def tts(model, text, spk, qF0s):
"""Convert text to speech waveform given a Tacotron model.
"""
if use_cuda:
model = model.cuda()
model.encoder.eval()
model.postnet.eval()
sequence = np.array(text)
spk = np.array([spk])
#sequence = np.array(text_to_sequence(text, [hparams.cleaners]))
sequence = Variable(torch.from_numpy(sequence)).unsqueeze(0)
spk = Variable(torch.from_numpy(spk))
qF0s = np.array(qF0s)
qF0s = Variable(torch.from_numpy(qF0s)).unsqueeze(0)
if use_cuda:
sequence = sequence.cuda()
spk = spk.cuda()
qF0s = qF0s.cuda()
# Greedy decoding
mel_outputs, linear_outputs, alignments = model(sequence, spk, qF0s.long())
linear_output = linear_outputs[0].cpu().data.numpy()
spectrogram = audio.denormalize(linear_output)
alignment = alignments[0].cpu().data.numpy()
# Predicted audio signal
waveform = audio.inv_spectrogram(linear_output.T)
return waveform, alignment, spectrogram
def tts(model, text):
"""Convert text to speech waveform given a Tacotron model.
"""
if use_cuda:
model = model.cuda()
# TODO: Turning off dropout of decoder's prenet causes serious performance
# regression, not sure why.
# model.decoder.eval()
model.encoder.eval()
model.postnet.eval()
sequence = np.array(text)
#sequence = np.array(text_to_sequence(text, [hparams.cleaners]))
sequence = Variable(torch.from_numpy(sequence)).unsqueeze(0)
if use_cuda:
sequence = sequence.cuda()
# Greedy decoding
mel_outputs, linear_outputs, alignments = model(sequence)
linear_output = linear_outputs[0].cpu().data.numpy()
spectrogram = audio.denormalize(linear_output)
alignment = alignments[0].cpu().data.numpy()
# Predicted audio signal
waveform = audio.inv_spectrogram(linear_output.T)
return waveform, alignment, spectrogram
def tts(model, text, mel):
"""Convert text to speech waveform given a Tacotron model.
"""
if use_cuda:
model = model.cuda()
model.encoder.eval()
model.postnet.eval()
sequence = np.array(text)
sequence = Variable(torch.from_numpy(sequence)).unsqueeze(0)
mel = Variable(torch.from_numpy(mel)).unsqueeze(0)
if use_cuda:
sequence = sequence.cuda()
mel = mel.cuda()
mel_outputs, linear_outputs, alignments = model.forward_generate_gst(
sequence, mel)
linear_output = linear_outputs[0].cpu().data.numpy()
spectrogram = audio.denormalize(linear_output)
alignment = alignments[0].cpu().data.numpy()
waveform = audio.inv_spectrogram(linear_output.T)
return waveform, alignment, spectrogram
def tts(model, text, tones):
"""Convert text to speech waveform given a Tacotron model.
"""
if use_cuda:
model = model.cuda()
model.encoder.eval()
model.postnet.eval()
sequence = np.array(text)
sequence = Variable(torch.from_numpy(sequence)).unsqueeze(0)
tones = np.array(tones)
tones = Variable(torch.from_numpy(tones)).unsqueeze(0)
if use_cuda:
sequence = sequence.cuda()
tones = tones.cuda()
mel_outputs, linear_outputs = model(sequence, tones)
linear_output = linear_outputs[0].cpu().data.numpy()
spectrogram = audio.denormalize(linear_output)
waveform = audio.inv_spectrogram(linear_output.T)
return waveform, spectrogram
def synthesize(model, mspec, spk):
"""Convert text to speech waveform given a Tacotron model.
"""
if use_cuda:
model = model.cuda()
model.eval()
sequence = np.array(mspec)
sequence = Variable(torch.from_numpy(sequence)).unsqueeze(0)
spk = np.array(spk)
spk = Variable(torch.from_numpy(spk)).unsqueeze(0)
if use_cuda:
sequence = sequence.cuda()
spk = spk.cuda()
with torch.no_grad():
model.forward_getlatents(sequence)
mel_outputs, linear_outputs, = model.forward_eval(sequence, spk)
linear_output = linear_outputs[0].cpu().data.numpy()
spectrogram = audio.denormalize(linear_output)
waveform = audio.inv_spectrogram(linear_output.T)
return waveform
def display_mel(self, mel, tag='', sr=22050):
amp_mel = denormalize(mel, self.config)
img = tf.transpose(amp_mel)
buf = buffer_mel(img, sr=sr)
img_tf = tf.image.decode_png(buf.getvalue(), channels=3)
self.add_image(tag, tf.expand_dims(img_tf, 0))
