, -1.0])
->>> tensor2 = torch.tensor([-1.0, 0.0, 1.0])
->>> cosine_similarity(tensor1, tensor2)
--1.0
-
    -
    -
    - -
    Note:
    - -
    -

    This function assumes the input tensors are 1-dimensional and have the same shape.

    -
    -
    - - -
    -
    - - \ No newline at end of file diff --git a/senselab/utils/tasks/cross_correlation.html b/senselab/utils/tasks/cross_correlation.html deleted file mode 100644 index 531b06aa..00000000 --- a/senselab/utils/tasks/cross_correlation.html +++ /dev/null @@ -1,495 +0,0 @@ - - - - - - - senselab.utils.tasks.cross_correlation API documentation - - - - - - - - - - -
    -
    -

    -senselab.utils.tasks.cross_correlation

    - -

    This module contains functions for computing the normalized cross-correlation between two signals.

    -
    - - - - - - 
     1"""This module contains functions for computing the normalized cross-correlation between two signals."""
- 2
- 3import numpy as np
- 4import pydra
- 5import torch
- 6from scipy.signal import correlate
- 7
- 8
- 9def compute_normalized_cross_correlation(signal1: torch.Tensor, signal2: torch.Tensor) -> torch.Tensor:
-10    """Calculate the normalized cross-correlation between two signals.
-11
-12    Args:
-13        signal1 (torch.Tensor): The first input signal as a PyTorch tensor.
-14        signal2 (torch.Tensor): The second input signal as a PyTorch tensor.
-15
-16    Returns:
-17        torch.Tensor: The normalized cross-correlation value between the two input signals.
-18
-19    Examples:
-20        >>> signal1 = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
-21        >>> signal2 = torch.tensor([2.0, 3.0, 4.0])
-22        >>> normalized_cross_correlation(signal1, signal2)
-23        Tensor([0.30151134, 0.51298918, 0.77459667, 0.9486833 , 0.90453403, 0.70710678, 0.43643578])
-24
-25    Note:
-26        This function assumes the input signals are one-dimensional
-27        and contain sufficient elements for meaningful cross-correlation.
-28    """
-29    # Ensure the inputs are 1D tensors
-30    if signal1.ndim != 1 or signal2.ndim != 1:
-31        raise ValueError("Input signals must be one-dimensional")
-32
-33    # Convert PyTorch tensors to NumPy arrays
-34    signal1_np: np.ndarray = signal1.numpy()
-35    signal2_np: np.ndarray = signal2.numpy()
-36
-37    # Calculate the energy of each signal
-38    energy_signal1 = np.sum(signal1_np**2)
-39    energy_signal2 = np.sum(signal2_np**2)
-40
-41    # Check for zero energy to avoid division by zero
-42    if energy_signal1 == 0 or energy_signal2 == 0:
-43        raise ZeroDivisionError("One of the input signals has zero energy, causing division by zero in normalization")
-44
-45    # Compute the cross-correlation
-46    cross_correlation = correlate(signal1_np, signal2_np)
-47
-48    # Calculate the normalized cross-correlation
-49    normalized_cross_correlation = cross_correlation / np.sqrt(energy_signal1 * energy_signal2)
-50
-51    print(normalized_cross_correlation)
-52    return torch.Tensor(normalized_cross_correlation)
-53
-54
-55compute_normalized_cross_correlation_pt = pydra.mark.task(compute_normalized_cross_correlation)
-
    - - -
    -
    - -
    - - def - compute_normalized_cross_correlation(signal1: torch.Tensor, signal2: torch.Tensor) -> torch.Tensor: - - - -
    - - 
    10def compute_normalized_cross_correlation(signal1: torch.Tensor, signal2: torch.Tensor) -> torch.Tensor:
-11    """Calculate the normalized cross-correlation between two signals.
-12
-13    Args:
-14        signal1 (torch.Tensor): The first input signal as a PyTorch tensor.
-15        signal2 (torch.Tensor): The second input signal as a PyTorch tensor.
-16
-17    Returns:
-18        torch.Tensor: The normalized cross-correlation value between the two input signals.
-19
-20    Examples:
-21        >>> signal1 = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
-22        >>> signal2 = torch.tensor([2.0, 3.0, 4.0])
-23        >>> normalized_cross_correlation(signal1, signal2)
-24        Tensor([0.30151134, 0.51298918, 0.77459667, 0.9486833 , 0.90453403, 0.70710678, 0.43643578])
-25
-26    Note:
-27        This function assumes the input signals are one-dimensional
-28        and contain sufficient elements for meaningful cross-correlation.
-29    """
-30    # Ensure the inputs are 1D tensors
-31    if signal1.ndim != 1 or signal2.ndim != 1:
-32        raise ValueError("Input signals must be one-dimensional")
-33
-34    # Convert PyTorch tensors to NumPy arrays
-35    signal1_np: np.ndarray = signal1.numpy()
-36    signal2_np: np.ndarray = signal2.numpy()
-37
-38    # Calculate the energy of each signal
-39    energy_signal1 = np.sum(signal1_np**2)
-40    energy_signal2 = np.sum(signal2_np**2)
-41
-42    # Check for zero energy to avoid division by zero
-43    if energy_signal1 == 0 or energy_signal2 == 0:
-44        raise ZeroDivisionError("One of the input signals has zero energy, causing division by zero in normalization")
-45
-46    # Compute the cross-correlation
-47    cross_correlation = correlate(signal1_np, signal2_np)
-48
-49    # Calculate the normalized cross-correlation
-50    normalized_cross_correlation = cross_correlation / np.sqrt(energy_signal1 * energy_signal2)
-51
-52    print(normalized_cross_correlation)
-53    return torch.Tensor(normalized_cross_correlation)
-
    - - -

    Calculate the normalized cross-correlation between two signals.

    - -
    Arguments:
    - -
      -
    • signal1 (torch.Tensor): The first input signal as a PyTorch tensor.
      • -
    • signal2 (torch.Tensor): The second input signal as a PyTorch tensor.
      • -
    - -
    Returns:
    - -
    -

    torch.Tensor: The normalized cross-correlation value between the two input signals.

    -
    - -
    Examples:
    - -
    -
    -
    >>> signal1 = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
->>> signal2 = torch.tensor([2.0, 3.0, 4.0])
->>> normalized_cross_correlation(signal1, signal2)
-Tensor([0.30151134, 0.51298918, 0.77459667, 0.9486833 , 0.90453403, 0.70710678, 0.43643578])
-
    -
    -
    - -
    Note:
    - -
    -

    This function assumes the input signals are one-dimensional - and contain sufficient elements for meaningful cross-correlation.

    -
    -
    - - -
    -
    - -
    - - def - compute_normalized_cross_correlation_pt(signal1: torch.Tensor, signal2: torch.Tensor) -> torch.Tensor: - - - -
    - - 
    10def compute_normalized_cross_correlation(signal1: torch.Tensor, signal2: torch.Tensor) -> torch.Tensor:
-11    """Calculate the normalized cross-correlation between two signals.
-12
-13    Args:
-14        signal1 (torch.Tensor): The first input signal as a PyTorch tensor.
-15        signal2 (torch.Tensor): The second input signal as a PyTorch tensor.
-16
-17    Returns:
-18        torch.Tensor: The normalized cross-correlation value between the two input signals.
-19
-20    Examples:
-21        >>> signal1 = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
-22        >>> signal2 = torch.tensor([2.0, 3.0, 4.0])
-23        >>> normalized_cross_correlation(signal1, signal2)
-24        Tensor([0.30151134, 0.51298918, 0.77459667, 0.9486833 , 0.90453403, 0.70710678, 0.43643578])
-25
-26    Note:
-27        This function assumes the input signals are one-dimensional
-28        and contain sufficient elements for meaningful cross-correlation.
-29    """
-30    # Ensure the inputs are 1D tensors
-31    if signal1.ndim != 1 or signal2.ndim != 1:
-32        raise ValueError("Input signals must be one-dimensional")
-33
-34    # Convert PyTorch tensors to NumPy arrays
-35    signal1_np: np.ndarray = signal1.numpy()
-36    signal2_np: np.ndarray = signal2.numpy()
-37
-38    # Calculate the energy of each signal
-39    energy_signal1 = np.sum(signal1_np**2)
-40    energy_signal2 = np.sum(signal2_np**2)
-41
-42    # Check for zero energy to avoid division by zero
-43    if energy_signal1 == 0 or energy_signal2 == 0:
-44        raise ZeroDivisionError("One of the input signals has zero energy, causing division by zero in normalization")
-45
-46    # Compute the cross-correlation
-47    cross_correlation = correlate(signal1_np, signal2_np)
-48
-49    # Calculate the normalized cross-correlation
-50    normalized_cross_correlation = cross_correlation / np.sqrt(energy_signal1 * energy_signal2)
-51
-52    print(normalized_cross_correlation)
-53    return torch.Tensor(normalized_cross_correlation)
-
    - - -

    Calculate the normalized cross-correlation between two signals.

    - -
    Arguments:
    - -
      -
    • signal1 (torch.Tensor): The first input signal as a PyTorch tensor.
      • -
    • signal2 (torch.Tensor): The second input signal as a PyTorch tensor.
      • -
    - -
    Returns:
    - -
    -

    torch.Tensor: The normalized cross-correlation value between the two input signals.

    -
    - -
    Examples:
    - -
    -
    -
    >>> signal1 = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
->>> signal2 = torch.tensor([2.0, 3.0, 4.0])
->>> normalized_cross_correlation(signal1, signal2)
-Tensor([0.30151134, 0.51298918, 0.77459667, 0.9486833 , 0.90453403, 0.70710678, 0.43643578])
-
    -
    -
    - -
    Note:
    - -
    -

    This function assumes the input signals are one-dimensional - and contain sufficient elements for meaningful cross-correlation.

    -
    -
    - - -
    -
    - - \ No newline at end of file diff --git a/senselab/utils/tasks/dimensionality_reduction.html b/senselab/utils/tasks/dimensionality_reduction.html deleted file mode 100644 index e3af2c21..00000000 --- a/senselab/utils/tasks/dimensionality_reduction.html +++ /dev/null @@ -1,908 +0,0 @@ - - - - - - - senselab.utils.tasks.dimensionality_reduction API documentation - - - - - - - - - - -
    -
    -

    -senselab.utils.tasks.dimensionality_reduction

    - -

    This module provides the implementation of dimensionality reduction.

    - -

    Dimensionality reduction is a crucial preprocessing step in many machine learning and data analysis pipelines. -It aims to project a high-dimensional dataset into a lower-dimensional subspace while preserving as much of the -significant structure and information as possible. This process is particularly useful for visualization, -noise reduction, and improving computational efficiency in downstream tasks.

