def gen_figure_from_output(self, id_name, labels, hidden, hparams):
labels_post = self.dataset_train.postprocess_sample(
labels) # Labels come in as T x C.
org_raw = RawWaveformLabelGen.load_sample(
id_name, self.OutputGen.frame_rate_output_Hz)
# Get a data plotter.
plotter = DataPlotter()
net_name = os.path.basename(hparams.model_name)
id_name = os.path.basename(id_name).rsplit('.', 1)[0]
filename = os.path.join(hparams.out_dir, id_name + "." + net_name)
plotter.set_title(id_name + " - " + net_name)
grid_idx = 0
graphs = list()
graphs.append((org_raw, 'Org'))
graphs.append((labels_post, 'Wavenet'))
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs)
plotter.set_linewidth(grid_idx=grid_idx, linewidth=[0.1])
plotter.set_colors(grid_idx=grid_idx, alpha=0.8)
plotter.set_lim(grid_idx, ymin=-1, ymax=1)
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [' +
str(hparams.frame_rate_output_Hz) + ' Hz]',
ylabel='raw')
plotter.gen_plot()
plotter.save_to_file(filename + '.Raw' + hparams.gen_figure_ext)
def gen_figure_from_output(self, id_name, labels, hidden, hparams):
if labels.ndim < 2:
labels = np.expand_dims(labels, axis=1)
labels_post = self.OutputGen.postprocess_sample(labels,
identify_peaks=True,
peak_range=100)
lf0 = self.OutputGen.labels_to_lf0(labels_post, hparams.k)
lf0, vuv = interpolate_lin(lf0)
vuv = vuv.astype(np.bool)
# Load original lf0 and vuv.
world_dir = hparams.world_dir if hasattr(hparams, "world_dir") and hparams.world_dir is not None\
else os.path.join(self.OutputGen.dir_labels, self.dir_extracted_acoustic_features)
org_labels = WorldFeatLabelGen.load_sample(
id_name, world_dir, num_coded_sps=hparams.num_coded_sps)
_, original_lf0, original_vuv, _ = WorldFeatLabelGen.convert_to_world_features(
org_labels, num_coded_sps=hparams.num_coded_sps)
original_lf0, _ = interpolate_lin(original_lf0)
original_vuv = original_vuv.astype(np.bool)
phrase_curve = np.fromfile(os.path.join(
self.OutputGen.dir_labels, id_name + self.OutputGen.ext_phrase),
dtype=np.float32).reshape(-1, 1)
original_lf0 -= phrase_curve
len_diff = len(original_lf0) - len(lf0)
original_lf0 = WorldFeatLabelGen.trim_end_sample(
original_lf0, int(len_diff / 2.0))
original_lf0 = WorldFeatLabelGen.trim_end_sample(original_lf0,
int(len_diff / 2.0) +
1,
reverse=True)
org_labels = self.OutputGen.load_sample(id_name,
self.OutputGen.dir_labels,
len(hparams.thetas))
org_labels = self.OutputGen.trim_end_sample(org_labels,
int(len_diff / 2.0))
org_labels = self.OutputGen.trim_end_sample(org_labels,
int(len_diff / 2.0) + 1,
reverse=True)
org_atoms = self.OutputGen.labels_to_atoms(
org_labels, k=hparams.k, frame_size=hparams.frame_size_ms)
