from scipy.sparse import csr_matrix
from sklearn.preprocessing import StandardScaler
import pickle
import numpy as np
import os
import sys
sys.path.insert(0, '../')
import common
import librosa
import h5py
from random import shuffle
SECONDS = 15
SR = 22050
HR = 1024
N_FRAMES = int(SECONDS * SR / float(HR)) # 10 seconds of audio
N_SAMPLES=1
N_BINS = 96
SPECTRO_FOLDER='spectro_SUPER_cqt'
DATASET_NAME='SUPER'
Y_PATH='class_3'
MAX_N_SCALER=300000
MSD = False
def scale(X, scaler=None, max_N=MAX_N_SCALER):
shape = X.shape
X.shape = (shape[0], shape[2] * shape[3])
if not scaler:
scaler = StandardScaler()
N = min([len(X), max_N])
scaler.fit(X[:N])
X = scaler.transform(X)
X.shape = shape
return X, scaler
def save_sparse_csr(filename,array):
np.savez(filename,data = array.data ,indices=array.indices,
indptr =array.indptr, shape=array.shape )
def load_sparse_csr(filename):
loader = np.load(filename)
return csr_matrix(( loader['data'], loader['indices'], loader['indptr']),
shape = loader['shape'])
def sample_patch(mel_spec, n_frames):
"""Randomly sample a part of the mel spectrogram."""
if n_frames <= mel_spec.shape[0]:
r_idx = np.random.randint(0, high=mel_spec.shape[0] - n_frames + 1)
return mel_spec[r_idx:r_idx + n_frames]
else:
return np.vstack((mel_spec, np.zeros((n_frames - mel_spec.shape[0], mel_spec.shape[1]))))
def prepare_set(dataset_name, set_name, normalize=True, with_factors=True, scaler=None):
if not os.path.exists(common.PATCHES_DIR):
os.makedirs(common.PATCHES_DIR)
f = h5py.File(common.PATCHES_DIR+'/patches_%s_%s_%sx%s_tmp.hdf5' % (set_name,dataset_name,N_SAMPLES,SECONDS),'w')
spec_folder=common.SPECTRO_PATH+SPECTRO_FOLDER+"/"
items = open(common.DATASETS_DIR+'/items_index_%s_%s.tsv' % (set_name, dataset_name)).read().splitlines()
n_items = len(items) * N_SAMPLES
print n_items
x_dset = f.create_dataset("features", (n_items,1,N_FRAMES,N_BINS), dtype='f')
i_dset = f.create_dataset("index", (n_items,), maxshape=(n_items,), dtype='S18')
if with_factors:
factors = np.load(common.DATASETS_DIR+'/y_%s_%s_%s.npy' % (set_name, Y_PATH,dataset_name))
y_dset = f.create_dataset("targets", (n_items,factors.shape[1]), dtype='f')
k=0
itemset = []
itemset_index = []
for t,track_id in enumerate(items):
if MSD:
msd_folder = track_id[2]+"/"+track_id[3]+"/"+track_id[4]+"/"
else:
msd_folder = ""
file = spec_folder+msd_folder+track_id+".pk"
try:
spec = pickle.load(open(file))
spec = librosa.power_to_db(np.abs(spec) ** 2,ref=np.max).T
for i in range(0,N_SAMPLES):
try:
sample = sample_patch(spec,N_FRAMES)
x_dset[k,:,:,:] = sample.reshape(-1,sample.shape[0],sample.shape[1])
if with_factors:
y_dset[k,:] = factors[t]
i_dset[k] = track_id
itemset.append(track_id)
itemset_index.append(t)
k+=1
except Exception as e:
print 'Error',e
print file
except Exception as e:
print 'Error1',e
if t%1000==0:
print t
print x_dset.shape
# Clean empty spectrograms
print "Cleaning empty spectrograms"
f2 = h5py.File(common.PATCHES_DIR+'/patches_%s_%s_%sx%s.hdf5' % (set_name,dataset_name,N_SAMPLES,SECONDS),'w')
index = f['index'][:]
index_clean = np.where(index != "")[0]
n_items = len(index_clean)
x_dset2 = f2.create_dataset("features", (n_items,1,N_FRAMES,N_BINS), dtype='f')
i_dset2 = f2.create_dataset("index", (n_items,), maxshape=(n_items,), dtype='S18')
for i in range(0,len(index_clean)):
x_dset2[i] = x_dset[index_clean[i]]
i_dset2[i] = i_dset[index_clean[i]]
f.close()
os.remove(common.PATCHES_DIR+'/patches_%s_%s_%sx%s_tmp.hdf5' % (set_name,dataset_name,N_SAMPLES,SECONDS))
# Normalize
if normalize:
print "Normalizing"
block_step = 10000
for i in range(0,len(itemset),block_step):
x_block = x_dset2[i:min(len(itemset),i+block_step)]
x_norm, scaler = scale(x_block,scaler)
x_dset2[i:min(len(itemset),i+block_step)] = x_norm
scaler_file=common.PATCHES_DIR+'/scaler_%s_%sx%s.pk' % (DATASET_NAME,N_SAMPLES,SECONDS)
pickle.dump(scaler,open(scaler_file,'wb'))
return scaler
def prepare_testset(dataset_name):
spec_folder=common.SPECTRO_PATH+SPECTRO_FOLDER+"/"
test_folder=common.DATA_DIR+'/spectro_%s_testset/' % dataset_name
if not os.path.exists(test_folder):
os.makedirs(test_folder)
items = open(common.DATASETS_DIR+'/items_index_test_%s.tsv' % dataset_name).read().splitlines()
testset = []
testset_index = []
for t,track_id in enumerate(items):
if MSD:
msd_folder = track_id[2]+"/"+track_id[3]+"/"+track_id[4]+"/"
else:
msd_folder = ""
file = spec_folder+msd_folder+track_id+".pk"
try:
spec = pickle.load(open(file))
spec = librosa.power_to_db(np.abs(spec) ** 2,ref=np.max).T
pickle.dump(spec, open(test_folder+track_id+".pk","wb"))
testset.append(track_id)
testset_index.append(t)
if t%1000==0:
print t
except:
print "no exist", file
if __name__ == '__main__':
scaler = prepare_set(DATASET_NAME,"train", with_factors=False)
scaler = prepare_set(DATASET_NAME,"val",scaler=scaler, with_factors=False)
scaler = prepare_set(DATASET_NAME,"test",scaler=scaler, with_factors=False)
#prepare_testset(DATASET_NAME)
