def test_train_step(self):
input = torch.randint(0, 24, (8, 128)).long().to(device)
mel_spec = torch.rand(8, 30, c.num_mels).to(device)
linear_spec = torch.rand(8, 30, c.num_freq).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
criterion = L1LossMasked().to(device)
model = Tacotron(c.embedding_size, c.num_freq, c.num_mels,
c.r).to(device)
model.train()
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for i in range(5):
mel_out, linear_out, align = model.forward(input, mel_spec)
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
loss = 0.5 * loss + 0.5 * criterion(linear_out, linear_spec,
mel_lengths)
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
if count not in [139, 59]:
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def test_train_step():
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (8, )).long().to(device)
input_lengths[-1] = 128
mel_spec = torch.rand(8, 120, c.audio['num_mels']).to(device)
linear_spec = torch.rand(8, 120, c.audio['num_freq']).to(device)
mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
mel_lengths[-1] = 120
stop_targets = torch.zeros(8, 120, 1).float().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input_dummy.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze()
criterion = L1LossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron(
num_chars=32,
num_speakers=5,
gst=True,
postnet_output_dim=c.audio['num_freq'],
decoder_output_dim=c.audio['num_mels'],
r=c.r,
memory_size=c.memory_size
).to(device) #FIXME: missing num_speakers parameter to Tacotron ctor
model.train()
print(model)
print(" > Num parameters for Tacotron GST model:%s" %
(count_parameters(model)))
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for _ in range(10):
mel_out, linear_out, align, stop_tokens = model.forward(
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)
loss = loss + criterion(linear_out, linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
class Synthesizer(object):
def load_model(self, model_path, model_name, model_config, use_cuda):
model_config = os.path.join(model_path, model_config)
self.model_file = os.path.join(model_path, model_name)
print(" > Loading model ...")
print(" | > model config: ", model_config)
print(" | > model file: ", self.model_file)
config = load_config(model_config)
self.config = config
self.use_cuda = use_cuda
self.model = Tacotron(config.embedding_size, config.num_freq, config.num_mels, config.r)
self.ap = AudioProcessor(config.sample_rate, config.num_mels, config.min_level_db,
config.frame_shift_ms, config.frame_length_ms, config.preemphasis,
config.ref_level_db, config.num_freq, config.power, griffin_lim_iters=60)
# load model state
if use_cuda:
cp = torch.load(self.model_file)
else:
cp = torch.load(self.model_file, map_location=lambda storage, loc: storage)
# load the model
self.model.load_state_dict(cp['model'])
if use_cuda:
self.model.cuda()
self.model.eval()
def save_wav(self, wav, path):
wav *= 32767 / max(1e-8, np.max(np.abs(wav)))
# sf.write(path, wav.astype(np.int32), self.config.sample_rate, format='wav')
# wav = librosa.util.normalize(wav.astype(np.float), norm=np.inf, axis=None)
# wav = wav / wav.max()
# sf.write(path, wav.astype('float'), self.config.sample_rate, format='ogg')
scipy.io.wavfile.write(path, self.config.sample_rate, wav.astype(np.int16))
# librosa.output.write_wav(path, wav.astype(np.int16), self.config.sample_rate, norm=True)
def tts(self, text):
text_cleaner = [self.config.text_cleaner]
wavs = []
for sen in text.split('.'):
if len(sen) < 3:
continue
sen = sen.strip()
sen +='.'
print(sen)
sen = sen.strip()
seq = np.array(text_to_sequence(text, text_cleaner))
chars_var = torch.from_numpy(seq).unsqueeze(0)
if self.use_cuda:
chars_var = chars_var.cuda()
mel_out, linear_out, alignments, stop_tokens = self.model.forward(chars_var)
linear_out = linear_out[0].data.cpu().numpy()
wav = self.ap.inv_spectrogram(linear_out.T)
# wav = wav[:self.ap.find_endpoint(wav)]
out = io.BytesIO()
wavs.append(wav)
wavs.append(np.zeros(10000))
self.save_wav(wav, out)
return out
def test_train_step(self):
input = torch.randint(0, 24, (8, 128)).long().to(device)
mel_spec = torch.rand(8, 30, c.num_mels).to(device)
linear_spec = torch.rand(8, 30, c.num_freq).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
stop_targets = torch.zeros(8, 30, 1).float().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float()
criterion = L1LossMasked().to(device)
criterion_st = nn.BCELoss().to(device)
model = Tacotron(c.embedding_size, c.num_freq, c.num_mels,
c.r).to(device)
model.train()
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for i in range(5):
mel_out, linear_out, align, stop_tokens = model.forward(
input, mel_spec)
assert stop_tokens.data.max() <= 1.0
assert stop_tokens.data.min() >= 0.0
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)
loss = loss + criterion(linear_out, linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
if count not in [145, 59]:
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def tts(text,
model_path='model/best_model.pth.tar',
config_path='model/config.json',
use_cuda=False):
CONFIG = load_config(config_path)
model = Tacotron(CONFIG.embedding_size, CONFIG.num_freq, CONFIG.num_mels,
CONFIG.r)
if use_cuda:
cp = torch.load(model_path + seq_to_seq_test_model_fname,
map_location='cuda:0')
else:
cp = torch.load(model_path, map_location=lambda storage, loc: storage)
model.load_state_dict(cp['model'])
if use_cuda:
model.cuda()
model.eval()
model.decoder.max_decoder_steps = 250
ap = AudioProcessor(CONFIG.sample_rate,
CONFIG.num_mels,
CONFIG.min_level_db,
CONFIG.frame_shift_ms,
CONFIG.frame_length_ms,
CONFIG.ref_level_db,
CONFIG.num_freq,
CONFIG.power,
CONFIG.preemphasis,
griffin_lim_iters=50)
t_1 = time.time()
text_cleaner = [CONFIG.text_cleaner]
seq = np.array(text_to_sequence(text, text_cleaner))
chars_var = torch.from_numpy(seq).unsqueeze(0)
if use_cuda:
chars_var = chars_var.cuda()
linear_out = model.forward(chars_var.long())
linear_out = linear_out[0].data.cpu().numpy()
waveform = ap.inv_spectrogram(linear_out.T)
waveform = waveform[:ap.find_endpoint(waveform)]
out_path = 'static/samples/'
os.makedirs(out_path, exist_ok=True)
file_name = text.replace(" ", "_").replace(".", "") + ".wav"
out_path = os.path.join(out_path, file_name)
ap.save_wav(waveform, out_path)
# print(" > Run-time: {}".format(time.time() - t_1))
return file_name