# Get a data plotter.
net_name = os.path.basename(hparams.model_name)
filename = str(os.path.join(hparams.out_dir, id_name + '.' + net_name))
plotter = DataPlotter()
plotter.set_title(id_name + " - " + net_name)
graphs_output = list()
grid_idx = 0
for idx in reversed(range(labels.shape[1])):
graphs_output.append(
(labels[:, idx],
r'$\theta$=' + "{0:.3f}".format(hparams.thetas[idx])))
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='NN output')
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs_output)
# plotter.set_lim(grid_idx=0, ymin=-1.8, ymax=1.8)
grid_idx += 1
graphs_peaks = list()
for idx in reversed(range(labels_post.shape[1])):
graphs_peaks.append((labels_post[:, idx, 0], ))
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='NN post-processed')
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs_peaks)
plotter.set_area_list(grid_idx=grid_idx,
area_list=[(np.invert(vuv), '0.8', 1.0)])
plotter.set_lim(grid_idx=grid_idx, ymin=-1.8, ymax=1.8)
grid_idx += 1
graphs_target = list()
for idx in reversed(range(org_labels.shape[1])):
graphs_target.append((org_labels[:, idx, 0], ))
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='target')
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs_target)
plotter.set_area_list(grid_idx=grid_idx,
area_list=[(np.invert(original_vuv), '0.8', 1.0)
])
plotter.set_lim(grid_idx=grid_idx, ymin=-1.8, ymax=1.8)
grid_idx += 1
output_atoms = AtomLabelGen.labels_to_atoms(
labels_post,
hparams.k,
hparams.frame_size_ms,
amp_threshold=hparams.min_atom_amp)
wcad_lf0 = AtomLabelGen.atoms_to_lf0(org_atoms, len(labels))
output_lf0 = AtomLabelGen.atoms_to_lf0(output_atoms, len(labels))
graphs_lf0 = list()
graphs_lf0.append((wcad_lf0, "wcad lf0"))
graphs_lf0.append((original_lf0, "org lf0"))
graphs_lf0.append((output_lf0, "predicted lf0"))
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs_lf0)
plotter.set_area_list(grid_idx=grid_idx,
area_list=[(np.invert(original_vuv), '0.8', 1.0)
])
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='lf0')
amp_lim = max(np.max(np.abs(wcad_lf0)), np.max(
np.abs(output_lf0))) * 1.1
plotter.set_lim(grid_idx=grid_idx, ymin=-amp_lim, ymax=amp_lim)
plotter.set_linestyles(grid_idx=grid_idx, linestyles=[':', '--', '-'])
# plotter.set_lim(xmin=300, xmax=1100)
plotter.gen_plot()
plotter.save_to_file(filename + ".BASE" + hparams.gen_figure_ext)
def gen_figure_phrase(self, hparams, ids_input):
id_list = ModelTrainer._input_to_str_list(ids_input)
model_output, model_output_post = self._forward_batched(
hparams,
id_list,
hparams.batch_size_gen_figure,
synth=False,
benchmark=False,
gen_figure=False)
for id_name, outputs_post in model_output_post.items():
if outputs_post.ndim < 2:
outputs_post = np.expand_dims(outputs_post, axis=1)
lf0 = outputs_post[:, 0]
output_lf0, _ = interpolate_lin(lf0)
output_vuv = outputs_post[:, 1]
output_vuv[output_vuv < 0.5] = 0.0
output_vuv[output_vuv >= 0.5] = 1.0
output_vuv = output_vuv.astype(np.bool)
# Load original lf0 and vuv.
world_dir = hparams.world_dir if hasattr(hparams, "world_dir") and hparams.world_dir is not None\
else os.path.join(hparams.out_dir, self.dir_extracted_acoustic_features)
org_labels = WorldFeatLabelGen.load_sample(
id_name,
world_dir,
num_coded_sps=hparams.num_coded_sps,
num_bap=hparams.num_bap)[:len(output_lf0)]
_, original_lf0, original_vuv, _ = WorldFeatLabelGen.convert_to_world_features(
org_labels,
num_coded_sps=hparams.num_coded_sps,
num_bap=hparams.num_bap)
original_lf0, _ = interpolate_lin(original_lf0)
original_vuv = original_vuv.astype(np.bool)
phrase_curve = np.fromfile(os.path.join(
self.flat_trainer.atom_trainer.OutputGen.dir_labels,
id_name + self.OutputGen.ext_phrase),
dtype=np.float32).reshape(
-1, 1)[:len(original_lf0)]
f0_mse = (np.exp(original_lf0.squeeze(-1)) -
np.exp(phrase_curve.squeeze(-1)))**2
f0_rmse = math.sqrt(
(f0_mse * original_vuv[:len(output_lf0)]).sum() /
original_vuv[:len(output_lf0)].sum())
self.logger.info("RMSE of {} phrase curve: {} Hz.".format(
id_name, f0_rmse))
len_diff = len(original_lf0) - len(lf0)
original_lf0 = WorldFeatLabelGen.trim_end_sample(
original_lf0, int(len_diff / 2.0))
original_lf0 = WorldFeatLabelGen.trim_end_sample(
original_lf0, int(len_diff / 2.0) + 1, reverse=True)
