From 09b18e8ab84c6af978ddc7aec6f062dda00dfb03 Mon Sep 17 00:00:00 2001 From: makaveli10 Date: Thu, 14 Dec 2023 22:08:12 +0800 Subject: [PATCH 1/9] add model size option from server --- whisper_live/server.py | 82 +++++++++++++++++++++++++----------------- 1 file changed, 50 insertions(+), 32 deletions(-) diff --git a/whisper_live/server.py b/whisper_live/server.py index 18bf5950..c06d0cd6 100644 --- a/whisper_live/server.py +++ b/whisper_live/server.py @@ -101,9 +101,10 @@ def recv_audio(self, websocket): multilingual=options["multilingual"], language=options["language"], task=options["task"], - client_uid=options["uid"] + client_uid=options["uid"], + model_size=options["model_size"] ) - + self.clients[websocket] = client self.clients_start_time[websocket] = time.time() @@ -127,7 +128,8 @@ def recv_audio(self, websocket): except Exception as e: logging.error(e) - self.clients[websocket].cleanup() + if self.clients[websocket].model_size is not None: + self.clients[websocket].cleanup() self.clients.pop(websocket) self.clients_start_time.pop(websocket) logging.info("Connection Closed.") @@ -180,7 +182,16 @@ class ServeClient: SERVER_READY = "SERVER_READY" DISCONNECT = "DISCONNECT" - def __init__(self, websocket, task="transcribe", device=None, multilingual=False, language=None, client_uid=None): + def __init__( + self, + websocket, + task="transcribe", + device=None, + multilingual=False, + language=None, + client_uid=None, + model_size="small" + ): """ Initialize a ServeClient instance. The Whisper model is initialized based on the client's language and device availability. @@ -199,11 +210,23 @@ def __init__(self, websocket, task="transcribe", device=None, multilingual=False self.client_uid = client_uid self.data = b"" self.frames = b"" - self.language = language if multilingual else "en" + self.model_sizes = [ + "tiny", "base", "small", "medium", "large-v2" + ] + self.multilingual = multilingual + self.model_size = self.get_model_size(model_size) + self.language = language if self.multilingual else "en" self.task = task + self.websocket = websocket + device = "cuda" if torch.cuda.is_available() else "cpu" + + + if self.model_size == None: + return + self.transcriber = WhisperModel( - "small" if multilingual else "small.en", + self.model_size, device=device, compute_type="int8" if device=="cpu" else "float16", local_files_only=False, @@ -228,7 +251,6 @@ def __init__(self, websocket, task="transcribe", device=None, multilingual=False self.pick_previous_segments = 2 # threading - self.websocket = websocket self.trans_thread = threading.Thread(target=self.speech_to_text) self.trans_thread.start() self.websocket.send( @@ -240,34 +262,30 @@ def __init__(self, websocket, task="transcribe", device=None, multilingual=False ) ) - def fill_output(self, output): + def get_model_size(self, model_size): """ - Format the current incomplete transcription output by combining it with previous complete segments. - The resulting transcription is wrapped into two lines, each containing a maximum of 50 characters. + Returns the whisper model size based on multilingual. + """ + if model_size not in self.model_sizes: + self.websocket.send( + json.dumps( + { + "uid": self.client_uid, + "status": "ERROR", + "message": f"Invalid model size {model_size}. Available choices: {self.model_sizes}" + } + ) + ) + return None + + if model_size == "large-v2": + self.multilingual = True + return model_size - It ensures that the combined transcription fits within two lines, with a maximum of 50 characters per line. - Segments are concatenated in the order they exist in the list of previous segments, with the most - recent complete segment first and older segments prepended as needed to maintain the character limit. - If a 3-second pause is detected in the previous segments, any text preceding it is discarded to ensure - the transcription starts with the most recent complete content. The resulting transcription is returned - as a single string. + if not self.multilingual: + model_size = model_size + ".en" - Args: - output(str): The current incomplete transcription segment. - - Returns: - str: A formatted transcription wrapped in two lines. - """ - text = '' - pick_prev = min(len(self.text), self.pick_previous_segments) - for seg in self.text[-pick_prev:]: - # discard everything before a 3 second pause - if seg == '': - text = '' - else: - text += seg - wrapped = "".join(text + output) - return wrapped + return model_size def add_frames(self, frame_np): """ From 402fceb9f3800880b29a57c22bdd4c156c881eec Mon Sep 17 00:00:00 2001 From: makaveli10 Date: Fri, 15 Dec 2023 02:21:09 +0800 Subject: [PATCH 2/9] remove emptyline --- whisper_live/server.py | 1 - 1 file changed, 1 deletion(-) diff --git a/whisper_live/server.py b/whisper_live/server.py index c06d0cd6..1fbd531a 100644 --- a/whisper_live/server.py +++ b/whisper_live/server.py @@ -221,7 +221,6 @@ def __init__( device = "cuda" if torch.cuda.is_available() else "cpu" - if self.model_size == None: return From 7ffcad64ba65ece49e9a0d8fdbb8faf2c93e24d5 Mon Sep 