def compute_score(self, data, output, hparams):
dict_original_post = self.get_output_dict(data.keys(),
hparams,
chunk_size=hparams.get_value(
"n_frames_per_step",
default=1))
metric_dict = {}
for label_name in next(iter(data.values())).keys():
metric = Metrics(hparams.metrics)
for id_name, labels in data.items():
labels = labels[label_name]
output = WorldFeatLabelGen.convert_to_world_features(
sample=labels,
contains_deltas=False,
num_coded_sps=hparams.num_coded_sps,
num_bap=hparams.num_bap)
org = WorldFeatLabelGen.convert_to_world_features(
sample=dict_original_post[id_name],
contains_deltas=hparams.add_deltas,
num_coded_sps=hparams.num_coded_sps,
num_bap=hparams.num_bap)
current_metrics = metric.get_metrics(hparams.metrics, *org,
*output)
metric.accumulate(id_name, current_metrics)
metric.log()
metric_dict[label_name] = metric.get_cum_values()
return metric_dict
def synth_ref(hparams, file_id_list, feature_dir=None):
# Create reference audio files containing only the vocoder degradation.
logging.info("Synthesise references with {} for [{}].".format(
hparams.synth_vocoder,
", ".join([id_name for id_name in file_id_list])))
synth_dict = dict()
old_synth_file_suffix = hparams.synth_file_suffix
hparams.synth_file_suffix = '_ref'
if hparams.synth_vocoder == "WORLD":
for id_name in file_id_list:
# Load reference audio features.
try:
output = WorldFeatLabelGen.load_sample(
id_name,
feature_dir,
num_coded_sps=hparams.num_coded_sps)
except FileNotFoundError as e1:
try:
output = WorldFeatLabelGen.load_sample(
id_name,
feature_dir,
add_deltas=True,
num_coded_sps=hparams.num_coded_sps)
coded_sp, lf0, vuv, bap = WorldFeatLabelGen.convert_to_world_features(
output,
contains_deltas=True,
num_coded_sps=hparams.num_coded_sps)
length = len(output)
lf0 = lf0.reshape(length, 1)
vuv = vuv.reshape(length, 1)
bap = bap.reshape(length, 1)
output = np.concatenate((coded_sp, lf0, vuv, bap),
axis=1)
except FileNotFoundError as e2:
logging.error(
"Cannot find extracted WORLD features with or without deltas in {}."
.format(feature_dir))
raise Exception([e1, e2])
synth_dict[id_name] = output
# Add identifier to suffix.
old_synth_file_suffix = hparams.synth_file_suffix
hparams.synth_file_suffix += str(hparams.num_coded_sps) + 'sp'
Synthesiser.run_world_synth(synth_dict, hparams)
elif hparams.synth_vocoder == "raw":
for id_name in file_id_list:
# Use extracted data. Useful to create a reference.
raw = RawWaveformLabelGen.load_sample(
id_name, hparams.frame_rate_output_Hz)
synth_dict[id_name] = raw
Synthesiser.run_raw_synth(synth_dict, hparams)
else:
raise NotImplementedError("Unknown vocoder type {}.".format(
hparams.synth_vocoder))
# Restore identifier.
hparams.synth_file_suffix = old_synth_file_suffix
def run_world_synth(synth_output: Dict[str, np.ndarray],
hparams: ExtendedHParams,
epoch: int = None,
step: int = None,
use_model_name: bool = True,
has_deltas: bool = False) -> None:
"""Run the WORLD synthesize method."""
fft_size = pyworld.get_cheaptrick_fft_size(hparams.synth_fs)
save_dir = Synthesiser._get_synth_dir(hparams,
use_model_name,
epoch=epoch,
step=step)
for id_name, output in synth_output.items():
logging.info(
"Synthesise {} with the WORLD vocoder.".format(id_name))
coded_sp, lf0, vuv, bap = WorldFeatLabelGen.convert_to_world_features(
output,
contains_deltas=has_deltas,
num_coded_sps=hparams.num_coded_sps,
num_bap=hparams.num_bap)
amp_sp = AudioProcessing.decode_sp(
coded_sp,
hparams.sp_type,
hparams.synth_fs,
post_filtering=hparams.do_post_filtering).astype(np.double,
copy=False)
args = dict()
for attr in "preemphasis", "f0_silence_threshold", "lf0_zero":
if hasattr(hparams, attr):
args[attr] = getattr(hparams, attr)
