updated process_datasets.py, added argparsing so I can mostly stop manually editing things, and some other cleanup
This commit is contained in:
parent
debcc93e7e
commit
7cdfa3dc0c
|
|
@ -1,191 +1,104 @@
|
|||
import os
|
||||
import json
|
||||
import argparse
|
||||
import torch
|
||||
import torchaudio
|
||||
import numpy as np
|
||||
|
||||
from tqdm.auto import tqdm
|
||||
from pathlib import Path
|
||||
from vall_e.config import cfg
|
||||
|
||||
# things that could be args
|
||||
cfg.sample_rate = 24_000
|
||||
cfg.audio_backend = "encodec"
|
||||
"""
|
||||
cfg.inference.weight_dtype = "bfloat16"
|
||||
cfg.inference.dtype = torch.bfloat16
|
||||
cfg.inference.amp = True
|
||||
"""
|
||||
|
||||
from vall_e.emb.g2p import encode as valle_phonemize
|
||||
from vall_e.emb.qnt import encode as valle_quantize, _replace_file_extension
|
||||
|
||||
input_audio = "voices"
|
||||
input_metadata = "metadata"
|
||||
output_dataset = f"training-{'2' if cfg.sample_rate == 24_000 else '4'}{'8' if cfg.sample_rate == 48_000 else '4'}KHz-{cfg.audio_backend}"
|
||||
device = "cuda"
|
||||
|
||||
audio_extension = ".enc"
|
||||
if cfg.audio_backend == "dac":
|
||||
audio_extension = ".dac"
|
||||
elif cfg.audio_backend == "audiodec":
|
||||
audio_extension = ".dec"
|
||||
|
||||
slice = "auto"
|
||||
missing = {
|
||||
"transcription": [],
|
||||
"audio": []
|
||||
}
|
||||
dataset = []
|
||||
|
||||
def pad(num, zeroes):
|
||||
return str(num).zfill(zeroes+1)
|
||||
|
||||
for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
|
||||
if not os.path.isdir(f'./{input_audio}/{dataset_name}/'):
|
||||
print("Is not dir:", f'./{input_audio}/{dataset_name}/')
|
||||
continue
|
||||
def process_items( items, stride=0 ):
|
||||
items = sorted( items )
|
||||
return items if stride == 0 else [ item for i, item in enumerate( items ) if i % stride == 0 ]
|
||||
|
||||
for speaker_id in tqdm(sorted(os.listdir(f'./{input_audio}/{dataset_name}/')), desc=f"Processing speaker in {dataset_name}"):
|
||||
if not os.path.isdir(f'./{input_audio}/{dataset_name}/{speaker_id}'):
|
||||
print("Is not dir:", f'./{input_audio}/{dataset_name}/{speaker_id}')
|
||||
continue
|
||||
|
||||
os.makedirs(f'./{output_dataset}/{dataset_name}/{speaker_id}/', exist_ok=True)
|
||||
def process_dataset( args ):
|
||||
# encodec / vocos
|
||||
|
||||
if speaker_id == "Noise":
|
||||
for filename in sorted(os.listdir(f'./{input_audio}/{dataset_name}/{speaker_id}/')):
|
||||
inpath = Path(f'./{input_audio}/{dataset_name}/{speaker_id}/{filename}')
|
||||
outpath = Path(f'./{output_dataset}/{dataset_name}/{speaker_id}/{filename}')
|
||||
if args.audio_backend in ["encodec", "vocos"]:
|
||||
audio_extension = ".enc"
|
||||
cfg.sample_rate = 24_000
|
||||
cfg.model.resp_levels = 8
|
||||
elif args.audio_backend == "dac":
|
||||
audio_extension = ".dac"
|
||||
cfg.sample_rate = 44_100
|
||||
cfg.model.resp_levels = 9
|
||||
elif cfg.audio_backend == "audiodec":
|
||||
sample_rate = 48_000
|
||||
audio_extension = ".dec"
|
||||
cfg.model.resp_levels = 8 # ?
