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("--feature", '-f', required=True)
parser.add_argument("--config", '-c', required=True)
parser.add_argument("--restore_am", '-am', required=True)
parser.add_argument("--restore_iam", '-iam', required=True)
args = parser.parse_args()
# return strategy
STRATEGY = return_strategy()
# load and save config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
with open(config['speech_config']) as f:
speech_config = yaml.load(f, Loader=yaml.Loader)
config.update(speech_config)
config['n_mels'] = config['asr_features']
config['hop_size'] = config['asr_downsample'] * config['sample_rate'] * config['stride_ms'] // 1000
config.update(vars(args))
config["version"] = tensorflow_tts.__version__
for key, value in config.items():
logging.info(f"{key} = {value}")
with STRATEGY.scope():
generator = MelGANGenerator(
config=MultiBandMelGANGeneratorConfig(
**config["multiband_melgan_generator_params"]
),
name="multi_band_melgan_generator",
)
generator.set_shape(config['n_mels'])
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)
output = generator(mels=fake_mels, training=False)
y_hat = pqmf.synthesis(output)
print('y_hat', y_hat.shape)
generator.load_weights(args.resume)
elif args.feature.endswith('.wav'):
signal, _ = librosa.load(args.feature, sr=config['sample_rate'])
mels = speech_featurizer.tf_extract(signal)
with tf.device('/cpu:0'):
mels = conformer.encoder_inference(mels)
def test_multi_band_melgan(dict_g):
args_g = make_multi_band_melgan_generator_args(**dict_g)
args_g = MultiBandMelGANGeneratorConfig(**args_g)
generator = TFMelGANGenerator(args_g, name="multi_band_melgan")
generator._build()
pqmf = TFPQMF(args_g, name="pqmf")
fake_mels = tf.random.uniform(shape=[1, 100, 80], dtype=tf.float32)
fake_y = tf.random.uniform(shape=[1, 100 * 256, 1], dtype=tf.float32)
y_hat_subbands = generator(fake_mels)
y_hat = pqmf.synthesis(y_hat_subbands)
y_subbands = pqmf.analysis(fake_y)
assert np.shape(y_subbands) == np.shape(y_hat_subbands)
assert np.shape(fake_y) == np.shape(y_hat)
def build_iam(self, config, model_path):
generator = MelGANGenerator(
config=MultiBandMelGANGeneratorConfig(
**config["multiband_melgan_generator_params"]),
name="multi_band_melgan_generator",
)
generator.set_shape(config['n_mels'])
pqmf = TFPQMF(
MultiBandMelGANGeneratorConfig(
**config["multiband_melgan_generator_params"]),
dtype=tf.float32,
name="pqmf",
)
fake_mels = tf.random.uniform(shape=[1, 100, config['n_mels']],
dtype=tf.float32)
output = generator(mels=fake_mels, training=False)
y_hat = pqmf.synthesis(output)
print('loading iam...')
generator.load_weights(model_path)
return generator, pqmf
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",
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.",
)
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)
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["multiband_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 = 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 = TFMelGANGenerator(
MultiBandMelGANGeneratorConfig(**config["multiband_melgan_generator_params"]),
name="multi_band_melgan_generator",
)
discriminator = TFParallelWaveGANDiscriminator(
ParallelWaveGANDiscriminatorConfig(
**config["parallel_wavegan_discriminator_params"]
),
name="parallel_wavegan_discriminator",
)
pqmf = TFPQMF(
MultiBandMelGANGeneratorConfig(**config["multiband_melgan_generator_params"]), name="pqmf"
)
# dummy input to build model.
fake_mels = tf.random.uniform(shape=[1, 100, 80], dtype=tf.float32)
y_mb_hat = generator(fake_mels)
y_hat = pqmf.synthesis(y_mb_hat)
discriminator(y_hat)
if len(args.pretrained) > 2:
print("Loading pretrained weights...")
generator.load_weights(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 = RectifiedAdam(
learning_rate=discriminator_lr_fn, amsgrad=False
)
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.")
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("--feature", '-f', required=True)
parser.add_argument("--speaker", '-s', required=True)
parser.add_argument("--config", '-c', required=True)
parser.add_argument("--resume", '-r', required=True)
args = parser.parse_args()
# return strategy
STRATEGY = return_strategy()
# load and save config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
with open(config['speech_config']) as f:
speech_config = yaml.load(f, Loader=yaml.Loader)
config.update(speech_config)
config['hop_size'] = config['sample_rate'] * config['stride_ms'] // 1000
config['sampling_rate'] = config['sample_rate']
config.update(vars(args))
config["version"] = tensorflow_tts.__version__
for key, value in config.items():
logging.info(f"{key} = {value}")
with STRATEGY.scope():
encoder = Encoder(**config['encoder'])
generator = MelGANGeneratorVQ(
encoder=encoder,
config=MultiBandMelGANGeneratorConfig(
**config["multiband_melgan_generator_params"]),
name="multi_band_melgan_generator",
)
generator.set_shape(config['n_mels'], config['gc_channels'])
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)
fake_gc = tf.random.uniform(shape=[1, config['gc_channels']],
dtype=tf.float32)
y_mb_hat = generator(mels=fake_mels, gc=fake_gc,
training=False)['y_mb_hat']
y_hat = pqmf.synthesis(y_mb_hat)
generator.load_weights(args.resume)
generator.summary()
speech_featurizer = TFSpeechFeaturizer(speech_config)
if args.feature.endswith('_mel.npy'):
mels = tf.constant(np.load(args.feature), tf.float32)
else:
signal, _ = librosa.load(args.feature, sr=config['sample_rate'])
mels = speech_featurizer.tf_extract(signal)
mels = tf.reshape(mels, [1, -1, config['n_mels']])
gc = tf.constant(
np.load(args.speaker).reshape([1, config['gc_channels']]), tf.float32)
# gc = tf.constant(np.zeros(256).reshape([1, config['gc_channels']]), tf.float32)
output = generator(mels=mels, gc=gc, training=False)['y_mb_hat']
y_hat = pqmf.synthesis(output).numpy().reshape([-1])
print('output:', y_hat.shape)
save_name = args.feature.replace('.wav', '_gen_vc.wav')
save_name = args.feature.replace('_mel.npy', '_gen_vc.wav')
save_name = save_name.split('/')[-1]
wavfile.write(save_name, config['sample_rate'], y_hat)
def depreemphasis(signal: np.ndarray, coeff=0.97):
if not coeff or coeff <= 0.0: return signal
x = np.zeros(signal.shape[0], dtype=np.float32)
x[0] = signal[0]
for n in range(1, signal.shape[0], 1):
x[n] = coeff * x[n - 1] + signal[n]
return x
y_hat = depreemphasis(y_hat)
wavfile.write(save_name.replace('.wav', '_depre.wav'),
config['sample_rate'], y_hat)
def main():
"""Run melgan decoding from folder."""
