def test_pqmf(subbands):
pqmf = PQMF(subbands)
x = torch.randn(1, 1, subbands * 32)
y = pqmf.analysis(x)
assert y.shape[2] * subbands == x.shape[2]
x_hat = pqmf.synthesis(y)
assert x.shape[2] == x_hat.shape[2]
def main():
"""Run decoding process."""
parser = argparse.ArgumentParser(
description=
"Decode dumped features with trained Parallel WaveGAN Generator "
"(See detail in parallel_wavegan/bin/decode.py).")
parser.add_argument("--feats-scp",
"--scp",
default=None,
type=str,
help="kaldi-style feats.scp file. "
"you need to specify either feats-scp or dumpdir.")
parser.add_argument("--dumpdir",
default=None,
type=str,
help="directory including feature files. "
"you need to specify either feats-scp or dumpdir.")
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(
"--config",
default=None,
type=str,
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
if args.config is None:
dirname = os.path.dirname(args.checkpoint)
args.config = os.path.join(dirname, "config.yml")
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
config.update(vars(args))
# check arguments
if (args.feats_scp is not None and args.dumpdir is not None) or \
(args.feats_scp is None and args.dumpdir is None):
raise ValueError("Please specify either --dumpdir or --feats-scp.")
# get dataset
if args.dumpdir is not None:
if config["format"] == "hdf5":
mel_query = "*.h5"
mel_load_fn = lambda x: read_hdf5(x, "feats") # NOQA
elif config["format"] == "npy":
mel_query = "*-feats.npy"
mel_load_fn = np.load
else:
raise ValueError("support only hdf5 or npy format.")
dataset = MelDataset(
args.dumpdir,
mel_query=mel_query,
mel_load_fn=mel_load_fn,
return_utt_id=True,
)
else:
dataset = MelSCPDataset(
feats_scp=args.feats_scp,
return_utt_id=True,
)
logging.info(f"The number of features to be decoded = {len(dataset)}.")
# setup
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
model_class = getattr(
parallel_wavegan.models,
config.get("generator_type", "ParallelWaveGANGenerator"))
model = model_class(**config["generator_params"])
model.load_state_dict(
torch.load(args.checkpoint, map_location="cpu")["model"]["generator"])
logging.info(f"Loaded model parameters from {args.checkpoint}.")
model.remove_weight_norm()
model = model.eval().to(device)
use_noise_input = not isinstance(model,
parallel_wavegan.models.MelGANGenerator)
pad_fn = torch.nn.ReplicationPad1d(config["generator_params"].get(
"aux_context_window", 0))
if config["generator_params"]["out_channels"] > 1:
pqmf = PQMF(config["generator_params"]["out_channels"]).to(device)
# start generation
total_rtf = 0.0
with torch.no_grad(), tqdm(dataset, desc="[decode]") as pbar:
for idx, (utt_id, c) in enumerate(pbar, 1):
# setup input
x = ()
if use_noise_input:
z = torch.randn(1, 1, len(c) * config["hop_size"]).to(device)
x += (z, )
c = pad_fn(
torch.tensor(c, dtype=torch.float).unsqueeze(0).transpose(
2, 1)).to(device)
x += (c, )
# generate
start = time.time()
if config["generator_params"]["out_channels"] == 1:
y = model(*x).view(-1).cpu().numpy()
else:
y = pqmf.synthesis(model(*x)).view(-1).cpu().numpy()
rtf = (time.time() - start) / (len(y) / config["sampling_rate"])
pbar.set_postfix({"RTF": rtf})
total_rtf += rtf
# save as PCM 16 bit wav file
sf.write(os.path.join(config["outdir"], f"{utt_id}_gen.wav"), y,
config["sampling_rate"], "PCM_16")
# report average RTF
logging.info(
f"Finished generation of {idx} utterances (RTF = {total_rtf / idx:.03f})."
