dropout=hp.tts_dropout).to(device)
tts_model.restore('quick_start/tts_weights/latest_weights.pyt')
if input_text:
inputs = [text_to_sequence(input_text.strip(), hp.tts_cleaner_names)]
else:
with open('sentences.txt') as f:
inputs = [
text_to_sequence(l.strip(), hp.tts_cleaner_names) for l in f
]
voc_k = voc_model.get_step() // 1000
tts_k = tts_model.get_step() // 1000
r = tts_model.get_r()
simple_table([('WaveRNN', str(voc_k) + 'k'),
(f'Tacotron(r={r})', str(tts_k) + 'k'),
('Generation Mode', 'Batched' if batched else 'Unbatched'),
('Target Samples', target if batched else 'N/A'),
('Overlap Samples', overlap if batched else 'N/A')])
for i, x in enumerate(inputs, 1):
print(f'\n| Generating {i}/{len(inputs)}')
_, m, attention = tts_model.generate(x)
if input_text:
save_path = f'quick_start/__input_{input_text[:10]}_{tts_k}k.wav'
else:
r, lr, max_step, batch_size = session
if current_step < max_step:
train_set, attn_example = get_tts_dataset(
paths.data, batch_size, r)
model.set_r(r)
training_steps = max_step - current_step
simple_table([(f'Steps with r={r}',
str(training_steps // 1000) + 'k Steps'),
('Batch Size', batch_size),
('Learning Rate', lr),
('Outputs/Step (r)', model.get_r())])
tts_train_loop(model, optimiser, train_set, lr, training_steps,
attn_example)
print('Training Complete.')
print(
'To continue training increase tts_total_steps in hparams.py or use --force_train\n'
)
print('Creating Ground Truth Aligned Dataset...\n')
train_set, attn_example = get_tts_dataset(paths.data, 8, model.get_r())
create_gta_features(model, train_set, paths.gta)
print(
def TTS_Wave(self):
os.makedirs('quick_start/tts_weights/', exist_ok=True)
os.makedirs('quick_start/voc_weights/', exist_ok=True)
zip_ref = zipfile.ZipFile('pretrained/ljspeech.wavernn.mol.800k.zip', 'r')
zip_ref.extractall('quick_start/voc_weights/')
zip_ref.close()
zip_ref = zipfile.ZipFile('pretrained/ljspeech.tacotron.r2.180k.zip', 'r')
zip_ref.extractall('quick_start/tts_weights/')
zip_ref.close()
# Parse Arguments
parser = argparse.ArgumentParser(description='TTS Generator')
parser.add_argument('-name', metavar='name', type=str,help='name of pdf')
parser.add_argument('--input_text', '-i', type=str, help='[string] Type in something here and TTS will generate it!')
parser.add_argument('--batched', '-b', dest='batched', action='store_true', help='Fast Batched Generation (lower quality)')
parser.add_argument('--unbatched', '-u', dest='batched', action='store_false', help='Slower Unbatched Generation (better quality)')
parser.add_argument('--target', '-t', type=int, help='[int] number of samples in each batch index')
parser.add_argument('--overlap', '-o', type=int, help='[int] number of crossover samples')
parser.add_argument('--force_cpu', '-c', action='store_true', help='Forces CPU-only training, even when in CUDA capable environment')
parser.set_defaults(batched=hp.voc_gen_batched)
parser.set_defaults(target=hp.voc_target)
parser.set_defaults(overlap=hp.voc_overlap)
parser.set_defaults(input_text=None)
parser.set_defaults(weights_path=None)
args = parser.parse_args()
batched = args.batched
target = args.target
overlap = args.overlap
input_text = args.input_text
weights_path = args.weights_path
if not args.force_cpu and torch.cuda.is_available():
device = torch.device('cuda')
torch.cuda.set_device(0)
else:
device = torch.device('cpu')
print('Using device:', device)
print('\nInitialising WaveRNN Model...\n')
# Instantiate WaveRNN Model
voc_model = WaveRNN(rnn_dims=hp.voc_rnn_dims,
fc_dims=hp.voc_fc_dims,
bits=hp.bits,
pad=hp.voc_pad,
upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels,
compute_dims=hp.voc_compute_dims,
res_out_dims=hp.voc_res_out_dims,
res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length,
sample_rate=hp.sample_rate,
mode='MOL').to(device)
voc_model.restore('quick_start/voc_weights/latest_weights.pyt')
print('\nInitialising Tacotron Model...\n')
# Instantiate Tacotron Model
tts_model = Tacotron(embed_dims=hp.tts_embed_dims,
num_chars=len(symbols),
encoder_dims=hp.tts_encoder_dims,
decoder_dims=hp.tts_decoder_dims,
n_mels=hp.num_mels,
fft_bins=hp.num_mels,
postnet_dims=hp.tts_postnet_dims,
encoder_K=hp.tts_encoder_K,
lstm_dims=hp.tts_lstm_dims,
postnet_K=hp.tts_postnet_K,
num_highways=hp.tts_num_highways,
dropout=hp.tts_dropout).to(device)
tts_model.restore('quick_start/tts_weights/latest_weights.pyt')
if input_text:
inputs = [text_to_sequence(input_text.strip(), hp.tts_cleaner_names)]
else:
with open('final.txt') as f:
inputs = [text_to_sequence(l.strip(), hp.tts_cleaner_names) for l in f]
voc_k = voc_model.get_step() // 1000
tts_k = tts_model.get_step() // 1000
r = tts_model.get_r()
simple_table([('WaveRNN', str(voc_k) + 'k'),
(f'Tacotron(r={r})', str(tts_k) + 'k'),
('Generation Mode', 'Batched' if batched else 'Unbatched'),
('Target Samples', target if batched else 'N/A'),
('Overlap Samples', overlap if batched else 'N/A')])
for i, x in enumerate(inputs, 1):
print("f'\n| Generating {i}/{len(inputs)}'")
_, m, attention = tts_model.generate(x)
if input_text:
save_path = './output_audio/'+str(i)+'.wav'
else:
save_path = './output_audio/'+str(i)+'.wav'
# save_attention(attention, save_path)
m = torch.tensor(m).unsqueeze(0)
m = (m + 4) / 8
voc_model.generate(m, save_path, batched, hp.voc_target, hp.voc_overlap, hp.mu_law)
if i == 2:
temp1 = AudioSegment.from_wav("./output_audio/"+str(i-1)+".wav")
temp2 = AudioSegment.from_wav("./output_audio/"+str(i)+".wav")
combined_sounds = temp1 + temp2
os.remove("./output_audio/"+str(i-1)+".wav")
os.remove("./output_audio/"+str(i)+".wav")
combined_sounds.export("./output_audio/"+self.path[:-4]+".wav", format="wav")
elif i > 2:
preTemp = AudioSegment.from_wav("./output_audio/"+self.path[:-4]+".wav")
newTemp = AudioSegment.from_wav("./output_audio/"+str(i)+".wav")
combined_sounds = preTemp + newTemp
os.remove("./output_audio/"+self.path[:-4]+".wav")
os.remove("./output_audio/"+str(i)+".wav")
combined_sounds.export("./output_audio/"+self.path[:-4]+".wav", format="wav")
print("Done")
