def feature_generation(audio_path, save_path, win_length=0.02, win_step=0.01, mode='fbank',
feature_len=40, noise_name='clean', noiseSNR=0.5,csv=None):
'''
<input>
audio_path = '/sda3/DATA/jsbae/Google_Speech_Command'
save_path = '/home/jsbae/STT2/KWS/feature_saved'
win_length: default=0.02, "specify the window length of feature"
win_step: default=0.01, "specify the window step length of feature"
mode: choices=['mfcc', 'fbank']
feature_len: default=40,'Features length'
<output>
No output. Save featuere and label(int) to npy filetpye.
'''
# Read testing_list and validation_list and make it to likst.
testing_list_path = os.path.join(audio_path, 'testing_list.txt')
validation_list_path = os.path.join(audio_path, 'validation_list.txt')
def read_txt(path):
text_file = open(path,'r')
lines = text_file.read().split('\n')
return lines
testing_list = read_txt(testing_list_path)
validation_list = read_txt(validation_list_path)
# end.
#
# make dirs.
if noise_name == 'clean':
dirs = [f for f in os.listdir(audio_path) if os.path.isdir(os.path.join(audio_path, f))] #label save at dirs
dirs.sort()
if '_background_noise_' in dirs:
dirs.remove('_background_noise_')
else:
audio_path = os.path.join(audio_path,noise_name)
dirs = [f for f in os.listdir(audio_path) if os.path.isdir(os.path.join(audio_path, f))] #label save at dirs
'''
dirs=[]
dirs1 = [f for f in os.listdir(audio_path) if os.path.isdir(os.path.join(audio_path, f))] #label save at dirs
dirs1.sort()
for dirs1_ in dirs1:
dirs1_path = os.path.join(audio_path,dirs1_)
dirs2 = [f for f in os.listdir(dirs1_path) if os.path.isdir(os.path.join(dirs1_path,f))]
for dirs2_ in dirs2:
dirs.append(dirs1_ + '/' + dirs2_)
'''
print(dirs)
print('Number of labels: ' + str(len(dirs)))
# end.
#
# Make directory if not exits.
noise_name = noise_name + '_SNR' + str(noiseSNR)# change noise_name with SNR
def make_dir(path):
if not os.path.exists(path):
os.makedirs(path)
make_dir(os.path.join(save_path,'TEST',mode, noise_name))
make_dir(os.path.join(save_path,'VALID',mode, noise_name))
make_dir(os.path.join(save_path,'TRAIN',mode, noise_name))
make_dir(os.path.join(save_path,'TEST','label'))
make_dir(os.path.join(save_path,'VALID','label'))
make_dir(os.path.join(save_path,'TRAIN','label'))
#end.
#
############# TO COMPUTE TOTAL SNR #############
total_SNR = 0
count = 0
# Make feature and label files.
for dirname in dirs:
full_dirname = os.path.join(audio_path,dirname)
cprint('Processing in '+full_dirname,'yellow')
teCount, vaCount, trCount = 0,0,0
for filename in os.listdir(full_dirname):
full_filename = os.path.join(full_dirname,filename)
#print full_filename
filenameNoSuffix = os.path.splitext(full_filename)[0]
# print filenameNoSuffix: output: /sda3/DATA/jsbae/Google_Speech_Command/nine/24b82192_nohash_2
ext = os.path.splitext(full_filename)[-1]
if ext == '.wav':
#print dirname #label
#print full_filename #wavfile path
rate = None
sig = None
#(rate,sig)= wav.read(full_filename)
'''
else:
(c_rate,c_sig)= wav.read(full_filename)
(n_rate,n_sig)= wav.read(bgmfile)
assert c_rate == n_rate == 16000
rate = c_rate
n_startpoint = randint(0,len(n_sig)-len(c_sig))
while n_startpoint == 0:
cprint('n_startpoint 0','red')
n_startpoint = randint(0,len(n_sig)-len(c_sig))
sig = (1-noiseSNR) * c_sig + noiseSNR * n_sig[n_startpoint:n_startpoint+len(c_sig)]
snr_ = SNR(sig,n_sig[n_startpoint:n_startpoint+len(c_sig)]*noiseSNR)
total_SNR += snr_
count += 1
csv.write(str(count)+','+str(snr_)+'\n')
csv.flush()
'''
try:
(rate,sig)= wav.read(full_filename)
except ValueError as e:
if e.message == "File format 'NIST'... not understood.":
sf = Sndfile(full_filename, 'r')
nframes = sf.nframes
sig = sf.read_frames(nframes)
rate = sf.samplerate
feat = calcfeat_delta_delta(sig,rate,win_length=win_length,win_step=win_step,mode=mode,feature_len=feature_len)
feat = preprocessing.scale(feat)
feat = np.transpose(feat)
#print(np.max(feat),np.min(feat),feat.shape) #(120, almost 99)
label = text_to_label(dirname)
#print label
if label == 30: raise ValueError('wrong')