)
def main():
parser = argparse.ArgumentParser(
description="TTS decoder running RETURNN TTS and an MB-MelGAN vocoder")
parser.add_argument("--returnn_config",
type=str,
help="RETURNN config file (.config)")
parser.add_argument("--vocab_file",
type=str,
help="RETURNN vocab file (.pkl)")
parser.add_argument("--pronunciation_lexicon",
type=str,
help="CMU style pronuncation lexicon")
parser.add_argument("--pwg_config",
type=str,
help="ParallelWaveGAN config (.yaml)")
parser.add_argument("--pwg_checkpoint",
type=str,
help="ParallelWaveGAN checkpoint (.pkl)")
args = parser.parse_args()
# Initialize RETURNN
rnn.init(args.returnn_config)
rnn.engine.use_search_flag = True # enable search mode
rnn.engine.init_network_from_config(rnn.config)
returnn_vocab = Vocabulary(vocab_file=args.vocab_file, unknown_label=None)
returnn_output_dict = {
'output':
rnn.engine.network.get_default_output_layer().output.placeholder
}
# Initialize PWG
pwg_config = yaml.load(open(args.pwg_config), Loader=yaml.Loader)
pyt_device = torch.device("cpu")
generator = pwg_models.MelGANGenerator(**pwg_config['generator_params'])
generator.load_state_dict(
torch.load(args.pwg_checkpoint,
map_location="cpu")["model"]["generator"])
generator.remove_weight_norm()
pwg_model = generator.eval().to(pyt_device)
pwg_pad_fn = torch.nn.ReplicationPad1d(pwg_config["generator_params"].get(
"aux_context_window", 0))
pwg_pqmf = PQMF(
pwg_config["generator_params"]["out_channels"]).to(pyt_device)
# load a CMU dict style pronunciation table
pronunciation_dictionary = {}
with open(args.pronunciation_lexicon, "rt") as lexicon:
for lexicon_entry in lexicon.readlines():
word, phonemes = lexicon_entry.strip().split(" ", maxsplit=1)
pronunciation_dictionary[word] = phonemes.split(" ")
# Tokenizer perl command
tokenizer = [
"perl", "./scripts/tokenizer/tokenizer.perl", "-l", "en", "-no-escape"
]
audios = []
for line in sys.stdin.readlines():
line = line.strip().lower()
# run perl tokenizer as external script
p = subprocess.Popen(tokenizer,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
line = p.communicate(
input=line.encode("UTF-8"))[0].decode("UTF-8").strip()
p.terminate()
print(line)
# apply num2wordsn and pronunciation dict
words = list(map(number_convert, line.split(" ")))
print(words)
phoneme_sequence = " _ ".join([
" ".join(pronunciation_dictionary[w]) for w in words
if w in pronunciation_dictionary.keys()
])
phoneme_sequence += " _ ~"
try:
classes = numpy.asarray(returnn_vocab.get_seq(phoneme_sequence),
dtype="int32")
feed_dict = {'classes': classes}
dataset = StaticDataset([feed_dict],
output_dim={'classes': (77, 1)})
result = rnn.engine.run_single(dataset, 0, returnn_output_dict)
except Exception as e:
print(e)
raise e
feature_data = numpy.squeeze(result['output']).T
print(feature_data.shape)
with torch.no_grad():
input_features = pwg_pad_fn(
torch.from_numpy(feature_data).unsqueeze(0)).to(pyt_device)
audio_waveform = pwg_pqmf.synthesis(
pwg_model(input_features)).view(-1).cpu().numpy()
audios.append(
numpy.asarray(audio_waveform * (2**15 - 1),
dtype="int16").tobytes())
for i, audio in enumerate(audios):
wave_writer = wave.open("out_%i.wav" % i, "wb")
wave_writer.setnchannels(1)
wave_writer.setframerate(16000)
wave_writer.setsampwidth(2)
wave_writer.writeframes(audio)
wave_writer.close()