17 00:00:00 2001 From: makaveli10 Date: Fri, 15 Dec 2023 02:21:35 +0800 Subject: [PATCH 3/9] update readme --- README.md | 25 ++++++++++++++++++++----- 1 file changed, 20 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 69e20e3b..37fc8814 100644 --- a/README.md +++ b/README.md @@ -28,18 +28,33 @@ Unlike traditional speech recognition systems that rely on continuous audio stre - To transcribe an audio file: ```python from whisper_live.client import TranscriptionClient - client = TranscriptionClient("localhost", 9090, is_multilingual=True, lang="hi", translate=True) - client(audio_file_path) + client = TranscriptionClient( + "localhost", + 9090, + is_multilingual=False, + lang="en", + translate=False, + model_size="small" + ) + + client("tests/jfk.wav") ``` - This command transcribes the specified audio file (audio.wav) using the Whisper model. It connects to the server running on localhost at port 9090. It also enables the multilingual feature, allowing transcription in multiple languages. The language option specifies the target language for transcription, in this case, Hindi ("hi"). The translate option should be set to `True` if we want to translate from the source language to English and `False` if we want to transcribe in the source language. + This command transcribes the specified audio file (audio.wav) using the Whisper model. It connects to the server running on localhost at port 9090. It can also enable the multilingual feature, allowing transcription in multiple languages. The language option specifies the target language for transcription, in this case, English ("en"). The translate option should be set to `True` if we want to translate from the source language to English and `False` if we want to transcribe in the source language. - To transcribe from microphone: ```python from whisper_live.client import TranscriptionClient - client = TranscriptionClient(host, port, is_multilingual=True, lang="hi", translate=True) + client = TranscriptionClient( + "localhost", + 9090, + is_multilingual=True, + lang="hi", + translate=True, + model_size="small" + ) client() ``` - This command captures audio from the microphone and sends it to the server for transcription. It uses the same options as the previous command, enabling the multilingual feature and specifying the target language and task. + This command captures audio from the microphone and sends it to the server for transcription. It uses the multilingual option with `hi` as the selectelanguage, enabling the multilingual feature and specifying the target language and task. We use whisper `small` by default but can be changed to any other option based on the requirements and the hardware running the server. - To trasncribe from a HLS stream: ```python From 14beb4f9429b5932b0375b536786cd2882b8fad9 Mon Sep 17 00:00:00 2001 From: makaveli10 Date: Thu, 14 Dec 2023 23:52:25 +0530 Subject: [PATCH 4/9] update chrome plugin with model size dropdown --- Audio-Transcription-Chrome/background.js | 3 +- Audio-Transcription-Chrome/options.js | 3 +- Audio-Transcription-Chrome/popup.html | 13 +- Audio-Transcription-Chrome/popup.js | 20 +- Audio-Transcription-Chrome/test.py | 243 +++++++++++++++++++++++ 5 files changed, 277 insertions(+), 5 deletions(-) create mode 100644 Audio-Transcription-Chrome/test.py diff --git a/Audio-Transcription-Chrome/background.js b/Audio-Transcription-Chrome/background.js index f860209b..a5028a78 100644 --- a/Audio-Transcription-Chrome/background.js +++ b/Audio-Transcription-Chrome/background.js @@ -156,7 +156,8 @@ async function startCapture(options) { port: options.port, multilingual: options.useMultilingual, language: options.language, - task: options.task + task: options.task, + modelSize: options.modelSize }, }); } else { diff --git a/Audio-Transcription-Chrome/options.js b/Audio-Transcription-Chrome/options.js index af77b386..9717856a 100644 --- a/Audio-Transcription-Chrome/options.js +++ b/Audio-Transcription-Chrome/options.js @@ -102,7 +102,8 @@ async function startRecord(option) { uid: uuid, multilingual: option.multilingual, language: option.language, - task: option.task + task: option.task, + model_size: option.modelSize }) ); }; diff --git a/Audio-Transcription-Chrome/popup.html b/Audio-Transcription-Chrome/popup.html index 4f2b6ec7..79f18ca9 100644 --- a/Audio-Transcription-Chrome/popup.html +++ b/Audio-Transcription-Chrome/popup.html @@ -125,11 +125,22 @@ + diff --git a/Audio-Transcription-Chrome/popup.js b/Audio-Transcription-Chrome/popup.js index f67f8b70..a1dd210c 100644 --- a/Audio-Transcription-Chrome/popup.js +++ b/Audio-Transcription-Chrome/popup.js @@ -7,8 +7,10 @@ document.addEventListener("DOMContentLoaded", function () { const useMultilingualCheckbox = document.getElementById('useMultilingualCheckbox'); const languageDropdown = document.getElementById('languageDropdown'); const taskDropdown = document.getElementById('taskDropdown'); + const modelSizeDropdown = document.getElementById('modelSizeDropdown'); let selectedLanguage = null; let selectedTask = taskDropdown.value; + let selectedModelSize = modelSizeDropdown.value; // Add click event listeners to the buttons startButton.addEventListener("click", startCapture); @@ -52,6 +54,13 @@ document.addEventListener("DOMContentLoaded", function () { } }); + chrome.storage.local.get("selectedModelSize", ({ selectedModelSize: storedModelSize }) => { + if (storedModelSize !