# Get a data plotter.
net_name = os.path.basename(hparams.model_name)
filename = str(
os.path.join(hparams.out_dir, id_name + '.' + net_name))
plotter = DataPlotter()
# plotter.set_title(id_name + " - " + net_name)
grid_idx = 0
graphs_lf0 = list()
graphs_lf0.append((original_lf0, "Original"))
graphs_lf0.append((phrase_curve, "Predicted"))
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs_lf0)
plotter.set_area_list(grid_idx=grid_idx,
area_list=[(np.invert(original_vuv), '0.8',
1.0, 'Reference unvoiced')])
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='LF0')
# amp_lim = max(np.max(np.abs(wcad_lf0)), np.max(np.abs(output_lf0))) * 1.1
# plotter.set_lim(grid_idx=grid_idx, ymin=-amp_lim, ymax=amp_lim)
plotter.set_lim(grid_idx=grid_idx, ymin=4.2, ymax=5.4)
# plotter.set_linestyles(grid_idx=grid_idx, linestyles=[':', '--', '-'])
# plotter.set_lim(xmin=300, xmax=1100)
plotter.gen_plot()
plotter.save_to_file(filename + ".PHRASE" + hparams.gen_figure_ext)
def gen_figure_from_output(self,
id_name,
labels,
hidden,
hparams,
clustering=None,
filters_out=None):
if labels is None or filters_out is None:
input_labels = self.InputGen[id_name][:, None, ...]
labels = self.model_handler.forward(input_labels, hparams)[0][:, 0]
filters_out = self.filters_forward(input_labels, hparams)[:, 0,
...]
intern_amps = labels[:, 2:]
labels = labels[:, :2]
# Retrieve data from label.
labels_post = self.OutputGen.postprocess_sample(labels)
output_vuv = labels_post[:, 1]
output_vuv[output_vuv < 0.5] = 0.0
output_vuv[output_vuv >= 0.5] = 1.0
output_vuv = output_vuv.astype(bool)
output_lf0 = labels_post[:, 0]
# Load original lf0 and vuv.
org_labels = self.OutputGen.load_sample(id_name,
self.OutputGen.dir_labels)
original_lf0, original_vuv = self.OutputGen.convert_to_world_features(
org_labels)
# original_lf0, _ = interpolate_lin(original_lf0)
# phrase_curve = self.OutputGen.get_phrase_curve(id_name)
# original_lf0 -= phrase_curve[:len(original_lf0)]
original_lf0 = original_lf0[:len(output_lf0)]
f0_mse = (np.exp(original_lf0) - np.exp(output_lf0))**2
f0_rmse = math.sqrt((f0_mse * original_vuv[:len(output_lf0)]).sum() /
original_vuv[:len(output_lf0)].sum())
self.logger.info("RMSE of {}: {} Hz.".format(id_name, f0_rmse))
org_labels = self.flat_trainer.atom_trainer.OutputGen.load_sample(
id_name, self.flat_trainer.atom_trainer.OutputGen.dir_labels,
len(self.flat_trainer.atom_trainer.OutputGen.theta_interval),
self.flat_trainer.atom_trainer.OutputGen.dir_world_labels)
org_vuv = org_labels[:, 0, 0]
org_vuv = org_vuv.astype(bool)
thetas = self.model_handler.model.thetas_approx()
# Get a data plotter
net_name = os.path.basename(hparams.model_name)
filename = str(os.path.join(hparams.out_dir, id_name + '.' + net_name))
plotter = DataPlotter()
plot_id = 0
graphs_intern = list()
for idx in reversed(range(intern_amps.shape[1])):
graphs_intern.append(
(intern_amps[:, idx], r'$\theta$={0:.3f}'.format(thetas[idx])))
plotter.set_data_list(grid_idx=plot_id, data_list=graphs_intern)
plotter.set_area_list(grid_idx=plot_id,
area_list=[(np.invert(output_vuv), '0.75', 1.0)])
plotter.set_label(grid_idx=plot_id, ylabel='command')
amp_max = 0.04
amp_min = -amp_max
plotter.set_lim(grid_idx=plot_id, ymin=amp_min, ymax=amp_max)
plot_id += 1
graphs_filters = list()
for idx in reversed(range(filters_out.shape[1])):
graphs_filters.append((filters_out[:, idx], ))
plotter.set_data_list(grid_idx=plot_id, data_list=graphs_filters)
plotter.set_area_list(grid_idx=plot_id,
area_list=[(np.invert(output_vuv), '0.75', 1.0,
'Unvoiced')])
plotter.set_label(grid_idx=plot_id, ylabel='filtered')
amp_max = 0.1
amp_min = -amp_max
plotter.set_lim(grid_idx=plot_id, ymin=amp_min, ymax=amp_max)
plot_id += 1
graphs_lf0 = list()
graphs_lf0.append((original_lf0, "Original"))
graphs_lf0.append((output_lf0, "Predicted"))
plotter.set_data_list(grid_idx=plot_id, data_list=graphs_lf0)
plotter.set_hatchstyles(grid_idx=plot_id, hatchstyles=['\\\\'])
plotter.set_area_list(grid_idx=plot_id,