    - -

    This module specifically implements three popular dimensionality reduction techniques: Principal Component Analysis -(PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP).

    - -

    Task Overview

    - -
      -
    • Input: High-dimensional data (typically represented as a matrix or tensor), i.e. A tensor of shape (100, 10)
      • -
    • Output: Lower-dimensional representation of the input data, i.e. A tensor of shape (100, 2)
      • -
    • Goal: Preserve important structures or relationships in the data while reducing its dimensionality
      • -
    - -

    Evaluation Metrics

    - -

    The effectiveness of dimensionality reduction techniques can be evaluated using several metrics, including but not -limited to:

    - -
      -
    • Reconstruction Error: Measures the difference between the original data and its reduced representation when -projected back to the original space. Commonly used for techniques like PCA.
      • -
    • Trustworthiness: Assesses how much the model preserves neighbor relationships between the high-dimensional -space and the low-dimensional space.
      • -
    • Continuity: Evaluates how well the local structure of the data is preserved in the reduced space.
      • -
    - -

    Supported models

    - -
      -

    • Principal Component Analysis (PCA): PCA is a widely recognized technique in the field of data science -and machine learning. It excels in linear dimensionality reduction, making it an invaluable tool for -simplifying complex datasets. PCA's primary strength lies in its ability to capture the most significant -variance within the data. By identifying the principal components that account for the majority of the variation, -PCA allows researchers and analysts to reduce the dimensionality of their data while retaining its most important -features. This makes PCA particularly useful for tasks like data visualization and feature extraction.

      • -

    • t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is primarily utilized as a visualization technique, -setting it apart from more general dimensionality reduction methods. It is able to preserve local structures -within data when projecting it into lower-dimensional spaces, typically two or three dimensions. This property -makes t-SNE exceptionally good at creating visually interpretable embeddings that can reveal clusters and patterns -in complex datasets. However, t-SNE is not suitable for tasks that require the preservation of global structures.

      • -

    • Uniform Manifold Approximation and Projection (UMAP): UMAP is a relatively newer technique that has gained -significant attention due to its versatility and performance. UMAP is able to balance the preservation of both -global and local structures within data. Like t-SNE, UMAP can be used visualizing high-dimensional data in two -or three dimensions. However, UMAP is also effective as a general-purpose non-linear dimensionality reduction -technique, making it suitable for a wide range of applications where preserving the overall structure of the -data in lower dimensions is crucial.

      • -
    - -

    Summary:

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    FeaturePCAt-SNEUMAP
    TypeLinearNon-linearNon-linear
    PreservesGlobal structure (variance)Local structure, cluster patternsLocal and global structure to some extent
    Filters outNoise, less important varianceGlobal structure, distances between clustersLess important variance, noise
    Best for- Understanding main sources of variation
    - Data compression
    - Preprocessing
    - Linear relationships    		- Visualizing high-dimensional data
    - Discovering clusters or patterns
    - Exploratory data analysis    		- General-purpose dimensionality reduction
    - Balancing local and global structure preservation
    Limitations- Assumes linear relationships
    - May miss complex patterns    		- Can distort global structure
    - Non-deterministic    		- Sensitive to parameters
    - Does not completely preserve density
    - -

    References:

    - - - -

    Notes:

    - -

    This module leverages the implementations from scikit-learn for PCA and t-SNE, and the umap-learn library for UMAP. -The reduced data is returned as a PyTorch tensor, allowing seamless integration with PyTorch-based workflows.

    - -

    Learn more:

    - - -
    - - - - - - 
      1"""This module provides the implementation of dimensionality reduction.
-  2
-  3Dimensionality reduction is a crucial preprocessing step in many machine learning and data analysis pipelines.
-  4It aims to project a high-dimensional dataset into a lower-dimensional subspace while preserving as much of the
-  5significant structure and information as possible. This process is particularly useful for visualization,
-  6noise reduction, and improving computational efficiency in downstream tasks.
-  7
-  8This module specifically implements three popular dimensionality reduction techniques: Principal Component Analysis
-  9(PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP).
- 10
- 11### Task Overview
- 12- Input: High-dimensional data (typically represented as a matrix or tensor), i.e. A tensor of shape (100, 10)
- 13- Output: Lower-dimensional representation of the input data, i.e. A tensor of shape (100, 2)
- 14- Goal: Preserve important structures or relationships in the data while reducing its dimensionality
- 15
- 16### Evaluation Metrics
- 17The effectiveness of dimensionality reduction techniques can be evaluated using several metrics, including but not
- 18limited to:
- 19- **Reconstruction Error**: Measures the difference between the original data and its reduced representation when
- 20    projected back to the original space. Commonly used for techniques like PCA.
- 21- **Trustworthiness**: Assesses how much the model preserves neighbor relationships between the high-dimensional
- 22    space and the low-dimensional space.
- 23- **Continuity**: Evaluates how well the local structure of the data is preserved in the reduced space.
- 24
- 25### Supported models
- 26- **Principal Component Analysis (PCA)**: PCA is a widely recognized technique in the field of data science
- 27    and machine learning. It excels in linear dimensionality reduction, making it an invaluable tool for
- 28    simplifying complex datasets. PCA's primary strength lies in its ability to capture the most significant
- 29    variance within the data. By identifying the principal components that account for the majority of the variation,
- 30    PCA allows researchers and analysts to reduce the dimensionality of their data while retaining its most important
- 31    features. This makes PCA particularly useful for tasks like data visualization and feature extraction.
- 32
- 33- **t-Distributed Stochastic Neighbor Embedding (t-SNE)**: t-SNE is primarily utilized as a visualization technique,
- 34    setting it apart from more general dimensionality reduction methods. It is able to preserve local structures
- 35    within data when projecting it into lower-dimensional spaces, typically two or three dimensions. This property
- 36    makes t-SNE exceptionally good at creating visually interpretable embeddings that can reveal clusters and patterns
- 37    in complex datasets. However, t-SNE is not suitable for tasks that require the preservation of global structures.
- 38
- 39- **Uniform Manifold Approximation and Projection (UMAP)**: UMAP is a relatively newer technique that has gained
- 40    significant attention due to its versatility and performance. UMAP is able to balance the preservation of both
- 41    global and local structures within data. Like t-SNE, UMAP can be used visualizing high-dimensional data in two
- 42    or three dimensions. However, UMAP is also effective as a general-purpose non-linear dimensionality reduction
- 43    technique, making it suitable for a wide range of applications where preserving the overall structure of the
- 44    data in lower dimensions is crucial.
- 45
- 46#### Summary:
- 47.. include:: ./dimensionality_reduction_table.md
- 48
- 49References:
- 50- PCA: https://www.nature.com/articles/s43586-022-00184-w
- 51- t-SNE: https://jmlr.org/papers/v9/vandermaaten08a.html
- 52- UAMP: https://arxiv.org/abs/1802.03426
- 53
- 54### Notes:
- 55This module leverages the implementations from scikit-learn for PCA and t-SNE, and the umap-learn library for UMAP.
- 56The reduced data is returned as a PyTorch tensor, allowing seamless integration with PyTorch-based workflows.
- 57
- 58Learn more:
- 59- PCA: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
- 60- t-SNE: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
- 61- UMAP: https://umap-learn.readthedocs.io/
- 62"""
- 63
- 64from typing import Literal
- 65
- 66import pydra
- 67import torch
- 68import umap
- 69from sklearn.decomposition import PCA as sklearn_PCA
- 70from sklearn.manifold import TSNE as sklearn_TSNE
- 71
- 72
- 73def compute_dimensionality_reduction(
- 74    data: torch.Tensor, model: Literal["pca", "tsne", "umap"] = "pca", n_components: int = 2, **kwargs: object
- 75) -> torch.Tensor:
- 76    """Reduce the dimensionality of the given data using the specified model.
- 77
- 78    This function provides three methods: PCA, t-SNE, or UMAP. The reduced data is returned as a PyTorch tensor.
- 79
- 80    Args:
- 81        data (torch.Tensor): Input data tensor of shape (n_samples, n_features).
- 82        model ({"pca", "tsne", "umap"}, optional): The dimensionality reduction model to use.
- 83            Choices are:
- 84                "pca" for Principal Component Analysis (default),
- 85                "tsne" for t-Distributed Stochastic Neighbor Embedding, or
- 86                "umap" for Uniform Manifold Approximation and Projection
- 87        n_components (int, optional): Number of dimensions in the output.
- 88            Must be less than or equal to the number of features in the input data.
- 89            Defaults to 2.
- 90        **kwargs: Additional keyword arguments to pass to the chosen model.
- 91            (See below for links to each model's documentation for available parameters.)
- 92
- 93    Returns:
- 94        torch.Tensor: The reduced data tensor of shape (n_samples, n_components).
- 95
- 96    Raises:
- 97        ValueError: If an invalid model choice is provided or if n_components is
- 98            greater than the number of features in the input data or less than 0.
- 99
-100    Examples:
-101        >>> data = torch.randn(100, 10)  # 100 samples, 10 features
-102        >>> data.shape
-103        torch.Size([100, 10])
-104
-105        >>> reduced_data = compute_dimensionality_reduction(data)
-106        >>> print(reduced_data.shape)
-107        torch.Size([100, 2])
-108
-109        >>> reduced_data = compute_dimensionality_reduction(data, model="pca", n_components=2, svd_solver="full")
-110        >>> print(reduced_data.shape)
-111        torch.Size([100, 2])
-112
-113        >>> reduced_data = compute_dimensionality_reduction(data, model="tsne", n_components=3, perplexity=30)
-114        >>> print(reduced_data.shape)
-115        torch.Size([100, 3])
-116
-117        >>> reduced_data = compute_dimensionality_reduction(data, model="umap", n_components=4,
-118            force_approximation_algorithm=False, init='spectral', learning_rate=1.0, n_neighbors=5)
-119        >>> print(reduced_data.shape)
-120        torch.Size([100, 4])
-121
-122    Notes:
-123        This function uses implementations from scikit-learn for PCA and t-SNE, and the umap-learn
-124        library for UMAP. For detailed information about each method and its parameters, please
-125        refer to the following documentation:
-126
-127        - PCA: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
-128        - t-SNE: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
-129        - UMAP: https://umap-learn.readthedocs.io/en/latest/api.html
-130    """
-131    if n_components > data.shape[1]:
-132        raise ValueError("n_components must be less than or equal to the number of features in the input data")
-133
-134    if n_components < 1:
-135        raise ValueError("n_components must be greater than 0")
-136
-137    if data.numel() == 0:
-138        raise ValueError("Input data must not be empty")
-139
-140    if model == "pca":
-141        # Perform PCA
-142        reducer = sklearn_PCA(n_components=n_components, **kwargs)
-143        reduced_data = reducer.fit_transform(data)
-144
-145    elif model == "tsne":
-146        # Perform t-SNE
-147        reducer = sklearn_TSNE(n_components=n_components, **kwargs)
-148        reduced_data = reducer.fit_transform(data)
-149
-150    elif model == "umap":
-151        # Perform UMAP
-152        reducer = umap.UMAP(n_components=n_components, **kwargs)
-153        reduced_data = reducer.fit_transform(data)
-154
-155    else:
-156        raise ValueError(f"Invalid model choice: {model}")
-157
-158    return torch.from_numpy(reduced_data).float()
-159
-160
-161compute_dimensionality_reduction_pt = pydra.mark.task(compute_dimensionality_reduction)
-
    - - -
    -
    - -
    - - def - compute_dimensionality_reduction( data: torch.Tensor, model: Literal['pca', 'tsne', 'umap'] = 'pca', n_components: int = 2, **kwargs: object) -> torch.Tensor: - - - -
    - - 
     74def compute_dimensionality_reduction(
- 75    data: torch.Tensor, model: Literal["pca", "tsne", "umap"] = "pca", n_components: int = 2, **kwargs: object
- 76) -> torch.Tensor:
- 77    """Reduce the dimensionality of the given data using the specified model.
- 78
- 79    This function provides three methods: PCA, t-SNE, or UMAP. The reduced data is returned as a PyTorch tensor.
- 80
- 81    Args:
- 82        data (torch.Tensor): Input data tensor of shape (n_samples, n_features).
- 83        model ({"pca", "tsne", "umap"}, optional): The dimensionality reduction model to use.
- 84            Choices are:
- 85                "pca" for Principal Component Analysis (default),
- 86                "tsne" for t-Distributed Stochastic Neighbor Embedding, or
- 87                "umap" for Uniform Manifold Approximation and Projection
- 88        n_components (int, optional): Number of dimensions in the output.
- 89            Must be less than or equal to the number of features in the input data.
- 90            Defaults to 2.
- 91        **kwargs: Additional keyword arguments to pass to the chosen model.
- 92            (See below for links to each model's documentation for available parameters.)
- 93
- 94    Returns:
- 95        torch.Tensor: The reduced data tensor of shape (n_samples, n_components).
- 96
- 97    Raises:
- 98        ValueError: If an invalid model choice is provided or if n_components is
- 99            greater than the number of features in the input data or less than 0.
-100
-101    Examples:
-102        >>> data = torch.randn(100, 10)  # 100 samples, 10 features
-103        >>> data.shape
-104        torch.Size([100, 10])
-105
-106        >>> reduced_data = compute_dimensionality_reduction(data)
-107        >>> print(reduced_data.shape)
-108        torch.Size([100, 2])
-109
-110        >>> reduced_data = compute_dimensionality_reduction(data, model="pca", n_components=2, svd_solver="full")
-111        >>> print(reduced_data.shape)
-112        torch.Size([100, 2])
-113
-114        >>> reduced_data = compute_dimensionality_reduction(data, model="tsne", n_components=3, perplexity=30)
-115        >>> print(reduced_data.shape)
-116        torch.Size([100, 3])
-117
-118        >>> reduced_data = compute_dimensionality_reduction(data, model="umap", n_components=4,
-119            force_approximation_algorithm=False, init='spectral', learning_rate=1.0, n_neighbors=5)
-120        >>> print(reduced_data.shape)
-121        torch.Size([100, 4])
-122
-123    Notes:
-124        This function uses implementations from scikit-learn for PCA and t-SNE, and the umap-learn
-125        library for UMAP. For detailed information about each method and its parameters, please
-126        refer to the following documentation:
-127
-128        - PCA: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
-129        - t-SNE: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
-130        - UMAP: https://umap-learn.readthedocs.io/en/latest/api.html
-131    """
-132    if n_components > data.shape[1]:
-133        raise ValueError("n_components must be less than or equal to the number of features in the input data")
-134
-135    if n_components < 1:
-136        raise ValueError("n_components must be greater than 0")
-137
-138    if data.numel() == 0:
-139        raise ValueError("Input data must not be empty")
-140
-141    if model == "pca":
-142        # Perform PCA
-143        reducer = sklearn_PCA(n_components=n_components, **kwargs)
-144        reduced_data = reducer.fit_transform(data)
-145
-146    elif model == "tsne":
-147        # Perform t-SNE
-148        reducer = sklearn_TSNE(n_components=n_components, **kwargs)
-149        reduced_data = reducer.fit_transform(data)
-150
-151    elif model == "umap":
-152        # Perform UMAP
-153        reducer = umap.UMAP(n_components=n_components, **kwargs)
-154        reduced_data = reducer.fit_transform(data)
-155
-156    else:
-157        raise ValueError(f"Invalid model choice: {model}")
-158
-159    return torch.from_numpy(reduced_data).float()
-
    - - -

