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import argparse
import glob
import numpy as np
import librosa
from essentia.standard import (NSGConstantQ,
NSGIConstantQ)
import hparams
import utils
def parse_files(path, source):
if source == 'mixture':
path = path + 'Mixtures/Dev/*/' + str(source) + '.wav'
paths = sorted(glob.glob(path))
else:
path = path + 'Sources/Dev/*/' + str(source) + '.wav'
paths = sorted(glob.glob(path))
return paths
def forward_transform(y, min_f, max_f, bpo, gamma):
# Parameters
params = {
# Backward transform needs to know the signal size.
'inputSize': y.size,
'minFrequency': min_f,
'maxFrequency': max_f,
'binsPerOctave': bpo,
# Minimum number of FFT bins per CQ channel.
'minimumWindow': 4,
'gamma': gamma
}
# Forward and backward transforms
constantq, dcchannel, nfchannel = NSGConstantQ(**params)(y)
return constantq, dcchannel, nfchannel
def backward_transform(c, dc, nf, orig_size, min_f, max_f, bpo, gamma):
# Parameters
params = {
# Backward transform needs to know the signal size.
'inputSize': orig_size,
'minFrequency': min_f,
'maxFrequency': max_f,
'binsPerOctave': bpo,
# Minimum number of FFT bins per CQ channel.
'minimumWindow': 4,
'gamma': gamma
}
# Forward and backward transforms
y = NSGIConstantQ(**params)(c, dc, nf)
return y
def make_chunks(c):
cqt = np.abs(c).astype(np.float16)
cqt = np.asfortranarray(cqt)
padded_cqt = librosa.util.fix_length(cqt,hparams.chunk_size*np.ceil(cqt.shape[-1]/hparams.chunk_size).astype(int))
framed_cqt = librosa.util.frame(padded_cqt,hparams.chunk_size,hparams.chunk_size)
samples = np.transpose(framed_cqt,(2,0,1))
cqt_input = np.expand_dims(samples,-1)
return cqt_input
if __name__ == '__main__':
args = argparse.ArgumentParser()
args.add_argument('Path',metavar='path',type=str,help='Path to DSD100')
args.add_argument('Source',metavar='source',type=str,help='Desired source to preprocess for separation. Use mixture to preprocess the mixtures')
args.add_argument('Output_path',metavar='output_path',type=str,help='Output path for the pikled spectrograms')
args = args.parse_args()
path = args.Path
source = args.Source
outpath = args.Output_path
if path[-1] != '/':
path = path + '/'
if outpath[-1] != '/':
outpath = outpath + '/'
files = parse_files(path, source)
mag_lf_array = []
mag_hf_array = []
for i in range(0,len(files)):
print(files[i])
y, sr = librosa.load(files[i], hparams.sr, mono = True)
C_lf,_,_ = forward_transform(y,hparams.lf_params['min_f'],hparams.lf_params['max_f'],hparams.lf_params['bins_per_octave'], hparams.lf_params['gamma'])
C_hf,_,_ = forward_transform(y,hparams.hf_params['min_f'],hparams.hf_params['max_f'],hparams.hf_params['bins_per_octave'], hparams.hf_params['gamma'])
c_lf = make_chunks(C_lf)
c_hf = make_chunks(C_hf)
mag_lf_array.append(c_lf)
mag_hf_array.append(c_hf)
if i == 1:
break
mag_lf = utils.list_to_array(mag_lf_array)
mag_hf = utils.list_to_array(mag_hf_array)
filename_lf = source + '_lf.npy'
filename_hf = source + '_hf.npy'
utils.pickle(mag_lf, outpath, filename_lf)
utils.pickle(mag_hf, outpath, filename_hf)
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