== undefined) { + modelSizeDropdown.value = storedModelSize; + selectedModelSize = storedModelSize; + } + }); + // Function to handle the start capture button click event async function startCapture() { // Ignore click if the button is disabled @@ -64,7 +73,7 @@ document.addEventListener("DOMContentLoaded", function () { // Send a message to the background script to start capturing let host = "localhost"; - let port = "9090"; + let port = "5901"; const useCollaboraServer = useServerCheckbox.checked; if (useCollaboraServer){ host = "transcription.kurg.org" @@ -79,7 +88,8 @@ document.addEventListener("DOMContentLoaded", function () { port: port, useMultilingual: useMultilingualCheckbox.checked, language: selectedLanguage, - task: selectedTask + task: selectedTask, + modelSize: selectedModelSize }, () => { // Update capturing state in storage and toggle the buttons chrome.storage.local.set({ capturingState: { isCapturing: true } }, () => { @@ -120,6 +130,7 @@ document.addEventListener("DOMContentLoaded", function () { stopButton.disabled = !isCapturing; useServerCheckbox.disabled = isCapturing; useMultilingualCheckbox.disabled = isCapturing; + modelSizeDropdown.disabled = isCapturing; startButton.classList.toggle("disabled", isCapturing); stopButton.classList.toggle("disabled", !isCapturing); @@ -157,6 +168,11 @@ document.addEventListener("DOMContentLoaded", function () { chrome.storage.local.set({ selectedTask }); }); + modelSizeDropdown.addEventListener('change', function() { + selectedModelSize = modelSizeDropdown.value; + chrome.storage.local.set({ selectedModelSize }); + }); + chrome.runtime.onMessage.addListener(async (request, sender, sendResponse) => { if (request.action === "updateSelectedLanguage") { const detectedLanguage = request.detectedLanguage; diff --git a/Audio-Transcription-Chrome/test.py b/Audio-Transcription-Chrome/test.py new file mode 100644 index 00000000..ddd69c91 --- /dev/null +++ b/Audio-Transcription-Chrome/test.py @@ -0,0 +1,243 @@ +import configparser + +import numpy as np +import torch + +from tensorrt_llm._utils import str_dtype_to_torch, torch_to_numpy +from tensorrt_llm.functional import LayerNormPositionType, LayerNormType + +layernorm_type_map = {i.name: i.value for i in LayerNormType} +layernorm_position_map = {i.name: i.value for i in LayerNormPositionType} + +def parse_config(ini_file, component, args): + config = configparser.ConfigParser() + config.read(ini_file) + if component == 'encoder': + args.n_layer = config.getint(component, 'n_layer') + args.n_head = config.getint(component, 'n_head') + args.hidden_size = config.getint(component, 'n_state') + args.n_ctx = config.getint(component, 'n_ctx') + args.ffn_hidden_size = config.getint(component, 'ffn_hidden_size') + args.vocab_size = config.getint(component, 'vocab_size') + args.n_positions = config.getint(component, 'n_positions') + args.has_position_embedding = config.getboolean( + component, 'has_position_embedding', fallback=False) + args.has_token_type_embedding = config.getboolean( + component, 'has_token_type_embedding', fallback=False) + args.has_embedding_layernorm = config.getboolean( + component, 'has_embedding_layernorm', fallback=False) + args.has_embedding_scale = config.getboolean(component, + 'has_embedding_scale', + fallback=False) + args.q_scaling = config.getfloat(component, 'q_scaling', fallback=1.0) + args.has_attention_qkvo_bias = config.getboolean( + component, 'has_attention_qkvo_bias', fallback=False) + args.has_mlp_bias = config.getboolean(component, + 'has_mlp_bias', + fallback=False) + args.has_model_final_layernorm = config.getboolean( + component, 'has_model_final_layernorm', fallback=False) + args.layernorm_eps = config.getfloat(component, + 'layernorm_eps', + fallback=1e-5) + args.layernorm_position = layernorm_position_map[config.get( + component, 'layernorm_position')] + args.layernorm_type = layernorm_type_map[config.get( + component, 'layernorm_type')] + args.hidden_act = config.get(component, 'hidden_act') + args.relative_attention = config.getboolean(component, + 'relative_attention', + fallback=False) + return args + +def fuse_qkv(q, k, v): + qkv_weight = np.concatenate((q, k, v)) + return qkv_weight + +def load_whisper_from_pytorch(tllm_model, + pytorch_ckpt_path, + component, + model_size, + multilingual=False, + dtype="float32"): + torch_dtype = str_dtype_to_torch(dtype) + model_name = "whisper-" + f"{model_size}" + if not multilingual: + model_name = model_name + ".en" + + pytorch_ckpt = torch.load(pytorch_ckpt_path + f"{model_name}.ckpt") + pytorch_model = { + key: torch_to_numpy(value.to(torch_dtype)) + for key, value in pytorch_ckpt.items() + } + + if component == "encoder": + # set conv1d + tllm_model.conv1.weight.value = pytorch_model['encoder.conv1.weight'].unsqueeze(3) + tllm_model.conv1.bias.value = pytorch_model['encoder.conv1.bias'] + tllm_model.conv2.weight.value = pytorch_model['encoder.conv2.weight'].unsqueeze(3) + tllm_model.conv2.bias.value = pytorch_model['encoder.conv2.bias'] + # if tllm_model.embedding.position_embedding: + # tllm_model.embedding.position_embedding.weight.value = pytorch_model[ + # 'encoder.embed_positions.weight'] + + for i in range(tllm_model.num_layers): + layer = tllm_model.encoder_layers[i] + layer_prefix = f'encoder.layers.