|
||||
else:
|
||||
raise Exception(f"Unknown audio backend: {args.audio_backend}")
|
||||
|
||||
if _replace_file_extension(outpath, audio_extension).exists():
|
||||
continue
|
||||
# prepare from args
|
||||
cfg.audio_backend = args.audio_backend # "encodec"
|
||||
cfg.inference.weight_dtype = args.dtype # "bfloat16"
|
||||
cfg.inference.amp = args.amp # False
|
||||
|
||||
waveform, sample_rate = torchaudio.load(inpath)
|
||||
qnt = valle_quantize(waveform, sr=sample_rate, device=device)
|
||||
# import after because we've overriden the config above
|
||||
from vall_e.emb.g2p import encode as valle_phonemize
|
||||
from vall_e.emb.qnt import encode as valle_quantize, _replace_file_extension
|
||||
|
||||
if cfg.audio_backend == "dac":
|
||||
np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
|
||||
"codes": qnt.codes.cpu().numpy().astype(np.uint16),
|
||||
"metadata": {
|
||||
"original_length": qnt.original_length,
|
||||
"sample_rate": qnt.sample_rate,
|
||||
|
||||
"input_db": qnt.input_db.cpu().numpy().astype(np.float32),
|
||||
"chunk_length": qnt.chunk_length,
|
||||
"channels": qnt.channels,
|
||||
"padding": qnt.padding,
|
||||
"dac_version": "1.0.0",
|
||||
},
|
||||
})
|
||||
else:
|
||||
np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
|
||||
"codes": qnt.cpu().numpy().astype(np.uint16),
|
||||
"metadata": {
|
||||
"original_length": waveform.shape[-1],
|
||||
"sample_rate": sample_rate,
|
||||
},
|
||||
})
|
||||
input_audio = args.input_audio # "voice""
|
||||
input_metadata = args.input_metadata # "metadata"
|
||||
output_group = f"{args.output_group}-{'2' if cfg.sample_rate == 24_000 else '4'}{'8' if cfg.sample_rate == 48_000 else '4'}KHz-{cfg.audio_backend}" # "training"
|
||||
device = args.device # "cuda"
|
||||
raise_exceptions = args.raise_exceptions # False
|
||||
stride = args.stride # 0
|
||||
slice = args.slice # "auto"
|
||||
|
||||
continue
|
||||
|
||||
metadata_path = Path(f'./{input_metadata}/{dataset_name}/{speaker_id}/whisper.json')
|
||||
if not metadata_path.exists():
|
||||
missing["transcription"].append(str(metadata_path))
|
||||
language_map = {} # k = group, v = language
|
||||
|
||||
ignore_groups = [] # skip these groups
|
||||
ignore_speakers = [] # skip these speakers
|
||||
|
||||
only_groups = [] # only process these groups
|
||||
only_speakers = [] # only process these speakers
|
||||
|
||||
always_slice_groups = [] # always slice from this group
|
||||
|
||||
missing = {
|
||||
"transcription": [],
|
||||
"audio": []
|
||||
}
|
||||
dataset = []
|
||||
|
||||
for group_name in sorted(os.listdir(f'./{input_audio}/')):
|
||||
if not os.path.isdir(f'./{input_audio}/{group_name}/'):
|
||||
print("Is not dir:", f'./{input_audio}/{group_name}/')
|
||||
continue
|
||||
|
||||
try:
|
||||
metadata = json.loads(open(metadata_path, "r", encoding="utf-8").read())
|
||||
except Exception as e:
|
||||
missing["transcription"].append(str(metadata_path))
|
||||
if group_name in ignore_groups:
|
||||
continue
|
||||
if only_groups and group_name not in only_groups:
|
||||
continue
|
||||
|
||||
if f'{dataset_name}/{speaker_id}' not in dataset:
|
||||
dataset.append(f'{dataset_name}/{speaker_id}')
|
||||
|
||||
txts = []
|
||||
wavs = []
|
||||
|
||||
use_slices = slice == True or (slice == "auto" and len(metadata.keys()) == 1) or dataset_name in ["LibriVox", "Audiobooks"]
|
||||
|
||||