area_list=[(np.invert(org_vuv.astype(bool)),
'0.75', 1.0, 'Reference unvoiced')])
plotter.set_label(grid_idx=plot_id,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='LF0')
plotter.set_lim(grid_idx=plot_id, ymin=3, ymax=6)
plotter.set_linestyles(grid_idx=plot_id, linestyles=['-.', '-'])
plotter.set_colors(grid_idx=plot_id,
colors=['C3', 'C2', 'C0'],
alpha=1)
plotter.gen_plot()
# plotter.gen_plot(True)
plotter.save_to_file(filename + ".PHRASE" + hparams.gen_figure_ext)
if clustering is None:
return
plotter = DataPlotter()
def cluster(array, mean=False):
if mean:
return np.array([
np.take(array, i, axis=-1).mean() for i in clustering
]).transpose()
return np.array([
np.take(array, i, axis=-1).sum(-1) for i in clustering
]).transpose()
clustered_amps = cluster(intern_amps)
clustered_thetas = cluster(thetas, True)
clustered_filters = cluster(filters_out)
plot_id = 0
graphs_intern = list()
for idx in reversed(range(clustered_amps.shape[1])):
graphs_intern.append(
(clustered_amps[:, idx],
r'$\theta$={0:.3f}'.format(clustered_thetas[idx])))
plotter.set_data_list(grid_idx=plot_id, data_list=graphs_intern)
plotter.set_area_list(grid_idx=plot_id,
area_list=[(np.invert(output_vuv), '0.75', 1.0,
'Unvoiced')])
plotter.set_label(grid_idx=plot_id, ylabel='cluster command')
amp_max = 0.04
amp_min = -amp_max
plotter.set_lim(grid_idx=plot_id, ymin=amp_min, ymax=amp_max)
plot_id += 1
graphs_filters = list()
for idx in reversed(range(clustered_filters.shape[1])):
graphs_filters.append((clustered_filters[:, idx], ))
plotter.set_data_list(grid_idx=plot_id, data_list=graphs_filters)
plotter.set_area_list(grid_idx=plot_id,
area_list=[(np.invert(output_vuv), '0.75', 1.0)])
plotter.set_label(grid_idx=plot_id, ylabel='filtered')
amp_max = 0.175
amp_min = -amp_max
plotter.set_lim(grid_idx=plot_id, ymin=amp_min, ymax=amp_max)
plot_id += 1
graphs_lf0 = list()
graphs_lf0.append((original_lf0, "Original"))
graphs_lf0.append((output_lf0, "Predicted"))
plotter.set_data_list(grid_idx=plot_id, data_list=graphs_lf0)
plotter.set_hatchstyles(grid_idx=plot_id, hatchstyles=['\\\\'])
plotter.set_area_list(grid_idx=plot_id,
area_list=[(np.invert(org_vuv.astype(bool)),
'0.75', 1.0, 'Reference unvoiced')])
plotter.set_label(grid_idx=plot_id,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='lf0')
# amp_lim = max(np.max(np.abs(wcad_lf0)), np.max(np.abs(output_lf0))) * 1.1
amp_lim = 1
plotter.set_lim(grid_idx=plot_id, ymin=-amp_lim, ymax=amp_lim)
plotter.set_linestyles(grid_idx=plot_id, linestyles=['-.', '-'])
plotter.set_colors(grid_idx=plot_id,
colors=['C3', 'C2', 'C0'],
alpha=1)
plotter.gen_plot()
# plotter.gen_plot(True)
plotter.save_to_file(filename + ".CLUSTERS" + hparams.gen_figure_ext)
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):
_, 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,
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)
questions = QuestionLabelGen.load_sample(
id_name,
os.path.join("experiments", hparams.voice, "questions"),
num_questions=hparams.num_questions)[:len(alphas)]
phoneme_indices = QuestionLabelGen.questions_to_phoneme_indices(
questions, hparams.phoneme_indices)
alpha_vec = self.phonemes_to_alpha_tensor[phoneme_indices % len(
self.phonemes_to_alpha_tensor)]
# 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, '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)])
# grid_idx += 1
# 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=WorldFeatLabelGen.mgc_to_sp(original_mgc, hparams.synth_fs))
#
# 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=sp)
grid_idx += 1
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [' + str(hparams.frame_size_ms) +
' ms]',
ylabel='alpha')
graphs = list()
graphs.append((alpha_vec, 'Original alpha'))
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)])
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)
plotter.gen_plot()
plotter.save_to_file(filename + '.VTLN' + hparams.gen_figure_ext)
def gen_figure_from_output(self, id_name, label, hidden, hparams):
