From 4d17ab9418c6b1b1cd2597270674538528d5a433 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C4=83zvan=20Purdel?= <104128753+rpurdel@users.noreply.github.com> Date: Wed, 20 Nov 2024 09:16:05 +0200 Subject: [PATCH] deps: use pypi provided silero vad, upgrade to latest --- poetry.lock | 18 +- pyproject.toml | 1 + requirements.txt | 1 + skynet/modules/stt/streaming_whisper/cfg.py | 6 +- .../models/vad/silero_vad.jit | Bin 1439299 -> 0 bytes .../stt/streaming_whisper/utils/utils.py | 5 +- .../stt/streaming_whisper/utils/vad_utils.py | 247 ------------------ 7 files changed, 24 insertions(+), 254 deletions(-) delete mode 100644 skynet/modules/stt/streaming_whisper/models/vad/silero_vad.jit delete mode 100644 skynet/modules/stt/streaming_whisper/utils/vad_utils.py diff --git a/poetry.lock b/poetry.lock index a6f5c3c..4f3565e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4544,6 +4544,22 @@ files = [ {file = "shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"}, ] +[[package]] +name = "silero-vad" +version = "5.1.2" +description = "Voice Activity Detector (VAD) by Silero" +optional = false +python-versions = ">=3.8" +files = [ + {file = "silero_vad-5.1.2-py3-none-any.whl", hash = "sha256:93b41953d7774b165407fda6b533c119c5803864e367d5034dc626c82cfdf661"}, + {file = "silero_vad-5.1.2.tar.gz", hash = "sha256:c442971160026d2d7aa0ad83f0c7ee86c89797a65289fe625c8ea59fc6fb828d"}, +] + +[package.dependencies] +onnxruntime = ">=1.16.1" +torch = ">=1.12.0" +torchaudio = ">=0.12.0" + [[package]] name = "six" version = "1.16.0" @@ -5941,4 +5957,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "~3.11" -content-hash = "495e3c5a0cb347d13376eaef3bfd532e89798cb752ea67c17a4979a87311882e" +content-hash = "e06203dbf380719b2ebe9dc19e868d3d018569b78d9833a9c9b256d30b5f28fd" diff --git a/pyproject.toml b/pyproject.toml index 32eea13..0fa0036 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,6 +42,7 @@ langchain-openai = "^0.2.0" av = "^12.3.0" pybase64 = "^1.4.0" vllm = "0.6.2" +silero-vad = "^5.1.2" [build-system] build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt index 5077415..93cd6f9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -119,6 +119,7 @@ s3transfer==0.10.2 ; python_version >= "3.11" and python_version < "3.12" safetensors==0.4.5 ; python_version >= "3.11" and python_version < "3.12" sentencepiece==0.2.0 ; python_version >= "3.11" and python_version < "3.12" setuptools==75.1.0 ; python_version >= "3.11" and python_version < "3.12" +silero-vad==5.1.2 ; python_version >= "3.11" and python_version < "3.12" six==1.16.0 ; python_version >= "3.11" and python_version < "3.12" sniffio==1.3.1 ; python_version >= "3.11" and python_version < "3.12" sqlalchemy==2.0.35 ; python_version >= "3.11" and python_version < "3.12" diff --git a/skynet/modules/stt/streaming_whisper/cfg.py b/skynet/modules/stt/streaming_whisper/cfg.py index d2e42d9..8299973 100644 --- a/skynet/modules/stt/streaming_whisper/cfg.py +++ b/skynet/modules/stt/streaming_whisper/cfg.py @@ -1,6 +1,5 @@ -import os - from faster_whisper import WhisperModel +from silero_vad import load_silero_vad from skynet.env import ( device, @@ -11,12 +10,11 @@ whisper_model_path, ) from skynet.logs import get_logger -from skynet.modules.stt.streaming_whisper.utils import vad_utils as vad log = get_logger(__name__) -vad_model = vad.init_jit_model(f'{os.getcwd()}/skynet/modules/stt/streaming_whisper/models/vad/silero_vad.jit') +vad_model = load_silero_vad(onnx=False) device = whisper_device if whisper_device != 'auto' else device log.info(f'Using {device}') diff --git a/skynet/modules/stt/streaming_whisper/models/vad/silero_vad.jit b/skynet/modules/stt/streaming_whisper/models/vad/silero_vad.jit deleted file mode 100644 index 501cb7c9ee9a8ac4f518acc05f14694c2a1e8f52..0000000000000000000000000000000000000000 GIT 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z&0hHBbq>D9uc?3+5JeNo>;0Bn;_>fz_Di#%5nU82)K^h0@*@L0pa*!s0Liitd}l3h zuKh0!NU6V1!35gB1t`e1A3C#h9H1inaE7tbETHB(Kwp7e`)KQ(wT!y<_PR`#xIoPj zC*~m6e#L(0I6y`Bzu#+*T>Ci3owdBV_P><>`+f1qkzA$gpK$;>0`zypZ+?N{_shKZ{S6Sx z|Mr#Ujf%s+_YD7N&tFpd{SIB$-=KhhUi?2`zfQ9K1^4?6xeQ2ftUl0i|6IeXtAUHl z_s<8Iu=fA^QN)xE;_K(%I_G};KN^5Td;y;WQW_X+X#U6l5x=ghfdh0+h|jQKAb$VH G@BR Tuple[bool, iter]: chunk_duration = convert_bytes_to_seconds(audio) wav_header = get_wav_header([audio], chunk_duration_s=chunk_duration) stream = wav_header + b'' + audio - audio = cfg.vad.read_audio(stream) - st = cfg.vad.get_speech_timestamps(audio, model=cfg.vad_model, return_seconds=True) + audio = read_audio(stream) + st = get_speech_timestamps(audio, model=cfg.vad_model, return_seconds=True) log.debug(f'Detected speech timestamps: {st}') silent = True if len(st) == 0 else False return silent, st diff --git a/skynet/modules/stt/streaming_whisper/utils/vad_utils.py b/skynet/modules/stt/streaming_whisper/utils/vad_utils.py deleted file mode 100644 index 417f5a1..0000000 --- a/skynet/modules/stt/streaming_whisper/utils/vad_utils.py +++ /dev/null @@ -1,247 +0,0 @@ -# MIT License -# -# Copyright (c) 2020-present Silero Team -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import io -import warnings -from typing import Callable - -import torch -import torchaudio - - -def read_audio(path: str, sampling_rate: int = 16000): - wav, sr = torchaudio.load(io.BytesIO(path)) - if wav.size(0) > 1: - wav = wav.mean(dim=0, keepdim=True) - - if sr != sampling_rate: - transform = torchaudio.transforms.Resample(orig_freq=sr, new_freq=sampling_rate) - wav = transform(wav) - sr = sampling_rate - - assert sr == sampling_rate - return wav.squeeze(0) - - -def init_jit_model(model_path: str, device=torch.device('cpu')): - torch.set_grad_enabled(False) - model = torch.jit.load(model_path, map_location=device) - model.eval() - return model - - -def get_speech_timestamps( - audio: torch.Tensor, - model, - threshold: float = 0.5, - sampling_rate: int = 16000, - min_speech_duration_ms: int = 250, - max_speech_duration_s: float = float('inf'), - min_silence_duration_ms: int = 100, - window_size_samples: int = 512, - speech_pad_ms: int = 30, - return_seconds: bool = False, - progress_tracking_callback: Callable[[float], None] = None, -): - """ - This method is used for splitting long audios into speech chunks using silero VAD - - Parameters - ---------- - audio: torch.Tensor, one dimensional - One dimensional float torch.Tensor, other types are casted to torch if possible - - model: preloaded .jit silero VAD model - - threshold: float (default - 0.5) - Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH. - It is better to tune this parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets. - - sampling_rate: int (default - 16000) - Currently silero VAD models support 8000 and 16000 sample rates - - min_speech_duration_ms: int (default - 250 milliseconds) - Final speech chunks shorter min_speech_duration_ms are thrown out - - max_speech_duration_s: int (default - inf) - Maximum duration of speech chunks in seconds - Chunks longer than max_speech_duration_s will be split at the timestamp of the last silence that lasts more than 100s (if any), to prevent agressive cutting. - Otherwise, they will be split aggressively just before max_speech_duration_s. - - min_silence_duration_ms: int (default - 100 milliseconds) - In the end of each speech chunk wait for min_silence_duration_ms before separating it - - window_size_samples: int (default - 1536 samples) - Audio chunks of window_size_samples size are fed to the silero VAD model. - WARNING! Silero VAD models were trained using 512, 1024, 1536 samples for 16000 sample rate and 256, 512, 768 samples for 8000 sample rate. - Values other than these may affect model perfomance!! - - speech_pad_ms: int (default - 30 milliseconds) - Final speech chunks are padded by speech_pad_ms each side - - return_seconds: bool (default - False) - whether return timestamps in seconds (default - samples) - - progress_tracking_callback: Callable[[float], None] (default - None) - callback function taking progress in percents as an argument - - Returns - ---------- - speeches: list of dicts - list containing ends and beginnings of speech chunks (samples or seconds based on return_seconds) - """ - - if not torch.is_tensor(audio): - try: - audio = torch.Tensor(audio) - except: - raise TypeError("Audio cannot be casted to tensor. Cast it manually") - - if len(audio.shape) > 1: - for i in range(len(audio.shape)): # trying to squeeze empty dimensions - audio = audio.squeeze(0) - if len(audio.shape) > 1: - raise ValueError("More than one dimension in audio. Are you trying to process audio with 2 channels?") - - if sampling_rate > 16000 and (sampling_rate % 16000 == 0): - step = sampling_rate // 16000 - sampling_rate = 16000 - audio = audio[::step] - warnings.warn('Sampling rate is a multiply of 16000, casting to 16000 manually!') - else: - step = 1 - - if sampling_rate == 8000 and window_size_samples > 768: - warnings.warn( - 