for filename in sorted(metadata.keys()):
|
||||
inpath = Path(f'./{input_audio}/{dataset_name}/{speaker_id}/{filename}')
|
||||
if not inpath.exists():
|
||||
missing["audio"].append(str(inpath))
|
||||
for speaker_id in tqdm(process_items(os.listdir(f'./{input_audio}/{group_name}/'), stride=stride), desc=f"Processing speaker in {group_name}"):
|
||||
if not os.path.isdir(f'./{input_audio}/{group_name}/{speaker_id}'):
|
||||
print("Is not dir:", f'./{input_audio}/{group_name}/{speaker_id}')
|
||||
continue
|
||||
|
||||
extension = os.path.splitext(filename)[-1][1:]
|
||||
fname = filename.replace(f'.{extension}', "")
|
||||
if speaker_id in ignore_speakers:
|
||||
continue
|
||||
if only_speakers and speaker_id not in only_speakers:
|
||||
continue
|
||||
|
||||
waveform, sample_rate = None, None
|
||||
language = metadata[filename]["language"] if "language" in metadata[filename] else "en"
|
||||
os.makedirs(f'./{output_group}/{group_name}/{speaker_id}/', exist_ok=True)
|
||||
|
||||
if len(metadata[filename]["segments"]) == 0 or not use_slices:
|
||||
outpath = Path(f'./{output_dataset}/{dataset_name}/{speaker_id}/{fname}.{extension}')
|
||||
text = metadata[filename]["text"]
|
||||
|
||||
if len(text) == 0:
|
||||
continue
|
||||
|
||||
if _replace_file_extension(outpath, audio_extension).exists():
|
||||
continue
|
||||
|
||||
if waveform is None:
|
||||
waveform, sample_rate = torchaudio.load(inpath)
|
||||
if waveform.shape[0] > 1:
|
||||
waveform = torch.mean(waveform, dim=0, keepdim=True)
|
||||
|
||||
wavs.append((
|
||||
outpath,
|
||||
text,
|
||||
language,
|
||||
waveform,
|
||||
sample_rate
|
||||
))
|
||||
else:
|
||||
i = 0
|
||||
for segment in metadata[filename]["segments"]:
|
||||
id = pad(i, 4)
|
||||
i = i + 1
|
||||
|
||||
outpath = Path(f'./{output_dataset}/{dataset_name}/{speaker_id}/{fname}_{id}.{extension}')
|
||||
text = segment["text"]
|
||||
|
||||
if len(text) == 0:
|
||||
continue
|
||||
if speaker_id == "Noise":
|
||||
for filename in sorted(os.listdir(f'./{input_audio}/{group_name}/{speaker_id}/')):
|
||||
inpath = Path(f'./{input_audio}/{group_name}/{speaker_id}/{filename}')
|
||||
outpath = Path(f'./{output_group}/{group_name}/{speaker_id}/{filename}')
|
||||
|
||||
if _replace_file_extension(outpath, audio_extension).exists():
|
||||
continue
|
||||
|
||||
if waveform is None:
|
||||
waveform, sample_rate = torchaudio.load(inpath)
|
||||
if waveform.shape[0] > 1:
|
||||
waveform = torch.mean(waveform, dim=0, keepdim=True)
|
||||
|
||||
start = int(segment['start'] * sample_rate)
|
||||
end = int(segment['end'] * sample_rate)
|
||||
|
||||
if start < 0:
|
||||
start = 0
|
||||
if end >= waveform.shape[-1]:
|
||||
end = waveform.shape[-1] - 1
|
||||
|
||||
if end - start < 0:
|
||||
continue
|
||||
|
||||
wavs.append((
|
||||
outpath,
|
||||
text,
|
||||
language,
|
||||
waveform[:, start:end],
|
||||
sample_rate
|
||||
))
|
||||
|
||||
if len(wavs) > 0:
|
||||
for job in tqdm(wavs, desc=f"Quantizing: {speaker_id}"):
|
||||
try:
|
||||
outpath, text, language, waveform, sample_rate = job
|
||||
|
||||
phones = valle_phonemize( text, language=language )
|
||||
waveform, sample_rate = torchaudio.load(inpath)
|
||||
qnt = valle_quantize(waveform, sr=sample_rate, device=device)
|
||||
|
||||
if cfg.audio_backend == "dac":
|
||||
|
|
@ -200,10 +113,6 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
|
|||
"channels": qnt.channels,
|
||||