# Retrieve data from label.
output_amps = label[:, 1:-1]
output_pos = label[:, -1]
labels_post = self.OutputGen.postprocess_sample(label)
output_vuv = labels_post[:, 0, 1].astype(bool)
output_atoms = self.OutputGen.labels_to_atoms(labels_post, k=hparams.k, amp_threshold=hparams.min_atom_amp)
output_lf0 = self.OutputGen.atoms_to_lf0(output_atoms, len(label))
# Load original lf0 and vuv.
world_dir = hparams.world_dir if hasattr(hparams, "world_dir") and hparams.world_dir is not None\
else os.path.join(self.OutputGen.dir_labels, self.dir_extracted_acoustic_features)
org_labels = LF0LabelGen.load_sample(id_name, world_dir)
original_lf0, _ = LF0LabelGen.convert_to_world_features(org_labels)
original_lf0, _ = interpolate_lin(original_lf0)
phrase_curve = np.fromfile(os.path.join(self.OutputGen.dir_labels, id_name + self.OutputGen.ext_phrase),
dtype=np.float32).reshape(-1, 1)
original_lf0[:len(phrase_curve)] -= phrase_curve[:len(original_lf0)]
original_lf0 = original_lf0[:len(output_lf0)]
org_labels = self.OutputGen.load_sample(id_name,
self.OutputGen.dir_labels,
len(hparams.thetas),
self.OutputGen.dir_world_labels)
org_vuv = org_labels[:, 0, 0].astype(bool)
org_labels = org_labels[:, 1:]
len_diff = len(org_labels) - len(labels_post)
org_labels = self.OutputGen.trim_end_sample(org_labels, int(len_diff / 2.0))
org_labels = self.OutputGen.trim_end_sample(org_labels, int(len_diff / 2.0) + 1)
org_atoms = AtomLabelGen.labels_to_atoms(org_labels, k=hparams.k, frame_size=hparams.frame_size_ms)
wcad_lf0 = self.OutputGen.atoms_to_lf0(org_atoms, len(org_labels))
# Get a data plotter
net_name = os.path.basename(hparams.model_name)
filename = str(os.path.join(hparams.out_dir, id_name + '.' + net_name))
plotter = DataPlotter()
plotter.set_title(id_name + " - " + net_name)
grid_idx = 0
graphs_output = list()
for idx in reversed(range(output_amps.shape[1])):
graphs_output.append((output_amps[:, idx], r'$\theta$={0:.3f}'.format(hparams.thetas[idx])))
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs_output)
plotter.set_label(grid_idx=grid_idx, ylabel='NN amps')
amp_max = np.max(output_amps) * 1.1
amp_min = np.min(output_amps) * 1.1
plotter.set_lim(grid_idx=grid_idx, ymin=amp_min, ymax=amp_max)
grid_idx += 1
graphs_pos_flag = list()
graphs_pos_flag.append((output_pos,))
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs_pos_flag)
plotter.set_label(grid_idx=grid_idx, ylabel='NN pos')
grid_idx += 1
graphs_peaks = list()
for idx in reversed(range(label.shape[1] - 2)):
graphs_peaks.append((labels_post[:, 1 + idx, 0],))
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs_peaks)
plotter.set_area_list(grid_idx=grid_idx, area_list=[(np.invert(output_vuv), '0.75', 1.0, 'Unvoiced')])
plotter.set_label(grid_idx=grid_idx, ylabel='NN peaks')
plotter.set_lim(grid_idx=grid_idx, ymin=-1.8, ymax=1.8)
grid_idx += 1
graphs_target = list()
for idx in reversed(range(org_labels.shape[1])):
graphs_target.append((org_labels[:, idx, 0],))
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs_target)
plotter.set_hatchstyles(grid_idx=grid_idx, hatchstyles=['\\\\'])
plotter.set_area_list(grid_idx=grid_idx, area_list=[(np.invert(org_vuv.astype(bool)), '0.75', 1.0, 'Reference unvoiced')])
plotter.set_label(grid_idx=grid_idx, ylabel='target')
plotter.set_lim(grid_idx=grid_idx, ymin=-1.8, ymax=1.8)
grid_idx += 1
graphs_lf0 = list()
graphs_lf0.append((wcad_lf0, "wcad lf0"))
graphs_lf0.append((original_lf0, "org lf0"))
graphs_lf0.append((output_lf0, "predicted lf0"))
plotter.set_data_list(grid_idx=grid_idx, data_list=graphs_lf0)
plotter.set_area_list(grid_idx=grid_idx, area_list=[(np.invert(org_vuv.astype(bool)), '0.75', 1.0)])
plotter.set_hatchstyles(grid_idx=grid_idx, hatchstyles=['\\\\'])
plotter.set_label(grid_idx=grid_idx, xlabel='frames [' + str(hparams.frame_size_ms) + ' ms]', ylabel='lf0')
amp_lim = max(np.max(np.abs(wcad_lf0)), np.max(np.abs(output_lf0))) * 1.1
plotter.set_lim(grid_idx=grid_idx, ymin=-amp_lim, ymax=amp_lim)
plotter.set_linestyles(grid_idx=grid_idx, linestyles=[':', '--', '-'])
