def call(self, audio, target_audio):
loss = 0.0
loss_ops = []
f_n = self.sample_rate / 2
f_max = f_n
f_min = 0.0
m_max = self._hz_to_mel(f_max)
m_min = self._hz_to_mel(f_min)
n_mels_max = int((m_max - m_min) / self.n_bands / 4)
m_all = np.linspace(m_min, m_max, self.n_bands + 1)
m_los = m_all[:-1]
m_his = m_all[1:]
f_los = self._mel_to_hz(m_los)
f_his = self._mel_to_hz(m_his)
d_m = (m_his - m_los) / n_mels_max
d_fs = self._df_dm(m_los) * d_m
for i, f_lo in enumerate(f_los):
f_hi = f_his[i]
d_f = d_fs[i]
for j, n_fft in enumerate(
self._get_closest_n_fft(self.sample_rate, d_f,
self.N_FFT_OPTIONS)):
n_mels = int(n_mels_max / 2**(2 * j))
loss_op = functools.partial(compute_mel,
sample_rate=self.sample_rate,
lo_hz=f_lo,
hi_hz=f_hi,
bins=n_mels,
fft_size=n_fft)
loss_ops.append(loss_op)
# Compute loss for each fft size.
for i, loss_op in enumerate(loss_ops):
target_mag = loss_op(target_audio)
value_mag = loss_op(audio)
# Add magnitude loss.
if self.mag_weight > 0:
loss += self.mag_weight * mean_difference(
target_mag, value_mag, self.loss_type)
# Add logmagnitude loss, reusing spectrogram.
if self.logmag_weight > 0:
target = spectral_ops.safe_log(target_mag)
value = spectral_ops.safe_log(value_mag)
loss += self.logmag_weight * mean_difference(
target, value, self.loss_type)
return loss
def call(self, audio, target_audio):
loss = 0.0
loss_ops = []
n_layers = len(self.fft_sizes)
f_n = self.sample_rate / 2
f_bands_ids = np.arange(0,
self.n_bands).repeat(n_layers / self.n_bands)
band_width = f_n / self.n_bands
for i, n_fft in enumerate(self.fft_sizes):
n_mels = int(
n_fft / 16
) # TODO: this is ad-hoc; change for something more motivated
f_lo = f_bands_ids[i] * band_width
f_hi = f_lo + band_width
loss_op = functools.partial(compute_mel,
sample_rate=self.sample_rate,
lo_hz=f_lo,
hi_hz=f_hi,
bins=n_mels,
fft_size=n_fft)
loss_ops.append(loss_op)
# Compute loss for each fft size.
for i, loss_op in enumerate(loss_ops):
target_mag = loss_op(target_audio)
value_mag = loss_op(audio)
# Add magnitude loss.
if self.mag_weight > 0:
loss += self.mag_weight * mean_difference(
target_mag, value_mag, self.loss_type)
# Add logmagnitude loss, reusing spectrogram.
if self.logmag_weight > 0:
target = spectral_ops.safe_log(target_mag)
value = spectral_ops.safe_log(value_mag)
loss += self.logmag_weight * mean_difference(
target, value, self.loss_type)
return loss
def call(self, audio, target_audio):
loss = 0.0
# Compute loss for each fft size.
for loss_op in self.loss_ops:
target_mag = loss_op(target_audio)
value_mag = loss_op(audio)
# Add magnitude loss.
if self.mag_weight > 0:
loss += self.mag_weight * mean_difference(
target_mag, value_mag, self.loss_type)
# Add logmagnitude loss, reusing spectrogram.
if self.logmag_weight > 0:
target = spectral_ops.safe_log(target_mag)
value = spectral_ops.safe_log(value_mag)
loss += self.logmag_weight * mean_difference(
target, value, self.loss_type)
return loss
def get_loss(self, target_mag, value_mag, weights=None, keep_batch=False):
"""Computes a loss for each timestep"""
loss = 0
if self.mag_weight > 0:
loss += self.mag_weight * mean_difference(
target_mag, value_mag, self.loss_type, weights=weights, axis=[2, 3])
if self.spectral_centroid_weight > 0:
target = spectral_ops.spectral_centroid(target_mag)
value = spectral_ops.spectral_centroid(value_mag)
loss += self.spectral_centroid_weight * mean_difference(
target, value, self.loss_type, weights=weights, axis=[2])
if self.blurred_spectral_weight > 0.0:
target = blur(target_mag)
value = blur(value_mag)
loss += self.blurred_spectral_weight * mean_difference(
target, value, self.loss_type, weights=weights, axis=[2, 3])
return loss
def error_heatmap(audio,
audio_gen,
step=12000,
name='',
tag='error_heatmap',
fft_sizes=(768, ),
log_smooth=('logmag', None, 1, 10, 100)):
for size in fft_sizes:
target_mag = ddsp.spectral_ops.compute_mag(tf_float32(audio),
size=size)
value_mag = ddsp.spectral_ops.compute_mag(tf_float32(audio_gen),
size=size)
for i in range(len(target_mag)):
for s in log_smooth:
if s is None:
t, v = scale(target_mag, value_mag)
title = f"Magnitude Spectrum ({size})"
elif s == 'logmag':
t = safe_log(target_mag)
v = safe_log(value_mag)
t, v = scale(t, v)
title = f"Logmag Spectrum ({size})"
else:
t = unskew(target_mag, s)
v = unskew(value_mag, s)
t, v = scale(t, v)
title = f"Magnitude Spectrum ({size}) with s={s}"
j = mean_difference(t, v, 'L1')
title += f" (diff = {j:.3f})"
img = get_error_heatmap(t[i], v[i])
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
img = np.rot90(img)
ax.imshow(img, aspect='auto')
ax.set_title(title)
ax.set_xticks([])
ax.set_yticks([])
# Format and save plot to image
tag_i = f'{tag}/{name}{i+1}-s={s}'
def committment_loss(self, z, z_q):
"""Encourage encoder to output embeddings close to the current centroids."""
loss = losses.mean_difference(z, tf.stop_gradient(z_q), loss_type='L2')
return self.commitment_loss_weight * loss
