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test python #35

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Dicksonchin93 Jun 19, 2020
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Dicksonchin93 Jun 19, 2020
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Dicksonchin93 Jun 19, 2020
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Dicksonchin93 Jun 19, 2020
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Empty file added __init__.py
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Empty file added lib/__init__.py
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Empty file added lib/matlab_py/__init__.py
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7 changes: 6 additions & 1 deletion lib/matlab_py/mrcg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,12 @@ def loudness(freq):
# pressure level dB. The equation is taken from section 4 of BS3383.
# Written by ZZ Jin, and adapted by DLW in Jan'07
dB=60
fmat = sio.loadmat('./lib/matlab_py/f_af_bf_cf.mat')
fmat = sio.loadmat(
os.path.join(
os.path.dirname(os.path.abspath(__file__)),
'f_af_bf_cf.mat',
)
)
# Stores parameters of equal-loudness functions from BS3383,"Normal equal-loudness level
# contours for pure tones under free-field listening conditions", table 1.
# f (or ff) is the tone frequency, af and bf are frequency-dependent coefficients, and
Expand Down
69 changes: 24 additions & 45 deletions lib/matlab_py/utils.py
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Original file line number Diff line number Diff line change
@@ -1,20 +1,14 @@
import os

import librosa
import numpy as np
import scipy.io as sio
# from scipy.stats import threshold
import lib.matlab_py.mrcg as mrcg

from . import mrcg as mrcg

def th_classifier(input, th):

output = (input >= th).choose(input, 1)
output = (output < th).choose(output, 0)
# output = np.clip(input, a_min=th, a_max=None)
# output = np.divide(output, th)
# output = np.subtract(1, output)
# output = np.clip(output, a_min=1 - th, a_max=None)
# output = np.subtract(1, output)
return output


Expand All @@ -32,7 +26,7 @@ def binary_saver(name_file, data, num_file ):
return fid_file, fid_txt


def Frame_Length( x,overlap,nwind ):
def Frame_Length( x, overlap, nwind):
nx = len(x)
noverlap = nwind - overlap
framelen = int((nx - noverlap) / (nwind - noverlap))
Expand Down Expand Up @@ -83,7 +77,9 @@ def frame2rawlabel(label, win_len, win_step):
break
else:
temp_label = label[i]
raw_label[start_indx+1:start_indx+win_len] = raw_label[start_indx+1:start_indx+win_len] + temp_label
raw_label[
start_indx+1:start_indx+win_len
] = raw_label[start_indx+1:start_indx+win_len] + temp_label
i += 1

start_indx = start_indx + win_step
Expand All @@ -92,25 +88,6 @@ def frame2rawlabel(label, win_len, win_step):

return raw_label

# def frame2rawlabel(label, win_len, win_step):
# num_frame = len(label)
# total_len = (num_frame - 1) * win_step + win_len
# raw_label = np.zeros([1, total_len])
# start_indx = 0
# i = 0
# while 1 :
# if (start_indx+win_len>total_len):
# break
#
# temp = label[i]*np.ones([1,win_len])
# temp2 = raw_label[0][start_indx : start_indx + win_len]
# raw_label[0][start_indx : start_indx + win_len] = temp2 + temp
# i = i+1
# start_indx = start_indx+win_step
#
# raw_label = th_classifier(raw_label, 1)
# return raw_label


def frame2inpt(label, win_len, win_step):
num_frame = len(label)
Expand All @@ -121,22 +98,22 @@ def frame2inpt(label, win_len, win_step):
while 1:
if (start_indx + win_len > total_len):
break

temp = label[i] * np.ones([1, win_len])
raw_label[start_indx: start_indx + win_len] = raw_label[start_indx: start_indx + win_len] + temp
raw_label[
start_indx: start_indx + win_len
] = raw_label[start_indx: start_indx + win_len] + temp
i = i + 1
start_indx = start_indx + win_step

raw_label = th_classifier(raw_label, 1)
return raw_label


def mrcg_extract(audio_dir) :
def mrcg_extract(audio_dir, save_dir):
noisy_speech, audio_sr = librosa.load(audio_dir, sr=16000)
y_label = np.zeros([len(noisy_speech), 1])
os.mkdir('./sample_data')
os.mkdir('./sample_data/Labels')
save_dir = './sample_data'
os.makedirs(save_dir, exist_ok=True)
os.makedirs("{}/Labels".format(save_dir), exist_ok=True)
name_mrcg = save_dir + '/mrcg'
name_label = save_dir + '/Labels/label'
mrcg_mat = np.transpose(mrcg.mrcg_features(noisy_speech, audio_sr))
Expand All @@ -155,14 +132,22 @@ def mrcg_extract(audio_dir) :
binary_saver(name_label, framed_label[1: len(mrcg_mat), 1], num )
data_len = len(mrcg_mat)
sio.savemat(save_dir+'/normalize_factor',{'train_mean': train_mean, 'train_std': train_std})
# save([save_dir, '/normalize_factor'], 'train_mean', 'train_std')

print('MRCG extraction is successifully done.')
return data_len, winlen, winstep


def vad_func(audio_dir, mode, th, output_type, is_default, off_on_length=20, on_off_length=20, hang_before=10,
hang_over=10):
def vad_func(
audio_dir,
mode,
th,
output_type,
is_default,
off_on_length=20,
on_off_length=20,
hang_before=10,
hang_over=10
):

os.system('rm -rf result')
os.system('rm -rf sample_data')
Expand All @@ -177,9 +162,8 @@ def vad_func(audio_dir, mode, th, output_type, is_default, off_on_length=20, on_

os.system(order)

pred_result = sio.loadmat('./result/pred.mat')
pred_result = sio.loadmat('result/pred.mat')
pp = pred_result['pred']
result = np.zeros([len(pp), 1])
result = th_classifier(pp, th)
result = vad_post(result, off_on_length, on_off_length, hang_before, hang_over)
if output_type == 1:
Expand All @@ -189,9 +173,6 @@ def vad_func(audio_dir, mode, th, output_type, is_default, off_on_length=20, on_


def vad_post(post_label, off_on_length=20, on_off_length=20, hang_before=10, hang_over=10):
# plt.subplot(4,1,1)
# plt.plot(post_label)