    Reduce the dimensionality of the given data using the specified model.

    - -

    This function provides three methods: PCA, t-SNE, or UMAP. The reduced data is returned as a PyTorch tensor.

    - -
    Arguments:
    - -
      -
    • data (torch.Tensor): Input data tensor of shape (n_samples, n_features).
      • -
    • model ({"pca", "tsne", "umap"}, optional): The dimensionality reduction model to use. -Choices are: - "pca" for Principal Component Analysis (default), - "tsne" for t-Distributed Stochastic Neighbor Embedding, or - "umap" for Uniform Manifold Approximation and Projection
      • -
    • n_components (int, optional): Number of dimensions in the output. -Must be less than or equal to the number of features in the input data. -Defaults to 2.
      • -
    • **kwargs: Additional keyword arguments to pass to the chosen model. -(See below for links to each model's documentation for available parameters.)
      • -
    - -
    Returns:
    - -
    -

    torch.Tensor: The reduced data tensor of shape (n_samples, n_components).

    -
    - -
    Raises:
    - -
      -
    • ValueError: If an invalid model choice is provided or if n_components is -greater than the number of features in the input data or less than 0.
      • -
    - -
    Examples:
    - -
    -
    -
    >>> data = torch.randn(100, 10)  # 100 samples, 10 features
->>> data.shape
-torch.Size([100, 10])
-
    -
    - -
    -
    >>> reduced_data = compute_dimensionality_reduction(data)
->>> print(reduced_data.shape)
-torch.Size([100, 2])
-
    -
    - -
    -
    >>> reduced_data = compute_dimensionality_reduction(data, model="pca", n_components=2, svd_solver="full")
->>> print(reduced_data.shape)
-torch.Size([100, 2])
-
    -
    - -
    -
    >>> reduced_data = compute_dimensionality_reduction(data, model="tsne", n_components=3, perplexity=30)
->>> print(reduced_data.shape)
-torch.Size([100, 3])
-
    -
    - -
    -
    >>> reduced_data = compute_dimensionality_reduction(data, model="umap", n_components=4,
-    force_approximation_algorithm=False, init='spectral', learning_rate=1.0, n_neighbors=5)
->>> print(reduced_data.shape)
-torch.Size([100, 4])
-
    -
    -
    - -
    Notes:
    - -
    -

    This function uses implementations from scikit-learn for PCA and t-SNE, and the umap-learn - library for UMAP. For detailed information about each method and its parameters, please - refer to the following documentation:

    - - -
    -
    - - -
    -
    - -
    - - def - compute_dimensionality_reduction_pt( data: torch.Tensor, model: Literal['pca', 'tsne', 'umap'] = 'pca', n_components: int = 2, **kwargs: object) -> torch.Tensor: - - - -
    - - 
     74def compute_dimensionality_reduction(
- 75    data: torch.Tensor, model: Literal["pca", "tsne", "umap"] = "pca", n_components: int = 2, **kwargs: object
- 76) -> torch.Tensor:
- 77    """Reduce the dimensionality of the given data using the specified model.
- 78
- 79    This function provides three methods: PCA, t-SNE, or UMAP. The reduced data is returned as a PyTorch tensor.
- 80
- 81    Args:
- 82        data (torch.Tensor): Input data tensor of shape (n_samples, n_features).
- 83        model ({"pca", "tsne", "umap"}, optional): The dimensionality reduction model to use.
- 84            Choices are:
- 85                "pca" for Principal Component Analysis (default),
- 86                "tsne" for t-Distributed Stochastic Neighbor Embedding, or
- 87                "umap" for Uniform Manifold Approximation and Projection
- 88        n_components (int, optional): Number of dimensions in the output.
- 89            Must be less than or equal to the number of features in the input data.
- 90            Defaults to 2.
- 91        **kwargs: Additional keyword arguments to pass to the chosen model.
- 92            (See below for links to each model's documentation for available parameters.)
- 93
- 94    Returns:
- 95        torch.Tensor: The reduced data tensor of shape (n_samples, n_components).
- 96
- 97    Raises:
- 98        ValueError: If an invalid model choice is provided or if n_components is
- 99            greater than the number of features in the input data or less than 0.
-100
-101    Examples:
-102        >>> data = torch.randn(100, 10)  # 100 samples, 10 features
-103        >>> data.shape
-104        torch.Size([100, 10])
-105
-106        >>> reduced_data = compute_dimensionality_reduction(data)
-107        >>> print(reduced_data.shape)
-108        torch.Size([100, 2])
-109
-110        >>> reduced_data = compute_dimensionality_reduction(data, model="pca", n_components=2, svd_solver="full")
-111        >>> print(reduced_data.shape)
-112        torch.Size([100, 2])
-113
-114        >>> reduced_data = compute_dimensionality_reduction(data, model="tsne", n_components=3, perplexity=30)
-115        >>> print(reduced_data.shape)
-116        torch.Size([100, 3])
-117
-118        >>> reduced_data = compute_dimensionality_reduction(data, model="umap", n_components=4,
-119            force_approximation_algorithm=False, init='spectral', learning_rate=1.0, n_neighbors=5)
-120        >>> print(reduced_data.shape)
-121        torch.Size([100, 4])
-122
-123    Notes:
-124        This function uses implementations from scikit-learn for PCA and t-SNE, and the umap-learn
-125        library for UMAP. For detailed information about each method and its parameters, please
-126        refer to the following documentation:
-127
-128        - PCA: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
-129        - t-SNE: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
-130        - UMAP: https://umap-learn.readthedocs.io/en/latest/api.html
-131    """
-132    if n_components > data.shape[1]:
-133        raise ValueError("n_components must be less than or equal to the number of features in the input data")
-134
-135    if n_components < 1:
-136        raise ValueError("n_components must be greater than 0")
-137
-138    if data.numel() == 0:
-139        raise ValueError("Input data must not be empty")
-140
-141    if model == "pca":
-142        # Perform PCA
-143        reducer = sklearn_PCA(n_components=n_components, **kwargs)
-144        reduced_data = reducer.fit_transform(data)
-145
-146    elif model == "tsne":
-147        # Perform t-SNE
-148        reducer = sklearn_TSNE(n_components=n_components, **kwargs)
-149        reduced_data = reducer.fit_transform(data)
-150
-151    elif model == "umap":
-152        # Perform UMAP
-153        reducer = umap.UMAP(n_components=n_components, **kwargs)
-154        reduced_data = reducer.fit_transform(data)
-155
-156    else:
-157        raise ValueError(f"Invalid model choice: {model}")
-158
-159    return torch.from_numpy(reduced_data).float()
-
    - - -

    Reduce the dimensionality of the given data using the specified model.