waveform = WorldFeatLabelGen.world_features_to_raw(
amp_sp,
lf0,
vuv,
bap,
fs=hparams.synth_fs,
n_fft=fft_size,
**args)
# Always save as wav file first and convert afterwards if necessary.
file_name = (os.path.basename(id_name) +
hparams.synth_file_suffix + '_' +
str(hparams.num_coded_sps) + hparams.sp_type +
"_WORLD")
file_path = os.path.join(save_dir, file_name)
soundfile.write(file_path + ".wav", waveform, hparams.synth_fs)
# Use PyDub for special audio formats.
if hparams.synth_ext.lower() != 'wav':
as_wave = pydub.AudioSegment.from_wav(file_path + ".wav")
file = as_wave.export(file_path + "." + hparams.synth_ext,
format=hparams.synth_ext)
file.close()
os.remove(file_path + ".wav")
def run_r9y9wavenet_mulaw_world_feats_synth(synth_output, hparams):
# If no path is given, use pre-trained model.
if not hasattr(
hparams,
"synth_vocoder_path") or hparams.synth_vocoder_path is None:
parent_dirs = os.path.realpath(__file__).split(os.sep)
dir_root = str.join(
os.sep, parent_dirs[:parent_dirs.index("IdiapTTS") + 1])
hparams.synth_vocoder_path = os.path.join(
dir_root, "idiaptts", "misc", "pretrained",
"r9y9wavenet_quantized_16k_world_feats_English.nn")
# Default quantization is with mu=255.
if not hasattr(hparams, "mu") or hparams.mu is None:
hparams.add_hparam("mu", 255)
if hasattr(hparams, 'frame_rate_output_Hz'):
org_frame_rate_output_Hz = hparams.frame_rate_output_Hz
hparams.frame_rate_output_Hz = 16000
else:
org_frame_rate_output_Hz = None
hparams.add_hparam("frame_rate_output_Hz", 16000)
synth_output = copy.copy(synth_output)
if hparams.do_post_filtering:
for id_name, output in synth_output.items():
coded_sp, lf0, vuv, bap = WorldFeatLabelGen.convert_to_world_features(
output,
contains_deltas=False,
num_coded_sps=hparams.num_coded_sps)
coded_sp = merlin_post_filter(
coded_sp,
WorldFeatLabelGen.fs_to_mgc_alpha(hparams.synth_fs))
synth_output[
id_name] = WorldFeatLabelGen.convert_from_world_features(
coded_sp, lf0, vuv, bap)
if hasattr(hparams, 'bit_depth'):
org_bit_depth = hparams.bit_depth
hparams.bit_depth = 16
else:
org_bit_depth = None
hparams.add_hparam("bit_depth", 16)
Synthesiser.run_wavenet_vocoder(synth_output, hparams)
# Restore identifier.
hparams.setattr_no_type_check(
"bit_depth", org_bit_depth) # Can be None, thus no type check.
hparams.setattr_no_type_check("frame_rate_output_Hz",
org_frame_rate_output_Hz) # Can be None.
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, 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 run_world_synth(synth_output, hparams):
"""Run the WORLD synthesize method."""
fft_size = pyworld.get_cheaptrick_fft_size(hparams.synth_fs)
save_dir = hparams.synth_dir if hparams.synth_dir is not None\
else hparams.out_dir if hparams.out_dir is not None\
else os.path.curdir
for id_name, output in synth_output.items():
logging.info(
"Synthesise {} with the WORLD vocoder.".format(id_name))
coded_sp, lf0, vuv, bap = WorldFeatLabelGen.convert_to_world_features(
output,
contains_deltas=False,
num_coded_sps=hparams.num_coded_sps)
amp_sp = WorldFeatLabelGen.decode_sp(
coded_sp,
hparams.sp_type,
hparams.synth_fs,
post_filtering=hparams.do_post_filtering).astype(np.double,
copy=False)
args = dict()
for attr in "preemphasize", "f0_silence_threshold", "lf0_zero":
if hasattr(hparams, attr):
args[attr] = getattr(hparams, attr)
