From ee80bd21bd7780e95f3c1b8e9c27c7ed8848d6d8 Mon Sep 17 00:00:00 2001 From: makaveli10 Date: Mon, 20 Nov 2023 19:04:31 +0800 Subject: [PATCH 1/2] update faster_whisper backend --- whisper_live/__version__.py | 2 +- whisper_live/server.py | 87 +++------ whisper_live/transcriber.py | 345 +++++++++++++++++++++++++----------- whisper_live/vad.py | 115 ------------ 4 files changed, 272 insertions(+), 277 deletions(-) delete mode 100644 whisper_live/vad.py diff --git a/whisper_live/__version__.py b/whisper_live/__version__.py index 10ce742b..f1442715 100644 --- a/whisper_live/__version__.py +++ b/whisper_live/__version__.py @@ -1 +1 @@ -__version__="0.0.7" \ No newline at end of file +__version__="0.0.8" \ No newline at end of file diff --git a/whisper_live/server.py b/whisper_live/server.py index 4e5283eb..18bf5950 100644 --- a/whisper_live/server.py +++ b/whisper_live/server.py @@ -13,7 +13,6 @@ import numpy as np import time from whisper_live.transcriber import WhisperModel -from whisper_live.vad import VoiceActivityDetection class TranscriptionServer: @@ -35,8 +34,6 @@ class TranscriptionServer: def __init__(self): # voice activity detection model - self.vad_model = VoiceActivityDetection() - self.vad_threshold = 0.4 self.clients = {} self.websockets = {} @@ -115,15 +112,6 @@ def recv_audio(self, websocket): frame_data = websocket.recv() frame_np = np.frombuffer(frame_data, dtype=np.float32) - try: - speech_prob = self.vad_model(torch.from_numpy(frame_np.copy()), self.RATE).item() - if speech_prob < self.vad_threshold: - continue - - except Exception as e: - logging.error(e) - return - self.clients[websocket].add_frames(frame_np) elapsed_time = time.time() - self.clients_start_time[websocket] @@ -322,25 +310,6 @@ def speech_to_text(self): Exception: If there is an issue with audio processing or WebSocket communication. """ - # detect language - if self.language is None: - # wait for 30s of audio - while self.frames_np is None or self.frames_np.shape[0] < 30*self.RATE: - time.sleep(1) - input_bytes = self.frames_np[-30*self.RATE:].copy() - self.frames_np = None - duration = input_bytes.shape[0] / self.RATE - - self.language, lang_prob = self.transcriber.transcribe( - input_bytes, - initial_prompt=None, - language=self.language, - task=self.task - ) - logging.info(f"Detected language {self.language} with probability {lang_prob}") - self.websocket.send(json.dumps( - {"uid": self.client_uid, "language": self.language, "language_prob": lang_prob})) - while True: if self.exit: logging.info("Exiting speech to text thread") @@ -358,24 +327,31 @@ def speech_to_text(self): samples_take = max(0, (self.timestamp_offset - self.frames_offset)*self.RATE) input_bytes = self.frames_np[int(samples_take):].copy() duration = input_bytes.shape[0] / self.RATE - if duration<1.0: + if duration<1.0: continue try: input_sample = input_bytes.copy() - # set previous complete segment as initial prompt - if len(self.text) and self.text[-1] != '': - initial_prompt = self.text[-1] - else: - initial_prompt = None # whisper transcribe with prompt - result = self.transcriber.transcribe( + result, info = self.transcriber.transcribe( input_sample, - initial_prompt=initial_prompt, + initial_prompt=None, language=self.language, - task=self.task + task=self.task, + vad_filter=True, + vad_parameters={"threshold": 0.5} ) + if self.language is None: + if info.language_probability > 0.5: + self.language = info.language + logging.info(f"Detected language {self.language} with probability {info.language_probability}") + self.websocket.send(json.dumps( + {"uid": self.client_uid, "language": self.language, "language_prob": info.language_probability})) + else: + # detect language again + continue + if len(result): self.t_start = None last_segment = self.update_segments(result, duration) @@ -384,17 +360,7 @@ def speech_to_text(self): else: segments = self.transcript[-self.send_last_n_segments:] if last_segment is not None: - segments = segments + [last_segment] - - try: - self.websocket.send( - json.dumps({ - "uid": self.client_uid, - "segments": segments - }) - ) - except Exception as e: - logging.error(f"[ERROR]: {e}") + segments = segments + [last_segment] else: # show previous output if there is pause i.e. no output from whisper segments = [] @@ -410,15 +376,16 @@ def speech_to_text(self): if time.time() - self.t_start > self.add_pause_thresh: self.text.append('') - try: - self.websocket.send( - json.dumps({ - "uid": self.client_uid, - "segments": segments - }) - ) - except Exception as e: - logging.error(f"[ERROR]: {e}") + try: + self.websocket.send( + json.dumps({ + "uid": self.client_uid, + "segments": segments + }) + ) + except Exception as e: + logging.error(f"[ERROR]: {e}") + except Exception as e: logging.error(f"[ERROR]: {e}") time.sleep(0.01) diff --git a/whisper_live/transcriber.py b/whisper_live/transcriber.py index ec6e28ca..a275878b 100644 --- a/whisper_live/transcriber.py +++ b/whisper_live/transcriber.py @@ -4,7 +4,6 @@ import logging import os import zlib -import logging from typing import BinaryIO, Iterable, List, NamedTuple, Optional, Tuple, Union @@ -14,21 +13,16 @@ from faster_whisper.audio import decode_audio from faster_whisper.feature_extractor import FeatureExtractor -from faster_whisper.tokenizer import Tokenizer -from faster_whisper.utils import download_model, format_timestamp +from faster_whisper.tokenizer import _LANGUAGE_CODES, Tokenizer +from faster_whisper.utils import download_model, format_timestamp, get_logger from faster_whisper.vad import ( SpeechTimestampsMap, + VadOptions, collect_chunks, get_speech_timestamps, ) -# implement logger not available in faster_whisper==0.4.1 -def get_logger(): - """Returns the module logger.""" - return logging.getLogger("faster_whisper") - - class Word(NamedTuple): start: float end: float @@ -37,18 +31,17 @@ class Word(NamedTuple): class Segment(NamedTuple): + id: int + seek: int start: float end: float text: str - words: Optional[List[Word]] - avg_log_prob: float + tokens: List[int] + temperature: float + avg_logprob: float + compression_ratio: float no_speech_prob: float - - -class AudioInfo(NamedTuple): - language: str - language_probability: float - duration: float + words: Optional[List[Word]] class TranscriptionOptions(NamedTuple): @@ -56,12 +49,15 @@ class TranscriptionOptions(NamedTuple): best_of: int patience: float length_penalty: float + repetition_penalty: float + no_repeat_ngram_size: int log_prob_threshold: Optional[float] no_speech_threshold: Optional[float] compression_ratio_threshold: Optional[float] condition_on_previous_text: bool + prompt_reset_on_temperature: float temperatures: List[float] - initial_prompt: Optional[str] + initial_prompt: Optional[Union[str, Iterable[int]]] prefix: Optional[str] suppress_blank: bool suppress_tokens: Optional[List[int]] @@ -72,6 +68,16 @@ class TranscriptionOptions(NamedTuple): append_punctuations: str +class TranscriptionInfo(NamedTuple): + language: str + language_probability: float + duration: float + duration_after_vad: float + all_language_probs: Optional[List[Tuple[str, float]]] + transcription_options: TranscriptionOptions + vad_options: VadOptions + + class WhisperModel: def __init__( self, @@ -82,14 +88,15 @@ def __init__( cpu_threads: int = 0, num_workers: int = 1, download_root: Optional[str] = None, - local_files_only: bool = True, + local_files_only: bool = False, ): """Initializes the Whisper model. Args: model_size_or_path: Size of the model to use (tiny, tiny.en, base, base.en, - small, small.en, medium, medium.en, large-v1, or large-v2) or a path to a converted - model directory. When a size is configured, the converted model is downloaded + small, small.en, medium, medium.en, large-v1, large-v2, or large), a path to a converted + model directory, or a CTranslate2-converted Whisper model ID from the Hugging Face Hub. + When a size or a model ID is configured, the converted model is downloaded