def test_get_static_stream_sizes():
stream_sizes = [180, 3, 1, 3]
has_dynamic_features = [True, True, False, True]
num_windows = 3
static_stream_sizes = get_static_stream_sizes(stream_sizes, has_dynamic_features, num_windows)
print(static_stream_sizes)
assert np.all(static_stream_sizes == [60, 1, 1, 1])
def test_get_static_stream_sizes():
stream_sizes = [180, 3, 1, 3]
has_dynamic_features = [True, True, False, True]
num_windows = 3
static_stream_sizes = get_static_stream_sizes(stream_sizes,
has_dynamic_features,
num_windows)
print(static_stream_sizes)
assert np.all(static_stream_sizes == [60, 1, 1, 1])
def get_selected_static_stream(y_hat_static):
static_stream_sizes = get_static_stream_sizes(hp.stream_sizes,
hp.has_dynamic_features,
len(hp.windows))
y_hat_selected = select_streams(y_hat_static,
static_stream_sizes,
streams=hp.adversarial_streams)
# 0-th mgc with adversarial trainging affects speech quality
# ref: saito17asja_gan.pdf
if hp.mask_0th_mgc_for_adv_loss:
assert hp == hparams.tts_acoustic
y_hat_selected = y_hat_selected[:, :, 1:]
return y_hat_selected
def split_streams(y_static, Y_data_mean, Y_data_std):
# static domain
mgc_dim, lf0_dim, vuv_dim, bap_dim = get_static_stream_sizes(
hp.stream_sizes, hp.has_dynamic_features, len(hp.windows))
mgc_start_idx = 0
lf0_start_idx = mgc_dim
vuv_start_idx = lf0_start_idx + lf0_dim
bap_start_idx = vuv_start_idx + vuv_dim
mgc = y_static[:, :, :lf0_start_idx]
lf0 = y_static[:, :, lf0_start_idx:vuv_start_idx]
vuv = y_static[:, :, vuv_start_idx]
bap = y_static[:, :, bap_start_idx:]
return inv_scale(mgc, lf0, vuv, bap, Y_data_mean, Y_data_std)
X_data_min, X_data_max = P.minmax(X[phase])
Y_data_mean, Y_data_var = P.meanvar(Y[phase])
Y_data_std = np.sqrt(Y_data_var)
np.save(join(data_dir, "X_{}_data_min".format(ty)), X_data_min)
np.save(join(data_dir, "X_{}_data_max".format(ty)), X_data_max)
np.save(join(data_dir, "Y_{}_data_mean".format(ty)), Y_data_mean)
np.save(join(data_dir, "Y_{}_data_var".format(ty)), Y_data_var)
if hp.generator_params["in_dim"] is None:
hp.generator_params["in_dim"] = X_data_min.shape[-1]
if hp.generator_params["out_dim"] is None:
hp.generator_params["out_dim"] = Y_data_mean.shape[-1]
if hp.discriminator_params["in_dim"] is None:
sizes = get_static_stream_sizes(hp.stream_sizes,
hp.has_dynamic_features,
len(hp.windows))
hp.discriminator_params["in_dim"] = int(np.sum(sizes))
dataset_loaders = get_tts_data_loaders(X, Y, X_data_min, X_data_max,
Y_data_mean, Y_data_std)
# Models
model_g = getattr(gantts.models, hp.generator)(**hp.generator_params)
model_d = getattr(gantts.models,
hp.discriminator)(**hp.discriminator_params)
print("Generator:", model_g)
print("Discriminator:", model_d)
# Reference discriminator model to compute spoofing rate
if checkpoint_path_r is not None:
reference_discriminator = getattr(
