mirror of https://github.com/coqui-ai/TTS.git
32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import io
|
|
import time
|
|
import librosa
|
|
import torch
|
|
import numpy as np
|
|
from .text import text_to_sequence, phoneme_to_sequence, sequence_to_phoneme
|
|
from .visual import visualize
|
|
from matplotlib import pylab as plt
|
|
|
|
|
|
def synthesis(m, s, CONFIG, use_cuda, ap):
|
|
text_cleaner = [CONFIG.text_cleaner]
|
|
if CONFIG.use_phonemes:
|
|
seq = np.asarray(
|
|
phoneme_to_sequence(s, text_cleaner, CONFIG.phoneme_language),
|
|
dtype=np.int32)
|
|
else:
|
|
seq = np.asarray(text_to_sequence(s, text_cleaner), dtype=np.int32)
|
|
chars_var = torch.from_numpy(seq).unsqueeze(0)
|
|
if use_cuda:
|
|
chars_var = chars_var.cuda()
|
|
decoder_output, postnet_output, alignments, stop_tokens = m.inference(
|
|
chars_var.long())
|
|
postnet_output = postnet_output[0].data.cpu().numpy()
|
|
decoder_output = decoder_output[0].data.cpu().numpy()
|
|
alignment = alignments[0].cpu().data.numpy()
|
|
if CONFIG.model == "Tacotron":
|
|
wav = ap.inv_spectrogram(postnet_output.T)
|
|
else:
|
|
wav = ap.inv_mel_spectrogram(postnet_output.T)
|
|
wav = wav[:ap.find_endpoint(wav)]
|
|
return wav, alignment, decoder_output, postnet_output, stop_tokens |