waveform = WorldFeatLabelGen.world_features_to_raw(
amp_sp,
lf0,
vuv,
bap,
fs=hparams.synth_fs,
n_fft=fft_size,
**args)
# f0 = np.exp(lf0, dtype=np.float64)
# vuv[f0 < WorldFeatLabelGen.f0_silence_threshold] = 0 # WORLD throws an error for too small f0 values.
# f0[vuv == 0] = 0.0
# ap = pyworld.decode_aperiodicity(np.ascontiguousarray(bap.reshape(-1, 1), np.float64),
# hparams.synth_fs,
# fft_size)
#
# waveform = pyworld.synthesize(f0, amp_sp, ap, hparams.synth_fs)
# waveform = waveform.astype(np.float32, copy=False) # Does inplace conversion, if possible.
# Always save as wav file first and convert afterwards if necessary.
file_path = os.path.join(
save_dir, "{}{}{}{}".format(
os.path.basename(id_name), "_" + hparams.model_name
if hparams.model_name is not None else "",
hparams.synth_file_suffix, "_WORLD"))
makedirs_safe(hparams.synth_dir)
soundfile.write(file_path + ".wav", waveform, hparams.synth_fs)
# Use PyDub for special audio formats.
if hparams.synth_ext.lower() != 'wav':
as_wave = pydub.AudioSegment.from_wav(file_path + ".wav")
file = as_wave.export(file_path + "." + hparams.synth_ext,
format=hparams.synth_ext)
file.close()
os.remove(file_path + ".wav")
class AcousticModelTrainer(ModelTrainer):
"""
Implementation of a ModelTrainer for the generation of acoustic data.
Use question labels as input and WORLD features w/o deltas/double deltas (specified in hparams.add_deltas) as output.
Synthesize audio from model output with MLPG smoothing.
"""
logger = logging.getLogger(__name__)
#########################
# Default constructor
#
def __init__(self,
dir_world_features,
dir_question_labels,
id_list,
num_questions,
hparams=None):
"""Default constructor.
:param dir_world_features: Path to the directory containing the world features.
:param dir_question_labels: Path to the directory containing the question labels.
:param id_list: List of ids, can contain a speaker directory.
:param num_questions: Number of questions in question file.
:param hparams: Set of hyper parameters.
"""
if hparams is None:
hparams = self.create_hparams()
hparams.out_dir = os.path.curdir
# Write missing default parameters.
if hparams.variable_sequence_length_train is None:
hparams.variable_sequence_length_train = hparams.batch_size_train > 1
if hparams.variable_sequence_length_test is None:
hparams.variable_sequence_length_test = hparams.batch_size_test > 1
if hparams.synth_dir is None:
hparams.synth_dir = os.path.join(hparams.out_dir, "synth")
super(AcousticModelTrainer, self).__init__(id_list, hparams)
self.InputGen = QuestionLabelGen(dir_question_labels, num_questions)
self.InputGen.get_normalisation_params(
dir_question_labels, hparams.input_norm_params_file_prefix)
self.OutputGen = WorldFeatLabelGen(dir_world_features,
add_deltas=hparams.add_deltas,
num_coded_sps=hparams.num_coded_sps,
sp_type=hparams.sp_type)
self.OutputGen.get_normalisation_params(
dir_world_features, hparams.output_norm_params_file_prefix)
self.dataset_train = LabelGensDataset(self.id_list_train,
self.InputGen,
self.OutputGen,
hparams,
match_lengths=True)
self.dataset_val = LabelGensDataset(self.id_list_val,
self.InputGen,
self.OutputGen,
hparams,
match_lengths=True)
if self.loss_function is None:
self.loss_function = torch.nn.MSELoss(reduction='none')
if hparams.scheduler_type == "default":
hparams.scheduler_type = "Plateau"
hparams.add_hparams(plateau_verbose=True)
@staticmethod
def create_hparams(hparams_string=None, verbose=False):
"""Create model hyper parameter container. Parse non default from given string."""
hparams = ModelTrainer.create_hparams(hparams_string, verbose=False)
hparams.add_hparams(
num_questions=None,
question_file=None, # Used to add labels in plot.
