94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
# BSD 3-Clause License
|
|
|
|
# Copyright (c) 2018-2020, NVIDIA Corporation
|
|
# All rights reserved.
|
|
|
|
# Redistribution and use in source and binary forms, with or without
|
|
# modification, are permitted provided that the following conditions are met:
|
|
|
|
# * Redistributions of source code must retain the above copyright notice, this
|
|
# list of conditions and the following disclaimer.
|
|
|
|
# * Redistributions in binary form must reproduce the above copyright notice,
|
|
# this list of conditions and the following disclaimer in the documentation
|
|
# and/or other materials provided with the distribution.
|
|
|
|
# * Neither the name of the copyright holder nor the names of its
|
|
# contributors may be used to endorse or promote products derived from
|
|
# this software without specific prior written permission.
|
|
|
|
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
|
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
|
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
|
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
|
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
|
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
|
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
|
|
"""https://github.com/NVIDIA/tacotron2"""
|
|
|
|
import matplotlib
|
|
matplotlib.use("Agg")
|
|
import matplotlib.pylab as plt
|
|
import numpy as np
|
|
|
|
|
|
def save_figure_to_numpy(fig):
|
|
# save it to a numpy array.
|
|
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
|
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
|
return data
|
|
|
|
|
|
def plot_alignment_to_numpy(alignment, info=None):
|
|
fig, ax = plt.subplots(figsize=(6, 4))
|
|
im = ax.imshow(alignment, aspect='auto', origin='lower',
|
|
interpolation='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.tight_layout()
|
|
|
|
fig.canvas.draw()
|
|
data = save_figure_to_numpy(fig)
|
|
plt.close()
|
|
return data
|
|
|
|
|
|
def plot_spectrogram_to_numpy(spectrogram):
|
|
fig, ax = plt.subplots(figsize=(12, 3))
|
|
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
|
interpolation='none')
|
|
plt.colorbar(im, ax=ax)
|
|
plt.xlabel("Frames")
|
|
plt.ylabel("Channels")
|
|
plt.tight_layout()
|
|
|
|
fig.canvas.draw()
|
|
data = save_figure_to_numpy(fig)
|
|
plt.close()
|
|
return data
|
|
|
|
|
|
def plot_gate_outputs_to_numpy(gate_targets, gate_outputs):
|
|
fig, ax = plt.subplots(figsize=(12, 3))
|
|
ax.scatter(range(len(gate_targets)), gate_targets, alpha=0.5,
|
|
color='green', marker='+', s=1, label='target')
|
|
ax.scatter(range(len(gate_outputs)), gate_outputs, alpha=0.5,
|
|
color='red', marker='.', s=1, label='predicted')
|
|
|
|
plt.xlabel("Frames (Green target, Red predicted)")
|
|
plt.ylabel("Gate State")
|
|
plt.tight_layout()
|
|
|
|
fig.canvas.draw()
|
|
data = save_figure_to_numpy(fig)
|
|
plt.close()
|
|
return data
|