# # Compute F0 RMSE for sample and add it to title.
# org_f0 = (np.exp(lf0.squeeze() + phrase_curve[:len(lf0)].squeeze()) * vuv)[:len(output_lf0)] # Fix minor negligible length mismatch.
# output_f0 = np.exp(output_lf0 + phrase_curve[:len(output_lf0)].squeeze()) * output_vuv[:len(output_lf0)]
# f0_mse = (org_f0 - output_f0) ** 2
# # non_zero_count = np.logical_and(vuv[:len(output_lf0)], output_vuv).sum()
# f0_rmse = math.sqrt(f0_mse.sum() / (np.logical_and(vuv[:len(output_lf0)], output_vuv).sum()))
# # Compute vuv error rate.
# num_errors = (vuv[:len(output_lf0)] != output_vuv)
# vuv_error_rate = float(num_errors.sum()) / len(output_lf0)
# plotter.set_title(id_name + " - " + net_name + " - F0_RMSE_" + "{:4.2f}Hz".format(f0_rmse) + " - VUV_" + "{:2.2f}%".format(vuv_error_rate * 100))
# plotter.set_lim(xmin=300, xmax=1100)g
plotter.gen_plot(monochrome=True)
plotter.gen_plot()
plotter.save_to_file(filename + ".VUV_DIST_POS" + hparams.gen_figure_ext)
def plot_world_features(plotter: DataPlotter,
plotter_config: DataPlotter.Config,
grid_indices: List[int],
id_name: str,
features: np.ndarray,
contains_deltas: bool,
num_coded_sps: int,
num_bap: int,
hparams: ExtendedHParams,
plot_mgc: bool = True,
mgc_label: str = "Original spectrogram",
plot_lf0: bool = True,
sps_slices: slice = None,
lf0_label: str = "Original LF0",
plot_vuv: bool = True,
vuv_colour_alpha: List[Union[str, float]] = ('red',
0.2),
*args,
**kwargs):
mgc, lf0, vuv, _ = WorldFeatLabelGen.convert_to_world_features(
features,
contains_deltas=contains_deltas,
num_coded_sps=num_coded_sps,
num_bap=num_bap)
lf0, _ = interpolate_lin(lf0)
if grid_indices is None:
grid_idx = plotter.get_next_free_grid_idx()
else:
grid_idx = grid_indices[0]
if plot_lf0:
plotter.set_label(grid_idx=grid_idx,
xlabel='frames [{} ms]'.format(
hparams.frame_size_ms),
ylabel='log(f0)')
# plotter.set_lim(grid_idx=0, ymin=math.log(60), ymax=math.log(250))
plotter.set_data_list(grid_idx=grid_idx,
data_list=[(lf0, lf0_label)])
if plot_vuv:
plotter.set_area_list(grid_idx=grid_idx,
area_list=[(np.invert(vuv.astype(bool)),
*vuv_colour_alpha)])
if plot_mgc:
if grid_indices is None:
grid_idx = plotter.get_next_free_grid_idx()
else:
grid_idx = grid_indices[1]
AcousticModelTrainer.plot_mgc(plotter=plotter,
plotter_config=plotter_config,
grid_indices=[grid_idx],
id_name=id_name,
features=mgc,
synth_fs=hparams.synth_fs,
spec_slice=sps_slices,
labels=('frames [{} ms]'.format(
hparams.frame_size_ms),
mgc_label),
*args,
**kwargs)