222 lines
6.5 KiB
Python
222 lines
6.5 KiB
Python
import os
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import json
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import torch
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import torchaudio
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import numpy as np
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from tqdm.auto import tqdm
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from pathlib import Path
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from vall_e.config import cfg
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# things that could be args
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cfg.sample_rate = 44_000
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cfg.inference.audio_backend = "dac"
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"""
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cfg.inference.weight_dtype = "bfloat16"
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cfg.inference.dtype = torch.bfloat16
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cfg.inference.amp = True
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"""
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from vall_e.emb.g2p import encode as valle_phonemize
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from vall_e.emb.qnt import encode as valle_quantize, _replace_file_extension
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input_audio = "voices"
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input_metadata = "metadata"
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output_dataset = f"training-{'2' if cfg.sample_rate == 24_000 else '4'}4KHz-{cfg.inference.audio_backend}"
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device = "cuda"
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audio_extension = ".dac" if cfg.inference.audio_backend == "dac" else ".enc"
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slice = "auto"
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missing = {
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"transcription": [],
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"audio": []
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}
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dataset = []
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def pad(num, zeroes):
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return str(num).zfill(zeroes+1)
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for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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if not os.path.isdir(f'./{input_audio}/{dataset_name}/'):
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print("Is not dir:", f'./{input_audio}/{dataset_name}/')
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continue
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for speaker_id in tqdm(sorted(os.listdir(f'./{input_audio}/{dataset_name}/')), desc=f"Processing speaker in {dataset_name}"):
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if not os.path.isdir(f'./{input_audio}/{dataset_name}/{speaker_id}'):
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print("Is not dir:", f'./{input_audio}/{dataset_name}/{speaker_id}')
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continue
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os.makedirs(f'./{output_dataset}/{dataset_name}/{speaker_id}/', exist_ok=True)
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if speaker_id == "Noise":
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for filename in sorted(os.listdir(f'./{input_audio}/{dataset_name}/{speaker_id}/')):
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inpath = Path(f'./{input_audio}/{dataset_name}/{speaker_id}/{filename}')
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outpath = Path(f'./{output_dataset}/{dataset_name}/{speaker_id}/{filename}')
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if _replace_file_extension(outpath, audio_extension).exists():
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continue
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waveform, sample_rate = torchaudio.load(inpath)
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qnt = valle_quantize(waveform, sr=sample_rate, device=device)
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if cfg.inference.audio_backend == "dac":
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np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
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"codes": qnt.codes.cpu().numpy().astype(np.uint16),
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"metadata": {
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"original_length": qnt.original_length,
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"sample_rate": qnt.sample_rate,
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"input_db": qnt.input_db.cpu().numpy().astype(np.float32),
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"chunk_length": qnt.chunk_length,
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"channels": qnt.channels,
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"padding": qnt.padding,
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"dac_version": "1.0.0",
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},
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})
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else:
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np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
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"codes": qnt.cpu().numpy().astype(np.uint16),
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"metadata": {
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"original_length": waveform.shape[-1],
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"sample_rate": sample_rate,
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},
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})
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continue
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metadata_path = Path(f'./{input_metadata}/{dataset_name}/{speaker_id}/whisper.json')
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if not metadata_path.exists():
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missing["transcription"].append(str(metadata_path))
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continue
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try:
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metadata = json.loads(open(metadata_path, "r", encoding="utf-8").read())
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except Exception as e:
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missing["transcription"].append(str(metadata_path))
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continue
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if f'{dataset_name}/{speaker_id}' not in dataset:
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dataset.append(f'{dataset_name}/{speaker_id}')
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txts = []
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wavs = []
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use_slices = slice == True or (slice == "auto" and len(metadata.keys()) == 1) or dataset_name in ["LibriVox", "Audiobooks"]
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for filename in sorted(metadata.keys()):
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inpath = Path(f'./{input_audio}/{dataset_name}/{speaker_id}/{filename}')
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if not inpath.exists():
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missing["audio"].append(str(inpath))
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continue
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extension = os.path.splitext(filename)[-1][1:]
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fname = filename.replace(f'.{extension}', "")
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waveform, sample_rate = None, None
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language = metadata[filename]["language"] if "language" in metadata[filename] else "en"
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if len(metadata[filename]["segments"]) == 0 or not use_slices:
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outpath = Path(f'./{output_dataset}/{dataset_name}/{speaker_id}/{fname}.{extension}')
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text = metadata[filename]["text"]
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if len(text) == 0:
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continue
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if _replace_file_extension(outpath, audio_extension).exists():
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continue
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if waveform is None:
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waveform, sample_rate = torchaudio.load(inpath)
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if waveform.shape[0] > 1:
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waveform = torch.mean(waveform, dim=0, keepdim=True)
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wavs.append((
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outpath,
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text,
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language,
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waveform,
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sample_rate
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))
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else:
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i = 0
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for segment in metadata[filename]["segments"]:
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id = pad(i, 4)
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i = i + 1
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outpath = Path(f'./{output_dataset}/{dataset_name}/{speaker_id}/{fname}_{id}.{extension}')
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text = segment["text"]
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if len(text) == 0:
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continue
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if _replace_file_extension(outpath, audio_extension).exists():
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continue
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if waveform is None:
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waveform, sample_rate = torchaudio.load(inpath)
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if waveform.shape[0] > 1:
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waveform = torch.mean(waveform, dim=0, keepdim=True)
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start = int(segment['start'] * sample_rate)
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end = int(segment['end'] * sample_rate)
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if start < 0:
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start = 0
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if end >= waveform.shape[-1]:
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end = waveform.shape[-1] - 1
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if end - start < 0:
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continue
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wavs.append((
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outpath,
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text,
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language,
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waveform[:, start:end],
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sample_rate
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))
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if len(wavs) > 0:
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for job in tqdm(wavs, desc=f"Quantizing: {speaker_id}"):
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try:
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outpath, text, language, waveform, sample_rate = job
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phones = valle_phonemize(text)
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qnt = valle_quantize(waveform, sr=sample_rate, device=device)
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if cfg.inference.audio_backend == "dac":
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np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
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"codes": qnt.codes.cpu().numpy().astype(np.uint16),
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"metadata": {
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"original_length": qnt.original_length,
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"sample_rate": qnt.sample_rate,
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"input_db": qnt.input_db.cpu().numpy().astype(np.float32),
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"chunk_length": qnt.chunk_length,
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"channels": qnt.channels,
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"padding": qnt.padding,
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"dac_version": "1.0.0",
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"text": text.strip(),
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"phonemes": "".join(phones),
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"language": language,
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},
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})
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else:
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np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
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"codes": qnt.cpu().numpy().astype(np.uint16),
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"metadata": {
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"original_length": waveform.shape[-1],
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"sample_rate": sample_rate,
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"text": text.strip(),
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"phonemes": "".join(phones),
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"language": language,
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},
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})
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except Exception as e:
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print(f"Failed to quantize: {outpath}:", e)
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continue
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open("./missing.json", 'w', encoding='utf-8').write(json.dumps(missing))
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open("./dataset_list.json", 'w', encoding='utf-8').write(json.dumps(dataset)) |