    - -

    This function provides three methods: PCA, t-SNE, or UMAP. The reduced data is returned as a PyTorch tensor.

    - -
    Arguments:
    - -
      -
    • data (torch.Tensor): Input data tensor of shape (n_samples, n_features).
      • -
    • model ({"pca", "tsne", "umap"}, optional): The dimensionality reduction model to use. -Choices are: - "pca" for Principal Component Analysis (default), - "tsne" for t-Distributed Stochastic Neighbor Embedding, or - "umap" for Uniform Manifold Approximation and Projection
      • -
    • n_components (int, optional): Number of dimensions in the output. -Must be less than or equal to the number of features in the input data. -Defaults to 2.
      • -
    • **kwargs: Additional keyword arguments to pass to the chosen model. -(See below for links to each model's documentation for available parameters.)
      • -
    - -
    Returns:
    - -
    -

    torch.Tensor: The reduced data tensor of shape (n_samples, n_components).

    -
    - -
    Raises:
    - -
      -
    • ValueError: If an invalid model choice is provided or if n_components is -greater than the number of features in the input data or less than 0.
      • -
    - -
    Examples:
    - -
    -
    -
    >>> data = torch.randn(100, 10)  # 100 samples, 10 features
->>> data.shape
-torch.Size([100, 10])
-
    -
    - -
    -
    >>> reduced_data = compute_dimensionality_reduction(data)
->>> print(reduced_data.shape)
-torch.Size([100, 2])
-
    -
    - -
    -
    >>> reduced_data = compute_dimensionality_reduction(data, model="pca", n_components=2, svd_solver="full")
->>> print(reduced_data.shape)
-torch.Size([100, 2])
-
    -
    - -
    -
    >>> reduced_data = compute_dimensionality_reduction(data, model="tsne", n_components=3, perplexity=30)
->>> print(reduced_data.shape)
-torch.Size([100, 3])
-
    -
    - -
    -
    >>> reduced_data = compute_dimensionality_reduction(data, model="umap", n_components=4,
-    force_approximation_algorithm=False, init='spectral', learning_rate=1.0, n_neighbors=5)
->>> print(reduced_data.shape)
-torch.Size([100, 4])
-
    -
    -
    - -
    Notes:
    - -
    -

    This function uses implementations from scikit-learn for PCA and t-SNE, and the umap-learn - library for UMAP. For detailed information about each method and its parameters, please - refer to the following documentation:

    - - -
    -
    - - -
    -
    - - \ No newline at end of file diff --git a/senselab/utils/tasks/eer.html b/senselab/utils/tasks/eer.html deleted file mode 100644 index 904773a7..00000000 --- a/senselab/utils/tasks/eer.html +++ /dev/null @@ -1,359 +0,0 @@ - - - - - - - senselab.utils.tasks.eer API documentation - - - - - - - - - - -
    -
    -

    -senselab.utils.tasks.eer

    - -

    This module implements some utilities for computing the Equal Error Rate (EER).

    -
    - - - - - - 
     1"""This module implements some utilities for computing the Equal Error Rate (EER)."""
- 2
- 3from typing import Tuple
- 4
- 5import pydra
- 6import torch
- 7from speechbrain.utils.metric_stats import EER
- 8
- 9
-10def compute_eer(predictions: torch.Tensor, targets: torch.Tensor) -> Tuple[float, float]:
-11    """Compute the Equal Error Rate (EER).
-12
-13    Args:
-14        predictions (torch.Tensor): A 1D tensor of predictions.
-15        targets (torch.Tensor): A 1D tensor of targets.
-16
-17    Returns:
-18        Tuple[float, float]: The EER and the threshold for the EER.
-19    """
-20    return EER(predictions, targets)
-21
-22
-23compute_eer_pt = pydra.mark.task(compute_eer)
-
    - - -
    -
    - -
    - - def - compute_eer(predictions: torch.Tensor, targets: torch.Tensor) -> Tuple[float, float]: - - - -
    - - 
    11def compute_eer(predictions: torch.Tensor, targets: torch.Tensor) -> Tuple[float, float]:
-12    """Compute the Equal Error Rate (EER).
-13
-14    Args:
-15        predictions (torch.Tensor): A 1D tensor of predictions.
-16        targets (torch.Tensor): A 1D tensor of targets.
-17
-18    Returns:
-19        Tuple[float, float]: The EER and the threshold for the EER.
-20    """
-21    return EER(predictions, targets)
-
    - - -

    Compute the Equal Error Rate (EER).

    - -
    Arguments:
    - -
      -
    • predictions (torch.Tensor): A 1D tensor of predictions.
      • -
    • targets (torch.Tensor): A 1D tensor of targets.
      • -
    - -
    Returns:
    - -
    -

    Tuple[float, float]: The EER and the threshold for the EER.

    -
    -
    - - -
    -
    - -
    - - def - compute_eer_pt(predictions: torch.Tensor, targets: torch.Tensor) -> Tuple[float, float]: - - - -
    - - 
    11def compute_eer(predictions: torch.Tensor, targets: torch.Tensor) -> Tuple[float, float]:
-12    """Compute the Equal Error Rate (EER).
-13
-14    Args:
-15        predictions (torch.Tensor): A 1D tensor of predictions.
-16        targets (torch.Tensor): A 1D tensor of targets.
-17
-18    Returns:
-19        Tuple[float, float]: The EER and the threshold for the EER.
-20    """
-21    return EER(predictions, targets)
-
    - - -

    Compute the Equal Error Rate (EER).

    - -
    Arguments:
    - -
      -
    • predictions (torch.Tensor): A 1D tensor of predictions.
      • -
    • targets (torch.Tensor): A 1D tensor of targets.
      • -
    - -
    Returns:
    - -
    -

    Tuple[float, float]: The EER and the threshold for the EER.

    -
    -
    - - -
    -
    - - \ No newline at end of file diff --git a/senselab/utils/tasks/input_output.html b/senselab/utils/tasks/input_output.html deleted file mode 100644 index 3c5862bf..00000000 --- a/senselab/utils/tasks/input_output.html +++ /dev/null @@ -1,947 +0,0 @@ - - - - - - - senselab.utils.tasks.input_output API documentation - - - - - - - - - - -
    -
    -

    -senselab.utils.tasks.input_output

    - -

    This module implements some IOTask utilities.

    - -

    This modules is deprecated and will be removed soon.

