def load_tts_model():
MODEL_PATH = dirpath + '/tts_model/best_model.pth.tar'
CONFIG_PATH = dirpath + '/tts_model/config.json'
CONFIG = load_config(CONFIG_PATH)
use_cuda = False
num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)
model = Tacotron(num_chars, CONFIG.embedding_size, CONFIG.audio['num_freq'], CONFIG.audio['num_mels'], CONFIG.r, attn_windowing=False)
num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)
model = Tacotron(num_chars, CONFIG.embedding_size, CONFIG.audio['num_freq'], CONFIG.audio['num_mels'], CONFIG.r, attn_windowing=False)
# load the audio processor
# CONFIG.audio["power"] = 1.3
CONFIG.audio["preemphasis"] = 0.97
ap = AudioProcessor(**CONFIG.audio)
# load model state
if use_cuda:
cp = torch.load(MODEL_PATH)
else:
cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)
# load the model
model.load_state_dict(cp['model'])
if use_cuda:
model.cuda()
#model.eval()
model.decoder.max_decoder_steps = 1000
return model, ap, MODEL_PATH, CONFIG, use_cuda
def say(self, text, output):
# load the model
model = Tacotron(self.CONFIG.embedding_size, self.CONFIG.num_freq,
self.CONFIG.num_mels, self.CONFIG.r)
# load the audio processor
ap = AudioProcessor(self.CONFIG.sample_rate, self.CONFIG.num_mels,
self.CONFIG.min_level_db,
self.CONFIG.frame_shift_ms,
self.CONFIG.frame_length_ms,
self.CONFIG.ref_level_db, self.CONFIG.num_freq,
self.CONFIG.power, self.CONFIG.preemphasis, 60)
# load model state
if self.use_cuda:
cp = torch.load(self.MODEL_PATH)
else:
cp = torch.load(self.MODEL_PATH,
map_location=lambda storage, loc: storage)
# load the model
model.load_state_dict(cp['model'])
if self.use_cuda:
model.cuda()
model.eval()
model.decoder.max_decoder_steps = 400
wavs = self.text2audio(text, model, self.CONFIG, self.use_cuda, ap)
audio = np.concatenate(wavs)
ap.save_wav(audio, output)
return
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 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
def load_model(self, MODEL_PATH, sentence, CONFIG, use_cuda, OUT_FILE):
# load the model
num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)
model = Tacotron(num_chars,
CONFIG.embedding_size,
CONFIG.audio['num_freq'],
CONFIG.audio['num_mels'],
CONFIG.r,
attn_windowing=False)
# load the audio processor
# CONFIG.audio["power"] = 1.3
CONFIG.audio["preemphasis"] = 0.97
ap = AudioProcessor(**CONFIG.audio)
# load model state
if use_cuda:
cp = torch.load(MODEL_PATH)
else:
cp = torch.load(MODEL_PATH,
map_location=lambda storage, loc: storage)
# load the model
model.load_state_dict(cp['model'])
if use_cuda:
model.cuda()
model.eval()
model.decoder.max_decoder_steps = 1000
align, spec, stop_tokens, wav_norm = self.tts(model, sentence, CONFIG,
use_cuda, ap, OUT_FILE)
return wav_norm
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(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
def __init__(self):
# Set constants
ROOT_PATH = 'TTS/tts_model/'
MODEL_PATH = ROOT_PATH + '/best_model.pth.tar'
# MODEL_PATH_TMP = ROOT_PATH + '/best_model.pth.tar'
CONFIG_PATH = ROOT_PATH + '/config.json'
OUT_FOLDER = ROOT_PATH + '/test'
self.CONFIG = load_config(CONFIG_PATH)
self.use_cuda = True # True
# load the model
self.model = Tacotron(self.CONFIG.embedding_size, self.CONFIG.num_freq,
self.CONFIG.num_mels, self.CONFIG.r)
# load the audio processor
self.ap = AudioProcessor(self.CONFIG.sample_rate, self.CONFIG.num_mels,
self.CONFIG.min_level_db,
self.CONFIG.frame_shift_ms,
self.CONFIG.frame_length_ms,
self.CONFIG.ref_level_db,
self.CONFIG.num_freq, self.CONFIG.power,
self.CONFIG.preemphasis, 60)
# load model state
if self.use_cuda:
cp = torch.load(MODEL_PATH)
else:
cp = torch.load(MODEL_PATH,
map_location=lambda storage, loc: storage)
# load the model
self.model.load_state_dict(cp['model'])
if self.use_cuda:
self.model.cuda()
self.model.eval()
self.model.decoder.max_decoder_steps = 500
self.nlp = spacy.load("en")
def load_tts_model(self):
CONFIG = load_config(CONFIG_PATH)
model = Tacotron(len(phonemes),
CONFIG.embedding_size,
CONFIG.audio["num_freq"],
CONFIG.audio["num_mels"],
CONFIG.r,
attn_windowing=False)
# load the audio processor
ap = AudioProcessor(**CONFIG.audio)
# load model state
cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)
# load the model
model.load_state_dict(cp["model"])
model.decoder.max_decoder_steps = 650
return model, ap, MODEL_PATH, CONFIG, use_cuda
def load_model(self):
# load the model
self.num_chars = len(phonemes) if self.CONFIG.use_phonemes else len(symbols)
self.model = Tacotron(self.num_chars, self.CONFIG.embedding_size, self.CONFIG.audio['num_freq'], self.CONFIG.audio['num_mels'], self.CONFIG.r, attn_windowing=False)
self.CONFIG.audio["preemphasis"] = 0.97
self.ap = AudioProcessor(**self.CONFIG.audio)
# load model state
if self.use_cuda:
self.cp = torch.load(self.MODEL_PATH)
else:
self.cp = torch.load(self.MODEL_PATH, map_location=lambda storage, loc: storage)
# load the model
self.model.load_state_dict(self.cp['model'])
if self.use_cuda:
self.model.cuda()
self.model.decoder.max_decoder_steps = 1000
self.sentence = self.message
align, spec, stop_tokens = self.tts(self.model, self.sentence, self.CONFIG, self.use_cuda, self.ap)
except:
pass
CONFIG = load_config(CONFIG_PATH)
use_cuda = torch.cuda.is_available()
VOCODER_MODEL_PATH = 'WaveRNN/saver.pth.tar'
VOCODER_CONFIG_PATH = 'WaveRNN/config_16K.json'
VOCODER_CONFIG = load_config(VOCODER_CONFIG_PATH)
# load the model
ap2 = AudioProcessor(**VOCODER_CONFIG.audio)
ap = AudioProcessor(**CONFIG.audio)
num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)
model = Tacotron(num_chars, CONFIG.embedding_size, ap.num_freq, ap.num_mels,
CONFIG.r, CONFIG.memory_size)
# load model state
if use_cuda:
cp = torch.load(MODEL_PATH)
else:
cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)
# load the model
model.load_state_dict(cp['model'])
if use_cuda:
model.cuda()
model.eval()
bits = 10