def __call__(self, example):
def maybe_add_channel(signal):
if signal.ndim == 1:
return np.expand_dims(signal, axis=0)
elif signal.ndim == 2:
return signal
else:
raise ValueError('Either the signal has ndim 1 or 2',
signal.shape)
example[M_K.OBSERVATION_STFT] = self.stft(
maybe_add_channel(example[OBSERVATION]))
example[M_K.OBSERVATION_ABS] = np.abs(
example[M_K.OBSERVATION_STFT]).astype(np.float32)
example[NUM_FRAMES] = example[M_K.OBSERVATION_STFT].shape[-2]
if SPEECH_IMAGE in example and NOISE_IMAGE in example:
speech = self.stft(maybe_add_channel(example[SPEECH_IMAGE]))
noise = self.stft(maybe_add_channel(example[NOISE_IMAGE]))
target_mask, noise_mask = biased_binary_mask(
np.stack([speech, noise], axis=0),
low_cut=self.opts.low_cut,
high_cut=self.opts.high_cut if self.opts.high_cut >= 0 else
speech.shape[-1] + self.opts.high_cut)
example[M_K.SPEECH_MASK_TARGET] = target_mask.astype(np.float32)
example[M_K.NOISE_MASK_TARGET] = noise_mask.astype(np.float32)
return example
def change_example_structure(example):
stft = pb.transform.stft
audio_data = example[K.AUDIO_DATA]
net_input = dict()
net_input['observation_stft'] = stft(audio_data[K.OBSERVATION]).astype(
np.complex64)
net_input['observation_abs'] = np.abs(
net_input['observation_stft']).astype(np.float32)
speech_image = stft(audio_data[K.SPEECH_IMAGE])
noise_image = stft(audio_data[K.NOISE_IMAGE])
target_mask, noise_mask = biased_binary_mask(
np.stack([speech_image, noise_image], axis=0))
net_input['speech_mask_target'] = target_mask.astype(np.float32)
net_input['noise_mask_target'] = noise_mask.astype(np.float32)
return net_input
def prepare_data(example):
stft = pb.transform.STFT(shift=256, size=1024)
net_input = dict()
audio_data = dict()
for key in ['observation', 'speech_image', 'noise_image']:
audio_data[key] = stft(
np.array([
pb.io.load_audio(audio) for audio in example['audio_path'][key]
]))
net_input['observation_abs'] = np.abs(audio_data['observation']).astype(
np.float32)
target_mask, noise_mask = biased_binary_mask(
np.stack([audio_data['speech_image'], audio_data['noise_image']],
axis=0))
net_input['speech_mask_target'] = target_mask.astype(np.float32)
net_input['noise_mask_target'] = noise_mask.astype(np.float32)
return net_input