{i}.' + + # attention table for all layers + layer.attention.rel_attn_table.value = relative_attention_table + + layer.attention.qkv.weight.value = fuse_qkv( + pytorch_model[f'{layer_prefix}self_attn.q.weight'], + pytorch_model[f'{layer_prefix}self_attn.k.weight'], + pytorch_model[f'{layer_prefix}self_attn.v.weight']) + layer.attention.dense.weight.value = pytorch_model[ + f'{layer_prefix}self_attn.out_proj.weight'] + + if tllm_model.has_attention_qkvo_bias: + layer.attention.qkv.bias.value = fuse_qkv( + pytorch_model[f'{layer_prefix}self_attn.q.bias'], + torch.zeros(pytorch_model[f'{layer_prefix}self_attn.q.bias'].shape), # no bias for k + pytorch_model[f'{layer_prefix}self_attn.v.bias'] + ) + layer.attention.dense.bias.value = pytorch_model[ + f'{layer_prefix}self_attn.out_proj.bias'] + + layer.attention_layernorm.weight.value = pytorch_model[ + f'{layer_prefix}self_attn_layer_norm.weight'] + if tllm_model.layernorm_type != LayerNormType.RmsNorm: + layer.attention_layernorm.bias.value = pytorch_model[ + f'{layer_prefix}self_attn_layer_norm.bias'] + + layer.mlp.fc.weight.value = pytorch_model[ + f'{layer_prefix}fc1.weight'] + layer.mlp.proj.weight.value = pytorch_model[ + f'{layer_prefix}fc2.weight'] + + if tllm_model.has_mlp_bias: + layer.mlp.fc.bias.value = pytorch_model[ + f'{layer_prefix}fc1.bias'] + layer.mlp.proj.bias.value = pytorch_model[ + f'{layer_prefix}fc2.bias'] + + layer.mlp_layernorm.weight.value = pytorch_model[ + f'{layer_prefix}final_layer_norm.weight'] + if tllm_model.layernorm_type != LayerNormType.RmsNorm: + layer.mlp_layernorm.bias.value = pytorch_model[ + f'{layer_prefix}final_layer_norm.bias'] + + if tllm_model.final_layernorm: + tllm_model.final_layernorm.weight.value = pytorch_model[ + 'encoder.layer_norm.weight'] + if tllm_model.layernorm_type != LayerNormType.RmsNorm: + tllm_model.final_layernorm.bias.value = pytorch_model[ + 'encoder.layer_norm.bias'] + + if component == "decoder": + tllm_model.embedding.vocab_embedding.weight.value = pytorch_model[ + 'shared.weight'] + if tllm_model.embedding.position_embedding: + tllm_model.embedding.position_embedding.weight.value = pytorch_model[ + 'decoder.embed_positions.weight'] + if tllm_model.embedding.token_type_embedding: + tllm_model.embedding.token_type_embedding.weight.value = pytorch_model[ + 'decoder.embed_token_type.weight'] + + # all layers use 1st layer's attn table + # transpose from [num_buckets, num_heads] --> [num_heads, num_buckets] + # ascontiguousarray is very important! otherwise TRT always receives the original layout + relative_attention_table = np.ascontiguousarray(pytorch_model[ + f'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight'] + .T) + + for i in range(tllm_model.num_layers): + layer = tllm_model.decoder_layers[i] + layer_prefix = f'decoder.block.{i}.' + + # attention table for all layers + layer.self_attention.rel_attn_table.value = relative_attention_table + + # self attn + layer.self_attention.qkv.weight.value = fuse_qkv( + pytorch_model[f'{layer_prefix}layer.0.SelfAttention.q.weight'], + pytorch_model[f'{layer_prefix}layer.0.SelfAttention.k.weight'], + pytorch_model[f'{layer_prefix}layer.0.SelfAttention.v.weight']) + layer.self_attention.dense.weight.value = pytorch_model[ + f'{layer_prefix}layer.0.SelfAttention.o.weight'] + + if tllm_model.has_attention_qkvo_bias: + layer.self_attention.qkv.bias.value = fuse_qkv( + pytorch_model[ + f'{layer_prefix}layer.0.SelfAttention.q.bias'], + pytorch_model[ + f'{layer_prefix}layer.0.SelfAttention.k.bias'], + pytorch_model[f'{layer_prefix}layer.0.SelfAttention.v.bias'] + ) + layer.self_attention.dense.bias.value = pytorch_model[ + f'{layer_prefix}layer.0.SelfAttention.o.bias'] + + layer.self_attention_layernorm.weight.value = pytorch_model[ + f'{layer_prefix}layer.0.layer_norm.weight'] + if tllm_model.layernorm_type != LayerNormType.RmsNorm: + layer.self_attention_layernorm.bias.value = pytorch_model[ + f'{layer_prefix}layer.0.layer_norm.bias'] + + # cross attn + layer.cross_attention.qkv.weight.value = fuse_qkv( + pytorch_model[ + f'{layer_prefix}layer.1.EncDecAttention.q.weight'], + pytorch_model[ + f'{layer_prefix}layer.1.EncDecAttention.k.weight'], + pytorch_model[f'{layer_prefix}layer.1.EncDecAttention.v.weight'] + ) + layer.cross_attention.dense.weight.value = pytorch_model[ + f'{layer_prefix}layer.1.EncDecAttention.o.weight'] + + if tllm_model.has_attention_qkvo_bias: + layer.cross_attention.qkv.bias.value = fuse_qkv( + pytorch_model[ + f'{layer_prefix}layer.1.EncDecAttention.q.bias'], + pytorch_model[ + f'{layer_prefix}layer.1.EncDecAttention.k.bias'], + pytorch_model[ + f'{layer_prefix}layer.1.EncDecAttention.v.bias']) + layer.cross_attention.dense.bias.value = pytorch_model[ + f'{layer_prefix}layer.1.EncDecAttention.o.bias'] + + layer.cross_attention_layernorm.weight.value = pytorch_model[ + f'{layer_prefix}layer.1.layer_norm.weight'] + if tllm_model.layernorm_type != LayerNormType.RmsNorm: + layer.cross_attention_layernorm.bias.value = pytorch_model[ + f'{layer_prefix}layer.1.layer_norm.bias'] + + layer.mlp.fc.weight.value = pytorch_model[ + f'{layer_prefix}layer.2.DenseReluDense.wi.weight'] + layer.mlp.proj.weight.value = pytorch_model[ + f'{layer_prefix}layer.2.DenseReluDense.wo.weight'] + + if tllm_model.has_mlp_bias: + layer.mlp.fc.bias.value = pytorch_model[ + f'{layer_prefix}layer.2.DenseReluDense.wi.bias'] + layer.mlp.proj.bias.value = pytorch_model[ + f'{layer_prefix}layer.2.DenseReluDense.wo.bias'] + + layer.mlp_layernorm.weight.value = pytorch_model[ + f'{layer_prefix}layer.2.layer_norm.weight'] + if tllm_model.layernorm_type != LayerNormType.RmsNorm: + layer.mlp_layernorm.bias.value = pytorch_model[ + f'{layer_prefix}layer.2.layer_norm.bias'] + + if tllm_model.final_layernorm: + tllm_model.final_layernorm.weight.value = pytorch_model[ + 'decoder.final_layer_norm.weight'] + if tllm_model.layernorm_type != LayerNormType.RmsNorm: + tllm_model.final_layernorm.bias.value = pytorch_model[ + 'decoder.final_layer_norm.bias'] + + tllm_model.lm_head.weight.value = pytorch_model['lm_head.weight'] + +if __name__=="__main__": + # print(layernorm_type_map) + # print(layernorm_position_map) + load_whisper_from_pytorch( \ No newline at end of file From 1e1349cd802fc900b1bd6cc7fd248a53b3bdc725 Mon Sep 17 00:00:00 2001 From: makaveli10 Date: Thu, 14 Dec 2023 23:52:51 +0530 Subject: [PATCH 5/9] update firefox plugin with model size dropdown --- Audio-Transcription-Firefox/content.js | 3 ++- Audio-Transcription-Firefox/popup.html | 11 +++++++++++ Audio-Transcription-Firefox/popup.js | 21 +++++++++++++++++++-- 3 files changed, 32 insertions(+), 3 deletions(-) diff --git a/Audio-Transcription-Firefox/content.js b/Audio-Transcription-Firefox/content.js index 1d866c8c..682b142a 100644 --- a/Audio-Transcription-Firefox/content.js +++ b/Audio-Transcription-Firefox/content.js @@ -77,7 +77,8 @@ function startRecording(data) { uid: uuid, multilingual: data.useMultilingual, language: data.language, - task: data.task + task: data.task, + model_size: data.modelSize }) ); }; diff --git a/Audio-Transcription-Firefox/popup.html b/Audio-Transcription-Firefox/popup.html index 7ca020ab..2755a762 100644 --- a/Audio-Transcription-Firefox/popup.html +++ b/Audio-Transcription-Firefox/popup.html @@ -133,5 +133,16 @@ + \ No newline at end of file diff --git a/Audio-Transcription-Firefox/popup.js b/Audio-Transcription-Firefox/popup.js index a707fcab..b6785dd9 100644 --- a/Audio-Transcription-Firefox/popup.js +++ b/Audio-Transcription-Firefox/popup.js @@ -6,8 +6,11 @@ document.addEventListener("DOMContentLoaded", function() { const useMultilingualCheckbox = document.getElementById('useMultilingualCheckbox'); const languageDropdown = document.getElementById('languageDropdown'); const taskDropdown = document.getElementById('taskDropdown'); + const modelSizeDropdown = document.getElementById('modelSizeDropdown'); let selectedLanguage = null; let selectedTask = taskDropdown.value; + let selectedModelSize = modelSizeDropdown.value; + browser.storage.local.get("capturingState") .then(function(result) { @@ -54,9 +57,16 @@ document.addEventListener("DOMContentLoaded", function() { } }); + browser.storage.local.get("selectedModelSize", ({ selectedModelSize: storedModelSize }) => { + if (storedModelSize !== undefined) { + modelSizeDropdown.value = storedModelSize; + selectedModelSize = storedModelSize; + } + }); + startButton.addEventListener("click", function() { let host = "localhost"; - let port = "9090"; + let port = "5901"; const useCollaboraServer = useServerCheckbox.checked; if (useCollaboraServer){ @@ -75,7 +85,8 @@ document.addEventListener("DOMContentLoaded", function() { port: port, useMultilingual: useMultilingualCheckbox.checked, language: selectedLanguage, - task: selectedTask + task: selectedTask, + modelSize: selectedModelSize } }); toggleCaptureButtons(true); @@ -115,6 +126,7 @@ document.addEventListener("DOMContentLoaded", function() { stopButton.disabled = !isCapturing; useServerCheckbox.disabled = isCapturing; useMultilingualCheckbox.disabled = isCapturing; + modelSizeDropdown.disabled = isCapturing; startButton.classList.toggle("disabled", isCapturing); stopButton.classList.toggle("disabled", !isCapturing); @@ -152,6 +164,11 @@ document.addEventListener("DOMContentLoaded", function() { browser.storage.local.set({ selectedTask }); }); + modelSizeDropdown.addEventListener('change', function() { + selectedModelSize = modelSizeDropdown.value; + browser.storage.local.set({ selectedModelSize }); + }); + browser.runtime.onMessage.addListener((request, sender, sendResponse) => { if 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script --- Audio-Transcription-Chrome/test.py | 243 ----------------------------- 1 file changed, 243 deletions(-) delete mode 100644 Audio-Transcription-Chrome/test.py diff --git a/Audio-Transcription-Chrome/test.py b/Audio-Transcription-Chrome/test.py deleted file mode 100644 index ddd69c91..00000000 --- a/Audio-Transcription-Chrome/test.py +++ /dev/null @@ -1,243 +0,0 @@ -import configparser - -import numpy as np -import torch - -from tensorrt_llm._utils import str_dtype_to_torch, torch_to_numpy -from tensorrt_llm.functional import LayerNormPositionType, LayerNormType - -layernorm_type_map = {i.name: i.value for i in LayerNormType} -layernorm_position_map = {i.name: i.value for i in LayerNormPositionType} - -def parse_config(ini_file, component, args): - config = configparser.ConfigParser() - config.read(ini_file) - if component == 'encoder': - args.n_layer = config.getint(component, 'n_layer') - args.n_head = config.getint(component, 'n_head') - args.hidden_size = config.getint(component, 'n_state') - args.n_ctx = config.getint(component, 'n_ctx') - args.ffn_hidden_size = config.getint(component, 'ffn_hidden_size') - args.vocab_size = config.getint(component, 'vocab_size') - args.n_positions = config.getint(component, 'n_positions') - args.has_position_embedding = config.getboolean( - component, 'has_position_embedding', fallback=False) - args.has_token_type_embedding = config.getboolean( - component, 'has_token_type_embedding', fallback=False) - args.has_embedding_layernorm = config.getboolean( - component, 'has_embedding_layernorm', fallback=False) - args.has_embedding_scale = config.getboolean(component, - 'has_embedding_scale', - fallback=False) - args.q_scaling = config.getfloat(component, 'q_scaling', fallback=1.0) - args.has_attention_qkvo_bias = config.getboolean( - component, 'has_attention_qkvo_bias', fallback=False) - args.has_mlp_bias = config.getboolean(component, - 'has_mlp_bias', - fallback=False) - args.has_model_final_layernorm = config.getboolean( - component, 'has_model_final_layernorm', fallback=False) - args.layernorm_eps = config.getfloat(component, - 'layernorm_eps', - fallback=1e-5) - args.layernorm_position = layernorm_position_map[config.get( - component, 'layernorm_position')] - args.layernorm_type = layernorm_type_map[config.get( - component, 'layernorm_type')] - args.hidden_act = config.get(component, 'hidden_act') - args.relative_attention = config.getboolean(component, - 'relative_attention', - fallback=False) - return args - -def fuse_qkv(q, k, v): - qkv_weight = np.concatenate((q, k, v)) - return qkv_weight - -def load_whisper_from_pytorch(tllm_model, - pytorch_ckpt_path, - component, - model_size, - multilingual=False, - dtype="float32"): - torch_dtype = str_dtype_to_torch(dtype) - model_name = "whisper-" + f"{model_size}" - if not multilingual: - model_name = model_name + ".en" - - pytorch_ckpt = torch.load(pytorch_ckpt_path + f"{model_name}.ckpt") - pytorch_model = { - key: torch_to_numpy(value.to(torch_dtype)) - for key, value in pytorch_ckpt.items() - } - - if component == "encoder": - # set conv1d - tllm_model.conv1.weight.value = pytorch_model['encoder.conv1.weight'].unsqueeze(3) - tllm_model.conv1.bias.value = pytorch_model['encoder.conv1.bias'] - tllm_model.conv2.weight.value = pytorch_model['encoder.conv2.weight'].unsqueeze(3) - tllm_model.conv2.bias.value = pytorch_model['encoder.conv2.bias'] - # if tllm_model.embedding.position_embedding: - # tllm_model.embedding.position_embedding.weight.value = pytorch_model[ - # 'encoder.embed_positions.weight'] - - for i in range(tllm_model.num_layers): - layer = tllm_model.encoder_layers[i] - layer_prefix = f'encoder.layers.{i}.' - - # attention table for all layers - layer.attention.rel_attn_table.value = relative_attention_table - - layer.attention.qkv.weight.value = fuse_qkv( - pytorch_model[f'{layer_prefix}self_attn.q.weight'], - pytorch_model[f'{layer_prefix}self_attn.k.weight'], - pytorch_model[f'{layer_prefix}self_attn.v.weight']) - layer.attention.dense.weight.value = pytorch_model[ - f'{layer_prefix}self_attn.out_proj.weight'] - - if tllm_model.has_attention_qkvo_bias: - layer.attention.qkv.bias.value = fuse_qkv( - pytorch_model[f'{layer_prefix}self_attn.q.bias'], - torch.zeros(pytorch_model[f'{layer_prefix}self_attn.q.bias'].shape), # no bias for k - pytorch_model[f'{layer_prefix}self_attn.v.bias'] - ) - layer.attention.dense.bias.value = pytorch_model[ - f'{layer_prefix}self_attn.out_proj.bias'] - - layer.attention_layernorm.weight.value = pytorch_model[ - f'{layer_prefix}self_attn_layer_norm.weight'] - if tllm_model.layernorm_type != LayerNormType.RmsNorm: - layer.attention_layernorm.bias.value = pytorch_model[ - f'{layer_prefix}self_attn_layer_norm.bias'] - - layer.mlp.fc.weight.value = pytorch_model[ - f'{layer_prefix}fc1.weight'] - layer.mlp.proj.weight.value = pytorch_model[ - f'{layer_prefix}fc2.weight'] - - if tllm_model.has_mlp_bias: - layer.mlp.fc.bias.value = pytorch_model[ - f'{layer_prefix}fc1.bias'] - layer.mlp.proj.bias.value = pytorch_model[ - f'{layer_prefix}fc2.bias'] - - layer.mlp_layernorm.weight.value = pytorch_model[ - f'{layer_prefix}final_layer_norm.weight'] - if tllm_model.layernorm_type != LayerNormType.RmsNorm: - layer.mlp_layernorm.bias.value = pytorch_model[ - f'{layer_prefix}final_layer_norm.bias'] - - if tllm_model.final_layernorm: - tllm_model.final_layernorm.weight.value = pytorch_model[ - 'encoder.layer_norm.weight'] - if tllm_model.layernorm_type != LayerNormType.RmsNorm: - tllm_model.final_layernorm.bias.value = pytorch_model[ - 'encoder.layer_norm.bias'] - - if component == "decoder": - tllm_model.embedding.vocab_embedding.weight.value = pytorch_model[ - 'shared.weight'] - if tllm_model.embedding.position_embedding: - tllm_model.embedding.position_embedding.weight.value = pytorch_model[ - 'decoder.embed_positions.weight'] - if tllm_model.embedding.token_type_embedding: - tllm_model.embedding.token_type_embedding.weight.value = pytorch_model[ - 'decoder.embed_token_type.weight'] - - # all layers use 1st layer's attn table - # transpose from [num_buckets, num_heads] --> [num_heads, num_buckets] - # ascontiguousarray is very important! otherwise TRT always receives the original layout - relative_attention_table = np.ascontiguousarray(pytorch_model[ - f'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight'] - .T) - - for i in range(tllm_model.num_layers): - layer = tllm_model.decoder_layers[i] - layer_prefix = f'decoder.block.{i}.' - - # attention table for all layers - layer.self_attention.rel_attn_table.value = relative_attention_table - - # self attn - layer.self_attention.qkv.weight.value = fuse_qkv( - pytorch_model[f'{layer_prefix}layer.0.SelfAttention.q.weight'], - pytorch_model[f'{layer_prefix}layer.0.SelfAttention.k.weight'], - pytorch_model[f'{layer_prefix}layer.0.SelfAttention.v.weight']) - layer.self_attention.dense.weight.value = pytorch_model[ - f'{layer_prefix}layer.0.SelfAttention.o.weight'] - - if tllm_model.has_attention_qkvo_bias: - layer.self_attention.qkv.bias.value = fuse_qkv( - pytorch_model[ - f'{layer_prefix}layer.0.SelfAttention.q.bias'], - pytorch_model[ - f'{layer_prefix}layer.0.SelfAttention.k.bias'], - pytorch_model[f'{layer_prefix}layer.0.SelfAttention.v.bias'] - ) - layer.self_attention.dense.bias.value = pytorch_model[ - f'{layer_prefix}layer.0.SelfAttention.o.bias'] - - layer.self_attention_layernorm.weight.value = pytorch_model[ - f'{layer_prefix}layer.0.layer_norm.weight'] - if tllm_model.layernorm_type != LayerNormType.RmsNorm: - layer.self_attention_layernorm.bias.value = pytorch_model[ - f'{layer_prefix}layer.0.layer_norm.bias'] - - # cross attn - layer.cross_attention.qkv.weight.value = fuse_qkv( - pytorch_model[ - f'{layer_prefix}layer.1.EncDecAttention.q.weight'], - pytorch_model[ - f'{layer_prefix}layer.1.EncDecAttention.k.weight'], - pytorch_model[f'{layer_prefix}layer.1.EncDecAttention.v.weight'] - ) - layer.cross_attention.dense.weight.value = pytorch_model[ - f'{layer_prefix}layer.1.EncDecAttention.o.weight'] - - if tllm_model.has_attention_qkvo_bias: - layer.cross_attention.qkv.bias.value = fuse_qkv( - pytorch_model[ - f'{layer_prefix}layer.1.EncDecAttention.q.bias'], - pytorch_model[ - f'{layer_prefix}layer.1.EncDecAttention.k.bias'], - pytorch_model[ - f'{layer_prefix}layer.1.EncDecAttention.v.bias']) - layer.cross_attention.dense.bias.value = pytorch_model[ - f'{layer_prefix}layer.1.EncDecAttention.o.bias'] - - layer.cross_attention_layernorm.weight.value = pytorch_model[ - f'{layer_prefix}layer.1.layer_norm.weight'] - if tllm_model.layernorm_type != LayerNormType.RmsNorm: - layer.cross_attention_layernorm.bias.value = pytorch_model[ - f'{layer_prefix}layer.1.layer_norm.bias'] - - layer.mlp.fc.weight.value = pytorch_model[ - f'{layer_prefix}layer.2.DenseReluDense.wi.weight'] - layer.mlp.proj.weight.value = pytorch_model[ - f'{layer_prefix}layer.2.DenseReluDense.wo.weight'] - - if tllm_model.has_mlp_bias: - layer.mlp.fc.bias.value = pytorch_model[ - f'{layer_prefix}layer.2.DenseReluDense.wi.bias'] - layer.mlp.proj.bias.value = pytorch_model[ - f'{layer_prefix}layer.2.DenseReluDense.wo.bias'] - - layer.mlp_layernorm.weight.value = pytorch_model[ - f'{layer_prefix}layer.2.layer_norm.weight'] - if tllm_model.layernorm_type != LayerNormType.RmsNorm: - layer.mlp_layernorm.bias.value = pytorch_model[ - f'{layer_prefix}layer.2.layer_norm.bias'] - - if tllm_model.final_layernorm: - tllm_model.final_layernorm.weight.value = pytorch_model[ - 'decoder.final_layer_norm.weight'] - if tllm_model.layernorm_type != LayerNormType.RmsNorm: - tllm_model.final_layernorm.bias.value = pytorch_model[ - 'decoder.final_layer_norm.bias'] - - tllm_model.lm_head.weight.value = pytorch_model['lm_head.weight'] - -if __name__=="__main__": - # print(layernorm_type_map) - # print(layernorm_position_map) - load_whisper_from_pytorch( \ No newline at end of file From a52dc0cbf8eb03b1f0b2c40eccf4e5d00ff5aa4c Mon Sep 17 00:00:00 2001 From: makaveli10 Date: Fri, 