# Save to TEST, VALID, TRAIN folder.
if os.path.join(dirname,filename) in testing_list:
featureFilename = os.path.join(save_path,'TEST',mode, noise_name, dirname+'_'+filenameNoSuffix.split('/')[-1]+'.npy')
labelFilename = os.path.join(save_path,'TEST','label', dirname+'_'+filenameNoSuffix.split('/')[-1]+'.npy')
assert label == np.load(labelFilename)
print featureFilename
np.save(featureFilename, feat)
#np.save(labelFilename, label)
teCount +=1
elif os.path.join(dirname,filename) in validation_list:
featureFilename = os.path.join(save_path,'VALID',mode, noise_name, dirname+'_'+filenameNoSuffix.split('/')[-1]+'.npy')
labelFilename = os.path.join(save_path,'VALID','label', dirname+'_'+filenameNoSuffix.split('/')[-1]+'.npy')
#np.save(featureFilename, feat)
#np.save(labelFilename, label)
vaCount += 1
raise ValueError
else:
featureFilename = os.path.join(save_path,'TRAIN',mode, noise_name, dirname+'_'+filenameNoSuffix.split('/')[-1]+'.npy')
labelFilename = os.path.join(save_path,'TRAIN','label', dirname+'_'+filenameNoSuffix.split('/')[-1]+'.npy')
#np.save(featureFilename, feat)
#np.save(labelFilename, label)
trCount +=1
raise ValueError
print trCount, vaCount, teCount
def wav2feature(root_directory, save_directory, name, win_len, win_step, mode,
feature_len, seq2seq, save):
count = 0
dirid = 0
level = 'cha' if seq2seq is False else 'seq2seq'
data_dir = os.path.join(root_directory, name)
print(data_dir)
preprocess(data_dir)
for subdir, dirs, files in os.walk(data_dir):
for f in files:
fullFilename = os.path.join(subdir, f)
filenameNoSuffix = os.path.splitext(fullFilename)[0]
if f.endswith('.wav'):
rate = None
sig = None
try:
(rate, sig) = wav.read(fullFilename)
except ValueError as e:
if e.message == "File format 'NIST'... not understood.":
sf = Sndfile(fullFilename, 'r')
nframes = sf.nframes
sig = sf.read_frames(nframes)
rate = sf.samplerate
feat = calcfeat_delta_delta(sig,
rate,
win_length=win_len,
win_step=win_step,
mode=mode,
feature_len=feature_len)
feat = preprocessing.scale(feat)
feat = np.transpose(feat)
print(feat.shape)
labelFilename = filenameNoSuffix + '.label'
with open(labelFilename, 'r') as f:
characters = f.readline().strip().lower()
targets = []
if seq2seq is True:
targets.append(28)
for c in characters:
if c == ' ':
targets.append(0)
elif c == "'":
targets.append(27)
else:
targets.append(ord(c) - 96)
if seq2seq is True:
targets.append(29)
print(targets)
if save:
count += 1
if count % 4000 == 0:
dirid += 1
print('file index:', count)
print('dir index:', dirid)
label_dir = os.path.join(save_directory, level, name,
str(dirid), 'label')
feat_dir = os.path.join(save_directory, level, name,
str(dirid), 'feature')
if not os.path.isdir(label_dir):
os.makedirs(label_dir)
if not os.path.isdir(feat_dir):
os.makedirs(feat_dir)
featureFilename = os.path.join(
feat_dir,
filenameNoSuffix.split('/')[-1] + '.npy')
np.save(featureFilename, feat)
t_f = os.path.join(
label_dir,
filenameNoSuffix.split('/')[-1] + '.npy')
print(t_f)
np.save(t_f, targets)
def wav2feature(rootdir, save_directory, mode, feature_len,level, keywords, win_len, win_step, seq2seq, save):
feat_dir = os.path.join(os.path.join(os.path.join(save_directory, level), keywords), mode)
label_dir = os.path.join(os.path.join(os.path.join(save_directory, level), keywords), 'label')
if not os.path.exists(label_dir):
os.makedirs(label_dir)
if not os.path.exists(feat_dir):
os.makedirs(feat_dir)
count = 0
for subdir, dirs, files in os.walk(rootdir):
for file in files:
fullFilename = os.path.join(subdir, file)