from the Hugging Face Hub. device: Device to use for computation ("cpu", "cuda", "auto"). device_index: Device ID to use. @@ -104,8 +111,10 @@ def __init__( having multiple workers enables true parallelism when running the model (concurrent calls to self.model.generate() will run in parallel). This can improve the global throughput at the cost of increased memory usage. - download_root: Directory where the model should be saved. If not set, the model - is saved in the standard Hugging Face cache directory. + download_root: Directory where the models should be saved. If not set, the models + are saved in the standard Hugging Face cache directory. + local_files_only: If True, avoid downloading the file and return the path to the + local cached file if it exists. """ self.logger = get_logger() @@ -147,6 +156,11 @@ def __init__( self.time_precision = 0.02 self.max_length = 448 + @property + def supported_languages(self) -> List[str]: + """The languages supported by the model.""" + return list(_LANGUAGE_CODES) if self.model.is_multilingual else ["en"] + def transcribe( self, audio: Union[str, BinaryIO, np.ndarray], @@ -156,6 +170,8 @@ def transcribe( best_of: int = 5, patience: float = 1, length_penalty: float = 1, + repetition_penalty: float = 1, + no_repeat_ngram_size: int = 0, temperature: Union[float, List[float], Tuple[float, ...]] = [ 0.0, 0.2, @@ -168,7 +184,8 @@ def transcribe( log_prob_threshold: Optional[float] = -1.0, no_speech_threshold: Optional[float] = 0.6, condition_on_previous_text: bool = True, - initial_prompt: Optional[str] = None, + prompt_reset_on_temperature: float = 0.5, + initial_prompt: Optional[Union[str, Iterable[int]]] = None, prefix: Optional[str] = None, suppress_blank: bool = True, suppress_tokens: Optional[List[int]] = [-1], @@ -178,8 +195,8 @@ def transcribe( prepend_punctuations: str = "\"'“¿([{-", append_punctuations: str = "\"'.。,，!！?？:：”)]}、", vad_filter: bool = False, - vad_parameters: Optional[dict] = None, - ) -> Tuple[Iterable[Segment], AudioInfo]: + vad_parameters: Optional[Union[dict, VadOptions]] = None, + ) -> Tuple[Iterable[Segment], TranscriptionInfo]: """Transcribes an input file. Arguments: @@ -192,6 +209,9 @@ def transcribe( best_of: Number of candidates when sampling with non-zero temperature. patience: Beam search patience factor. length_penalty: Exponential length penalty constant. + repetition_penalty: Penalty applied to the score of previously generated tokens + (set > 1 to penalize). + no_repeat_ngram_size: Prevent repetitions of ngrams with this size (set 0 to disable). temperature: Temperature for sampling. It can be a tuple of temperatures, which will be successively used upon failures according to either `compression_ratio_threshold` or `log_prob_threshold`. @@ -206,7 +226,10 @@ def transcribe( as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. - initial_prompt: Optional text to provide as a prompt for the first window. + prompt_reset_on_temperature: Resets prompt if temperature is above this value. + Arg has effect only if condition_on_previous_text is True. + initial_prompt: Optional text string or iterable of token ids to provide as a + prompt for the first window. prefix: Optional text to provide as a prefix for the first window. suppress_blank: Suppress blank outputs at the beginning of the sampling. suppress_tokens: List of token IDs to suppress. -1 will suppress a default set @@ -222,14 +245,14 @@ def transcribe( vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio without speech. This step is using the Silero VAD model https://github.com/snakers4/silero-vad. - vad_parameters: Dictionary of Silero VAD parameters (see available parameters and - default values in the function `get_speech_timestamps`). + vad_parameters: Dictionary of Silero VAD parameters or VadOptions class (see available + parameters and default values in the class `VadOptions`). Returns: A tuple with: - a generator over transcribed segments - - an