    -
    - - - - - - 
      1"""This module implements some IOTask utilities.
-  2
-  3This modules is deprecated and will be removed soon.
-  4"""
-  5
-  6import os
-  7from typing import Any, Dict, List, Union
-  8
-  9import pydra
- 10from datasets import Audio, Dataset, Image, load_dataset
- 11
- 12from senselab.utils.data_structures.file import File, from_strings_to_files
- 13from senselab.utils.data_structures.model import check_hf_repo_exists
- 14
- 15
- 16def read_files_from_disk(files: Union[str, List[str]]) -> Dict[str, Any]:
- 17    """Read files from disk and create a Hugging Face `Dataset` object."""
- 18    if isinstance(files, str):
- 19        files = [files]
- 20    formatted_files = from_strings_to_files(files)
- 21
- 22    def _from_files_to_dataset(files: List[File]) -> Dataset:
- 23        """Reading files from disk and create a HuggingFace `Dataset` object."""
- 24        # Checking if all files are of the same type
- 25        if not all(file.type == files[0].type for file in files):
- 26            raise ValueError("All files must be of the same type.")
- 27
- 28        # Loading file paths
- 29        file_data = [str(file.filepath) for file in files]
- 30
- 31        # Creating the Dataset object
- 32        dict_obj = {files[0].type: file_data}
- 33        # Using the type of the first file as the key
- 34
- 35        return _from_dict_to_hf_dataset(dict_obj)
- 36
- 37    dataset = _from_files_to_dataset(formatted_files)
- 38    return _from_hf_dataset_to_dict(dataset)
- 39
- 40
- 41def read_dataset_from_disk(input_path: str, split: str, streaming: bool = False) -> Dict[str, Any]:
- 42    """Loads a Hugging Face `Dataset` object from disk.
- 43
- 44    It determines the format based on the file extension or directory.
- 45    """
- 46    # Determine the input format.
- 47    if os.path.isdir(input_path):
- 48        input_format = "arrow"
- 49    else:
- 50        input_format = os.path.splitext(input_path)[1].strip(".")
- 51
- 52    # Load the dataset
- 53    try:
- 54        dataset = load_dataset(
- 55            input_format,
- 56            split=split,
- 57            data_files=input_path,
- 58            streaming=streaming,
- 59        )
- 60        return _from_hf_dataset_to_dict(dataset)
- 61    except Exception as e:
- 62        # Generic error handling, e.g., network issues, data loading issues
- 63        raise RuntimeError(f"An error occurred while loading the dataset: {str(e)}")
- 64
- 65
- 66def read_dataset_from_hub(
- 67    remote_repository: str,
- 68    revision: str = "main",
- 69    split: str = "all",
- 70) -> Dict[str, Any]:
- 71    """Loads a Hugging Face `Dataset` object from the Hugging Face Hub.
- 72
- 73    It includes support for private repositories.
- 74    """
- 75    if not check_hf_repo_exists(remote_repository, "main", "dataset"):
- 76        raise RuntimeError(
- 77            f"The repository {remote_repository} - {revision} - {split}" " does not exist or could not be accessed."
- 78        )
- 79
- 80    # Load the dataset
- 81    try:
- 82        dataset = load_dataset(path=remote_repository, revision=revision, split=split)
- 83    except Exception as e:
- 84        # Generic error handling, e.g., network issues, data loading issues
- 85        raise RuntimeError(f"An error occurred while loading the dataset: {str(e)}")
- 86
- 87    return _from_hf_dataset_to_dict(dataset)
- 88
- 89
- 90def push_dataset_to_hub(
- 91    dataset: Dict[str, Any], remote_repository: str, revision: str = "main", split: str = "all"
- 92) -> None:
- 93    """Uploads a Hugging Face `Dataset` object to the Hugging Face Hub."""
- 94    hf_dataset = _from_dict_to_hf_dataset(dataset)
- 95
- 96    try:
- 97        # Upload the dataset to the Hugging Face Hub
- 98        hf_dataset.push_to_hub(repo_id=remote_repository, revision=revision, split=split)
- 99    except Exception as e:
-100        raise RuntimeError(f"Failed to push dataset to the hub: {str(e)}")
-101    return
-102
-103
-104def save_dataset_to_disk(
-105    dataset: Dict[str, Any],
-106    output_directory: str,
-107    output_name: str,
-108    output_format: str = "parquet",
-109) -> None:
-110    """Saves a Hugging Face `Dataset` object to disk."""
-111    # TODO: optimize saving process (playing with batch size and num of cpus)
-112    hf_dataset = _from_dict_to_hf_dataset(dataset)
-113
-114    # Prepare the output path differently if the format is Arrow
-115    if output_format == "arrow":
-116        output_path = os.path.join(output_directory, output_name)
-117        # No extension for Arrow, it's a directory
-118    else:
-119        output_path = os.path.join(output_directory, f"{output_name}.{output_format}")
-120
-121    # Create the output directory, ignore error if it already exists
-122    os.makedirs(output_directory, exist_ok=True)
-123
-124    if output_format == "parquet":
-125
-126        def _save_hf_dataset_as_parquet(dataset: Dataset, output_path: str) -> None:
-127            """Saves a Hugging Face `Dataset` object to parquet format."""
-128            dataset.to_parquet(output_path)
-129
-130        _save_hf_dataset_as_parquet(hf_dataset, output_path)
-131    elif output_format == "json":
-132
-133        def _save_hf_dataset_as_json(dataset: Dataset, output_path: str) -> None:
-134            """Saves a Hugging Face `Dataset` object to json format."""
-135            dataset.to_json(output_path)
-136
-137        _save_hf_dataset_as_json(hf_dataset, output_path)
-138    elif output_format == "csv":
-139
-140        def _save_hf_dataset_as_csv(dataset: Dataset, output_path: str) -> None:
-141            """Saves a Hugging Face `Dataset` object to csv format."""
-142            dataset.to_csv(output_path)
-143
-144        _save_hf_dataset_as_csv(hf_dataset, output_path)
-145    elif output_format == "sql":
-146
-147        def _save_hf_dataset_as_sql(dataset: Dataset, output_path: str) -> None:
-148            """Saves a Hugging Face `Dataset` object to sql format."""
-149            dataset.to_sql(output_path)
-150
-151        _save_hf_dataset_as_sql(hf_dataset, output_path)
-152    elif output_format == "arrow":
-153
-154        def _save_hf_dataset_as_arrow(dataset: Dataset, output_path: str) -> None:
-155            """Saves a Hugging Face `Dataset` object in Apache Arrow format."""
-156            dataset.save_to_disk(output_path)
-157
-158        _save_hf_dataset_as_arrow(hf_dataset, output_path)
-159    else:
-160        raise ValueError(f"Unsupported output format: {output_format}")
-161    return
-162
-163
-164def _from_hf_dataset_to_dict(dataset: Dataset) -> Dict[str, Any]:
-165    """Converts a Hugging Face `Dataset` object to a dictionary."""
-166    return dataset.to_dict()
-167
-168
-169def _from_dict_to_hf_dataset(
-170    data: Dict[str, Any],
-171    image_columns: list[str] = ["image"],
-172    audio_columns: list[str] = ["audio"],
-173) -> Dataset:
-174    """Converts a dictionary to a Hugging Face `Dataset` object.
-175
-176    It casts image and audio columns.
-177    """
-178    dataset = Dataset.from_dict(data)
-179
-180    # Cast image columns
-181    for column in image_columns:
-182        if column in dataset.column_names:
-183            dataset = dataset.cast_column(column, Image())
-184
-185    # Cast audio columns
-186    for column in audio_columns:
-187        if column in dataset.column_names:
-188            dataset = dataset.cast_column(column, Audio(mono=False))
-189
-190    return dataset
-191
-192
-193read_files_from_disk_pt = pydra.mark.task(read_files_from_disk)
-194read_dataset_from_disk_pt = pydra.mark.task(read_dataset_from_disk)
-195read_dataset_from_hub_pt = pydra.mark.task(read_dataset_from_hub)
-196push_dataset_to_hub_pt = pydra.mark.task(push_dataset_to_hub)
-197save_dataset_to_disk_pt = pydra.mark.task(save_dataset_to_disk)
-
    - - -
    -
    - -
    - - def - read_files_from_disk(files: Union[str, List[str]]) -> Dict[str, Any]: - - - -
    - - 
    17def read_files_from_disk(files: Union[str, List[str]]) -> Dict[str, Any]:
-18    """Read files from disk and create a Hugging Face `Dataset` object."""
-19    if isinstance(files, str):
-20        files = [files]
-21    formatted_files = from_strings_to_files(files)
-22
-23    def _from_files_to_dataset(files: List[File]) -> Dataset:
-24        """Reading files from disk and create a HuggingFace `Dataset` object."""
-25        # Checking if all files are of the same type
-26        if not all(file.type == files[0].type for file in files):
-27            raise ValueError("All files must be of the same type.")
-28
-29        # Loading file paths
-30        file_data = [str(file.filepath) for file in files]
-31
-32        # Creating the Dataset object
-33        dict_obj = {files[0].type: file_data}
-34        # Using the type of the first file as the key
-35
-36        return _from_dict_to_hf_dataset(dict_obj)
-37
-38    dataset = _from_files_to_dataset(formatted_files)
-39    return _from_hf_dataset_to_dict(dataset)
-
    - - -

    Read files from disk and create a Hugging Face Dataset object.

    -
    - - -
    -
    - -
    - - def - read_dataset_from_disk(input_path: str, split: str, streaming: bool = False) -> Dict[str, Any]: - - - -
    - - 
    42def read_dataset_from_disk(input_path: str, split: str, streaming: bool = False) -> Dict[str, Any]:
-43    """Loads a Hugging Face `Dataset` object from disk.
-44
-45    It determines the format based on the file extension or directory.
-46    """
-47    # Determine the input format.
-48    if os.path.isdir(input_path):
-49        input_format = "arrow"
-50    else:
-51        input_format = os.path.splitext(input_path)[1].strip(".")
-52
-53    # Load the dataset
-54    try:
-55        dataset = load_dataset(
-56            input_format,
-57            split=split,
-58            data_files=input_path,
-59            streaming=streaming,
-60        )
-61        return _from_hf_dataset_to_dict(dataset)
-62    except Exception as e:
-63        # Generic error handling, e.g., network issues, data loading issues
-64        raise RuntimeError(f"An error occurred while loading the dataset: {str(e)}")
-
    - - -

    Loads a Hugging Face Dataset object from disk.

    - -

    It determines the format based on the file extension or directory.

    -
    - - -
    -
    - -
    - - def - read_dataset_from_hub( remote_repository: str, revision: str = 'main', split: str = 'all') -> Dict[str, Any]: - - - -
    - - 
    67def read_dataset_from_hub(
-68    remote_repository: str,
-69    revision: str = "main",
-70    split: str = "all",
-71) -> Dict[str, Any]:
-72    """Loads a Hugging Face `Dataset` object from the Hugging Face Hub.
-73
-74    It includes support for private repositories.
-75    """
-76    if not check_hf_repo_exists(remote_repository, "main", "dataset"):
-77        raise RuntimeError(
-78            f"The repository {remote_repository} - {revision} - {split}" " does not exist or could not be accessed."
-79        )
-80
-81    # Load the dataset
-82    try:
-83        dataset = load_dataset(path=remote_repository, revision=revision, split=split)
-84    except Exception as e:
-85        # Generic error handling, e.g., network issues, data loading issues
-86        raise RuntimeError(f"An error occurred while loading the dataset: {str(e)}")
-87
-88    return _from_hf_dataset_to_dict(dataset)
-
    - - -

    Loads a Hugging Face Dataset object from the Hugging Face Hub.

    - -

    It includes support for private repositories.

    -
    - - -
    -
    - -
    - - def - push_dataset_to_hub( dataset: Dict[str, Any], remote_repository: str, revision: str = 'main', split: str = 'all') -> None: - - - -
    - - 
     91def push_dataset_to_hub(
- 92    dataset: Dict[str, Any], remote_repository: str, revision: str = "main", split: str = "all"
- 93) -> None:
- 94    """Uploads a Hugging Face `Dataset` object to the Hugging Face Hub."""
- 95    hf_dataset = _from_dict_to_hf_dataset(dataset)
- 96
- 97    try:
- 98        # Upload the dataset to the Hugging Face Hub
- 99        hf_dataset.push_to_hub(repo_id=remote_repository, revision=revision, split=split)
-100    except Exception as e:
-101        raise RuntimeError(f"Failed to push dataset to the hub: {str(e)}")
-102    return
-
    - - -

    Uploads a Hugging Face Dataset object to the Hugging Face Hub.

