mirror of https://github.com/coqui-ai/TTS.git
54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
import io
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import librosa
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import torch
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import numpy as np
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from TTS.utils.text import text_to_sequence
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from matplotlib import pylab as plt
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hop_length = 250
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def create_speech(m, s, CONFIG, use_cuda, ap):
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text_cleaner = [CONFIG.text_cleaner]
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seq = np.array(text_to_sequence(s, text_cleaner))
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# mel = np.zeros([seq.shape[0], CONFIG.num_mels, 1], dtype=np.float32)
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if use_cuda:
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chars_var = torch.autograd.Variable(
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torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()
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# mel_var = torch.autograd.Variable(torch.from_numpy(mel).type(torch.cuda.FloatTensor), volatile=True).cuda()
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else:
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chars_var = torch.autograd.Variable(
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torch.from_numpy(seq), volatile=True).unsqueeze(0)
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# mel_var = torch.autograd.Variable(torch.from_numpy(mel).type(torch.FloatTensor), volatile=True)
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mel_out, linear_out, alignments = m.forward(chars_var)
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linear_out = linear_out[0].data.cpu().numpy()
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alignment = alignments[0].cpu().data.numpy()
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spec = ap._denormalize(linear_out)
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wav = ap.inv_spectrogram(linear_out.T)
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wav = wav[:ap.find_endpoint(wav)]
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out = io.BytesIO()
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ap.save_wav(wav, out)
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return wav, alignment, spec
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def visualize(alignment, spectrogram, CONFIG):
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label_fontsize = 16
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plt.figure(figsize=(16, 16))
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plt.subplot(2, 1, 1)
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plt.imshow(alignment.T, aspect="auto", origin="lower", interpolation=None)
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plt.xlabel("Decoder timestamp", fontsize=label_fontsize)
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plt.ylabel("Encoder timestamp", fontsize=label_fontsize)
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plt.colorbar()
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plt.subplot(2, 1, 2)
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librosa.display.specshow(spectrogram.T, sr=CONFIG.sample_rate,
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hop_length=hop_length, x_axis="time", y_axis="linear")
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plt.xlabel("Time", fontsize=label_fontsize)
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plt.ylabel("Hz", fontsize=label_fontsize)
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plt.tight_layout()
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plt.colorbar()
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