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 test_meanvar():
# Pick acoustic features for testing
_, X = example_file_data_sources_for_acoustic_model()
X = FileSourceDataset(X)
lengths = [len(x) for x in X]
D = X[0].shape[-1]
X_mean, X_var = P.meanvar(X)
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 test_minmax():
# Pick linguistic features for testing
X, _ = example_file_data_sources_for_acoustic_model()
X = FileSourceDataset(X)
lengths = [len(x) for x in X]
D = X[0].shape[-1]
X_min, X_max = P.minmax(X)
assert np.isfinite(X_min).all()
assert np.isfinite(X_max).all()
x = X[0]
x_scaled = P.minmax_scale(x, X_min, X_max, feature_range=(0, 0.99))
assert np.max(x_scaled) <= 1
assert np.min(x_scaled) >= 0
assert np.isfinite(x_scaled).all()
# Need to specify (min, max) or (scale_, min_)
@raises(ValueError)
def __test_raise1(x, X_min, X_max):
P.minmax_scale(x)
@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)
fs, x = wavfile.read(path)
x = x.astype(np.float64)
f0, timeaxis = pyworld.dio(x, fs, frame_period=frame_period)
f0 = pyworld.stonemask(x, f0, timeaxis, fs)
spectrogram = pyworld.cheaptrick(x, f0, timeaxis, fs)
spectrogram = trim_zeros_frames(spectrogram)
mc = pysptk.sp2mc(spectrogram, order=order, alpha=alpha)
return mc
bdl_source = MyFileDataSource(data_root=DATA_ROOT,
speakers=["bdl"], max_files=max_files)
slt_source = MyFileDataSource(data_root=DATA_ROOT,
speakers=["slt"], max_files=max_files)
X = PaddedFileSourceDataset(bdl_source, 1200).asarray()
Y = PaddedFileSourceDataset(slt_source, 1200).asarray()
print(X.shape)
print(Y.shape)
# # Plotting util
# def plot_parallel(x,y):
# figure(figsize=(16,7))
# subplot(2,1,1)
# librosa.display.specshow(trim_zeros_frames(x).T, sr=fs, hop_length=hop_length, x_axis="time")
# colorbar()
# subplot(2,1,2)
# librosa.display.specshow(trim_zeros_frames(y).T, sr=fs, hop_length=hop_length, x_axis="time")
# colorbar()
#
# idx = 22 # any
def make_expected_dataset(data_root, use_delta):
from pathlib import Path
from nnmnkwii.datasets import PaddedFileSourceDataset
from nnmnkwii.datasets.cmu_arctic import CMUArcticWavFileDataSource
from nnmnkwii.metrics import melcd
from nnmnkwii.preprocessing import (delta_features, remove_zeros_frames,
trim_zeros_frames)
from nnmnkwii.preprocessing.alignment import DTWAligner
from nnmnkwii.util import apply_each2d_trim
max_files = 100 # number of utterances to be used.
test_size = 0.03
windows = DELTA_WINDOWS
class MyFileDataSource(CMUArcticWavFileDataSource):
def __init__(self, *args, **kwargs):
super(MyFileDataSource, self).__init__(*args, **kwargs)
self.test_paths = None
def collect_files(self):
paths = [
Path(path)
for path in super(MyFileDataSource, self).collect_files()
]
paths_train, paths_test = train_test_split(paths,
test_size=test_size,
random_state=1234)
# keep paths for later testing
self.test_paths = paths_test
return paths_train
def collect_features(self, path):
feature = kwiiyatta.analyze_wav(path)
s = trim_zeros_frames(feature.spectrum_envelope)
return feature.mel_cepstrum.data[:len(s)] # トリムするフレームが手前にずれてるのでは？
clb_source = MyFileDataSource(data_root=data_root,
speakers=["clb"],
max_files=max_files)
slt_source = MyFileDataSource(data_root=data_root,
speakers=["slt"],
max_files=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) dimension
X_aligned, Y_aligned = X_aligned[:, :, 1:], Y_aligned[:, :, 1:]
if use_delta:
X_aligned = apply_each2d_trim(delta_features, X_aligned, windows)
Y_aligned = apply_each2d_trim(delta_features, Y_aligned, windows)
XY = (np.concatenate((X_aligned, Y_aligned),
axis=-1).reshape(-1, X_aligned.shape[-1] * 2))
return remove_zeros_frames(XY)