    -
    - - -
    -
    - -
    - - def - save_dataset_to_disk( dataset: Dict[str, Any], output_directory: str, output_name: str, output_format: str = 'parquet') -> None: - - - -
    - - 
    105def save_dataset_to_disk(
-106    dataset: Dict[str, Any],
-107    output_directory: str,
-108    output_name: str,
-109    output_format: str = "parquet",
-110) -> None:
-111    """Saves a Hugging Face `Dataset` object to disk."""
-112    # TODO: optimize saving process (playing with batch size and num of cpus)
-113    hf_dataset = _from_dict_to_hf_dataset(dataset)
-114
-115    # Prepare the output path differently if the format is Arrow
-116    if output_format == "arrow":
-117        output_path = os.path.join(output_directory, output_name)
-118        # No extension for Arrow, it's a directory
-119    else:
-120        output_path = os.path.join(output_directory, f"{output_name}.{output_format}")
-121
-122    # Create the output directory, ignore error if it already exists
-123    os.makedirs(output_directory, exist_ok=True)
-124
-125    if output_format == "parquet":
-126
-127        def _save_hf_dataset_as_parquet(dataset: Dataset, output_path: str) -> None:
-128            """Saves a Hugging Face `Dataset` object to parquet format."""
-129            dataset.to_parquet(output_path)
-130
-131        _save_hf_dataset_as_parquet(hf_dataset, output_path)
-132    elif output_format == "json":
-133
-134        def _save_hf_dataset_as_json(dataset: Dataset, output_path: str) -> None:
-135            """Saves a Hugging Face `Dataset` object to json format."""
-136            dataset.to_json(output_path)
-137
-138        _save_hf_dataset_as_json(hf_dataset, output_path)
-139    elif output_format == "csv":
-140
-141        def _save_hf_dataset_as_csv(dataset: Dataset, output_path: str) -> None:
-142            """Saves a Hugging Face `Dataset` object to csv format."""
-143            dataset.to_csv(output_path)
-144
-145        _save_hf_dataset_as_csv(hf_dataset, output_path)
-146    elif output_format == "sql":
-147
-148        def _save_hf_dataset_as_sql(dataset: Dataset, output_path: str) -> None:
-149            """Saves a Hugging Face `Dataset` object to sql format."""
-150            dataset.to_sql(output_path)
-151
-152        _save_hf_dataset_as_sql(hf_dataset, output_path)
-153    elif output_format == "arrow":
-154
-155        def _save_hf_dataset_as_arrow(dataset: Dataset, output_path: str) -> None:
-156            """Saves a Hugging Face `Dataset` object in Apache Arrow format."""
-157            dataset.save_to_disk(output_path)
-158
-159        _save_hf_dataset_as_arrow(hf_dataset, output_path)
-160    else:
-161        raise ValueError(f"Unsupported output format: {output_format}")
-162    return
-
    - - -

    Saves a Hugging Face Dataset object to disk.

    -
    - - -
    -
    - -
    - - def - read_files_from_disk_pt(files: Union[str, List[str]]) -> Dict[str, Any]: - - - -
    - - 
    17def read_files_from_disk(files: Union[str, List[str]]) -> Dict[str, Any]:
-18    """Read files from disk and create a Hugging Face `Dataset` object."""
-19    if isinstance(files, str):
-20        files = [files]
-21    formatted_files = from_strings_to_files(files)
-22
-23    def _from_files_to_dataset(files: List[File]) -> Dataset:
-24        """Reading files from disk and create a HuggingFace `Dataset` object."""
-25        # Checking if all files are of the same type
-26        if not all(file.type == files[0].type for file in files):
-27            raise ValueError("All files must be of the same type.")
-28
-29        # Loading file paths
-30        file_data = [str(file.filepath) for file in files]
-31
-32        # Creating the Dataset object
-33        dict_obj = {files[0].type: file_data}
-34        # Using the type of the first file as the key
-35
-36        return _from_dict_to_hf_dataset(dict_obj)
-37
-38    dataset = _from_files_to_dataset(formatted_files)
-39    return _from_hf_dataset_to_dict(dataset)
-
    - - -

    Read files from disk and create a Hugging Face Dataset object.

    -
    - - -
    -
    - -
    - - def - read_dataset_from_disk_pt(input_path: str, split: str, streaming: bool = False) -> Dict[str, Any]: - - - -
    - - 
    42def read_dataset_from_disk(input_path: str, split: str, streaming: bool = False) -> Dict[str, Any]:
-43    """Loads a Hugging Face `Dataset` object from disk.
-44
-45    It determines the format based on the file extension or directory.
-46    """
-47    # Determine the input format.
-48    if os.path.isdir(input_path):
-49        input_format = "arrow"
-50    else:
-51        input_format = os.path.splitext(input_path)[1].strip(".")
-52
-53    # Load the dataset
-54    try:
-55        dataset = load_dataset(
-56            input_format,
-57            split=split,
-58            data_files=input_path,
-59            streaming=streaming,
-60        )
-61        return _from_hf_dataset_to_dict(dataset)
-62    except Exception as e:
-63        # Generic error handling, e.g., network issues, data loading issues
-64        raise RuntimeError(f"An error occurred while loading the dataset: {str(e)}")
-
    - - -

    Loads a Hugging Face Dataset object from disk.

    - -

    It determines the format based on the file extension or directory.

    -
    - - -
    -
    - -
    - - def - read_dataset_from_hub_pt( remote_repository: str, revision: str = 'main', split: str = 'all') -> Dict[str, Any]: - - - -
    - - 
    67def read_dataset_from_hub(
-68    remote_repository: str,
-69    revision: str = "main",
-70    split: str = "all",
-71) -> Dict[str, Any]:
-72    """Loads a Hugging Face `Dataset` object from the Hugging Face Hub.
-73
-74    It includes support for private repositories.
-75    """
-76    if not check_hf_repo_exists(remote_repository, "main", "dataset"):
-77        raise RuntimeError(
-78            f"The repository {remote_repository} - {revision} - {split}" " does not exist or could not be accessed."
-79        )
-80
-81    # Load the dataset
-82    try:
-83        dataset = load_dataset(path=remote_repository, revision=revision, split=split)
-84    except Exception as e:
-85        # Generic error handling, e.g., network issues, data loading issues
-86        raise RuntimeError(f"An error occurred while loading the dataset: {str(e)}")
-87
-88    return _from_hf_dataset_to_dict(dataset)
-
    - - -

    Loads a Hugging Face Dataset object from the Hugging Face Hub.

    - -

    It includes support for private repositories.

    -
    - - -
    -
    - -
    - - def - push_dataset_to_hub_pt( dataset: Dict[str, Any], remote_repository: str, revision: str = 'main', split: str = 'all') -> None: - - - -
    - - 
     91def push_dataset_to_hub(
- 92    dataset: Dict[str, Any], remote_repository: str, revision: str = "main", split: str = "all"
- 93) -> None:
- 94    """Uploads a Hugging Face `Dataset` object to the Hugging Face Hub."""
- 95    hf_dataset = _from_dict_to_hf_dataset(dataset)
- 96
- 97    try:
- 98        # Upload the dataset to the Hugging Face Hub
- 99        hf_dataset.push_to_hub(repo_id=remote_repository, revision=revision, split=split)
-100    except Exception as e:
-101        raise RuntimeError(f"Failed to push dataset to the hub: {str(e)}")
-102    return
-
    - - -

    Uploads a Hugging Face Dataset object to the Hugging Face Hub.

    -
    - - -
    -
    - -
    - - def - save_dataset_to_disk_pt( dataset: Dict[str, Any], output_directory: str, output_name: str, output_format: str = 'parquet') -> None: - - - -
    - - 
    105def save_dataset_to_disk(
-106    dataset: Dict[str, Any],
-107    output_directory: str,
-108    output_name: str,
-109    output_format: str = "parquet",
-110) -> None:
-111    """Saves a Hugging Face `Dataset` object to disk."""
-112    # TODO: optimize saving process (playing with batch size and num of cpus)
-113    hf_dataset = _from_dict_to_hf_dataset(dataset)
-114
-115    # Prepare the output path differently if the format is Arrow
-116    if output_format == "arrow":
-117        output_path = os.path.join(output_directory, output_name)
-118        # No extension for Arrow, it's a directory
-119    else:
-120        output_path = os.path.join(output_directory, f"{output_name}.{output_format}")
-121
-122    # Create the output directory, ignore error if it already exists
-123    os.makedirs(output_directory, exist_ok=True)
-124
-125    if output_format == "parquet":
-126
-127        def _save_hf_dataset_as_parquet(dataset: Dataset, output_path: str) -> None:
-128            """Saves a Hugging Face `Dataset` object to parquet format."""
-129            dataset.to_parquet(output_path)
-130
-131        _save_hf_dataset_as_parquet(hf_dataset, output_path)
-132    elif output_format == "json":
-133
-134        def _save_hf_dataset_as_json(dataset: Dataset, output_path: str) -> None:
-135            """Saves a Hugging Face `Dataset` object to json format."""
-136            dataset.to_json(output_path)
-137
-138        _save_hf_dataset_as_json(hf_dataset, output_path)
-139    elif output_format == "csv":
-140
-141        def _save_hf_dataset_as_csv(dataset: Dataset, output_path: str) -> None:
-142            """Saves a Hugging Face `Dataset` object to csv format."""
-143            dataset.to_csv(output_path)
-144
-145        _save_hf_dataset_as_csv(hf_dataset, output_path)
-146    elif output_format == "sql":
-147
-148        def _save_hf_dataset_as_sql(dataset: Dataset, output_path: str) -> None:
-149            """Saves a Hugging Face `Dataset` object to sql format."""
-150            dataset.to_sql(output_path)
-151
-152        _save_hf_dataset_as_sql(hf_dataset, output_path)
-153    elif output_format == "arrow":
-154
-155        def _save_hf_dataset_as_arrow(dataset: Dataset, output_path: str) -> None:
-156            """Saves a Hugging Face `Dataset` object in Apache Arrow format."""
-157            dataset.save_to_disk(output_path)
-158
-159        _save_hf_dataset_as_arrow(hf_dataset, output_path)
-160    else:
-161        raise ValueError(f"Unsupported output format: {output_format}")
-162    return
-
    - - -

    Saves a Hugging Face Dataset object to disk.

    -
    - - -
    -
    - - \ No newline at end of file diff --git a/senselab/utils/tasks/plotting.html b/senselab/utils/tasks/plotting.html deleted file mode 100644 index 59ac802b..00000000 --- a/senselab/utils/tasks/plotting.html +++ /dev/null @@ -1,540 +0,0 @@ - - - - - - - senselab.utils.tasks.plotting API documentation - - - - - - - - - - -
    -
    -

    -senselab.utils.tasks.plotting

    - -

    This module implements plotting methods for utilities.

