def _initialize(self):
"""
initialize with the necessary elements
"""
self.tts_checkpoint_path = os.path.join(self.directory, "assets",
"tts", "step-162000")
self.waveflow_checkpoint_path = os.path.join(self.directory, "assets",
"vocoder", "step-2000000")
self.waveflow_config_path = os.path.join(self.directory, "assets",
"vocoder",
"waveflow_ljspeech.yaml")
tts_config_path = os.path.join(self.directory, "assets", "tts",
"ljspeech.yaml")
with open(tts_config_path) as f:
self.tts_config = yaml.load(f, Loader=yaml.Loader)
with fluid.dygraph.guard(fluid.CPUPlace()):
self.tts_model = FastSpeechModel(
self.tts_config['network'],
num_mels=self.tts_config['audio']['num_mels'])
io.load_parameters(model=self.tts_model,
checkpoint_path=self.tts_checkpoint_path)
# Build vocoder.
args = AttrDict()
args.config = self.waveflow_config_path
args.use_fp16 = False
self.waveflow_config = io.add_yaml_config_to_args(args)
self.waveflow = WaveFlowModule(self.waveflow_config)
io.load_parameters(model=self.waveflow,
checkpoint_path=self.waveflow_checkpoint_path)
def _initialize(self):
"""
initialize with the necessary elements
"""
self.tts_checkpoint_path = os.path.join(self.directory, "assets",
"tts", "step-120000")
self.waveflow_checkpoint_path = os.path.join(self.directory, "assets",
"vocoder", "step-2000000")
self.waveflow_config_path = os.path.join(self.directory, "assets",
"vocoder",
"waveflow_ljspeech.yaml")
tts_config_path = os.path.join(self.directory, "assets", "tts",
"ljspeech.yaml")
with open(tts_config_path) as f:
self.tts_config = yaml.load(f, Loader=yaml.Loader)
# The max length of audio when synthsis.
self.max_len = 1000
# The threshold of stop token which indicates the time step should stop generate spectrum or not.
self.stop_threshold = 0.5
with fluid.dygraph.guard(fluid.CPUPlace()):
# Build TTS.
with fluid.unique_name.guard():
network_cfg = self.tts_config['network']
self.tts_model = TransformerTTSModel(
network_cfg['embedding_size'], network_cfg['hidden_size'],
network_cfg['encoder_num_head'],
network_cfg['encoder_n_layers'],
self.tts_config['audio']['num_mels'],
network_cfg['outputs_per_step'],
network_cfg['decoder_num_head'],
network_cfg['decoder_n_layers'])
io.load_parameters(model=self.tts_model,
checkpoint_path=self.tts_checkpoint_path)
# Build vocoder.
args = AttrDict()
args.config = self.waveflow_config_path
args.use_fp16 = False
self.waveflow_config = io.add_yaml_config_to_args(args)
self.waveflow = WaveFlowModule(self.waveflow_config)
io.load_parameters(model=self.waveflow,
checkpoint_path=self.waveflow_checkpoint_path)
def synthesis_with_waveflow(mel_output, args, checkpoint, place):
fluid.enable_dygraph(place)
args.config = args.config_vocoder
args.use_fp16 = False
config = io.add_yaml_config_to_args(args)
mel_spectrogram = fluid.layers.transpose(mel_output, [0, 2, 1])
# Build model.
waveflow = WaveFlowModule(config)
io.load_parameters(model=waveflow, checkpoint_path=checkpoint)
for layer in waveflow.sublayers():
if isinstance(layer, weight_norm.WeightNormWrapper):
layer.remove_weight_norm()
# Run model inference.
wav = waveflow.synthesize(mel_spectrogram, sigma=config.sigma)
return wav.numpy()[0]
iteration += 1
if iteration % config.test_every == 0:
# Run validation step.
model.valid_step(iteration)
if rank == 0 and iteration % config.save_every == 0:
# Save parameters.
model.save(iteration)
# Close TensorBoard.
if rank == 0:
vdl.close()
if __name__ == "__main__":
# Create parser.
parser = argparse.ArgumentParser(description="Train WaveFlow model")
#formatter_class='default_argparse')
add_options_to_parser(parser)
utils.add_config_options_to_parser(parser)
# Parse argument from both command line and yaml config file.
# For conflicting updates to the same field,
# the preceding update will be overwritten by the following one.
config = parser.parse_args()
config = io.add_yaml_config_to_args(config)
# Force to use fp32 in model training
vars(config)["use_fp16"] = False
train(config)