def main():
"""Run training process."""
parser = argparse.ArgumentParser(
description=
"Train MelGAN (See detail in tensorflow_tts/bin/train-melgan.py)")
parser.add_argument(
"--train-dir",
default=None,
type=str,
help="directory including training data. ",
)
parser.add_argument(
"--dev-dir",
default=None,
type=str,
help="directory including development data. ",
)
parser.add_argument("--use-norm",
default=1,
type=int,
help="use norm mels for training or raw.")
parser.add_argument("--outdir",
type=str,
required=True,
help="directory to save checkpoints.")
parser.add_argument("--config",
type=str,
required=True,
help="yaml format configuration file.")
parser.add_argument(
"--resume",
default="",
type=str,
nargs="?",
help='checkpoint file path to resume training. (default="")',
)
parser.add_argument(
"--verbose",
type=int,
default=1,
help="logging level. higher is more logging. (default=1)",
)
parser.add_argument(
"--generator_mixed_precision",
default=0,
type=int,
help="using mixed precision for generator or not.",
)
parser.add_argument(
"--discriminator_mixed_precision",
default=0,
type=int,
help="using mixed precision for discriminator or not.",
)
args = parser.parse_args()
# return strategy
STRATEGY = return_strategy()
# set mixed precision config
if args.generator_mixed_precision == 1 or args.discriminator_mixed_precision == 1:
tf.config.optimizer.set_experimental_options(
{"auto_mixed_precision": True})
args.generator_mixed_precision = bool(args.generator_mixed_precision)
args.discriminator_mixed_precision = bool(
args.discriminator_mixed_precision)
args.use_norm = bool(args.use_norm)
# set logger
if args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
stream=sys.stdout,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
elif args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
stream=sys.stdout,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
else:
logging.basicConfig(
level=logging.WARN,
stream=sys.stdout,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
logging.warning("Skip DEBUG/INFO messages")
# check directory existence
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# check arguments
if args.train_dir is None:
raise ValueError("Please specify --train-dir")
if args.dev_dir is None:
raise ValueError("Please specify either --valid-dir")
# load and save config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
config.update(vars(args))
config["version"] = tensorflow_tts.__version__
with open(os.path.join(args.outdir, "config.yml"), "w") as f:
yaml.dump(config, f, Dumper=yaml.Dumper)
for key, value in config.items():
logging.info(f"{key} = {value}")
# get dataset
if config["remove_short_samples"]:
mel_length_threshold = config["batch_max_steps"] // config[
"hop_size"] + 2 * config["melgan_generator_params"].get(
"aux_context_window", 0)
else:
mel_length_threshold = None
if config["format"] == "npy":
audio_query = "*-wave.npy"
mel_query = "*-raw-feats.npy" if args.use_norm is False else "*-norm-feats.npy"
audio_load_fn = np.load
mel_load_fn = np.load
else:
raise ValueError("Only npy are supported.")
# define train/valid dataset
train_dataset = AudioMelDataset(
root_dir=args.train_dir,
audio_query=audio_query,
mel_query=mel_query,
audio_load_fn=audio_load_fn,
mel_load_fn=mel_load_fn,
mel_length_threshold=mel_length_threshold,
).create(
is_shuffle=config["is_shuffle"],
map_fn=lambda items: collater(
items,
batch_max_steps=tf.constant(config["batch_max_steps"],
dtype=tf.int32),
hop_size=tf.constant(config["hop_size"], dtype=tf.int32),
),
allow_cache=config["allow_cache"],
batch_size=config["batch_size"] * STRATEGY.num_replicas_in_sync,
)
valid_dataset = AudioMelDataset(
root_dir=args.dev_dir,
audio_query=audio_query,
mel_query=mel_query,
audio_load_fn=audio_load_fn,
mel_load_fn=mel_load_fn,
mel_length_threshold=mel_length_threshold,
).create(
is_shuffle=config["is_shuffle"],
map_fn=lambda items: collater(
items,
batch_max_steps=tf.constant(config["batch_max_steps_valid"],
dtype=tf.int32),
hop_size=tf.constant(config["hop_size"], dtype=tf.int32),
),
allow_cache=config["allow_cache"],
batch_size=config["batch_size"] * STRATEGY.num_replicas_in_sync,
)
# define trainer
trainer = MelganTrainer(
steps=0,
epochs=0,
config=config,
strategy=STRATEGY,
is_generator_mixed_precision=args.generator_mixed_precision,
is_discriminator_mixed_precision=args.discriminator_mixed_precision,
)
# define generator and discriminator
with STRATEGY.scope():
generator = TFMelGANGenerator(
MELGAN_CONFIG.MelGANGeneratorConfig(
**config["melgan_generator_params"]),
name="melgan_generator",
)
discriminator = TFMelGANMultiScaleDiscriminator(
MELGAN_CONFIG.MelGANDiscriminatorConfig(
**config["melgan_discriminator_params"]),
name="melgan_discriminator",
)
