def build(self, input_shape):
if self.image_data_format == 'channels_first':
self.n_ch = input_shape[1]
self.n_freq = input_shape[2]
self.n_time = input_shape[3]
else:
self.n_ch = input_shape[3]
self.n_freq = input_shape[1]
self.n_time = input_shape[2]
if self.init == 'mel':
self.filterbank = K.variable(backend.filterbank_mel(sr=self.sr,
n_freq=self.n_freq,
n_mels=self.n_fbs,
fmin=self.fmin,
fmax=self.fmax).transpose(),
dtype=K.floatx())
elif self.init == 'log':
self.filterbank = K.variable(backend.filterbank_log(sr=self.sr,
n_freq=self.n_freq,
n_bins=self.n_fbs,
bins_per_octave=self.bins_per_octave,
fmin=self.fmin).transpose(),
dtype=K.floatx())
if self.trainable_fb:
self.trainable_weights.append(self.filterbank)
else:
self.non_trainable_weights.append(self.filterbank)
super(Filterbank, self).build(input_shape)
self.built = True
def test_filterbank_log():
"""test for backend.filterback_log"""
fblog_ref = np.load(
os.path.join(os.path.dirname(__file__), 'fblog_8000_512.npy'))
fblog = KPB.filterbank_log(sr=8000, n_freq=512)
assert fblog.shape == fblog_ref.shape
assert np.allclose(fblog, fblog_ref, atol=TOL)
def test_filterbank_log(sample_rate, n_freq, n_bins, bins_per_octave, f_min, spread):
"""It only tests if the function is a valid wrapper"""
log_fb = KPB.filterbank_log(
sample_rate=sample_rate,
n_freq=n_freq,
n_bins=n_bins,
bins_per_octave=bins_per_octave,
f_min=f_min,
spread=spread,
)
assert log_fb.dtype == K.floatx()
assert log_fb.shape == (n_freq, n_bins)
def test_fb_log_fail():
_ = KPB.filterbank_log(sample_rate=22050, n_freq=513, n_bins=300, bins_per_octave=12)