filenameNoSuffix = os.path.splitext(fullFilename)[0]
if file.endswith('.WAV'):
rate = None
sig = None
try:
(rate,sig)= wav.read(fullFilename)
except ValueError as e:
if e.message == "File format 'NIST'... not understood.":
sf = Sndfile(fullFilename, 'r')
nframes = sf.nframes
sig = sf.read_frames(nframes)
rate = sf.samplerate
feat = calcfeat_delta_delta(sig,rate,win_length=win_len,win_step=win_step,mode=mode,feature_len=feature_len)
feat = preprocessing.scale(feat)
feat = np.transpose(feat)
print(feat.shape)
if level == 'phn':
labelFilename = filenameNoSuffix + '.PHN'
phenome = []
with open(labelFilename,'r') as f:
if seq2seq is True:
phenome.append(len(phn)) # <start token>
for line in f.read().splitlines():
s=line.split(' ')[2]
p_index = phn.index(s)
phenome.append(p_index)
if seq2seq is True:
phenome.append(len(phn)+1) # <end token>
print(phenome)
phenome = np.array(phenome)
elif level == 'cha':
labelFilename = filenameNoSuffix + '.WRD'
phenome = []
sentence = ''
with open(labelFilename,'r') as f:
for line in f.read().splitlines():
s=line.split(' ')[2]
sentence += s+' '
if seq2seq is True:
phenome.append(28)
for c in s:
if c=="'":
phenome.append(27)
else:
phenome.append(ord(c)-96)
phenome.append(0)
phenome = phenome[:-1]
if seq2seq is True:
phenome.append(29)
print(phenome)
print(sentence)
count+=1
print('file index:',count)
if save:
featureFilename = os.path.join(feat_dir,filenameNoSuffix.split('/')[-1]+'.npy')
np.save(featureFilename,feat)
labelFilename = os.path.join(label_dir,filenameNoSuffix.split('/')[-1]+'.npy')
print(featureFilename,labelFilename)
np.save(labelFilename,phenome)
def wav2feature(root_directory, save_directory, name, win_len, win_step, mode,
feature_len, seq2seq, save):
"""
To run for WSJ corpus, you should download sph2pipe_v2.5 first!
"""
count = 0
dirid = 0
level = 'cha' if seq2seq is False else 'seq2seq'
for subdir, dirs, files in os.walk(root_directory):
for f in files:
fullFilename = os.path.join(subdir, f)
filenameNoSuffix = os.path.splitext(fullFilename)[0]
if f.endswith('.wv1') or f.endswith('.wav'):
rate = None
sig = None
try:
(rate, sig) = wav.read(fullFilename)
except ValueError as e:
sph2pipe = os.path.join(sph2pipe_dir, 'sph2pipe')
wav_name = fullFilename.replace('wv1', 'wav')
check_call(
['./sph2pipe', '-f', 'rif', fullFilename, wav_name])
os.remove(fullFilename)
print(wav_name)
(rate, sig) = wav.read(wav_name)
os.remove(fullFilename)
feat = calcfeat_delta_delta(sig,
rate,
win_length=win_len,
win_step=win_step,
feature_len=feature_len,
mode=mode)
feat = preprocessing.scale(feat)
feat = np.transpose(feat)
print(feat.shape)
labelFilename = filenameNoSuffix + '.label'
with open(labelFilename, 'r') as f:
characters = f.readline().strip().lower()
targets = []
if seq2seq is True:
targets.append(28)
for c in characters:
if c == ' ':
targets.append(0)
elif c == "'":
targets.append(27)
else:
targets.append(ord(c) - 96)
if seq2seq is True:
targets.append(29)
targets = np.array(targets)
print(targets)
if save:
count += 1
if count % 1000 == 0:
dirid += 1
print('file index:', count)
print('dir index:', dirid)
label_dir = os.path.join(save_directory, level, name,
str(dirid), 'label')
feat_dir = os.path.join(save_directory, level, name,
str(dirid), mode)
if not os.path.isdir(label_dir):
os.makedirs(label_dir)
if not os.path.isdir(feat_dir):
os.makedirs(feat_dir)
featureFilename = os.path.join(
feat_dir,
filenameNoSuffix.split('/')[-1] + '.npy')
np.save(featureFilename, feat)
t_f = os.path.join(
label_dir,
filenameNoSuffix.split('/')[-1] + '.npy')
print(t_f)
np.save(t_f, targets)