def log_audio(model: Tacotron2, iteration: int, logger: Tacotron2Logger, waveglow):
text = "Does it work yet?"
sequence = array(text_to_sequence(text, ['english_cleaners']))[None, :]
sequence = torch.autograd.Variable(
torch.from_numpy(sequence)).cuda().long()
mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
with torch.no_grad():
audio = waveglow.infer(mel_outputs_postnet, sigma=0.666)
logger.add_audio(text, audio[0].data.cpu(), global_step=iteration, sample_rate=hparams.sampling_rate)
def log_audio(model: Tacotron2, iteration: int, logger: Tacotron2Logger,
waveglow, inference_batch, text_encoded, mel):
# load source data to obtain rhythm using tacotron 2 as a forced aligner
x, y = model.parse_batch(inference_batch)
with torch.no_grad():
# get rhythm (alignment map) using tacotron 2
mel_outputs, mel_outputs_postnet, gate_outputs, rhythm = model.forward(
x)
rhythm = rhythm.permute(1, 0, 2)
for emotion in range(4):
emotion_id = torch.LongTensor([emotion]).cuda()
with torch.no_grad():
mel_outputs, mel_outputs_postnet, gate_outputs, _ = model.inference_noattention(
(text_encoded, mel, emotion_id, rhythm))
audio = waveglow.infer(mel_outputs_postnet, sigma=0.8)
logger.add_audio(f"Emotion {str(emotion)}",
audio[0].data.cpu(),
global_step=iteration,
sample_rate=hparams.sampling_rate)
