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import librosa
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.colors import LogNorm
matplotlib.use("Agg")
def plot_alignment(alignment, info=None, fig_size=(16, 10), title=None, output_fig=False, plot_log=False):
if isinstance(alignment, torch.Tensor):
alignment_ = alignment.detach().cpu().numpy().squeeze()
else:
alignment_ = alignment
alignment_ = alignment_.astype(np.float32) if alignment_.dtype == np.float16 else alignment_
fig, ax = plt.subplots(figsize=fig_size)
im = ax.imshow(
alignment_.T, aspect="auto", origin="lower", interpolation="none", norm=LogNorm() if plot_log else None
)
fig.colorbar(im, ax=ax)
xlabel = "Decoder timestep"
if info is not None:
xlabel += "\n\n" + info
plt.xlabel(xlabel)
plt.ylabel("Encoder timestep")
# plt.yticks(range(len(text)), list(text))
plt.tight_layout()
if title is not None:
plt.title(title)
if not output_fig:
plt.close()
return fig
def plot_spectrogram(spectrogram, ap=None, fig_size=(16, 10), output_fig=False):
if isinstance(spectrogram, torch.Tensor):
spectrogram_ = spectrogram.detach().cpu().numpy().squeeze().T
else:
spectrogram_ = spectrogram.T
spectrogram_ = spectrogram_.astype(np.float32) if spectrogram_.dtype == np.float16 else spectrogram_
if ap is not None:
spectrogram_ = ap.denormalize(spectrogram_) # pylint: disable=protected-access
fig = plt.figure(figsize=fig_size)
plt.imshow(spectrogram_, aspect="auto", origin="lower")
plt.colorbar()
plt.tight_layout()
if not output_fig:
plt.close()
return fig
def plot_pitch(pitch, spectrogram, ap=None, fig_size=(30, 10), output_fig=False):
"""Plot pitch curves on top of the spectrogram.
Args:
pitch (np.array): Pitch values.
spectrogram (np.array): Spectrogram values.
Shapes:
pitch: :math:`(T,)`
spec: :math:`(C, T)`
"""
if isinstance(spectrogram, torch.Tensor):
spectrogram_ = spectrogram.detach().cpu().numpy().squeeze().T
else:
spectrogram_ = spectrogram.T
spectrogram_ = spectrogram_.astype(np.float32) if spectrogram_.dtype == np.float16 else spectrogram_
if ap is not None:
spectrogram_ = ap.denormalize(spectrogram_) # pylint: disable=protected-access
old_fig_size = plt.rcParams["figure.figsize"]
if fig_size is not None:
plt.rcParams["figure.figsize"] = fig_size
fig, ax = plt.subplots()
ax.imshow(spectrogram_, aspect="auto", origin="lower")
ax.set_xlabel("time")
ax.set_ylabel("spec_freq")
ax2 = ax.twinx()
ax2.plot(pitch, linewidth=5.0, color="red")
ax2.set_ylabel("F0")
plt.rcParams["figure.figsize"] = old_fig_size
if not output_fig:
plt.close()
return fig
def plot_avg_pitch(pitch, chars, fig_size=(30, 10), output_fig=False):
"""Plot pitch curves on top of the input characters.
Args:
pitch (np.array): Pitch values.
chars (str): Characters to place to the x-axis.
Shapes:
pitch: :math:`(T,)`
"""
old_fig_size = plt.rcParams["figure.figsize"]
if fig_size is not None:
plt.rcParams["figure.figsize"] = fig_size
fig, ax = plt.subplots()
x = np.array(range(len(chars)))
my_xticks = chars
plt.xticks(x, my_xticks)
ax.set_xlabel("characters")
ax.set_ylabel("freq")
ax2 = ax.twinx()
ax2.plot(pitch, linewidth=5.0, color="red")
ax2.set_ylabel("F0")
plt.rcParams["figure.figsize"] = old_fig_size
if not output_fig:
plt.close()
return fig
def plot_avg_energy(energy, chars, fig_size=(30, 10), output_fig=False):
"""Plot energy curves on top of the input characters.
Args:
energy (np.array): energy values.
chars (str): Characters to place to the x-axis.
Shapes:
energy: :math:`(T,)`
"""
old_fig_size = plt.rcParams["figure.figsize"]
if fig_size is not None:
plt.rcParams["figure.figsize"] = fig_size
fig, ax = plt.subplots()
x = np.array(range(len(chars)))
my_xticks = chars
plt.xticks(x, my_xticks)
ax.set_xlabel("characters")
ax.set_ylabel("freq")
ax2 = ax.twinx()
ax2.plot(energy, linewidth=5.0, color="red")
ax2.set_ylabel("energy")
plt.rcParams["figure.figsize"] = old_fig_size
if not output_fig:
plt.close()
return fig
def visualize(
alignment,
postnet_output,
text,
hop_length,
CONFIG,
tokenizer,
stop_tokens=None,
decoder_output=None,
output_path=None,
figsize=(8, 24),
output_fig=False,
):
"""Intended to be used in Notebooks."""
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 = tokenizer.text_to_ids(text)
text = tokenizer.ids_to_text(seq)
print(text)
plt.yticks(range(len(text)), list(text))
plt.colorbar()
if stop_tokens is not None:
# 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()
if not output_fig:
plt.close()
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