def visualize(alignment,
spectrogram_postnet,
stop_tokens,
text,
hop_length,
CONFIG,
spectrogram=None,
output_path=None):
if spectrogram is not None:
num_plot = 4
else:
num_plot = 3
label_fontsize = 16
fig = plt.figure(figsize=(8, 24))
plt.subplot(num_plot, 1, 1)
plt.imshow(alignment.T, aspect="auto", origin="lower", interpolation=None)
plt.xlabel("Decoder timestamp", fontsize=label_fontsize)
plt.ylabel("Encoder timestamp", fontsize=label_fontsize)
if CONFIG.use_phonemes:
seq = phoneme_to_sequence(text, [CONFIG.text_cleaner],
CONFIG.phoneme_language,
CONFIG.enable_eos_bos_chars)
text = sequence_to_phoneme(seq)
print(text)
plt.yticks(range(len(text)), list(text))
plt.colorbar()
stop_tokens = stop_tokens.squeeze().detach().to('cpu').numpy()
plt.subplot(num_plot, 1, 2)
plt.plot(range(len(stop_tokens)), list(stop_tokens))
plt.subplot(num_plot, 1, 3)
librosa.display.specshow(spectrogram_postnet.T,
sr=CONFIG.audio['sample_rate'],
hop_length=hop_length,
x_axis="time",
y_axis="linear")
plt.xlabel("Time", fontsize=label_fontsize)
plt.ylabel("Hz", fontsize=label_fontsize)
plt.tight_layout()
plt.colorbar()
if spectrogram is not None:
plt.subplot(num_plot, 1, 4)
librosa.display.specshow(spectrogram.T,
sr=CONFIG.audio['sample_rate'],
hop_length=hop_length,
x_axis="time",
y_axis="linear")
plt.xlabel("Time", fontsize=label_fontsize)
plt.ylabel("Hz", fontsize=label_fontsize)
plt.tight_layout()
plt.colorbar()
if output_path:
print(output_path)
fig.savefig(output_path)
plt.close()
def visualize(alignment,
postnet_output,
stop_tokens,
text,
hop_length,
CONFIG,
decoder_output=None,
output_path=None,
figsize=(8, 24)):
if decoder_output is not None:
num_plot = 4
else:
num_plot = 3
label_fontsize = 16
fig = plt.figure(figsize=figsize)
plt.subplot(num_plot, 1, 1)
plt.imshow(alignment.T, aspect="auto", origin="lower", interpolation=None)
plt.xlabel("Decoder timestamp", fontsize=label_fontsize)
plt.ylabel("Encoder timestamp", fontsize=label_fontsize)
# compute phoneme representation and back
if CONFIG.use_phonemes:
seq = phoneme_to_sequence(
text, [CONFIG.text_cleaner],
CONFIG.phoneme_language,
CONFIG.enable_eos_bos_chars,
tp=CONFIG.characters if 'characters' in CONFIG.keys() else None)
text = sequence_to_phoneme(
seq,
tp=CONFIG.characters if 'characters' in CONFIG.keys() else None)
print(text)
plt.yticks(range(len(text)), list(text))
plt.colorbar()
# plot stopnet predictions
plt.subplot(num_plot, 1, 2)
plt.plot(range(len(stop_tokens)), list(stop_tokens))
# plot postnet spectrogram
plt.subplot(num_plot, 1, 3)
librosa.display.specshow(postnet_output.T,
sr=CONFIG.audio['sample_rate'],
hop_length=hop_length,
x_axis="time",
y_axis="linear",
fmin=CONFIG.audio['mel_fmin'],
fmax=CONFIG.audio['mel_fmax'])
plt.xlabel("Time", fontsize=label_fontsize)
plt.ylabel("Hz", fontsize=label_fontsize)
plt.tight_layout()
plt.colorbar()
if decoder_output is not None:
plt.subplot(num_plot, 1, 4)
librosa.display.specshow(decoder_output.T,
sr=CONFIG.audio['sample_rate'],
hop_length=hop_length,
x_axis="time",
y_axis="linear",
fmin=CONFIG.audio['mel_fmin'],
fmax=CONFIG.audio['mel_fmax'])
plt.xlabel("Time", fontsize=label_fontsize)
plt.ylabel("Hz", fontsize=label_fontsize)
plt.tight_layout()
plt.colorbar()
if output_path:
print(output_path)
fig.savefig(output_path)
plt.close()