def gen_figure_from_output(self, id_name, label, hidden, hparams):
_, alphas = hidden
labels_post = self.OutputGen.postprocess_sample(label)
coded_sp, lf0, vuv, bap = WorldFeatLabelGen.convert_to_world_features(
labels_post,
contains_deltas=False,
num_coded_sps=hparams.num_coded_sps)
sp = WorldFeatLabelGen.mcep_to_amp_sp(coded_sp, hparams.synth_fs)
lf0, _ = interpolate_lin(lf0)
# Load original LF0.
org_labels_post = WorldFeatLabelGen.load_sample(
id_name,
dir_out=self.OutputGen.dir_labels,
add_deltas=self.OutputGen.add_deltas,
num_coded_sps=hparams.num_coded_sps)
original_mgc, original_lf0, original_vuv, *_ = WorldFeatLabelGen.convert_to_world_features(
sample=org_labels_post,
contains_deltas=self.OutputGen.add_deltas,
num_coded_sps=hparams.num_coded_sps)
original_lf0, _ = interpolate_lin(original_lf0)
sp = sp[:, :150] # Zoom into spectral features.
# Get a data plotter.
grid_idx = -1
plotter = DataPlotter()
net_name = os.path.basename(hparams.model_name)
filename = str(os.path.join(hparams.out_dir, id_name + '.' + net_name))
plotter.set_title(id_name + ' - ' + net_name)
plotter.set_num_colors(3)
# plotter.set_lim(grid_idx=0, ymin=math.log(60), ymax=math.log(250))
# # Plot LF0
# grid_idx += 1
# graphs.append((original_lf0, 'Original LF0'))
# graphs.append((lf0, 'NN LF0'))
# plotter.set_data_list(grid_idx=grid_idx, data_list=graphs)
# plotter.set_area_list(grid_idx=grid_idx, area_list=[(np.invert(vuv.astype(bool)), '0.8', 1.0),
# (np.invert(original_vuv.astype(bool)), 'red', 0.2)])
# plotter.set_label(grid_idx=grid_idx, xlabel='frames [{}] ms'.format(hparams.frame_length), ylabel='log(f0)')
# Reverse the warping.
wl = self._get_dummy_warping_layer(hparams)
norm_params_no_deltas = (
self.OutputGen.norm_params[0][:hparams.num_coded_sps],
self.OutputGen.norm_params[1][:hparams.num_coded_sps])
pre_net_output, _ = wl.forward_sample(label, -alphas)
# Postprocess sample manually.
pre_net_output = pre_net_output.detach().cpu().numpy()
pre_net_mgc = pre_net_output[:, 0, :hparams.
num_coded_sps] * norm_params_no_deltas[
1] + norm_params_no_deltas[0]
# Plot spectral features predicted by pre-network.
grid_idx += 1
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [{}] ms'.format(
hparams.frame_size_ms),
ylabel='Pre-network')
plotter.set_specshow(grid_idx=grid_idx,
spec=np.abs(
WorldFeatLabelGen.mcep_to_amp_sp(
pre_net_mgc,
hparams.synth_fs)[:, :sp.shape[1]]))
# Plot final predicted spectral features.
grid_idx += 1
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [{}] ms'.format(
hparams.frame_size_ms),
ylabel='VTLN')
plotter.set_specshow(grid_idx=grid_idx, spec=np.abs(sp))
# Plot predicted alpha value and V/UV flag.
grid_idx += 1
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [{}] ms'.format(
hparams.frame_size_ms),
ylabel='alpha')
graphs = list()
graphs.append((alphas, 'NN alpha'))
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs)
plotter.set_area_list(grid_idx=grid_idx,
area_list=[(np.invert(vuv.astype(bool)), '0.8',
1.0),
(np.invert(original_vuv.astype(bool)),
'red', 0.2)])
