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audio_processor.py
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import librosa
import numpy as np
from math import floor
def compute_melgram(audio_path):
''' Compute a mel-spectrogram and returns it in a shape of (1,1,96,1366), where
96 == #mel-bins and 1366 == #time frame
parameters
----------
audio_path: path for the audio file.
Any format supported by audioread will work.
More info: http://librosa.github.io/librosa/generated/librosa.core.load.html#librosa.core.load
'''
# mel-spectrogram parameters
SR = 12000
N_FFT = 512
N_MELS = 96
HOP_LEN = 256
DURA = 29.12 # to make it 1366 frame..
src, sr = librosa.load(audio_path, sr=SR) # whole signal
n_sample = src.shape[0]
n_sample_fit = int(DURA*SR)
if n_sample < n_sample_fit: # if too short
src = np.hstack((src, np.zeros((int(DURA*SR) - n_sample,))))
elif n_sample > n_sample_fit: # if too long
src = src[(n_sample-n_sample_fit)/2:(n_sample+n_sample_fit)/2]
logam = librosa.logamplitude
melgram = librosa.feature.melspectrogram
ret = logam(melgram(y=src, sr=SR, hop_length=HOP_LEN,
n_fft=N_FFT, n_mels=N_MELS)**2,
ref_power=1.0)
ret = ret[np.newaxis, np.newaxis, :]
return ret
def compute_melgram_multiframe(audio_path, all_song=True):
''' Compute a mel-spectrogram in multiple frames of the song and returns it in a shape of (N,1,96,1366), where
96 == #mel-bins, 1366 == #time frame, and N=#frames
parameters
----------
audio_path: path for the audio file.
Any format supported by audioread will work.
More info: http://librosa.github.io/librosa/generated/librosa.core.load.html#librosa.core.load
'''
# mel-spectrogram parameters
SR = 12000
N_FFT = 512
N_MELS = 96
HOP_LEN = 256
DURA = 29.12 # to make it 1366 frame..
if all_song:
DURA_TRASH = 0
else:
DURA_TRASH = 20
src, sr = librosa.load(audio_path, sr=SR) # whole signal
n_sample = src.shape[0]
n_sample_fit = int(DURA*SR)
n_sample_trash = int(DURA_TRASH*SR)
#remove the trash at the beginning and at the end
src = src[n_sample_trash:(n_sample-n_sample_trash)]
n_sample=n_sample-2*n_sample_trash
#print n_sample
#print n_sample_fit
ret = np.zeros((0, 1, 96, 1366), dtype=np.float32)
if n_sample < n_sample_fit: # if too short
src = np.hstack((src, np.zeros((int(DURA*SR) - n_sample,))))
logam = librosa.logamplitude
melgram = librosa.feature.melspectrogram
ret = logam(melgram(y=src, sr=SR, hop_length=HOP_LEN,
n_fft=N_FFT, n_mels=N_MELS)**2,
ref_power=1.0)
ret = ret[np.newaxis, np.newaxis, :]
elif n_sample > n_sample_fit: # if too long
N=int(floor(n_sample/n_sample_fit))
src_total=src
for i in range(0,N):
src = src_total[(i*n_sample_fit):(i+1)*(n_sample_fit)]
logam = librosa.logamplitude
melgram = librosa.feature.melspectrogram
retI = logam(melgram(y=src, sr=SR, hop_length=HOP_LEN,
n_fft=N_FFT, n_mels=N_MELS)**2,
ref_power=1.0)
retI = retI[np.newaxis, np.newaxis, :]
#print retI.shape
ret = np.concatenate((ret, retI), axis=0)
return ret