instance of AudioInfo + - an instance of TranscriptionInfo """ sampling_rate = self.feature_extractor.sampling_rate @@ -237,19 +260,24 @@ def transcribe( audio = decode_audio(audio, sampling_rate=sampling_rate) duration = audio.shape[0] / sampling_rate + duration_after_vad = duration self.logger.info( "Processing audio with duration %s", format_timestamp(duration) ) if vad_filter: - vad_parameters = {} if vad_parameters is None else vad_parameters - speech_chunks = get_speech_timestamps(audio, **vad_parameters) + if vad_parameters is None: + vad_parameters = VadOptions() + elif isinstance(vad_parameters, dict): + vad_parameters = VadOptions(**vad_parameters) + speech_chunks = get_speech_timestamps(audio, vad_parameters) audio = collect_chunks(audio, speech_chunks) + duration_after_vad = audio.shape[0] / sampling_rate self.logger.info( "VAD filter removed %s of audio", - format_timestamp(duration - (audio.shape[0] / sampling_rate)), + format_timestamp(duration - duration_after_vad), ) if self.logger.isEnabledFor(logging.DEBUG): @@ -271,6 +299,7 @@ def transcribe( features = self.feature_extractor(audio) encoder_output = None + all_language_probs = None if language is None: if not self.model.is_multilingual: @@ -279,17 +308,27 @@ def transcribe( else: segment = features[:, : self.feature_extractor.nb_max_frames] encoder_output = self.encode(segment) - results = self.model.detect_language(encoder_output) - language_token, language_probability = results[0][0] - language = language_token[2:-2] + # results is a list of tuple[str, float] with language names and + # probabilities. + results = self.model.detect_language(encoder_output)[0] + # Parse language names to strip out markers + all_language_probs = [(token[2:-2], prob) for (token, prob) in results] + # Get top language token and probability + language, language_probability = all_language_probs[0] self.logger.info( "Detected language '%s' with probability %.2f", language, language_probability, ) - return language, language_probability else: + if not self.model.is_multilingual and language != "en": + self.logger.warning( + "The current model is English-only but the language parameter is set to '%s'; " + "using 'en' instead." % language + ) + language = "en" + language_probability = 1 tokenizer = Tokenizer( @@ -304,10 +343,13 @@ def transcribe( best_of=best_of, patience=patience, length_penalty=length_penalty, + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, log_prob_threshold=log_prob_threshold, no_speech_threshold=no_speech_threshold, compression_ratio_threshold=compression_ratio_threshold, condition_on_previous_text=condition_on_previous_text, + prompt_reset_on_temperature=prompt_reset_on_temperature, temperatures=( temperature if isinstance(temperature, (list, tuple)) else [temperature] ), @@ -327,13 +369,17 @@ def transcribe( if speech_chunks: segments = restore_speech_timestamps(segments, speech_chunks, sampling_rate) - audio_info = AudioInfo( + info = TranscriptionInfo( language=language, language_probability=language_probability, duration=duration, + duration_after_vad=duration_after_vad, + transcription_options=options, + vad_options=vad_parameters, + all_language_probs=all_language_probs, ) - return segments + return segments, info def generate_segments( self, @@ -343,14 +389,20 @@ def generate_segments( encoder_output: Optional[ctranslate2.StorageView] = None, ) -> Iterable[Segment]: content_frames = features.shape[-1] - self.feature_extractor.nb_max_frames + idx = 0 seek = 0 all_tokens = [] prompt_reset_since = 0 if options.initial_prompt is not None: - initial_prompt = " " + options.initial_prompt.strip() - initial_prompt_tokens = tokenizer.encode(initial_prompt) - all_tokens.extend(initial_prompt_tokens) + if isinstance(options.initial_prompt, str): + initial_prompt = " " + options.initial_prompt.strip() + initial_prompt_tokens = tokenizer.encode(initial_prompt) + all_tokens.extend(initial_prompt_tokens) + else: + all_tokens.extend(options.initial_prompt) + + last_speech_timestamp = 0.0 all_segments = [] while seek < content_frames: time_offset = seek * self.feature_extractor.time_per_frame @@ -373,12 +425,15 @@ def generate_segments( prefix=options.prefix if seek == 0 else None, ) - if encoder_output is None: + if seek > 0 or encoder_output is None: encoder_output = self.encode(segment) - result, avg_log_prob, temperature = self.generate_with_fallback( - encoder_output, prompt, tokenizer, options - ) + ( + result, + avg_logprob, + temperature, + compression_ratio, + ) = self.generate_with_fallback(encoder_output, prompt, tokenizer, options) if options.no_speech_threshold is not None: # no voice activity check @@ -386,7 +441,7 @@ def generate_segments( if ( options.log_prob_threshold is not None - and avg_log_prob > options.log_prob_threshold + and avg_logprob > options.log_prob_threshold ): # don't skip if the logprob is high enough, despite the no_speech_prob should_skip = False @@ -482,9 +537,6 @@ def generate_segments( seek += segment_size - if not options.condition_on_previous_text or temperature > 0.5: - prompt_reset_since = len(all_tokens) - if options.word_timestamps: self.add_word_timestamps( current_segments, @@ -493,12 +545,14 @@ def generate_segments( segment_size, options.prepend_punctuations, options.append_punctuations, + last_speech_timestamp=last_speech_timestamp, ) word_end_timestamps = [ w["end"] for s in current_segments for w in s["words"] ] - + if len(word_end_timestamps) > 0: + last_speech_timestamp = word_end_timestamps[-1] if not single_timestamp_ending and len(word_end_timestamps) > 0: seek_shift = round( (word_end_timestamps[-1] - time_offset) * self.frames_per_second @@ -507,8 +561,6 @@ def generate_segments( if seek_shift > 0: seek = previous_seek + seek_shift - encoder_output = None - for segment in current_segments: tokens = segment["tokens"] text = tokenizer.decode(tokens) @@ -517,19 +569,38 @@ def generate_segments( continue all_tokens.extend(tokens) + idx += 1 all_segments.append(Segment( + id=idx, + seek=seek, start=segment["start"], end=segment["end"], text=text, + tokens=tokens, + temperature=temperature, + avg_logprob=avg_logprob, + compression_ratio=compression_ratio, + no_speech_prob=result.no_speech_prob, words=( [Word(**word) for word in segment["words"]] if options.word_timestamps else None - ), - avg_log_prob=avg_log_prob, - no_speech_prob=result.no_speech_prob, + ), )) + + if ( + not options.condition_on_previous_text + or temperature > options.prompt_reset_on_temperature + ): + if options.condition_on_previous_text: + self.logger.debug( + "Reset prompt. prompt_reset_on_temperature threshold is met %f > %f", + temperature, + options.prompt_reset_on_temperature, + ) + + prompt_reset_since = len(all_tokens) return all_segments def encode(self, features: np.ndarray) -> ctranslate2.StorageView: @@ -548,10 +619,10 @@ def generate_with_fallback( prompt: List[int], tokenizer: Tokenizer, options: TranscriptionOptions, - ) -> Tuple[ctranslate2.models.WhisperGenerationResult, float, float]: - result = None - avg_log_prob = None - final_temperature = None + ) -> Tuple[ctranslate2.models.WhisperGenerationResult, float, float, float]: + decode_result = None + all_results = [] + below_cr_threshold_results = [] max_initial_timestamp_index = int( round(options.max_initial_timestamp / self.time_precision) @@ -571,11 +642,12 @@ def generate_with_fallback( "patience": options.patience, } - final_temperature = temperature result = self.model.generate( encoder_output, [prompt], length_penalty=options.length_penalty, + repetition_penalty=options.repetition_penalty, + no_repeat_ngram_size=options.no_repeat_ngram_size, max_length=self.max_length, return_scores=True, return_no_speech_prob=True, @@ -589,44 +661,63 @@ def generate_with_fallback( # Recover the average log prob from the returned score. seq_len = len(tokens) - cum_log_prob = result.scores[0] * (seq_len**options.length_penalty) - avg_log_prob = cum_log_prob / (seq_len + 1) + cum_logprob = result.scores[0] * (seq_len**options.length_penalty) + avg_logprob = cum_logprob / (seq_len + 1) text = tokenizer.decode(tokens).strip() compression_ratio = get_compression_ratio(text) + decode_result = ( + result, + avg_logprob, + temperature, + compression_ratio, + ) + all_results.append(decode_result) + needs_fallback = False - if ( - options.compression_ratio_threshold is not None - and compression_ratio > options.compression_ratio_threshold - ): - needs_fallback = True # too repetitive + if options.compression_ratio_threshold is not None: + if compression_ratio > options.compression_ratio_threshold: + needs_fallback = True # too repetitive - self.logger.debug( - "Compression ratio threshold is not met with temperature %.1f (%f > %f)", - temperature, - compression_ratio, - options.compression_ratio_threshold, - ) + self.logger.debug( + "Compression ratio threshold is not met with temperature %.1f (%f > %f)", + temperature, + compression_ratio, + options.compression_ratio_threshold, + ) + else: + below_cr_threshold_results.append(decode_result) if ( options.log_prob_threshold is not None - and avg_log_prob < options.log_prob_threshold + and avg_logprob < options.log_prob_threshold ): needs_fallback = True # average log probability is too low self.logger.debug( "Log probability threshold is not met with temperature %.1f (%f < %f)", temperature, - avg_log_prob, + avg_logprob, options.log_prob_threshold, ) + if ( + options.no_speech_threshold is not None + and result.no_speech_prob > options.no_speech_threshold + ): + needs_fallback = False # silence + if not needs_fallback: break + else: + # all failed, select the result with the highest average log probability + decode_result = max( + below_cr_threshold_results or all_results, key=lambda x: x[1] + ) - return result, avg_log_prob, final_temperature + return decode_result def get_prompt( self, @@ -650,6 +741,8 @@ def get_prompt( prefix_tokens = tokenizer.encode(" " + prefix.strip()) if len(prefix_tokens) >= self.max_length // 2: prefix_tokens = prefix_tokens[: self.max_length // 2 - 1] + if not without_timestamps: + prompt.append(tokenizer.timestamp_begin) prompt.extend(prefix_tokens) return prompt @@ -662,7 +755,8 @@ def add_word_timestamps( num_frames: int, prepend_punctuations: str, append_punctuations: str, - ): + last_speech_timestamp: float, + ) -> None: if len(segments) == 0: return @@ -675,6 +769,24 @@ def add_word_timestamps( alignment = self.find_alignment( tokenizer, text_tokens, encoder_output, num_frames ) + word_durations = np.array([word["end"] - word["start"] for word in alignment]) + word_durations = word_durations[word_durations.nonzero()] + median_duration = np.median(word_durations) if len(word_durations) > 0 else 0.0 + max_duration = median_duration * 2 + + # hack: truncate long words at sentence boundaries. + # a better segmentation algorithm based on VAD should be able to replace this. + if len(word_durations) > 0: + sentence_end_marks = ".。!！?？" + # ensure words at sentence boundaries + # are not longer than twice the median word duration. + for i in range(1, len(alignment)): + if alignment[i]["end"] - alignment[i]["start"] > max_duration: + if alignment[i]["word"] in sentence_end_marks: + alignment[i]["end"] = alignment[i]["start"] + max_duration + elif alignment[i - 1]["word"] in sentence_end_marks: + alignment[i]["start"] = alignment[i]["end"] - max_duration + merge_punctuations(alignment, prepend_punctuations, append_punctuations) time_offset = ( @@ -705,10 +817,51 @@ def add_word_timestamps( saved_tokens += len(timing["tokens"]) word_index += 1 + # hack: truncate long words at segment boundaries. + # a better segmentation algorithm based on VAD should be able to replace this. if len(words) > 0: - # adjust the segment-level timestamps based on the word-level timestamps - segment["start"] = words[0]["start"] - segment["end"] = words[-1]["end"] + # ensure the first