def dl_pretrained(base_dir: str, train_name: str = DEFAULT_WAVEGLOW, prep_name: Optional[str] = None, version: int = 3):
train_dir = get_train_dir(base_dir, train_name, create=True)
assert os.path.isdir(train_dir)
checkpoints_dir = get_checkpoints_dir(train_dir)
dest_path = get_checkpoint_pretrained(checkpoints_dir)
print("Downloading pretrained waveglow model from Nvida...")
dl_wg(
destination=dest_path,
version=version
)
print("Pretrained model is now beeing converted to be able to use it...")
convert_glow(
origin=dest_path,
destination=dest_path,
keep_orig=False
)
if prep_name is not None:
prep_dir = get_prepared_dir(base_dir, prep_name)
wholeset = load_filelist(prep_dir)
save_testset(train_dir, wholeset)
save_valset(train_dir, wholeset)
save_prep_name(train_dir, prep_name=prep_name)
def infer(base_dir: str, train_name: str, wav_path: str, custom_checkpoint: Optional[int] = None, sigma: float = 0.666, denoiser_strength: float = 0.00, custom_hparams: Optional[Dict[str, str]] = None):
train_dir = get_train_dir(base_dir, train_name, create=False)
assert os.path.isdir(train_dir)
checkpoint_path, iteration = get_custom_or_last_checkpoint(
get_checkpoints_dir(train_dir), custom_checkpoint)
infer_dir = get_infer_dir(train_dir, wav_path, iteration)
logger = prepare_logger(get_infer_log(infer_dir))
logger.info(f"Inferring {wav_path}...")
checkpoint = CheckpointWaveglow.load(checkpoint_path, logger)
wav, wav_sr, wav_mel, orig_mel = infer_core(
wav_path=wav_path,
denoiser_strength=denoiser_strength,
sigma=sigma,
checkpoint=checkpoint,
custom_hparams=custom_hparams,
logger=logger
)
save_infer_wav(infer_dir, wav_sr, wav)
save_infer_plot(infer_dir, wav_mel)
save_infer_orig_wav(infer_dir, wav_path)
save_infer_orig_plot(infer_dir, orig_mel)
score = save_diff_plot(infer_dir)
save_v(infer_dir)
logger.info(f"Imagescore: {score*100}%")
logger.info(f"Saved output to: {infer_dir}")
def try_load_checkpoint(base_dir: str, train_name: Optional[str],
checkpoint: Optional[int],
logger: Logger) -> Optional[CheckpointWaveglow]:
result = None
if train_name:
train_dir = get_train_dir(base_dir, train_name, False)
checkpoint_path, _ = get_custom_or_last_checkpoint(
get_checkpoints_dir(train_dir), checkpoint)
result = CheckpointWaveglow.load(checkpoint_path, logger)
return result
def validate(base_dir: str,
train_name: str,
entry_id: Optional[int] = None,
speaker: Optional[str] = None,
ds: str = "val",
custom_checkpoint: Optional[int] = None,
sigma: float = 0.666,
denoiser_strength: float = 0.00,
custom_hparams: Optional[Dict[str, str]] = None):
train_dir = get_train_dir(base_dir, train_name, create=False)
assert os.path.isdir(train_dir)
if ds == "val":
data = load_valset(train_dir)
elif ds == "test":
data = load_testset(train_dir)
else:
raise Exception()
speaker_id: Optional[int] = None
if speaker is not None:
prep_name = load_prep_name(train_dir)
prep_dir = get_prepared_dir(base_dir, prep_name, create=False)
speakers = load_prep_speakers_json(prep_dir)
speaker_id = speakers.get_id(speaker)
entry = data.get_for_validation(entry_id, speaker_id)
checkpoint_path, iteration = get_custom_or_last_checkpoint(
get_checkpoints_dir(train_dir), custom_checkpoint)
val_dir = get_val_dir(train_dir, entry, iteration)
logger = prepare_logger(get_val_log(val_dir))
logger.info(f"Validating {entry.wav_path}...")
checkpoint = CheckpointWaveglow.load(checkpoint_path, logger)
wav, wav_sr, wav_mel, orig_mel = infer(wav_path=entry.wav_path,
denoiser_strength=denoiser_strength,
sigma=sigma,
checkpoint=checkpoint,
custom_hparams=custom_hparams,
logger=logger)
save_val_wav(val_dir, wav_sr, wav)
save_val_plot(val_dir, wav_mel)
save_val_orig_wav(val_dir, entry.wav_path)
save_val_orig_plot(val_dir, orig_mel)
score = save_diff_plot(val_dir)
save_v(val_dir)
logger.info(f"Imagescore: {score*100}%")
logger.info(f"Saved output to: {val_dir}")
def continue_training(base_dir: str,
train_name: str,
custom_hparams: Optional[Dict[str, str]] = None):
train_dir = get_train_dir(base_dir, train_name, create=False)
assert os.path.isdir(train_dir)
logs_dir = get_train_logs_dir(train_dir)
logger = prepare_logger(get_train_log_file(logs_dir))
continue_train(custom_hparams=custom_hparams,
logdir=logs_dir,
trainset=load_trainset(train_dir),
valset=load_valset(train_dir),
save_checkpoint_dir=get_checkpoints_dir(train_dir),
debug_logger=logger)
def start_new_training(base_dir: str,
train_name: str,
prep_name: str,
test_size: float = 0.01,
validation_size: float = 0.01,
custom_hparams: Optional[Dict[str, str]] = None,
split_seed: int = 1234,
warm_start_train_name: Optional[str] = None,
warm_start_checkpoint: Optional[int] = None):
prep_dir = get_prepared_dir(base_dir, prep_name)
wholeset = load_filelist(prep_dir)
trainset, testset, valset = split_prepared_data_train_test_val(
wholeset,
test_size=test_size,
validation_size=validation_size,
seed=split_seed,
shuffle=True)
train_dir = get_train_dir(base_dir, train_name, create=True)
save_trainset(train_dir, trainset)
save_testset(train_dir, testset)
save_valset(train_dir, valset)
logs_dir = get_train_logs_dir(train_dir)
logger = prepare_logger(get_train_log_file(logs_dir), reset=True)
warm_model = try_load_checkpoint(base_dir=base_dir,
train_name=warm_start_train_name,
checkpoint=warm_start_checkpoint,
logger=logger)
save_prep_name(train_dir, prep_name)
train(
custom_hparams=custom_hparams,
logdir=logs_dir,
trainset=trainset,
valset=valset,
save_checkpoint_dir=get_checkpoints_dir(train_dir),
debug_logger=logger,
warm_model=warm_model,
)