mirror of https://github.com/coqui-ai/TTS.git
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
import numpy as np
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import librosa
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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def plot_alignment(alignment, info=None):
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fig, ax = plt.subplots(figsize=(16, 10))
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im = ax.imshow(
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alignment.T, aspect='auto', origin='lower', interpolation='none')
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fig.colorbar(im, ax=ax)
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xlabel = 'Decoder timestep'
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if info is not None:
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xlabel += '\n\n' + info
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plt.xlabel(xlabel)
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plt.ylabel('Encoder timestep')
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# plt.yticks(range(len(text)), list(text))
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plt.tight_layout()
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return fig
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def plot_spectrogram(linear_output, audio):
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spectrogram = audio._denormalize(linear_output)
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fig = plt.figure(figsize=(16, 10))
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plt.imshow(spectrogram.T, aspect="auto", origin="lower")
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plt.colorbar()
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plt.tight_layout()
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return fig
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def visualize(alignment, spectrogram, stop_tokens, text, hop_length, CONFIG, spectrogram2=None):
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if spectrogram2 is not None:
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num_plot = 4
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else:
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num_plot = 3
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label_fontsize = 16
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plt.figure(figsize=(16, 32))
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plt.subplot(num_plot, 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.yticks(range(len(text)), list(text))
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plt.colorbar()
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stop_tokens = stop_tokens.squeeze().detach().to('cpu').numpy()
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plt.subplot(num_plot, 1, 2)
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plt.plot(range(len(stop_tokens)), list(stop_tokens))
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plt.subplot(num_plot, 1, 3)
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librosa.display.specshow(spectrogram.T, sr=CONFIG.audio['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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if spectrogram2 is not None:
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plt.subplot(num_plot, 1, 4)
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librosa.display.specshow(spectrogram2.T, sr=CONFIG.audio['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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