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| # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/1B. Voice activity detection.ipynb. | |
| # %% auto 0 | |
| __all__ = [] | |
| # %% ../nbs/1B. Voice activity detection.ipynb 3 | |
| import os | |
| import torch | |
| import torchaudio | |
| from pathlib import Path | |
| from fastprogress import progress_bar | |
| from fastcore.script import call_parse | |
| import whisperx | |
| import random | |
| import numpy as np | |
| import webdataset as wds | |
| # %% ../nbs/1B. Voice activity detection.ipynb 5 | |
| # some of the original file names have a dot in their name | |
| # webdataset does not like it so let's patch it | |
| def fix_dots_in_names(name): | |
| name, ext = name.rsplit('.', 1) | |
| return ".".join((name.replace('.', '_'), ext)) | |
| def load_dataset(url, decode=True, rename_files=None): | |
| ds = wds.WebDataset(url, rename_files=rename_files) | |
| if not decode: return ds | |
| return ds.decode(wds.torch_audio) | |
| # %% ../nbs/1B. Voice activity detection.ipynb 7 | |
| def extract_segments(vad_result, max_duration): | |
| binarize = whisperx.vad.Binarize(max_duration=max_duration) | |
| segments = binarize(vad_result) | |
| return [(x.start, x.end) for x in segments.get_timeline()] | |
| def segment_audio(vad_model, audio, sr=16000): | |
| vad_result = vad_model({"waveform": audio, "sample_rate": sr}) | |
| return extract_segments(vad_result, 30) | |
| # %% ../nbs/1B. Voice activity detection.ipynb 13 | |
| def flac_to_vad_name(input): | |
| if '-flac-' in input: | |
| return input.rsplit("/", 1)[1].replace('flac', 'vad') + ".gz" | |
| else: | |
| return input.rsplit("/", 1)[1].replace('raw', 'vad') + ".gz" | |
| def process_shard( | |
| input:str, # input shard URL/path | |
| output:str=None, # output shard URL/path | |
| fix_dots:bool=False, # fix dots in LibriLight filenames | |
| ): | |
| if output is None: output = flac_to_vad_name(input) | |
| ds = torch.utils.data.DataLoader(load_dataset(input, rename_files=fix_dots_in_names if fix_dots else None), num_workers=2, batch_size=None) | |
| vad_model = whisperx.vad.load_vad_model('cuda') | |
| tmp = output+".tmp" | |
| with wds.TarWriter(tmp) as sink: | |
| for s in progress_bar(ds, total='noinfer'): | |
| audio, sr = s.get('flac', s.get('wav', (None, None))) | |
| if audio is None: | |
| print(f"warning: '{s['__key__']}' does not contain an audio file") | |
| continue | |
| sink.write({ | |
| "__key__": s['__key__'], | |
| "vad.npy": np.array(segment_audio(vad_model, audio, sr=sr), dtype=np.float16) | |
| }) | |
| os.rename(tmp, output) | |