num_coded_sps=60,
sp_type="mcep",
add_deltas=True,
synth_load_org_sp=False,
synth_load_org_lf0=False,
synth_load_org_vuv=False,
synth_load_org_bap=False)
if verbose:
logging.info(hparams.get_debug_string())
return hparams
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)
def compute_score(self, dict_outputs_post, dict_hiddens, hparams):
# Get data for comparision.
dict_original_post = dict()
for id_name in dict_outputs_post.keys():
dict_original_post[id_name] = WorldFeatLabelGen.load_sample(
id_name,
dir_out=self.OutputGen.dir_labels,
add_deltas=True,
num_coded_sps=hparams.num_coded_sps)
f0_rmse = 0.0
f0_rmse_max_id = "None"
f0_rmse_max = 0.0
all_rmse = []
vuv_error_rate = 0.0
vuv_error_max_id = "None"
vuv_error_max = 0.0
all_vuv = []
mcd = 0.0
mcd_max_id = "None"
mcd_max = 0.0
all_mcd = []
bap_error = 0.0
bap_error_max_id = "None"
bap_error_max = 0.0
all_bap_error = []
for id_name, labels in dict_outputs_post.items():
output_coded_sp, output_lf0, output_vuv, output_bap = self.OutputGen.convert_to_world_features(
sample=labels,
contains_deltas=False,
num_coded_sps=hparams.num_coded_sps)
output_vuv = output_vuv.astype(bool)
# Get data for comparision.
org_coded_sp, org_lf0, org_vuv, org_bap = self.OutputGen.convert_to_world_features(
sample=dict_original_post[id_name],
contains_deltas=self.OutputGen.add_deltas,
num_coded_sps=hparams.num_coded_sps)
# Compute f0 from lf0.
org_f0 = np.exp(org_lf0.squeeze())[:len(
output_lf0)] # Fix minor negligible length mismatch.
output_f0 = np.exp(output_lf0)
# Compute MCD.
org_coded_sp = org_coded_sp[:len(output_coded_sp)]
current_mcd = metrics.melcd(
output_coded_sp[:, 1:],
org_coded_sp[:, 1:]) # TODO: Use aligned mcd.
if current_mcd > mcd_max:
mcd_max_id = id_name
mcd_max = current_mcd
mcd += current_mcd
all_mcd.append(current_mcd)
# Compute RMSE.
f0_mse = (org_f0 - output_f0)**2
current_f0_rmse = math.sqrt(
(f0_mse * org_vuv[:len(output_lf0)]).sum() /
org_vuv[:len(output_lf0)].sum())
if current_f0_rmse != current_f0_rmse:
logging.error(
"Computed NaN for F0 RMSE for {}.".format(id_name))
else:
if current_f0_rmse > f0_rmse_max:
f0_rmse_max_id = id_name
f0_rmse_max = current_f0_rmse
f0_rmse += current_f0_rmse
all_rmse.append(current_f0_rmse)
# Compute error of VUV in percentage.
num_errors = (org_vuv[:len(output_lf0)] != output_vuv)
vuv_error_rate_tmp = float(num_errors.sum()) / len(output_lf0)
if vuv_error_rate_tmp > vuv_error_max:
vuv_error_max_id = id_name
vuv_error_max = vuv_error_rate_tmp
vuv_error_rate += vuv_error_rate_tmp
all_vuv.append(vuv_error_rate_tmp)
# Compute aperiodicity distortion.
org_bap = org_bap[:len(output_bap)]
if len(output_bap.shape) > 1 and output_bap.shape[1] > 1:
current_bap_error = metrics.melcd(
output_bap, org_bap) # TODO: Use aligned mcd?
else:
current_bap_error = math.sqrt(
((org_bap - output_bap)**
2).mean()) * (10.0 / np.log(10) * np.sqrt(2.0))
if current_bap_error > bap_error_max:
bap_error_max_id = id_name
bap_error_max = current_bap_error
bap_error += current_bap_error
all_bap_error.append(current_bap_error)
f0_rmse /= len(dict_outputs_post)
vuv_error_rate /= len(dict_outputs_post)
mcd /= len(dict_original_post)
bap_error /= len(dict_original_post)
self.logger.info("Worst MCD: {} {:4.2f}dB".format(mcd_max_id, mcd_max))
self.logger.info("Worst F0 RMSE: {} {:4.2f}Hz".format(
f0_rmse_max_id, f0_rmse_max))
self.logger.info("Worst VUV error: {} {:2.2f}%".format(
vuv_error_max_id, vuv_error_max * 100))
self.logger.info("Worst BAP error: {} {:4.2f}db".format(
bap_error_max_id, bap_error_max))
self.logger.info(
"Benchmark score: MCD {:4.2f}dB, F0 RMSE {:4.2f}Hz, VUV {:2.2f}%, BAP error {:4.2f}db"
.format(mcd, f0_rmse, vuv_error_rate * 100, bap_error))
return mcd, f0_rmse, vuv_error_rate, bap_error
def synthesize(self, id_list, synth_output, hparams):
"""
Depending on hparams override the network output with the extracted features,
then continue with normal synthesis pipeline.
