240 lines
7.1 KiB
Python
240 lines
7.1 KiB
Python
"""
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# Handles processing audio provided through --input-audio of adequately annotated transcriptions provided through --input-metadata (through transcribe.py)
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# Outputs NumPy objects containing quantized audio and adequate metadata for use of loading in the trainer through --output-dataset
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"""
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import os
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import json
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import argparse
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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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def pad(num, zeroes):
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return str(num).zfill(zeroes+1)
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def process_items( items, stride=0, stride_offset=0 ):
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items = sorted( items )
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return items if stride == 0 else [ item for i, item in enumerate( items ) if (i+stride_offset) % stride == 0 ]
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def process(
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audio_backend="encodec",
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input_audio="Emilia",
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output_dataset="training",
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raise_exceptions=False,
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stride=0,
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stride_offset=0,
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slice="auto",
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device="cuda",
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dtype="float16",
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amp=False,
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):
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# encodec / vocos
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if audio_backend in ["encodec", "vocos"]:
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audio_extension = ".enc"
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cfg.sample_rate = 24_000
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cfg.model.resp_levels = 8
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elif audio_backend == "dac":
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audio_extension = ".dac"
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cfg.sample_rate = 44_100
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cfg.model.resp_levels = 9
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elif cfg.audio_backend == "audiodec":
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sample_rate = 48_000
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audio_extension = ".dec"
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cfg.model.resp_levels = 8 # ?
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else:
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raise Exception(f"Unknown audio backend: {audio_backend}")
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# prepare from args
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cfg.audio_backend = audio_backend # "encodec"
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cfg.inference.weight_dtype = dtype # "bfloat16"
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cfg.inference.amp = amp # False
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# import after because we've overriden the config above
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# need to validate if this is even necessary anymore
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from vall_e.emb.g2p import encode as phonemize
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from vall_e.emb.qnt import encode as quantize, _replace_file_extension
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output_dataset = f"{output_dataset}/{'2' if cfg.sample_rate == 24_000 else '4'}{'8' if cfg.sample_rate == 48_000 else '4'}KHz-{cfg.audio_backend}" # "training"
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language_map = {} # k = group, v = language
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ignore_groups = [] # skip these groups
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ignore_speakers = [] # skip these speakers
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only_groups = [] # only process these groups
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only_speakers = [] # only process these speakers
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always_slice_groups = [] # always slice from this group
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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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# Layout: ./Emilia/JA/JA-B000000/JA_B00000_S00000_W000000.{json|mp3}
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for language in sorted(os.listdir(f'./{input_audio}/')):
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if not os.path.isdir(f'./{input_audio}/{language}/'):
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print("Is not dir:", f'./{input_audio}/{language}/')
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continue
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if language in ignore_groups:
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continue
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if only_groups and language not in only_groups:
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continue
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group_name = "Emilia"
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for speaker_id in tqdm(process_items(os.listdir(f'./{input_audio}/{language}/'), stride=stride, stride_offset=stride_offset), desc=f"Processing speaker in {language}"):
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if not os.path.isdir(f'./{input_audio}/{language}/{speaker_id}'):
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print("Is not dir:", f'./{input_audio}/{language}/{speaker_id}')
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continue
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if speaker_id in ignore_speakers:
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continue
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if only_speakers and speaker_id not in only_speakers:
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continue
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os.makedirs(f'./{output_dataset}/{group_name}/{speaker_id}/', exist_ok=True)
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if f'{group_name}/{speaker_id}' not in dataset:
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dataset.append(f'{group_name}/{speaker_id}')
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txts = []
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wavs = []
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for filename in os.listdir(f'./{input_audio}/{language}/{speaker_id}'):
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if ".mp3" not in filename:
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continue
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inpath = Path(f'./{input_audio}/{language}/{speaker_id}/{filename}')
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jsonpath = _replace_file_extension(inpath, ".json")
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if not inpath.exists() or not jsonpath.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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outpath = Path(f'./{output_dataset}/{group_name}/{speaker_id}/{fname}.{extension}')
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metadata = json.load(open(jsonpath, "r", encoding="utf-8"))
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if "text" not in metadata:
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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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text = metadata["text"]
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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.lower(),
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waveform,
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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 = phonemize(text, language=language)
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qnt = quantize(waveform, sr=sample_rate, device=device)
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if cfg.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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if raise_exceptions:
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raise e
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continue
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open(f"./{output_dataset}/missing.json", 'w', encoding='utf-8').write(json.dumps(missing))
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open(f"./{output_dataset}/dataset.json", 'w', encoding='utf-8').write(json.dumps(dataset))
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--audio-backend", type=str, default="encodec")
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parser.add_argument("--dtype", type=str, default="bfloat16")
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parser.add_argument("--amp", action="store_true")
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parser.add_argument("--input-audio", type=str, default="Emilia")
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parser.add_argument("--output-dataset", type=str, default="training/dataset")
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parser.add_argument("--device", type=str, default="cuda")
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parser.add_argument("--raise-exceptions", action="store_true")
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parser.add_argument("--stride", type=int, default=0)
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parser.add_argument("--stride-offset", type=int, default=0)
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parser.add_argument("--slice", type=str, default="auto")
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args = parser.parse_args()
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# do some assumption magic
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# to-do: find a nice way to spawn multiple processes where tqdm plays nicely
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if args.device.isnumeric():
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args.stride = torch.cuda.device_count()
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args.stride_offset = int(args.device)
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args.device = f'cuda:{args.device}'
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process(
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audio_backend=args.audio_backend,
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input_audio=args.input_audio,
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output_dataset=args.output_dataset,
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raise_exceptions=args.raise_exceptions,
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stride=args.stride,
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stride_offset=args.stride_offset,
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slice=args.slice,
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device=args.device,
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dtype=args.dtype,
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amp=args.amp,
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)
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if __name__ == "__main__":
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main() |