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def _get_small_datasets(padded=False, duration=False):
if duration:
X, Y = example_file_data_sources_for_duration_model()
else:
X, Y = example_file_data_sources_for_acoustic_model()
if padded:
X = PaddedFileSourceDataset(X, padded_length=1000)
Y = PaddedFileSourceDataset(Y, padded_length=1000)
else:
X = FileSourceDataset(X)
Y = FileSourceDataset(Y)
return X, Y
def _get_small_datasets(padded=False, duration=False, padded_length=1000):
if duration:
X, Y = example_file_data_sources_for_duration_model()
else:
X, Y = example_file_data_sources_for_acoustic_model()
if padded:
X = PaddedFileSourceDataset(X, padded_length=padded_length)
Y = PaddedFileSourceDataset(Y, padded_length=padded_length)
else:
X = FileSourceDataset(X)
Y = FileSourceDataset(Y)
return X, Y
def _get_small_datasets(padded=False, duration=False, padded_length=1000):
if duration:
X, Y = example_file_data_sources_for_duration_model()
else:
X, Y = example_file_data_sources_for_acoustic_model()
if padded:
X = PaddedFileSourceDataset(X, padded_length=padded_length)
Y = PaddedFileSourceDataset(Y, padded_length=padded_length)
else:
X = FileSourceDataset(X)
Y = FileSourceDataset(Y)
return X, Y
X_std = np.sqrt(X_var)
assert np.isfinite(X_mean).all()
assert np.isfinite(X_var).all()
assert X_mean.shape[-1] == D
assert X_var.shape[-1] == D
_, X_std_hat = P.meanstd(X)
assert np.allclose(X_std, X_std_hat)
x = X[0]
x_scaled = P.scale(x, X_mean, X_std)
assert np.isfinite(x_scaled).all()
# For padded dataset
_, X = example_file_data_sources_for_acoustic_model()
X = PaddedFileSourceDataset(X, 1000)
# Should get same results with padded features
X_mean_hat, X_var_hat = P.meanvar(X, lengths)
assert np.allclose(X_mean, X_mean_hat)
assert np.allclose(X_var, X_var_hat)
# Inverse transform
x = X[0]
x_hat = P.inv_scale(P.scale(x, X_mean, X_std), X_mean, X_std)
assert np.allclose(x, x_hat, atol=1e-5)
def _get_small_datasets(padded=False, duration=False):
if duration:
X, Y = example_file_data_sources_for_duration_model()
else:
X, Y = example_file_data_sources_for_acoustic_model()
if padded:
X = PaddedFileSourceDataset(X, padded_length=1000)
Y = PaddedFileSourceDataset(Y, padded_length=1000)
else:
X = FileSourceDataset(X)
Y = FileSourceDataset(Y)
return X, Y
@raises(ValueError)
def __test_raise2(x, X_min, X_max):
P.inv_minmax_scale(x)
__test_raise1(x, X_min, X_max)
__test_raise2(x, X_min, X_max)
# Explicit scale_ and min_
min_, scale_ = P.minmax_scale_params(X_min, X_max, feature_range=(0, 0.99))
x_scaled_hat = P.minmax_scale(x, min_=min_, scale_=scale_)
assert np.allclose(x_scaled, x_scaled_hat)
# For padded dataset
X, _ = example_file_data_sources_for_acoustic_model()
X = PaddedFileSourceDataset(X, 1000)
# Should get same results with padded features
X_min_hat, X_max_hat = P.minmax(X, lengths)
assert np.allclose(X_min, X_min_hat)
assert np.allclose(X_max, X_max_hat)
# Inverse transform
x = X[0]
x_hat = P.inv_minmax_scale(P.minmax_scale(x, X_min, X_max), X_min, X_max)
assert np.allclose(x, x_hat)
x_hat = P.inv_minmax_scale(
P.minmax_scale(x, scale_=scale_, min_=min_), scale_=scale_, min_=min_)
assert np.allclose(x, x_hat)
else:
spectrogram = pysptk.mc2sp(
mc.astype(np.float64), alpha=config.alpha, fftlen=config.fftlen)
waveform = pyworld.synthesize(
f0, spectrogram, aperiodicity, fs, config.frame_period)
return waveform
clb_source = MyFileDataSource(data_root=config.data_root,
speakers=["bdl"], max_files=config.max_files)
slt_source = MyFileDataSource(data_root=config.data_root,
speakers=["slt"], max_files=config.max_files)
X = PaddedFileSourceDataset(clb_source, 1200).asarray()
Y = PaddedFileSourceDataset(slt_source, 1200).asarray()
# Alignment
X_aligned, Y_aligned = DTWAligner(verbose=0, dist=melcd).transform((X, Y))
# Drop 1st (power) dim
X_aligned, Y_aligned = X_aligned[:, :, 1:], Y_aligned[:, :, 1:]
# apply MLPG
static_dim = X_aligned.shape[-1]
if config.use_delta:
X_aligned = apply_each2d_trim(delta_features, X_aligned, config.windows)
Y_aligned = apply_each2d_trim(delta_features, Y_aligned, config.windows)
XY = np.concatenate((X_aligned, Y_aligned), axis=-1).reshape(-1, X_aligned.shape[-1]*2)
# remove zero padding
XY = remove_zeros_frames(XY)
waveform = engine.synthesis(x, b)
else:
spectrogram = pysptk.mc2sp(
mc.astype(np.float64), alpha=config.alpha, fftlen=config.fftlen)
waveform = pyworld.synthesize(
f0, spectrogram, aperiodicity, fs, config.frame_period)
return waveform
clb_source = MyFileDataSource(data_root=config.data_root,
speakers=["bdl"], max_files=config.max_files)
slt_source = MyFileDataSource(data_root=config.data_root,
speakers=["slt"], max_files=config.max_files)
X = PaddedFileSourceDataset(clb_source, 1200).asarray()
Y = PaddedFileSourceDataset(slt_source, 1200).asarray()
# Alignment
X_aligned, Y_aligned = DTWAligner(verbose=0, dist=melcd).transform((X, Y))
# Drop 1st (power) dim
X_aligned, Y_aligned = X_aligned[:, :, 1:], Y_aligned[:, :, 1:]
# apply MLPG
static_dim = X_aligned.shape[-1]
if config.use_delta:
X_aligned = apply_each2d_trim(delta_features, X_aligned, config.windows)
Y_aligned = apply_each2d_trim(delta_features, Y_aligned, config.windows)
XY = np.concatenate((X_aligned, Y_aligned), axis=-1).reshape(-1, X_aligned.shape[-1]*2)
# remove zero padding