    -
    - - - - - - 
      1"""This module implements plotting methods for utilities."""
-  2
-  3from typing import List
-  4
-  5import matplotlib.cm as cm
-  6import matplotlib.pyplot as plt
-  7
-  8from senselab.utils.data_structures.script_line import ScriptLine
-  9
- 10
- 11def plot_transcript(transcript: ScriptLine) -> None:
- 12    """Plots the transcript visualization over time.
- 13
- 14    Args:
- 15        transcript (ScriptLine): The transcript object containing chunks of text with start and end times.
- 16
- 17    Returns:
- 18        None
- 19
- 20    Todo:
- 21        - Add option to save the plot
- 22        - Add option to choose the size of the Figure
- 23        - Add check if transcript contains chunks with time information
- 24    """
- 25    if transcript.chunks is None:
- 26        raise ValueError("The transcript does not contain any chunks.")
- 27
- 28    chunks = transcript.chunks
- 29
- 30    texts = [chunk.text for chunk in chunks]
- 31    start_times = []
- 32    end_times = []
- 33    for chunk in chunks:
- 34        # Ensure that chunks have start and end times
- 35        if chunk.start is None or chunk.end is None:
- 36            raise ValueError("Each chunk must have both start and end times.")
- 37        else:
- 38            start_times.append(chunk.start)
- 39            end_times.append(chunk.end)
- 40
- 41    # Create a figure and axis
- 42    _, ax = plt.subplots(figsize=(12, 6))
- 43
- 44    # Plot each text segment and add text label
- 45    for i, text in enumerate(texts):
- 46        if start_times[i] is not None and end_times[i] is not None:
- 47            ax.plot([start_times[i], end_times[i]], [i, i], marker="o")
- 48            ax.text((start_times[i] + end_times[i]) / 2, i, text, ha="center", va="bottom")
- 49
- 50    # Setting labels and title
- 51    ax.set_yticks(range(len(texts)))
- 52    ax.set_yticklabels([])
- 53    ax.set_xlabel("Time (seconds)")
- 54    ax.set_title("Transcript Visualization Over Time")
- 55
- 56    # Show the plot
- 57    plt.show()
- 58
- 59
- 60def plot_segment(segments: List[ScriptLine]) -> None:
- 61    """Plots the segments of the transcript over time.
- 62
- 63    Args:
- 64        segments (List[ScriptLine]): The segments object containing segments with start and end times and a label.
- 65
- 66    Returns:
- 67        None
- 68
- 69    Todo:
- 70        - Add option to save the plot
- 71        - Add option to choose the size of the Figure
- 72        - Add check if transcript contains segments with time information
- 73    """
- 74    start_times = []
- 75    end_times = []
- 76    labels = []
- 77
- 78    for segment in segments:
- 79        # Ensure that segments have start and end times and a label
- 80        if segment.start is None or segment.end is None or segment.speaker is None:
- 81            raise ValueError("Each segment must have start and end times and a label.")
- 82        else:
- 83            start_times.append(segment.start)
- 84            end_times.append(segment.end)
- 85            labels.append(segment.speaker)
- 86
- 87    # Create a figure and axis
- 88    _, ax = plt.subplots(figsize=(12, 6))
- 89
- 90    # Create a color map based on unique labels
- 91    unique_labels = list(set(labels))
- 92    color_map = cm.get_cmap("tab10", len(unique_labels))  # 'tab10' provides 10 distinct colors
- 93    label_to_color = {label: color_map(i) for i, label in enumerate(unique_labels)}
- 94    label_to_y_value = {label: i for i, label in enumerate(unique_labels)}  # Assign y-value based on label index
- 95
- 96    # Plot each segment and add text label with color
- 97    for i, label in enumerate(labels):
- 98        color = label_to_color[label]
- 99        y_value = label_to_y_value[label]  # Get y-value based on label
-100        ax.plot([start_times[i], end_times[i]], [y_value, y_value], marker="o", color=color, linewidth=2)
-101        ax.text((start_times[i] + end_times[i]) / 2, y_value, label, ha="center", va="bottom", color=color)
-102
-103    # Setting labels and title
-104    ax.set_yticks(range(len(unique_labels)))
-105    ax.set_yticklabels(unique_labels)
-106    ax.set_xlabel("Time (seconds)")
-107    ax.set_title("Segment Visualization Over Time")
-108
-109    # Show the plot
-110    plt.show()
-
    - - -
    -
    - -
    - - def - plot_transcript( transcript: senselab.utils.data_structures.script_line.ScriptLine) -> None: - - - -
    - - 
    12def plot_transcript(transcript: ScriptLine) -> None:
-13    """Plots the transcript visualization over time.
-14
-15    Args:
-16        transcript (ScriptLine): The transcript object containing chunks of text with start and end times.
-17
-18    Returns:
-19        None
-20
-21    Todo:
-22        - Add option to save the plot
-23        - Add option to choose the size of the Figure
-24        - Add check if transcript contains chunks with time information
-25    """
-26    if transcript.chunks is None:
-27        raise ValueError("The transcript does not contain any chunks.")
-28
-29    chunks = transcript.chunks
-30
-31    texts = [chunk.text for chunk in chunks]
-32    start_times = []
-33    end_times = []
-34    for chunk in chunks:
-35        # Ensure that chunks have start and end times
-36        if chunk.start is None or chunk.end is None:
-37            raise ValueError("Each chunk must have both start and end times.")
-38        else:
-39            start_times.append(chunk.start)
-40            end_times.append(chunk.end)
-41
-42    # Create a figure and axis
-43    _, ax = plt.subplots(figsize=(12, 6))
-44
-45    # Plot each text segment and add text label
-46    for i, text in enumerate(texts):
-47        if start_times[i] is not None and end_times[i] is not None:
-48            ax.plot([start_times[i], end_times[i]], [i, i], marker="o")
-49            ax.text((start_times[i] + end_times[i]) / 2, i, text, ha="center", va="bottom")
-50
-51    # Setting labels and title
-52    ax.set_yticks(range(len(texts)))
-53    ax.set_yticklabels([])
-54    ax.set_xlabel("Time (seconds)")
-55    ax.set_title("Transcript Visualization Over Time")
-56
-57    # Show the plot
-58    plt.show()
-
    - - -

    Plots the transcript visualization over time.

    - -
    Arguments:
    - -
      -
    • transcript (ScriptLine): The transcript object containing chunks of text with start and end times.
      • -
    - -
    Returns:
    - -
    -

    None

    -
    - -
    Todo:
    - -
    -
      -
    • Add option to save the plot
      • -
    • Add option to choose the size of the Figure
      • -
    • Add check if transcript contains chunks with time information
      • -
    -
    -
    - - -
    -
    - -
    - - def - plot_segment( segments: List[senselab.utils.data_structures.script_line.ScriptLine]) -> None: - - - -
    - - 
     61def plot_segment(segments: List[ScriptLine]) -> None:
- 62    """Plots the segments of the transcript over time.
- 63
- 64    Args:
- 65        segments (List[ScriptLine]): The segments object containing segments with start and end times and a label.
- 66
- 67    Returns:
- 68        None
- 69
- 70    Todo:
- 71        - Add option to save the plot
- 72        - Add option to choose the size of the Figure
- 73        - Add check if transcript contains segments with time information
- 74    """
- 75    start_times = []
- 76    end_times = []
- 77    labels = []
- 78
- 79    for segment in segments:
- 80        # Ensure that segments have start and end times and a label
- 81        if segment.start is None or segment.end is None or segment.speaker is None:
- 82            raise ValueError("Each segment must have start and end times and a label.")
- 83        else:
- 84            start_times.append(segment.start)
- 85            end_times.append(segment.end)
- 86            labels.append(segment.speaker)
- 87
- 88    # Create a figure and axis
- 89    _, ax = plt.subplots(figsize=(12, 6))
- 90
- 91    # Create a color map based on unique labels
- 92    unique_labels = list(set(labels))
- 93    color_map = cm.get_cmap("tab10", len(unique_labels))  # 'tab10' provides 10 distinct colors
- 94    label_to_color = {label: color_map(i) for i, label in enumerate(unique_labels)}
- 95    label_to_y_value = {label: i for i, label in enumerate(unique_labels)}  # Assign y-value based on label index
- 96
- 97    # Plot each segment and add text label with color
- 98    for i, label in enumerate(labels):
- 99        color = label_to_color[label]
-100        y_value = label_to_y_value[label]  # Get y-value based on label
-101        ax.plot([start_times[i], end_times[i]], [y_value, y_value], marker="o", color=color, linewidth=2)
-102        ax.text((start_times[i] + end_times[i]) / 2, y_value, label, ha="center", va="bottom", color=color)
-103
-104    # Setting labels and title
-105    ax.set_yticks(range(len(unique_labels)))
-106    ax.set_yticklabels(unique_labels)
-107    ax.set_xlabel("Time (seconds)")
-108    ax.set_title("Segment Visualization Over Time")
-109
-110    # Show the plot
-111    plt.show()
-
    - - -

    Plots the segments of the transcript over time.

    - -
    Arguments:
    - -
      -
    • segments (List[ScriptLine]): The segments object containing segments with start and end times and a label.
      • -
    - -
    Returns:
    - -
    -

    None

    -
    - -
    Todo:
    - -
    -
      -
    • Add option to save the plot
      • -
    • Add option to choose the size of the Figure
      • -
    • Add check if transcript contains segments with time information
      • -
    -
    -
    - - -
    -
    - - \ No newline at end of file diff --git a/senselab/utils/tasks/pooling.html b/senselab/utils/tasks/pooling.html deleted file mode 100644 index 4157504a..00000000 --- a/senselab/utils/tasks/pooling.html +++ /dev/null @@ -1,601 +0,0 @@ - - - - - - - senselab.utils.tasks.pooling API documentation - - - - - - - - - - -
    -
    -

    -senselab.utils.tasks.pooling

    - -

    This module implements pooling methods for torch tensors.