"""
if hparams.synth_load_org_sp\
or hparams.synth_load_org_lf0\
or hparams.synth_load_org_vuv\
or hparams.synth_load_org_bap:
for id_name in id_list:
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)
labels = WorldFeatLabelGen.load_sample(
id_name, world_dir, num_coded_sps=hparams.num_coded_sps)
len_diff = len(labels) - len(synth_output[id_name])
if len_diff > 0:
labels = WorldFeatLabelGen.trim_end_sample(labels,
int(len_diff /
2),
reverse=True)
labels = WorldFeatLabelGen.trim_end_sample(
labels, len_diff - int(len_diff / 2))
if hparams.synth_load_org_sp:
synth_output[
id_name][:len(labels), :self.OutputGen.
num_coded_sps] = labels[:, :self.OutputGen.
num_coded_sps]
if hparams.synth_load_org_lf0:
synth_output[id_name][:len(labels), -3] = labels[:, -3]
if hparams.synth_load_org_vuv:
synth_output[id_name][:len(labels), -2] = labels[:, -2]
if hparams.synth_load_org_bap:
synth_output[id_name][:len(labels), -1] = labels[:, -1]
# Run the vocoder.
ModelTrainer.synthesize(self, id_list, synth_output, hparams)
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)
def compute_score(self, data, output, hparams):
metrics_dict = super().compute_score(data, output, hparams)
dict_original_post = self.get_output_dict(
data.keys(),
hparams,
chunk_size=hparams.n_frames_per_step
if hparams.has_value("n_frames_per_step") else 1)
# Create a warping layer for manual 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])
# Compute MCD for different set of coefficients.
batch_size = len(data.keys())
for cep_coef_start in [1]:
for cep_coef_end in itertools.chain(range(10, 19), [-1]):
org_to_output_mcd = 0.0
org_to_pre_net_output_mcd = 0.0
for label_name in next(iter(data.values())).keys():
for id_name, labels in data.items():
labels = labels[label_name]
# alphas = output[id_name]['alphas']
alphas = [
output[id_name][key]
for key in output[id_name].keys()
if "alphas" in key
]
# batch_size = len(dict_outputs_post)
# for cep_coef_start in [1]:
# for cep_coef_end in itertools.chain(range(10, 19), [-1]):
# for id_name, labels in dict_outputs_post.items():
# # Split NN output.
# _, (output_alphas,) = dict_hiddens[id_name]
output_mgc_post, *_ = WorldFeatLabelGen.convert_to_world_features(
sample=labels,
contains_deltas=False,
num_coded_sps=hparams.num_coded_sps,
num_bap=hparams.num_bap)
# Reverse the warping.
pre_net_output, _ = wl.forward_sample(
labels[:, :hparams.num_coded_sps],
[-alpha for alpha in alphas])
# pre_net_output, _ = wl.forward_sample(labels, -output_alphas)
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]
# Load the original warped sample.
org_mgc_post = dict_original_post[id_name][:len(
output_mgc_post), :hparams.num_coded_sps]
# Compute mcd difference.
org_to_output_mcd += Metrics.mcd_k(
org_mgc_post,
output_mgc_post,
k=cep_coef_end,
start_bin=cep_coef_start)
org_to_pre_net_output_mcd += Metrics.mcd_k(
org_mgc_post,
pre_net_mgc,
k=cep_coef_end,
start_bin=cep_coef_start)
org_to_pre_net_output_mcd /= batch_size
org_to_output_mcd /= batch_size
self.logger.info("MCep from {} to {}:".format(
cep_coef_start, cep_coef_end))
self.logger.info(
"Original mgc to pre-net mgc error: {:4.2f}dB".format(
org_to_pre_net_output_mcd))
self.logger.info(
"Original mgc to nn mgc error: {:4.2f}dB".format(
org_to_output_mcd))
return metrics_dict
