def main():
_, dic = vad_import()
items = []
for key, _ in dic.items():
items.append(key) #vad order
directory = '/export/b13/jlai/scale/vad/open_sat/vad_nist/jeff_vast_dev_3'
if not os.path.exists(directory):
os.makedirs(directory)
#load model
print('Loading model...')
model = load_model('LSTM_model_1.h5')
#Evaluate
print('Evaluating...')
batch_size = 1
input_data = mfcc_array()
vad_predict = model.predict(input_data, batch_size=batch_size)
#vad_predict = model.predict_generator(mfcc_gen(), steps=math.ceil(25083/batch_size)) #return numpy array
vad_original = vad_array() #numpy array
print("vad_predict.shape is %s" % (vad_predict.shape, ))
print("vad_original.shape is %s" % (vad_original.shape, ))
assert vad_predict.shape == vad_original.shape, "vad shape not same"
f = open('vad2nist_3', 'wb')
pickle.dump(vad_predict, f)
pickle.dump(vad_original, f)
f.close()
def hmm_to_file():
f = open('vad2nist_3', 'rb')
_ = pickle.load(f)
vad_original = pickle.load(f)
f.close()
hmm_vad = hmm()
_, dic = vad_import()
items = []
for key, _ in dic.items():
items.append(key) #vad order
directory = '/export/b13/jlai/scale/vad/open_sat/vad_nist/jeff_vast_dev_5'
#masking
neg_count = 0
for i in np.arange(0, vad_original.shape[0]):
for j in np.arange(0, vad_original.shape[1]):
for k in np.arange(0, vad_original.shape[2]):
if vad_original[i][j][k] == -1:
neg_count += 1
hmm_vad[i][j][k] = -1
print("number of -1 in vad_original is %d" % neg_count)
#subsample --> sample --> utterance --> whole dataset
vad_index = index()
per5, count, utt = 0, 0, []
for sub_sample in hmm_vad:
utt.append(sub_sample)
per5 += 1
if per5 == vad_index[count]: #end of an utterance
utt = np.concatenate(utt, axis=0)
with open(directory + '/' + items[count] + '.txt', 'w') as f:
accu, diff = 0., 0.01
if utt[0] == 1:
prev_tag = 'speech'
if utt[0] == 0:
prev_tag = 'non-speech'
for vad_temp in utt[1:]:
if vad_temp == 1:
current_tag = 'speech'
if vad_temp == 0:
current_tag = 'non-speech'
if vad_temp == -1:
f.write("X\tX\tX\tSAD\t%s\t%.2f\t%.2f\t%s\t1.00\n" %
(items[count], accu, accu + diff, prev_tag))
break
if current_tag == prev_tag:
diff += 0.01 #10 ms
else:
f.write("X\tX\tX\tSAD\t%s\t%.2f\t%.2f\t%s\t1.00\n" %
(items[count], accu, accu + diff, prev_tag))
prev_tag = current_tag
accu = accu + diff
diff = 0.01
f.close()
per5 = 0
count += 1
utt = []
def eval():
f = open('vad2nist_3', 'rb')
vad_predict = pickle.load(f)
vad_original = pickle.load(f)
f.close()
#Convert vad_predict from probability to 0 and 1 and -1 (for masking)
threshold = 0.50
r = np.logical_or(vad_original == 0, vad_original == 1)
result = np.copy(vad_predict)
result[r] = (vad_predict[r] > threshold)
result[vad_original == -1] = -1
vad_predict = result
_, dic = vad_import()
items = []
for key, _ in dic.items():
items.append(key) #vad order
directory = '/export/b13/jlai/scale/vad/open_sat/vad_nist/jeff_vast_dev_4'
#subsample --> sample --> utterance --> whole dataset
vad_index = index()
per5, count, utt = 0, 0, []
for sub_sample in vad_predict:
utt.append(sub_sample)
per5 += 1
if per5 == vad_index[count]: #end of an utterance
utt = np.concatenate(utt, axis=0)
with open(directory + '/' + items[count] + '.txt', 'w') as f:
accu, diff = 0., 0.01
if utt[0] == 1:
prev_tag = 'speech'
if utt[0] == 0:
prev_tag = 'non-speech'
for vad_temp in utt[1:]:
if vad_temp == 1:
current_tag = 'speech'
if vad_temp == 0:
current_tag = 'non-speech'
if vad_temp == -1:
f.write("X\tX\tX\tSAD\t%s\t%.2f\t%.2f\t%s\t1.00\n" %
(items[count], accu, accu + diff, prev_tag))
break
if current_tag == prev_tag:
diff += 0.01 #10 ms
else:
f.write("X\tX\tX\tSAD\t%s\t%.2f\t%.2f\t%s\t1.00\n" %
(items[count], accu, accu + diff, prev_tag))
prev_tag = current_tag
accu = accu + diff
diff = 0.01
f.close()
per5 = 0
count += 1
utt = []
def nist():
"""
return mfcc, vad dictionary
"""
_, vad_dic = vad_import()
mfcc_dic = get_frame()
#Data Processing
for key in vad_dic.keys():
diff = len(vad_dic[key]) - len(mfcc_dic[key])
if diff > 0: #vad has more --> omit vad
temp = vad_dic[key]
temp = temp[:len(mfcc_dic[key])]
assert len(temp) == len(mfcc_dic[key])
vad_dic[key] = temp
else: #mfcc has more --> add 0 to vad:
temp = vad_dic[key]
for _ in np.arange(np.abs(diff)):
temp.append([0])
assert len(temp) == len(mfcc_dic[key])
vad_dic[key] = temp
assert len(vad_dic[key]) == len(mfcc_dic[key])
return vad_dic
def combine_mfcc_vad(): _, vad_dic = vad_import() mfcc_dic = get_frame() #Data Processing for key in vad_dic.keys(): diff = len(vad_dic[key])-len(mfcc_dic[key])