    -
    - - - - - - 
     1"""This module implements pooling methods for torch tensors."""
- 2
- 3from typing import Literal
- 4
- 5import pydra
- 6import torch
- 7
- 8
- 9def pooling(
-10    data: torch.Tensor,
-11    pool_type: Literal["mean", "min", "max"] = "mean",
-12    dimension: int = 0,
-13    keep_dimension: bool = False,
-14) -> torch.Tensor:
-15    """Apply pooling to the input tensor along the specified dimension.
-16
-17    This function provides three pooling methods: mean, min, or max. The pooled tensor is returned as a PyTorch tensor.
-18
-19    Args:
-20        data (torch.Tensor): Input tensor of shape (n_samples, n_features, n_channels).
-21        pool_type (str, optional): The pooling method to use.
-22            Choices are:
-23                * "mean" for mean pooling
-24                * "min" for min pooling
-25                * "max" for max pooling
-26            Defaults to "mean".
-27        dimension (int, optional): The dimension along which to apply the pooling.
-28            Defaults to 0.
-29        keep_dimension (bool, optional): Whether to keep the original tensor dimension after pooling.
-30
-31    Returns:
-32        torch.Tensor: The pooled tensor of shape (n_samples, n_features, n_channels).
-33
-34    Raises:
-35        ValueError: If an invalid pooling method choice is provided.
-36
-37    Examples:
-38        >>> data = torch.randn(10, 20, 30)  # 10 samples, 20 features, 30 channels
-39        >>> data.shape
-40        torch.Size([10, 20, 30])
-41
-42        >>> pooled_data = pooling(data, pool_type="mean", dimension=0, keep_dimension=True)
-43        >>> print(pooled_data.shape)
-44        torch.Size([1, 20, 30])
-45
-46        >>> pooled_data = pooling(data, pool_type="max", dimension=1, keep_dimension=False)
-47        >>> print(pooled_data.shape)
-48        torch.Size([10, 30])
-49
-50        >>> pooled_data = pooling(data, pool_type="min", dimension=2)
-51        >>> print(pooled_data.shape)
-52        torch.Size([10, 20])
-53    """
-54    # Ensure the dimension is valid
-55    assert dimension < len(data.shape), "Invalid dimension provided."
-56
-57    if data.numel() == 0:
-58        raise ValueError("Input data must not be empty")
-59
-60    # Applying the appropriate pooling based on the pool type
-61    if pool_type == "max":
-62        return torch.max(data, dim=dimension, keepdim=keep_dimension).values
-63    elif pool_type == "mean":
-64        return torch.mean(data, dim=dimension, keepdim=keep_dimension)
-65    elif pool_type == "min":
-66        return torch.min(data, dim=dimension, keepdim=keep_dimension).values
-67    else:
-68        raise ValueError("Unsupported pooling type. Choose 'max', 'avg', or 'min'.")
-69
-70
-71pooling_pt = pydra.mark.task(pooling)
-
    - - -
    -
    - -
    - - def - pooling( data: torch.Tensor, pool_type: Literal['mean', 'min', 'max'] = 'mean', dimension: int = 0, keep_dimension: bool = False) -> torch.Tensor: - - - -
    - - 
    10def pooling(
-11    data: torch.Tensor,
-12    pool_type: Literal["mean", "min", "max"] = "mean",
-13    dimension: int = 0,
-14    keep_dimension: bool = False,
-15) -> torch.Tensor:
-16    """Apply pooling to the input tensor along the specified dimension.
-17
-18    This function provides three pooling methods: mean, min, or max. The pooled tensor is returned as a PyTorch tensor.
-19
-20    Args:
-21        data (torch.Tensor): Input tensor of shape (n_samples, n_features, n_channels).
-22        pool_type (str, optional): The pooling method to use.
-23            Choices are:
-24                * "mean" for mean pooling
-25                * "min" for min pooling
-26                * "max" for max pooling
-27            Defaults to "mean".
-28        dimension (int, optional): The dimension along which to apply the pooling.
-29            Defaults to 0.
-30        keep_dimension (bool, optional): Whether to keep the original tensor dimension after pooling.
-31
-32    Returns:
-33        torch.Tensor: The pooled tensor of shape (n_samples, n_features, n_channels).
-34
-35    Raises:
-36        ValueError: If an invalid pooling method choice is provided.
-37
-38    Examples:
-39        >>> data = torch.randn(10, 20, 30)  # 10 samples, 20 features, 30 channels
-40        >>> data.shape
-41        torch.Size([10, 20, 30])
-42
-43        >>> pooled_data = pooling(data, pool_type="mean", dimension=0, keep_dimension=True)
-44        >>> print(pooled_data.shape)
-45        torch.Size([1, 20, 30])
-46
-47        >>> pooled_data = pooling(data, pool_type="max", dimension=1, keep_dimension=False)
-48        >>> print(pooled_data.shape)
-49        torch.Size([10, 30])
-50
-51        >>> pooled_data = pooling(data, pool_type="min", dimension=2)
-52        >>> print(pooled_data.shape)
-53        torch.Size([10, 20])
-54    """
-55    # Ensure the dimension is valid
-56    assert dimension < len(data.shape), "Invalid dimension provided."
-57
-58    if data.numel() == 0:
-59        raise ValueError("Input data must not be empty")
-60
-61    # Applying the appropriate pooling based on the pool type
-62    if pool_type == "max":
-63        return torch.max(data, dim=dimension, keepdim=keep_dimension).values
-64    elif pool_type == "mean":
-65        return torch.mean(data, dim=dimension, keepdim=keep_dimension)
-66    elif pool_type == "min":
-67        return torch.min(data, dim=dimension, keepdim=keep_dimension).values
-68    else:
-69        raise ValueError("Unsupported pooling type. Choose 'max', 'avg', or 'min'.")
-
    - - -

    Apply pooling to the input tensor along the specified dimension.

    - -

    This function provides three pooling methods: mean, min, or max. The pooled tensor is returned as a PyTorch tensor.

    - -
    Arguments:
    - -
      -
    • data (torch.Tensor): Input tensor of shape (n_samples, n_features, n_channels).
      • -
    • pool_type (str, optional): The pooling method to use. -Choices are: - * "mean" for mean pooling - * "min" for min pooling - * "max" for max pooling -Defaults to "mean".
      • -
    • dimension (int, optional): The dimension along which to apply the pooling. -Defaults to 0.
      • -
    • keep_dimension (bool, optional): Whether to keep the original tensor dimension after pooling.
      • -
    - -
    Returns:
    - -
    -

    torch.Tensor: The pooled tensor of shape (n_samples, n_features, n_channels).

    -
    - -
    Raises:
    - -
      -
    • ValueError: If an invalid pooling method choice is provided.
      • -
    - -
    Examples:
    - -
    -
    -
    >>> data = torch.randn(10, 20, 30)  # 10 samples, 20 features, 30 channels
->>> data.shape
-torch.Size([10, 20, 30])
-
    -
    - -
    -
    >>> pooled_data = pooling(data, pool_type="mean", dimension=0, keep_dimension=True)
->>> print(pooled_data.shape)
-torch.Size([1, 20, 30])
-
    -
    - -
    -
    >>> pooled_data = pooling(data, pool_type="max", dimension=1, keep_dimension=False)
->>> print(pooled_data.shape)
-torch.Size([10, 30])
-
    -
    - -
    -
    >>> pooled_data = pooling(data, pool_type="min", dimension=2)
->>> print(pooled_data.shape)
-torch.Size([10, 20])
-
    -
    -
    -
    - - -
    -
    - -
    - - def - pooling_pt( data: torch.Tensor, pool_type: Literal['mean', 'min', 'max'] = 'mean', dimension: int = 0, keep_dimension: bool = False) -> torch.Tensor: - - - -
    - - 
    10def pooling(
-11    data: torch.Tensor,
-12    pool_type: Literal["mean", "min", "max"] = "mean",
-13    dimension: int = 0,
-14    keep_dimension: bool = False,
-15) -> torch.Tensor:
-16    """Apply pooling to the input tensor along the specified dimension.
-17
-18    This function provides three pooling methods: mean, min, or max. The pooled tensor is returned as a PyTorch tensor.
-19
-20    Args:
-21        data (torch.Tensor): Input tensor of shape (n_samples, n_features, n_channels).
-22        pool_type (str, optional): The pooling method to use.
-23            Choices are:
-24                * "mean" for mean pooling
-25                * "min" for min pooling
-26                * "max" for max pooling
-27            Defaults to "mean".
-28        dimension (int, optional): The dimension along which to apply the pooling.
-29            Defaults to 0.
-30        keep_dimension (bool, optional): Whether to keep the original tensor dimension after pooling.
-31
-32    Returns:
-33        torch.Tensor: The pooled tensor of shape (n_samples, n_features, n_channels).
-34
-35    Raises:
-36        ValueError: If an invalid pooling method choice is provided.
-37
-38    Examples:
-39        >>> data = torch.randn(10, 20, 30)  # 10 samples, 20 features, 30 channels
-40        >>> data.shape
-41        torch.Size([10, 20, 30])
-42
-43        >>> pooled_data = pooling(data, pool_type="mean", dimension=0, keep_dimension=True)
-44        >>> print(pooled_data.shape)
-45        torch.Size([1, 20, 30])
-46
-47        >>> pooled_data = pooling(data, pool_type="max", dimension=1, keep_dimension=False)
-48        >>> print(pooled_data.shape)
-49        torch.Size([10, 30])
-50
-51        >>> pooled_data = pooling(data, pool_type="min", dimension=2)
-52        >>> print(pooled_data.shape)
-53        torch.Size([10, 20])
-54    """
-55    # Ensure the dimension is valid
-56    assert dimension < len(data.shape), "Invalid dimension provided."
-57
-58    if data.numel() == 0:
-59        raise ValueError("Input data must not be empty")
-60
-61    # Applying the appropriate pooling based on the pool type
-62    if pool_type == "max":
-63        return torch.max(data, dim=dimension, keepdim=keep_dimension).values
-64    elif pool_type == "mean":
-65        return torch.mean(data, dim=dimension, keepdim=keep_dimension)
-66    elif pool_type == "min":
-67        return torch.min(data, dim=dimension, keepdim=keep_dimension).values
-68    else:
-69        raise ValueError("Unsupported pooling type. Choose 'max', 'avg', or 'min'.")
-
    - - -

    Apply pooling to the input tensor along the specified dimension.

    - -

    This function provides three pooling methods: mean, min, or max. The pooled tensor is returned as a PyTorch tensor.

    - -
    Arguments:
    - -
      -
    • data (torch.Tensor): Input tensor of shape (n_samples, n_features, n_channels).
      • -
    • pool_type (str, optional): The pooling method to use. -Choices are: - * "mean" for mean pooling - * "min" for min pooling - * "max" for max pooling -Defaults to "mean".
      • -
    • dimension (int, optional): The dimension along which to apply the pooling. -Defaults to 0.
      • -
    • keep_dimension (bool, optional): Whether to keep the original tensor dimension after pooling.
      • -
    - -
    Returns:
    - -
    -

    torch.Tensor: The pooled tensor of shape (n_samples, n_features, n_channels).

    -
    - -
    Raises:
    - -
      -
    • ValueError: If an invalid pooling method choice is provided.
      • -
    - -
    Examples:
    - -
    -
    -
    >>> data = torch.randn(10, 20, 30)  # 10 samples, 20 features, 30 channels
->>> data.shape
-torch.Size([10, 20, 30])
-
    -
    - -
    -
    >>> pooled_data = pooling(data, pool_type="mean", dimension=0, keep_dimension=True)
->>> print(pooled_data.shape)
-torch.Size([1, 20, 30])
-
    -
    - -
    -
    >>> pooled_data = pooling(data, pool_type="max", dimension=1, keep_dimension=False)
->>> print(pooled_data.shape)
-torch.Size([10, 30])
-
    -
    - -
    -
    >>> pooled_data = pooling(data, pool_type="min", dimension=2)
->>> print(pooled_data.shape)
-torch.Size([10, 20])
-
    -
    -
    -
    - - -
    -
    - - \ No newline at end of file