def get_denoised_audio(noisyAudio):
noiseAudioFeatureExtractor = FeatureExtractor(noisyAudio,
windowLength=windowLength,
overlap=overlap,
sample_rate=sr)
noise_stft_features = noiseAudioFeatureExtractor.get_stft_spectrogram()
# Paper: Besides, spectral phase was not used in the training phase.
# At reconstruction, noisy spectral phase was used instead to
# perform in- verse STFT and recover human speech.
noisyPhase = np.angle(noise_stft_features)
noise_stft_features = np.abs(noise_stft_features)
mean = np.mean(noise_stft_features)
std = np.std(noise_stft_features)
noise_stft_features = (noise_stft_features - mean) / std
predictors = prepare_input_features(noise_stft_features)
predictors = np.reshape(
predictors,
(predictors.shape[0], predictors.shape[1], 1, predictors.shape[2]))
predictors = np.transpose(predictors, (3, 0, 1, 2)).astype(np.float32)
STFTFullyConvolutional = model.predict(predictors)
denoisedAudioFullyConvolutional = revert_features_to_audio(
STFTFullyConvolutional, noisyPhase, noiseAudioFeatureExtractor, mean,
std)
return denoisedAudioFullyConvolutional
def parallel_audio_processing(self, clean_filename):
clean_audio, _ = read_audio(clean_filename, self.sample_rate)
# remove silent frame from clean audio
clean_audio = self._remove_silent_frames(clean_audio)
# sample random fixed-sized snippets of audio
clean_audio = self._audio_random_crop(clean_audio, duration=self.audio_max_duration)
## extract stft features from clean audio ##
clean_audio_fe = FeatureExtractor(clean_audio, windowLength=self.window_length,
overlap=self.overlap, sample_rate=self.sample_rate)
clean_spectrogram = clean_audio_fe.get_stft_spectrogram()
## clean_spectrogram = cleanAudioFE.get_mel_spectrogram()
# get the clean phase
clean_phase = np.angle(clean_spectrogram)
# get the clean spectral magnitude
clean_magnitude = np.abs(clean_spectrogram)
# noise generation
noise_magnitude = self._gen_noise_stft(clean_magnitude, 0)
#clean_magnitude = self._phase_aware_scaling(clean_magnitude, clean_phase, noise_phase)
scaler = StandardScaler(copy=False, with_mean=True, with_std=True)
noise_magnitude = scaler.fit_transform(noise_magnitude)
clean_magnitude = scaler.transform(clean_magnitude)
return noise_magnitude, clean_magnitude, clean_phase
def parallel_audio_processing(self, clean_filename):
clean_audio, _ = read_audio(clean_filename, self.sample_rate)
# remove silent frame from clean audio
clean_audio = self._remove_silent_frames(clean_audio)
noise_filename = self._sample_noise_filename()
# read the noise filename
noise_audio, sr = read_audio(noise_filename, self.sample_rate)
# remove silent frame from noise audio
noise_audio = self._remove_silent_frames(noise_audio)
# sample random fixed-sized snippets of audio
clean_audio = self._audio_random_crop(clean_audio,
duration=self.audio_max_duration)
# add noise to input image
noiseInput = self._add_noise_to_clean_audio(clean_audio, noise_audio)
# extract stft features from noisy audio
noisy_input_fe = FeatureExtractor(noiseInput,
windowLength=self.window_length,
overlap=self.overlap,
sample_rate=self.sample_rate)
noise_spectrogram = noisy_input_fe.get_stft_spectrogram()
# Or get the phase angle (in radians)
# noisy_stft_magnitude, noisy_stft_phase = librosa.magphase(noisy_stft_features)
noise_phase = np.angle(noise_spectrogram)
# get the magnitude of the spectral
noise_magnitude = np.abs(noise_spectrogram)
# extract stft features from clean audio
clean_audio_fe = FeatureExtractor(clean_audio,
windowLength=self.window_length,
overlap=self.overlap,
sample_rate=self.sample_rate)
clean_spectrogram = clean_audio_fe.get_stft_spectrogram()
# clean_spectrogram = cleanAudioFE.get_mel_spectrogram()
# get the clean phase
clean_phase = np.angle(clean_spectrogram)
# get the clean spectral magnitude
clean_magnitude = np.abs(clean_spectrogram)
# clean_magnitude = 2 * clean_magnitude / np.sum(scipy.signal.hamming(self.window_length, sym=False))
clean_magnitude = self._phase_aware_scaling(clean_magnitude,
clean_phase, noise_phase)
scaler = StandardScaler(copy=False, with_mean=True, with_std=True)
noise_magnitude = scaler.fit_transform(noise_magnitude)
clean_magnitude = scaler.transform(clean_magnitude)
return noise_magnitude, clean_magnitude, noise_phase
return noise_stft_mag_features, clean_stft_magnitude, noise_stft_phase
train_dataset = tf.data.TFRecordDataset([train_tfrecords_filenames])
train_dataset = train_dataset.map(tf_record_parser)
train_dataset = train_dataset.repeat(1)
train_dataset = train_dataset.batch(1000)
train_dataset = train_dataset.prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
window_length = 256
overlap = 64
sr = 16000
feature_extractor = FeatureExtractor(None,
windowLength=window_length,
overlap=overlap,
sample_rate=sr)
def revert_features_to_audio(features, phase, cleanMean=None, cleanStd=None):
# scale the outpus back to the original range
if cleanMean and cleanStd:
features = cleanStd * features + cleanMean
phase = np.transpose(phase, (1, 0))
features = np.squeeze(features)
# features = librosa.db_to_amplitude(features)
# features = librosa.db_to_power(features)
features = features * np.exp(
1j * phase) # that fixes the abs() ope previously done
model.load_weights(
os.path.join(mozilla_basepath, 'denoiser_cnn_log_mel_generator.h5'))
cleanAudio, sr = read_audio(os.path.join(mozilla_basepath, 'clips',
'common_voice_en_16526.mp3'),
sample_rate=fs)
print("Min:", np.min(cleanAudio), "Max:", np.max(cleanAudio))
noiseAudio, sr = read_audio(os.path.join(urbansound_basepath, 'audio',
'fold10', '7913-3-0-0.wav'),
sample_rate=fs)
print("Min:", np.min(noiseAudio), "Max:", np.max(noiseAudio))
cleanAudioFeatureExtractor = FeatureExtractor(cleanAudio,
windowLength=windowLength,
overlap=overlap,
sample_rate=sr)
stft_features = cleanAudioFeatureExtractor.get_stft_spectrogram()
stft_features = np.abs(stft_features)
print("Min:", np.min(stft_features), "Max:", np.max(stft_features))
noisyAudio = add_noise_to_clean_audio(cleanAudio, noiseAudio)
noiseAudioFeatureExtractor = FeatureExtractor(noisyAudio,
windowLength=windowLength,
overlap=overlap,
sample_rate=sr)
noise_stft_features = noiseAudioFeatureExtractor.get_stft_spectrogram()
def revert_features_to_audio2(features,
phase,
cleanMean=None,