15 Dec 2023 00:09:55 +0530 Subject: [PATCH 8/9] update chrome/firefox plugin readme --- Audio-Transcription-Chrome/README.md | 1 + Audio-Transcription-Firefox/README.md | 1 + 2 files changed, 2 insertions(+) diff --git a/Audio-Transcription-Chrome/README.md b/Audio-Transcription-Chrome/README.md index 0d402901..7392f738 100644 --- a/Audio-Transcription-Chrome/README.md +++ b/Audio-Transcription-Chrome/README.md @@ -29,6 +29,7 @@ When using the Audio Transcription extension, you have the following options: - **Use Multilingual Model**: Enable this option to utilize the multilingual capabilities of OpenAI-whisper. - **Language**: Select the target language for transcription or translation. You can choose from a variety of languages supported by OpenAI-whisper. - **Task:** Choose the specific task to perform on the audio. You can select either "transcribe" for transcription or "translate" to translate the audio to English. + - **Model Size**: Select the whisper model size to run the server with. ### Getting Started - Make sure the transcription server is running properly. To know more about how to start the server, see the [documentation here](https://github.com/collabora/whisper-live). diff --git a/Audio-Transcription-Firefox/README.md b/Audio-Transcription-Firefox/README.md index 52e4ceb2..be48b786 100644 --- a/Audio-Transcription-Firefox/README.md +++ b/Audio-Transcription-Firefox/README.md @@ -27,6 +27,7 @@ When using the Audio Transcription extension, you have the following options: - **Use Multilingual Model**: Enable this option to utilize the multilingual capabilities of OpenAI-whisper. - **Language**: Select the target language for transcription or translation. You can choose from a variety of languages supported by OpenAI-whisper. - **Task:** Choose the specific task to perform on the audio. You can select either "transcribe" for transcription or "translate" to translate the audio to English. + - **Model Size**: Select the whisper model size to run the server with. ### Getting Started - Make sure the transcription server is running properly. To know more about how to start the server, see the [documentation here](https://github.com/collabora/whisper-live). From e006722da77ac638f968889845510b6308e2b9d6 Mon Sep 17 00:00:00 2001 From: makaveli10 Date: Wed, 20 Dec 2023 18:06:02 +0530 Subject: [PATCH 9/9] add model size option to client --- whisper_live/client.py | 30 +++++++++++++++++++----------- 1 file changed, 19 insertions(+), 11 deletions(-) diff --git a/whisper_live/client.py b/whisper_live/client.py index 070845db..633c1237 100644 --- a/whisper_live/client.py +++ b/whisper_live/client.py @@ -50,7 +50,7 @@ class Client: INSTANCES = {} def __init__( - self, host=None, port=None, is_multilingual=False, lang=None, translate=False + self, host=None, port=None, is_multilingual=False, lang=None, translate=False, model_size="small" ): """ Initializes a Client instance for audio recording and streaming to a server. @@ -80,7 +80,9 @@ def __init__( self.last_response_recieved = None self.disconnect_if_no_response_for = 15 self.multilingual = is_multilingual - self.language = lang if is_multilingual else "en" + self.language = lang + self.model_size = model_size + self.server_error = False if translate: self.task = "translate" @@ -140,11 +142,16 @@ def on_message(self, ws, message): print("[ERROR]: invalid client uid") return - if "status" in message.keys() and message["status"] == "WAIT": - self.waiting = True - print( - f"[INFO]:Server is full. Estimated wait time {round(message['message'])} minutes." - ) + if "status" in message.keys(): + if message["status"] == "WAIT": + self.waiting = True + print( + f"[INFO]:Server is full. Estimated wait time {round(message['message'])} minutes." + ) + elif message["status"] == "ERROR": + print(f"Message from Server: {message['message']}") + self.server_error = True + return if "message" in message.keys() and message["message"] == "DISCONNECT": print("[INFO]: Server overtime disconnected.") @@ -213,6 +220,7 @@ def on_open(self, ws): "multilingual": self.multilingual, "language": self.language, "task": self.task, + "model_size": self.model_size, } ) ) @@ -497,8 +505,8 @@ class TranscriptionClient: transcription_client() ``` """ - def __init__(self, host, port, is_multilingual=False, lang=None, translate=False): - self.client = Client(host, port, is_multilingual, lang, translate) + def __init__(self, host, port, is_multilingual=False, lang=None, translate=False, model_size="small"): + self.client = Client(host, port, is_multilingual, lang, translate, model_size) def __call__(self, audio=None, hls_url=None): """ @@ -514,10 +522,10 @@ def __call__(self, audio=None, hls_url=None): """ print("[INFO]: Waiting for server ready ...") while not self.client.recording: - if self.client.waiting: + if self.client.waiting or self.client.server_error: self.client.close_websocket() return - pass + print("[INFO]: Server Ready!") if hls_url is not None: self.client.process_hls_stream(hls_url)

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