# 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