parser = argparse.ArgumentParser(
description="Generate Audio from melspectrogram with trained melgan "
"(See detail in example/melgan/decode_melgan.py).")
parser.add_argument("--rootdir",
default=None,
type=str,
required=True,
help="directory including ids/durations files.")
parser.add_argument("--outdir",
type=str,
required=True,
help="directory to save generated speech.")
parser.add_argument("--checkpoint",
type=str,
required=True,
help="checkpoint file to be loaded.")
parser.add_argument("--use-norm",
type=int,
default=1,
help="Use norm or raw melspectrogram.")
parser.add_argument("--batch-size",
type=int,
default=8,
help="batch_size.")
parser.add_argument(
"--config",
default=None,
type=str,
required=True,
help="yaml format configuration file. if not explicitly provided, "
"it will be searched in the checkpoint directory. (default=None)")
parser.add_argument(
"--verbose",
type=int,
default=1,
help="logging level. higher is more logging. (default=1)")
args = parser.parse_args()
# set logger
if args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
elif args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
else:
logging.basicConfig(
level=logging.WARN,
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)
# load config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
config.update(vars(args))
if config["format"] == "npy":
mel_query = "*-norm-feats.npy" if args.use_norm == 1 else "*-raw-feats.npy"
mel_load_fn = np.load
else:
raise ValueError("Only npy is supported.")
# define data-loader
dataset = MelDataset(root_dir=args.rootdir,
mel_query=mel_query,
mel_load_fn=mel_load_fn,
return_utt_id=True)
dataset = dataset.create(batch_size=args.batch_size)
# define model and load checkpoint
mb_melgan = TFMelGANGenerator(
config=MultiBandMelGANGeneratorConfig(**config["generator_params"]),
name='melgan')
mb_melgan._build()
mb_melgan.load_weights(args.checkpoint)
pqmf = TFPQMF(
config=MultiBandMelGANGeneratorConfig(**config["generator_params"]),
name='pqmf')
for data in tqdm(dataset, desc="[Decoding]"):
utt_ids, mels, mel_lengths = data
# melgan inference.
generated_subbands = mb_melgan(mels)
generated_audios = pqmf.synthesis(generated_subbands)
# convert to numpy.
generated_audios = generated_audios.numpy() # [B, T]
# save to outdir
for i, audio in enumerate(generated_audios):
utt_id = utt_ids[i].numpy().decode("utf-8")
sf.write(os.path.join(args.outdir, f"{utt_id}.wav"),
audio[:mel_lengths[i].numpy() * config["hop_size"]],
config["sampling_rate"], "PCM_16")
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='*_mel.npy',
type=str,
help="suffix of mel file",
)
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",
'-v',
type=int,
default=1,
help="logging level. higher is more logging. (default=1)",
)
parser.add_argument(
"--generator_mixed_precision",
'-gmxp',
default=0,
type=int,
help="using mixed precision for generator or not.",
)
parser.add_argument(
"--discriminator_mixed_precision",
'-dmxp',
default=0,
type=int,
help="using mixed precision for discriminator or not.",
)
parser.add_argument(
"--pretrained",
'-p',
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['hop_size'] = 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
# include global condition
def collater_gc(items, **kwargs):
gc = items['gc']
items = collater(items, **kwargs)
items['gc'] = gc
return items
# define train/valid dataset
train_dataset = MelGC(
training=True,
n_mels=config['n_mels'],
gc_channels=config['gc_channels'],
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_gc(
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 = MelGC(
training=False,
n_mels=config['n_mels'],
gc_channels=config['gc_channels'],
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_gc(
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 = MultiBandMelganVQTrainer(
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():
encoder = Encoder(**config['encoder'])
generator = MelGANGeneratorVQ(
encoder=encoder,
config=MultiBandMelGANGeneratorConfig(
**config["multiband_melgan_generator_params"]),
name="multi_band_melgan_generator",
)
generator.set_shape(config['n_mels'], config['gc_channels'])
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)
fake_gc = tf.random.uniform(shape=[1, config['gc_channels']],
dtype=tf.float32)
y_mb_hat = generator(mels=fake_mels, gc=fake_gc, training=True)
for k in y_mb_hat:
print(k, y_mb_hat[k].shape)
y_hat = pqmf.synthesis(y_mb_hat['y_mb_hat'])
print('y_hat:', y_hat.shape)
discriminator(y_hat)
if len(args.pretrained) > 1:
generator.load_weights(args.pretrained)
logging.info(
f"Successfully loaded pretrained weight from {args.pretrained}."
)
encoder.summary()
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.")