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,
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)