"padding": qnt.padding,
|
||||
"dac_version": "1.0.0",
|
||||
|
||||
"text": text.strip(),
|
||||
"phonemes": "".join(phones),
|
||||
"language": language,
|
||||
},
|
||||
})
|
||||
else:
|
||||
|
|
@ -212,15 +121,168 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
|
|||
"metadata": {
|
||||
"original_length": waveform.shape[-1],
|
||||
"sample_rate": sample_rate,
|
||||
|
||||
"text": text.strip(),
|
||||
"phonemes": "".join(phones),
|
||||
"language": language,
|
||||
},
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"Failed to quantize: {outpath}:", e)
|
||||
continue
|
||||
|
||||
open("./missing.json", 'w', encoding='utf-8').write(json.dumps(missing))
|
||||
open("./dataset_list.json", 'w', encoding='utf-8').write(json.dumps(dataset))
|
||||
continue
|
||||
|
||||
metadata_path = Path(f'./{input_metadata}/{group_name}/{speaker_id}/whisper.json')
|
||||
if not metadata_path.exists():
|
||||
missing["transcription"].append(str(metadata_path))
|
||||
continue
|
||||
|
||||
try:
|
||||
metadata = json.loads(open(metadata_path, "r", encoding="utf-8").read())
|
||||
except Exception as e:
|
||||
missing["transcription"].append(str(metadata_path))
|
||||
continue
|
||||
|
||||
if f'{group_name}/{speaker_id}' not in dataset:
|
||||
dataset.append(f'{group_name}/{speaker_id}')
|
||||
|
||||
txts = []
|
||||
wavs = []
|
||||
|
||||
use_slices = slice == True or (slice == "auto" and len(metadata.keys()) == 1) or group_name in always_slice_groups
|
||||
|
||||
for filename in sorted(metadata.keys()):
|
||||
inpath = Path(f'./{input_audio}/{group_name}/{speaker_id}/{filename}')
|
||||
if not inpath.exists():
|
||||
missing["audio"].append(str(inpath))
|
||||
continue
|
||||
|
||||
extension = os.path.splitext(filename)[-1][1:]
|
||||
fname = filename.replace(f'.{extension}', "")
|
||||
|
||||
waveform, sample_rate = None, None
|
||||
language = language_map[group_name] if group_name in language_map else (metadata[filename]["language"] if "language" in metadata[filename] else "en")
|
||||
|
||||
if len(metadata[filename]["segments"]) == 0 or not use_slices:
|
||||
outpath = Path(f'./{output_group}/{group_name}/{speaker_id}/{fname}.{extension}')
|
||||
text = metadata[filename]["text"]
|
||||
|
||||
if len(text) == 0:
|
||||
continue
|
||||
|
||||
if _replace_file_extension(outpath, audio_extension).exists():
|
||||
continue
|
||||
|
||||
if waveform is None:
|
||||
waveform, sample_rate = torchaudio.load(inpath)
|
||||
if waveform.shape[0] > 1:
|
||||
waveform = torch.mean(waveform, dim=0, keepdim=True)
|
||||
|
||||
wavs.append((
|
||||
outpath,
|
||||
text,
|
||||
language,
|
||||
waveform,
|
||||
sample_rate
|
||||
))
|
||||
else:
|
||||
i = 0
|
||||
for segment in metadata[filename]["segments"]:
|
||||
id = pad(i, 4)
|
||||
i = i + 1
|
||||
|
||||
outpath = Path(f'./{output_group}/{group_name}/{speaker_id}/{fname}_{id}.{extension}')
|
||||
text = segment["text"]
|
||||
|
||||
if len(text) == 0:
|
||||
continue
|
||||
|
||||
if _replace_file_extension(outpath, audio_extension).exists():
|
||||
continue
|
||||
|
||||
if waveform is None:
|
||||
waveform, sample_rate = torchaudio.load(inpath)
|
||||
if waveform.shape[0] > 1:
|
||||
waveform = torch.mean(waveform, dim=0, keepdim=True)
|
||||
|
||||
start = int(segment['start'] * sample_rate)
|
||||
end = int(segment['end'] * sample_rate)
|
||||
|
||||
if start < 0:
|
||||
start = 0
|
||||