'window_size_samples is too big for 8000 sampling_rate! Better set window_size_samples to 256, 512 or 768 for 8000 sample rate!' - ) - if window_size_samples not in [256, 512, 768, 1024, 1536]: - warnings.warn( - 'Unusual window_size_samples! Supported window_size_samples:\n - [512, 1024, 1536] for 16000 sampling_rate\n - [256, 512, 768] for 8000 sampling_rate' - ) - - model.reset_states() - min_speech_samples = sampling_rate * min_speech_duration_ms / 1000 - speech_pad_samples = sampling_rate * speech_pad_ms / 1000 - max_speech_samples = sampling_rate * max_speech_duration_s - window_size_samples - 2 * speech_pad_samples - min_silence_samples = sampling_rate * min_silence_duration_ms / 1000 - min_silence_samples_at_max_speech = sampling_rate * 98 / 1000 - - audio_length_samples = len(audio) - - speech_probs = [] - for current_start_sample in range(0, audio_length_samples, window_size_samples): - chunk = audio[current_start_sample : current_start_sample + window_size_samples] - if len(chunk) < window_size_samples: - chunk = torch.nn.functional.pad(chunk, (0, int(window_size_samples - len(chunk)))) - speech_prob = model(chunk, sampling_rate).item() - speech_probs.append(speech_prob) - # caculate progress and seng it to callback function - progress = current_start_sample + window_size_samples - if progress > audio_length_samples: - progress = audio_length_samples - progress_percent = (progress / audio_length_samples) * 100 - if progress_tracking_callback: - progress_tracking_callback(progress_percent) - - triggered = False - speeches = [] - current_speech = {} - neg_threshold = threshold - 0.15 - temp_end = 0 # to save potential segment end (and tolerate some silence) - prev_end = next_start = 0 # to save potential segment limits in case of maximum segment size reached - - for i, speech_prob in enumerate(speech_probs): - if (speech_prob >= threshold) and temp_end: - temp_end = 0 - if next_start < prev_end: - next_start = window_size_samples * i - - if (speech_prob >= threshold) and not triggered: - triggered = True - current_speech['start'] = window_size_samples * i - continue - - if triggered and (window_size_samples * i) - current_speech['start'] > max_speech_samples: - if prev_end: - current_speech['end'] = prev_end - speeches.append(current_speech) - current_speech = {} - if next_start < prev_end: # previously reached silence (< neg_thres) and is still not speech (< thres) - triggered = False - else: - current_speech['start'] = next_start - prev_end = next_start = temp_end = 0 - else: - current_speech['end'] = window_size_samples * i - speeches.append(current_speech) - current_speech = {} - prev_end = next_start = temp_end = 0 - triggered = False - continue - - if (speech_prob < neg_threshold) and triggered: - if not temp_end: - temp_end = window_size_samples * i - if ( - (window_size_samples * i) - temp_end - ) > min_silence_samples_at_max_speech: # condition to avoid cutting in very short silence - prev_end = temp_end - if (window_size_samples * i) - temp_end < min_silence_samples: - continue - else: - current_speech['end'] = temp_end - if (current_speech['end'] - current_speech['start']) > min_speech_samples: - speeches.append(current_speech) - current_speech = {} - prev_end = next_start = temp_end = 0 - triggered = False - continue - - if current_speech and (audio_length_samples - current_speech['start']) > min_speech_samples: - current_speech['end'] = audio_length_samples - speeches.append(current_speech) - - for i, speech in enumerate(speeches): - if i == 0: - speech['start'] = int(max(0, speech['start'] - speech_pad_samples)) - if i != len(speeches) - 1: - silence_duration = speeches[i + 1]['start'] - speech['end'] - if silence_duration < 2 * speech_pad_samples: - speech['end'] += int(silence_duration // 2) - speeches[i + 1]['start'] = int(max(0, speeches[i + 1]['start'] - silence_duration // 2)) - else: - speech['end'] = int(min(audio_length_samples, speech['end'] + speech_pad_samples)) - speeches[i + 1]['start'] = int(max(0, speeches[i + 1]['start'] - speech_pad_samples)) - else: - speech['end'] = int(min(audio_length_samples, speech['end'] + speech_pad_samples)) - - if return_seconds: - for speech_dict in speeches: - speech_dict['start'] = round(speech_dict['start'] / sampling_rate, 1) - speech_dict['end'] = round(speech_dict['end'] / sampling_rate, 1) - elif step > 1: - for speech_dict in speeches: - speech_dict['start'] *= step - speech_dict['end'] *= step - - return speeches