and second word after a pause is not longer than + # twice the median word duration. + if words[0]["end"] - last_speech_timestamp > median_duration * 4 and ( + words[0]["end"] - words[0]["start"] > max_duration + or ( + len(words) > 1 + and words[1]["end"] - words[0]["start"] > max_duration * 2 + ) + ): + if ( + len(words) > 1 + and words[1]["end"] - words[1]["start"] > max_duration + ): + boundary = max( + words[1]["end"] / 2, words[1]["end"] - max_duration + ) + words[0]["end"] = words[1]["start"] = boundary + words[0]["start"] = max(0, words[0]["end"] - max_duration) + + # prefer the segment-level start timestamp if the first word is too long. + if ( + segment["start"] < words[0]["end"] + and segment["start"] - 0.5 > words[0]["start"] + ): + words[0]["start"] = max( + 0, min(words[0]["end"] - median_duration, segment["start"]) + ) + else: + segment["start"] = words[0]["start"] + + # prefer the segment-level end timestamp if the last word is too long. + if ( + segment["end"] > words[-1]["start"] + and segment["end"] + 0.5 < words[-1]["end"] + ): + words[-1]["end"] = max( + words[-1]["start"] + median_duration, segment["end"] + ) + else: + segment["end"] = words[-1]["end"] + + last_speech_timestamp = segment["end"] segment["words"] = words @@ -741,6 +894,8 @@ def find_alignment( text_tokens + [tokenizer.eot] ) word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0)) + if len(word_boundaries) <= 1: + return [] jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool) jump_times = time_indices[jumps] / self.tokens_per_second @@ -751,22 +906,6 @@ def find_alignment( for i, j in zip(word_boundaries[:-1], word_boundaries[1:]) ] - # hack: ensure the first and second word is not longer than twice the median word duration. - # a better segmentation algorithm based on VAD should be able to replace this. - word_durations = end_times - start_times - word_durations = word_durations[word_durations.nonzero()] - if len(word_durations) > 0: - median_duration = np.median(word_durations) - max_duration = median_duration * 2 - if len(word_durations) >= 2 and word_durations[1] > max_duration: - boundary = max(end_times[2] / 2, end_times[2] - max_duration) - end_times[0] = start_times[1] = boundary - if ( - len(word_durations) >= 1 - and end_times[0] - start_times[0] > max_duration - ): - start_times[0] = max(0, end_times[0] - max_duration) - return [ dict( word=word, tokens=tokens, start=start, end=end, probability=probability @@ -792,7 +931,8 @@ def restore_speech_timestamps( words = [] for word in segment.words: # Ensure the word start and end times are resolved to the same chunk. - chunk_index = ts_map.get_chunk_index(word.start) + middle = (word.start + word.end) / 2 + chunk_index = ts_map.get_chunk_index(middle) word = word._replace( start=ts_map.get_original_time(word.start, chunk_index), end=ts_map.get_original_time(word.end, chunk_index), @@ -811,7 +951,7 @@ def restore_speech_timestamps( end=ts_map.get_original_time(segment.end), ) - yield segment + return segments def get_ctranslate2_storage(segment: np.ndarray) -> ctranslate2.StorageView: @@ -825,7 +965,10 @@ def get_compression_ratio(text: str) -> float: return len(text_bytes) / len(zlib.compress(text_bytes)) -def get_suppressed_tokens(tokenizer, suppress_tokens): +def get_suppressed_tokens( + tokenizer: Tokenizer, + suppress_tokens: Optional[List[int]], +) -> Optional[List[int]]: if not suppress_tokens or -1 in suppress_tokens: return suppress_tokens @@ -846,7 +989,7 @@ def get_suppressed_tokens(tokenizer, suppress_tokens): return sorted(set(suppress_tokens)) -def merge_punctuations(alignment: List[dict], prepended: str, appended: str): +def merge_punctuations(alignment: List[dict], prepended: str, appended: str) -> None: # merge prepended punctuations i = len(alignment) - 2 j = len(alignment) - 1 diff --git a/whisper_live/vad.py b/whisper_live/vad.py deleted file mode 100644 index 22a06e4f..00000000 --- a/whisper_live/vad.py +++ /dev/null @@ -1,115 +0,0 @@ -# original: https://github.com/snakers4/silero-vad/blob/master/utils_vad.py - -import os -import subprocess -import torch -import numpy as np -import onnxruntime - - -class VoiceActivityDetection(): - - def __init__(self, force_onnx_cpu=True): - path = self.download() - opts = onnxruntime.SessionOptions() - opts.log_severity_level = 3 - - opts.inter_op_num_threads = 1 - opts.intra_op_num_threads = 1 - - if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers(): - self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'], sess_options=opts) - else: - self.session = onnxruntime.InferenceSession(path, providers=['CUDAExecutionProvider'], sess_options=opts) - - - self.reset_states() - self.sample_rates = [8000, 16000] - - def _validate_input(self, x, sr: int): - if x.dim() == 1: - x = x.unsqueeze(0) - if x.dim() > 2: - raise ValueError(f"Too many dimensions for input audio chunk {x.dim()}") - - if sr != 16000 and (sr % 16000 == 0): - step = sr // 16000 - x = x[:,::step] - sr = 16000 - - if sr not in self.sample_rates: - raise ValueError(f"Supported sampling rates: {self.sample_rates} (or multiply of 16000)") - - if sr / x.shape[1] > 31.25: - raise ValueError("Input audio chunk is too short") - - return x, sr - - def reset_states(self, batch_size=1): - self._h = np.zeros((2, batch_size, 64)).astype('float32') - self._c = np.zeros((2, batch_size, 64)).astype('float32') - self._last_sr = 0 - self._last_batch_size = 0 - - def __call__(self, x, sr: int): - - x, sr = self._validate_input(x, sr) - batch_size = x.shape[0] - - if not self._last_batch_size: - self.reset_states(batch_size) - if (self._last_sr) and (self._last_sr != sr): - self.reset_states(batch_size) - if (self._last_batch_size) and (self._last_batch_size != batch_size): - self.reset_states(batch_size) - - if sr in [8000, 16000]: - ort_inputs = {'input': x.numpy(), 'h': self._h, 'c': self._c, 'sr': np.array(sr, dtype='int64')} - ort_outs = self.session.run(None, ort_inputs) - out, self._h, self._c = ort_outs - else: - raise ValueError() - - self._last_sr = sr - self._last_batch_size = batch_size - - out = torch.tensor(out) - return out - - def audio_forward(self, x, sr: int, num_samples: int = 512): - outs = [] - x, sr = self._validate_input(x, sr) - - if x.shape[1] % num_samples: - pad_num = num_samples - (x.shape[1] % num_samples) - x = torch.nn.functional.pad(x, (0, pad_num), 'constant', value=0.0) - - self.reset_states(x.shape[0]) - for i in range(0, x.shape[1], num_samples): - wavs_batch = x[:, i:i+num_samples] - out_chunk = self.__call__(wavs_batch, sr) - outs.append(out_chunk) - - stacked = torch.cat(outs, dim=1) - return stacked.cpu() - - @staticmethod - def download(model_url="https://github.com/snakers4/silero-vad/raw/master/files/silero_vad.onnx"): - target_dir = os.path.expanduser("~/.cache/whisper-live/") - - # Ensure the target directory exists - os.makedirs(target_dir, exist_ok=True) - - # Define the target file path - model_filename = os.path.join(target_dir, "silero_vad.onnx") - - # Check if the model file already exists - if not os.path.exists(model_filename): - # If it doesn't exist, download the model using wget - print("Downloading VAD ONNX model...") - try: - subprocess.run(["wget", "-O", model_filename, model_url], check=True) - except subprocess.CalledProcessError: - print("Failed to download the model using wget.") - return model_filename - From 073cfc20f37567a3bf3c2d4a27baaad7a1bf4769 Mon Sep 17 00:00:00 2001 From: makaveli10 Date: Mon, 20 Nov 2023 19:05:02 +0800 Subject: [PATCH 2/2] update version faster whisper server requirements --- requirements/server.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/server.txt b/requirements/server.txt index 1c987507..0b19eaa9 100644 --- a/requirements/server.txt +++ b/requirements/server.txt @@ -1,5 +1,5 @@ PyAudio -faster-whisper==0.6.0 +faster-whisper==0.9.0 --extra-index-url https://download.pytorch.org/whl/cu111 torch==1.10.1 torchaudio==0.10.1