print('===' * 10, outdir)
audio = Audio(config_loader.config)
if args.checkpoint is not None:
all_weights = [args.checkpoint]
elif args.all_weights:
all_weights = [(config_loader.weights_dir / x.stem).as_posix()
for x in config_loader.weights_dir.iterdir()
if x.suffix == '.index']
else:
all_weights = [None] # default
if args.verbose:
print(f'\nWeights list: \n{all_weights}\n')
for weights in all_weights:
model = config_loader.load_model(weights)
file_name = f'{fname}_ttsstep{model.step}'
print(f'Output wav under {outdir}')
wavs = []
for i, text_line in enumerate(text):
out = model.predict(text_line,
spk_emb=spk_emb,
encode=True,
phoneme_max_duration=None,
speed_regulator=0.8)
mel = out['mel'].numpy().T
wav = audio.reconstruct_waveform(mel)
wavs.append(wav)
if args.store_mel:
np.save((outdir / (file_name + f'_{i}')).with_suffix('.mel'),
out['mel'].numpy())
fill_gaps = args.fill_mode_max or args.fill_mode_next
fix_jumps = args.fix_jumps
fill_mode = f"{f'max' * args.fill_mode_max}{f'next' * args.fill_mode_next}"
filling_tag = f"{f'(max)' * args.fill_mode_max}{f'(next)' * args.fill_mode_next}"
tag_description = ''.join([
f'{"_weighted" * weighted}{"_best" * (not weighted)}',
f'{"_binary" * binary}', f'{"_filled" * fill_gaps}{filling_tag}',
f'{"_fix_jumps" * fix_jumps}', f'_layer{args.extract_layer}'
])
writer_tag = f'DurationExtraction{tag_description}'
print(writer_tag)
config_manager = Config(config_path=args.config, model_kind='autoregressive')
config = config_manager.config
config_manager.print_config()
if args.autoregressive_weights != '':
model = config_manager.load_model(args.autoregressive_weights)
else:
model = config_manager.load_model()
if model.r != 1:
print(
f"ERROR: model's reduction factor is greater than 1, check config. (r={model.r}"
)
data_prep = AutoregressivePreprocessor.from_config(
config=config_manager, tokenizer=model.text_pipeline.tokenizer)
data_handler = TextMelDataset.from_config(config_manager,
preprocessor=data_prep,
kind='phonemized')
target_dir = config_manager.data_dir / f'durations'
target_dir.mkdir(exist_ok=True)