# dummy input to build model.
fake_mels = tf.random.uniform(shape=[1, 100, 80], dtype=tf.float32)
y_hat = generator(fake_mels)
discriminator(y_hat)
generator.summary()
discriminator.summary()
gen_optimizer = tf.keras.optimizers.Adam(
**config["generator_optimizer_params"])
dis_optimizer = tf.keras.optimizers.Adam(
**config["discriminator_optimizer_params"])
trainer.compile(
gen_model=generator,
dis_model=discriminator,
gen_optimizer=gen_optimizer,
dis_optimizer=dis_optimizer,
)
# start training
try:
trainer.fit(
train_dataset,
valid_dataset,
saved_path=os.path.join(config["outdir"], "checkpoints/"),
resume=args.resume,
)
except KeyboardInterrupt:
trainer.save_checkpoint()
logging.info(f"Successfully saved checkpoint @ {trainer.steps}steps.")
def main():
"""Run training process."""
parser = argparse.ArgumentParser(
description=
"Train MultiBand MelGAN (See detail in examples/multiband_melgan/train_multiband_melgan.py)"
)
parser.add_argument(
"--train-dir",
'-td',
default=None,
type=str,
help="directory including training data. ",
)
parser.add_argument(
"--dev-dir",
'-dd',
default=None,
type=str,
help="directory including development data. ",
)
parser.add_argument(
"--audio-query",
'-aq',
default='*_wav.npy',
type=str,
help="suffix of audio file",
)
parser.add_argument(
"--mel-query",
'-mq',
default='*_conformer_enc16.npy',
type=str,
help="suffix of mel file",
)
parser.add_argument("--use-norm",
default=1,
type=int,
help="use norm mels for training or raw.")
parser.add_argument("--outdir",
'-od',
type=str,
required=True,
help="directory to save checkpoints.")
parser.add_argument("--config",
'-c',
type=str,
required=True,
help="yaml format configuration file.")
parser.add_argument(
"--resume",
'-r',
default="",
type=str,
nargs="?",
help='checkpoint file path to resume training. (default="")',
)
parser.add_argument(
"--verbose",
type=int,
default=1,
help="logging level. higher is more logging. (default=1)",
)
parser.add_argument(
"--generator_mixed_precision",
default=0,
type=int,
help="using mixed precision for generator or not.",
)
parser.add_argument(
"--discriminator_mixed_precision",
default=0,
type=int,
help="using mixed precision for discriminator or not.",
)
parser.add_argument(
"--pretrained",
default="",
type=str,
nargs="?",
help="path of .h5 mb-melgan generator to load weights from",
)
args = parser.parse_args()
# return strategy
STRATEGY = return_strategy()
# set mixed precision config
if args.generator_mixed_precision == 1 or args.discriminator_mixed_precision == 1:
tf.config.optimizer.set_experimental_options(
{"auto_mixed_precision": True})
args.generator_mixed_precision = bool(args.generator_mixed_precision)
args.discriminator_mixed_precision = bool(
args.discriminator_mixed_precision)
# set logger
if args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
stream=sys.stdout,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
elif args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
stream=sys.stdout,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
else:
logging.basicConfig(
level=logging.WARN,
stream=sys.stdout,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
logging.warning("Skip DEBUG/INFO messages")
# check directory existence
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# check arguments
if args.train_dir is None:
raise ValueError("Please specify --train-dir")
if args.dev_dir is None:
raise ValueError("Please specify either --valid-dir")
# load and save config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
with open(config['speech_config']) as f:
mel_config = yaml.load(f, Loader=yaml.Loader)
config.update(mel_config)
config['n_mels'] = config['asr_features']
config['hop_size'] = config['asr_downsample'] * config[
'sample_rate'] * config['stride_ms'] // 1000
config['sampling_rate'] = config['sample_rate']
config.update(vars(args))
config["version"] = tensorflow_tts.__version__
