def test_melgan_trainable(dict_g, dict_d):
random_c = tf.random.uniform(shape=[4, 32, 80], dtype=tf.float32)
args_g = make_pwgan_generator_args(**dict_g)
args_d = make_pwgan_discriminator_args(**dict_d)
args_g = ParallelWaveGANGeneratorConfig(**args_g)
args_d = ParallelWaveGANDiscriminatorConfig(**args_d)
generator = TFParallelWaveGANGenerator(args_g)
generator._build()
discriminator = TFParallelWaveGANDiscriminator(args_d)
discriminator._build()
generated_audios = generator(random_c, training=True)
discriminator(generated_audios)
generator.summary()
discriminator.summary()
def main():
"""Run parallel_wavegan decoding from folder."""
parser = argparse.ArgumentParser(
description="Generate Audio from melspectrogram with trained melgan "
"(See detail in examples_tts/parallel_wavegan/decode_parallel_wavegan.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 = "*-fs-after-feats.npy" if "fastspeech" in args.rootdir else "*-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,
)
dataset = dataset.create(batch_size=args.batch_size)
# define model and load checkpoint
parallel_wavegan = TFParallelWaveGANGenerator(
config=ParallelWaveGANGeneratorConfig(
**config["parallel_wavegan_generator_params"]),
name="parallel_wavegan_generator",
)
parallel_wavegan._build()
parallel_wavegan.load_weights(args.checkpoint)
for data in tqdm(dataset, desc="[Decoding]"):
utt_ids, mels, mel_lengths = data["utt_ids"], data["mels"], data[
"mel_lengths"]
# pwgan inference.
generated_audios = parallel_wavegan.inference(mels)
# 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",
)