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import librosa
import numpy as np
import python_speech_features as psf
def get_fbanks(audio_file):
def normalize_frames(signal, epsilon=1e-12):
return np.array([(v - np.mean(v)) / max(np.std(v), epsilon) for v in signal])
y, sr = librosa.load(audio_file, sr=16000)
assert sr == 16000
trim_len = int(0.25 * sr)
if y.shape[0] < 1 * sr:
# if less than 1 seconds, don't use that audio
return None
y = y[trim_len:-trim_len]
# frame width of 25 ms with a stride of 15 ms. This will have an overlap of 10s
filter_banks, energies = psf.fbank(y, samplerate=sr, nfilt=64, winlen=0.025, winstep=0.01)
filter_banks = normalize_frames(signal=filter_banks)
filter_banks = filter_banks.reshape((filter_banks.shape[0], 64, 1))
return filter_banks
def extract_fbanks(path):
fbanks = get_fbanks(path)
num_frames = fbanks.shape[0]
# sample sets of 64 frames each
numpy_arrays = []
start = 0
while start < num_frames + 64:
slice_ = fbanks[start:start + 64]
if slice_ is not None and slice_.shape[0] == 64:
assert slice_.shape[0] == 64
assert slice_.shape[1] == 64
assert slice_.shape[2] == 1
slice_ = np.moveaxis(slice_, 2, 0)
slice_ = slice_.reshape((1, 1, 64, 64))
numpy_arrays.append(slice_)
start = start + 64
print('num samples extracted: {}'.format(len(numpy_arrays)))
return np.concatenate(numpy_arrays, axis=0) |