with open(os.path.join(args.outdir, "config.yml"), "w") as f:
yaml.dump(config, f, Dumper=yaml.Dumper)
for key, value in config.items():
logging.info(f"{key} = {value}")
# get dataset
if config["remove_short_samples"]:
mel_length_threshold = config["batch_max_steps"] // config["hop_size"] \
+ 2 * config["multiband_melgan_generator_params"].get("aux_context_window", 0)
else:
mel_length_threshold = None
audio_query = args.audio_query
mel_query = args.mel_query
audio_load_fn = np.load
mel_load_fn = np.load
# define train/valid dataset
train_dataset = Mel(
n_mels=config['n_mels'],
training=True,
root_dir=args.train_dir,
audio_query=audio_query,
mel_query=mel_query,
audio_load_fn=audio_load_fn,
mel_load_fn=mel_load_fn,
mel_length_threshold=mel_length_threshold,
).create(
is_shuffle=config["is_shuffle"],
map_fn=lambda items: collater(
items,
batch_max_steps=tf.constant(config["batch_max_steps"],
dtype=tf.int32),
hop_size=tf.constant(config["hop_size"], dtype=tf.int32),
),
allow_cache=config["allow_cache"],
batch_size=config["batch_size"] * STRATEGY.num_replicas_in_sync *
config["gradient_accumulation_steps"],
)
valid_dataset = Mel(
n_mels=config['n_mels'],
training=False,
root_dir=args.dev_dir,
audio_query=audio_query,
mel_query=mel_query,
audio_load_fn=audio_load_fn,
mel_load_fn=mel_load_fn,
mel_length_threshold=mel_length_threshold,
).create(
is_shuffle=config["is_shuffle"],
map_fn=lambda items: collater(
items,
batch_max_steps=tf.constant(config["batch_max_steps_valid"],
dtype=tf.int32),
hop_size=tf.constant(config["hop_size"], dtype=tf.int32),
),
allow_cache=config["allow_cache"],
batch_size=config["batch_size"] * STRATEGY.num_replicas_in_sync,
)
# define trainer
trainer = MultiBandMelganTrainer(
steps=0,
epochs=0,
config=config,
strategy=STRATEGY,
is_generator_mixed_precision=args.generator_mixed_precision,
is_discriminator_mixed_precision=args.discriminator_mixed_precision,
)
with STRATEGY.scope():
# define generator and discriminator
generator = MelGANGenerator(
config=MultiBandMelGANGeneratorConfig(
**config["multiband_melgan_generator_params"]),
name="multi_band_melgan_generator",
)
generator.set_shape(config['n_mels'])
discriminator = TFMelGANMultiScaleDiscriminator(
MultiBandMelGANDiscriminatorConfig(
**config["multiband_melgan_discriminator_params"]),
name="multi_band_melgan_discriminator",
)
pqmf = TFPQMF(
MultiBandMelGANGeneratorConfig(
**config["multiband_melgan_generator_params"]),
dtype=tf.float32,
name="pqmf",
)
# dummy input to build model.
fake_mels = tf.random.uniform(shape=[1, 100, config['n_mels']],
dtype=tf.float32)
y_mb_hat = generator(fake_mels)
y_hat = pqmf.synthesis(y_mb_hat)
discriminator(y_hat)
if len(args.pretrained) > 1:
generator.load_weights(args.pretrained)
logging.info(
f"Successfully loaded pretrained weight from {args.pretrained}."
)
generator.summary()
discriminator.summary()
# define optimizer
generator_lr_fn = getattr(
tf.keras.optimizers.schedules,
config["generator_optimizer_params"]["lr_fn"])(
**config["generator_optimizer_params"]["lr_params"])
discriminator_lr_fn = getattr(
tf.keras.optimizers.schedules,
config["discriminator_optimizer_params"]["lr_fn"],
)(**config["discriminator_optimizer_params"]["lr_params"])
gen_optimizer = tf.keras.optimizers.Adam(
learning_rate=generator_lr_fn,
amsgrad=config["generator_optimizer_params"]["amsgrad"],
)
dis_optimizer = tf.keras.optimizers.Adam(
learning_rate=discriminator_lr_fn,
amsgrad=config["discriminator_optimizer_params"]["amsgrad"],
)
trainer.compile(
gen_model=generator,
dis_model=discriminator,
gen_optimizer=gen_optimizer,
dis_optimizer=dis_optimizer,
pqmf=pqmf,
)
# start training
try:
trainer.fit(
train_dataset,
valid_dataset,
saved_path=os.path.join(config["outdir"], "checkpoints/"),
resume=args.resume,
)
except KeyboardInterrupt:
trainer.save_checkpoint()
logging.info(f"Successfully saved checkpoint @ {trainer.steps}steps.")
