def get_forward_model(model_path):
device = torch.device('cuda')
model = ForwardTacotron(embed_dims=hp.forward_embed_dims,
num_chars=len(symbols),
durpred_rnn_dims=hp.forward_durpred_rnn_dims,
durpred_conv_dims=hp.forward_durpred_conv_dims,
rnn_dim=hp.forward_rnn_dims,
postnet_k=hp.forward_postnet_K,
postnet_dims=hp.forward_postnet_dims,
prenet_k=hp.forward_prenet_K,
prenet_dims=hp.forward_prenet_dims,
highways=hp.forward_num_highways,
dropout=hp.forward_dropout,
n_mels=hp.num_mels).to(device)
model.load(model_path)
return model
pitch_rnn_dims=hp.forward_pitch_rnn_dims,
pitch_conv_dims=hp.forward_pitch_conv_dims,
pitch_dropout=hp.forward_pitch_dropout,
pitch_emb_dims=hp.forward_pitch_emb_dims,
pitch_proj_dropout=hp.forward_pitch_proj_dropout,
rnn_dim=hp.forward_rnn_dims,
postnet_k=hp.forward_postnet_K,
postnet_dims=hp.forward_postnet_dims,
prenet_k=hp.forward_prenet_K,
prenet_dims=hp.forward_prenet_dims,
highways=hp.forward_num_highways,
dropout=hp.forward_dropout,
n_mels=hp.num_mels).to(device)
tts_load_path = tts_weights if tts_weights else paths.forward_latest_weights
tts_model.load(tts_load_path)
if input_text:
text = clean_text(input_text.strip())
inputs = [text_to_sequence(text)]
else:
with open('sentences.txt') as f:
inputs = [clean_text(l.strip()) for l in f]
inputs = [text_to_sequence(t) for t in inputs]
tts_k = tts_model.get_step() // 1000
if args.vocoder == 'wavernn':
voc_k = voc_model.get_step() // 1000
simple_table([('Forward Tacotron', str(tts_k) + 'k'),
('Vocoder Type', 'WaveRNN'),
def main():
# Parse Arguments
parser = argparse.ArgumentParser(description='TTS Generator')
parser.add_argument(
'--tts_weights',
type=str,
help='[string/path] Load in different FastSpeech weights')
parser.add_argument('--hp_file',
metavar='FILE',
default='hparams.py',
help='The file to use for the hyperparameters')
parser.add_argument(
'--alpha',
type=float,
default=1.,
help='Parameter for controlling length regulator for speedup '
'or slow-down of generated speech, e.g. alpha=2.0 is double-time')
if not os.path.exists('onnx'):
os.mkdir('onnx')
args = parser.parse_args()
hp.configure(args.hp_file)
input_text = "the forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves."
tts_weights = args.tts_weights
paths = Paths(hp.data_path, hp.voc_model_id, hp.tts_model_id)
device = torch.device('cpu')
print('Using device:', device)
print('\nInitialising Forward TTS Model...\n')
tts_model = ForwardTacotron(embed_dims=hp.forward_embed_dims,
num_chars=len(symbols),
durpred_rnn_dims=hp.forward_durpred_rnn_dims,
durpred_conv_dims=hp.forward_durpred_conv_dims,
rnn_dim=hp.forward_rnn_dims,
postnet_k=hp.forward_postnet_K,
postnet_dims=hp.forward_postnet_dims,
prenet_k=hp.forward_prenet_K,
prenet_dims=hp.forward_prenet_dims,
highways=hp.forward_num_highways,
dropout=hp.forward_dropout,
n_mels=hp.num_mels).to(device)
tts_load_path = tts_weights or paths.forward_latest_weights
tts_model.load(tts_load_path)
encoder = DurationPredictor(tts_model)
decoder = Tacotron(tts_model)
tts_model.eval()
encoder.eval()
decoder.eval()
opset_version = 10
with torch.no_grad():
input_seq = text_to_sequence(input_text.strip(), hp.tts_cleaner_names)
input_seq = torch.as_tensor(input_seq, dtype=torch.long,
device=device).unsqueeze(0)
'''
FIRST STEP: predict symbols duration
'''
torch.onnx.export(encoder,
input_seq,
"./onnx/forward_tacotron_duration_prediction.onnx",
opset_version=opset_version,
do_constant_folding=True,
input_names=["input_seq"],
output_names=["embeddings", "duration"])
x, durations = encoder(input_seq)
'''
SECOND STEP: expand symbols by durations
'''
x = encoder.lr(x, durations)
'''
THIRD STEP: generate mel
'''
torch.onnx.export(decoder,
x,
"./onnx/forward_tacotron_regression.onnx",
opset_version=opset_version,
do_constant_folding=True,
input_names=["data"],
output_names=["mel"])
print('Done!')