if end >= waveform.shape[-1]:
|
||||
end = waveform.shape[-1] - 1
|
||||
|
||||
if end - start < 0:
|
||||
continue
|
||||
|
||||
wavs.append((
|
||||
outpath,
|
||||
text,
|
||||
language,
|
||||
waveform[:, start:end],
|
||||
sample_rate
|
||||
))
|
||||
|
||||
if len(wavs) > 0:
|
||||
for job in tqdm(wavs, desc=f"Quantizing: {speaker_id}"):
|
||||
try:
|
||||
outpath, text, language, waveform, sample_rate = job
|
||||
|
||||
phones = valle_phonemize(text, language=language)
|
||||
qnt = valle_quantize(waveform, sr=sample_rate, device=device)
|
||||
|
||||
|
||||
if cfg.audio_backend == "dac":
|
||||
np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
|
||||
"codes": qnt.codes.cpu().numpy().astype(np.uint16),
|
||||
"metadata": {
|
||||
"original_length": qnt.original_length,
|
||||
"sample_rate": qnt.sample_rate,
|
||||
|
||||
"input_db": qnt.input_db.cpu().numpy().astype(np.float32),
|
||||
"chunk_length": qnt.chunk_length,
|
||||
"channels": qnt.channels,
|
||||
"padding": qnt.padding,
|
||||
"dac_version": "1.0.0",
|
||||
|
||||
"text": text.strip(),
|
||||
"phonemes": "".join(phones),
|
||||
"language": language,
|
||||
},
|
||||
})
|
||||
else:
|
||||
np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
|
||||
"codes": qnt.cpu().numpy().astype(np.uint16),
|
||||
"metadata": {
|
||||
"original_length": waveform.shape[-1],
|
||||
"sample_rate": sample_rate,
|
||||
|
||||
"text": text.strip(),
|
||||
"phonemes": "".join(phones),
|
||||
"language": language,
|
||||
},
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"Failed to quantize: {outpath}:", e)
|
||||
if raise_exceptions:
|
||||
raise e
|
||||
continue
|
||||
|
||||
open("./missing.json", 'w', encoding='utf-8').write(json.dumps(missing))
|
||||
open("./dataset_list.json", 'w', encoding='utf-8').write(json.dumps(dataset))
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
parser.add_argument("--audio-backend", type=str, default="encodec")
|
||||
parser.add_argument("--dtype", type=str, default="bfloat16")
|
||||
parser.add_argument("--amp", action="store_true")
|
||||
parser.add_argument("--input-audio", type=str, default="voices")
|
||||
parser.add_argument("--input-metadata", type=str, default="metadata")
|
||||
parser.add_argument("--output_group", type=str, default="training")
|
||||
parser.add_argument("--device", type=str, default="cuda")
|
||||
parser.add_argument("--raise-exceptions", action="store_true")
|
||||
parser.add_argument("--stride", type=int, default=0)
|
||||
parser.add_argument("--slice", type=str, default="auto")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
process_dataset( args )
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -813,7 +813,7 @@ class Base(nn.Module):
|
|||
inputs_embeds=x,
|
||||
past_key_values=state,
|
||||
position_ids=position_ids,
|
||||
use_cache=True,
|
||||
use_cache=not self.training,
|
||||
# return_dict=True,
|
||||
)
|
||||
if self.n_experts > 1 and self.training:
|
||||
|
|
@ -1350,15 +1350,6 @@ class Base(nn.Module):
|
|||
x, m = list_to_tensor(x_list)
|
||||
|
||||
training = self.training
|
||||
# yes, there's a better way.
|
||||
"""
|
||||
training = False
|
||||
for batch_index, batch in enumerate(inputs):
|
||||
for name, input in batch:
|
||||
if name == "targ":
|
||||
training = True
|
||||
"""
|
||||
|
||||
device = x.device
|
||||
batch_size = len(x_list)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user