def test_dtw_aligner():
from nnmnkwii.preprocessing.alignment import DTWAligner, IterativeDTWAligner
x, fs = librosa.load(example_audio_file(), sr=None)
assert fs == 16000
x_fast = librosa.effects.time_stretch(x, 2.0)
X = _get_mcep(x, fs)
Y = _get_mcep(x_fast, fs)
D = X.shape[-1]
# Create padded pair
X, Y = adjust_frame_lengths(X, Y, divisible_by=2)
# Add utterance axis
X = X.reshape(1, -1, D)
Y = Y.reshape(1, -1, D)
X_aligned, Y_aligned = DTWAligner().transform((X, Y))
assert X_aligned.shape == Y_aligned.shape
assert np.linalg.norm(X_aligned - Y_aligned) < np.linalg.norm(X - Y)
X_aligned, Y_aligned = IterativeDTWAligner(n_iter=2,
max_iter_gmm=10,
n_components_gmm=2).transform(
(X, Y))
assert X_aligned.shape == Y_aligned.shape
assert np.linalg.norm(X_aligned - Y_aligned) < np.linalg.norm(X - Y)
# Custom dist function
from nnmnkwii.metrics import melcd
X_aligned, Y_aligned = DTWAligner(dist=melcd).transform((X, Y))
assert np.linalg.norm(X_aligned - Y_aligned) < np.linalg.norm(X - Y)
def test_mulaw_real():
fs, x = wavfile.read(example_audio_file())
x = (x / 32768.0).astype(np.float32)
mu = 256
y = P.mulaw_quantize(x, mu)
assert y.min() >= 0 and y.max() < mu
assert y.dtype == np.int
x = P.inv_mulaw_quantize(y, mu) * 32768
assert x.dtype == np.float32
x = x.astype(np.int16)
def test_trim_remove_zeros_frames():
fs, x = wavfile.read(example_audio_file())
frame_period = 5
x = x.astype(np.float64)
f0, timeaxis = pyworld.dio(x, fs, frame_period=frame_period)
spectrogram = pyworld.cheaptrick(x, f0, timeaxis, fs)
aperiodicity = pyworld.d4c(x, f0, timeaxis, fs)
for mat in [spectrogram, aperiodicity]:
trimmed = trim_zeros_frames(mat)
assert trimmed.shape[1] == mat.shape[1]
for mat in [spectrogram, aperiodicity]:
trimmed = remove_zeros_frames(mat)
assert trimmed.shape[1] == mat.shape[1]