# Add phoneme annotations if given.
if hasattr(hparams, "phoneme_indices") and hparams.phoneme_indices is not None \
and hasattr(hparams, "question_file") and hparams.question_file is not None:
questions = QuestionLabelGen.load_sample(
id_name,
os.path.join("experiments", hparams.voice, "questions"),
num_questions=hparams.num_questions)[:len(lf0)]
np_phonemes = QuestionLabelGen.questions_to_phonemes(
questions, hparams.phoneme_indices, hparams.question_file)
plotter.set_annotations(grid_idx, np_phonemes)
# Plot reference spectral features.
grid_idx += 1
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [{}] ms'.format(
hparams.frame_size_ms),
ylabel='Original spectrogram')
plotter.set_specshow(grid_idx=grid_idx,
spec=np.abs(
WorldFeatLabelGen.mcep_to_amp_sp(
original_mgc,
hparams.synth_fs)[:, :sp.shape[1]]))
plotter.gen_plot()
plotter.save_to_file(filename + '.VTLN' + hparams.gen_figure_ext)
def gen_figure_from_output(self, id_name, label, hidden, hparams):
labels_post = self.OutputGen.postprocess_sample(label)
coded_sp, lf0, vuv, bap = WorldFeatLabelGen.convert_to_world_features(
labels_post,
contains_deltas=False,
num_coded_sps=hparams.num_coded_sps)
lf0, _ = interpolate_lin(lf0)
# Load original lf0.
org_labels_post = WorldFeatLabelGen.load_sample(
id_name,
self.OutputGen.dir_labels,
add_deltas=self.OutputGen.add_deltas,
num_coded_sps=hparams.num_coded_sps)
original_mgc, original_lf0, original_vuv, *_ = WorldFeatLabelGen.convert_to_world_features(
org_labels_post,
contains_deltas=self.OutputGen.add_deltas,
num_coded_sps=hparams.num_coded_sps)
original_lf0, _ = interpolate_lin(original_lf0)
# Get a data plotter.
grid_idx = 0
plotter = DataPlotter()
net_name = os.path.basename(hparams.model_name)
filename = str(os.path.join(hparams.out_dir, id_name + '.' + net_name))
plotter.set_title(id_name + ' - ' + net_name)
plotter.set_num_colors(3)
# plotter.set_lim(grid_idx=0, ymin=math.log(60), ymax=math.log(250))
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='log(f0)')
graphs = list()
graphs.append((original_lf0, 'Original lf0'))
graphs.append((lf0, 'PyTorch lf0'))
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs)
plotter.set_area_list(grid_idx=grid_idx,
area_list=[(np.invert(vuv.astype(bool)), '0.8',
1.0),
(np.invert(original_vuv.astype(bool)),
'red', 0.2)])
grid_idx += 1
import librosa
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='Original spectrogram')
plotter.set_specshow(grid_idx=grid_idx,
spec=librosa.amplitude_to_db(np.absolute(
WorldFeatLabelGen.mcep_to_amp_sp(
original_mgc, hparams.synth_fs)),
top_db=None))
grid_idx += 1
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='NN spectrogram')
plotter.set_specshow(grid_idx=grid_idx,
spec=librosa.amplitude_to_db(np.absolute(
WorldFeatLabelGen.mcep_to_amp_sp(
coded_sp, hparams.synth_fs)),
top_db=None))
if hasattr(hparams, "phoneme_indices") and hparams.phoneme_indices is not None \
and hasattr(hparams, "question_file") and hparams.question_file is not None:
questions = QuestionLabelGen.load_sample(
id_name,
"experiments/" + hparams.voice + "/questions/",
num_questions=hparams.num_questions)[:len(lf0)]
np_phonemes = QuestionLabelGen.questions_to_phonemes(
questions, hparams.phoneme_indices, hparams.question_file)
plotter.set_annotations(grid_idx, np_phonemes)
plotter.gen_plot()
plotter.save_to_file(filename + '.Org-PyTorch' +
hparams.gen_figure_ext)