'''fill 1 to short valley'''
offset = False
onset = False
Expand All @@ -209,8 +190,6 @@ def vad_post(post_label, off_on_length=20, on_off_length=20, hang_before=10, han
offset = False

'''remove impulse like detection'''
# plt.subplot(4,1,2)
# plt.plot(post_label)
post_label = np.concatenate([np.zeros((1, 1)), post_label], axis=0)

for i in range(post_label.shape[0]):
Expand Down
121 changes: 39 additions & 82 deletions lib/python/VAD_test.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,8 @@
import sys
sys.path.insert(0, './lib/python')
import VAD_Proposed as Vp
import VAD_DNN as Vd
import VAD_bDNN as Vb
import VAD_LSTM_2 as Vl
import scipy.io as sio
import graph_test as graph_test

from . import graph_test
import os, getopt
import glob

Expand All @@ -17,127 +14,87 @@
# model_dir = "./saved_model"
# valid_batch_size = 4134

if __name__ == '__main__':

try:
opts, args = getopt.getopt(sys.argv[1:], 'hm:l:d:', ["data_dir=", "norm_dir=", "model_dir="])
except getopt.GetoptError as err:
print(str(err))
sys.exit(1)

if len(opts) != 6:
print("arguments are not enough.")
sys.exit(1)

for opt, arg in opts:
if opt == '-h':
sys.exit(0)
elif opt == '-m':
mode = int(arg)
elif opt == '-l':
data_len = int(arg)
elif opt == '-d':
is_default = int(arg)
elif opt == '--data_dir':
data_dir = str(arg)
elif opt == '--norm_dir':
norm_dir = str(arg)
elif opt == '--model_dir':
model_dir = str(arg)

def get_predictions(mode, data_len, is_default, data_dir, norm_dir, model_dir):
if mode == 0:
# Vp.test_config(c_test_dir=data_dir,
# c_norm_dir=norm_dir,
# c_initial_logs_dir=model_dir, c_batch_size_eval=batch_size,
# c_data_len=data_len)
#
# pred, label = Vp.main()

if is_default:
graph_list = sorted(glob.glob(model_dir + '/backup/backup_pb/frozen_model_ACAM.pb'))
norm_dir = model_dir + '/backup/backup_norm'
pred, label = graph_test.do_test(graph_list[-1], data_dir, norm_dir, data_len, is_default, mode)
else:
graph_list = sorted(glob.glob(model_dir + '/graph/ACAM/*.pb'))
print(graph_list)
pred, label = graph_test.do_test(graph_list[-1], data_dir, norm_dir, data_len, is_default, mode)

elif mode == 1:

print(os.path.abspath('./configure/bDNN'))
sys.path.insert(0, os.path.abspath('./configure/bDNN'))

import config as cg
if is_default:
graph_list = sorted(glob.glob(model_dir + '/backup/backup_pb/frozen_model_bDNN.pb'))
norm_dir = model_dir + '/backup/backup_norm'
pred, label = graph_test.do_test(graph_list[-1], data_dir, norm_dir, data_len, is_default, mode)
else:
graph_list = sorted(glob.glob(model_dir + '/graph/bDNN/*.pb'))
print(graph_list)
pred, label = graph_test.do_test(graph_list[-1], data_dir, norm_dir, data_len, is_default, mode)

# Vb.test_config(c_test_dir=data_dir,
# c_norm_dir=norm_dir,
# c_initial_logs_dir=model_dir, c_batch_size_eval=batch_size,
# c_data_len=data_len)
# Vb.test_config(c_test_dir=data_dir,
# c_norm_dir='/home/sbie/storage3/github/VAD_Toolkit/VAD/saved_model/backup_norm',
# c_initial_logs_dir='/home/sbie/storage3/github/VAD_Toolkit/VAD/saved_model/backup_ckpt', c_batch_size_eval=batch_size,
# c_data_len=data_len)
#
# pred, label = Vb.main()

elif mode == 2:

start_time = time()
print(os.path.abspath('./configure/DNN'))
sys.path.insert(0, os.path.abspath('./configure/DNN'))

import config as cg

if is_default:
graph_list = sorted(glob.glob(model_dir + '/backup/backup_pb/frozen_model_DNN.pb'))
norm_dir = model_dir + '/backup/backup_norm'
pred, label = graph_test.do_test(graph_list[-1], data_dir, norm_dir, data_len, is_default, mode)
else:
graph_list = sorted(glob.glob(model_dir + '/graph/DNN/*.pb'))
pred, label = graph_test.do_test(graph_list[-1], data_dir, norm_dir, data_len, is_default, mode)

# Vd.test_config(c_test_dir=data_dir,
# c_norm_dir=norm_dir,
# c_initial_logs_dir=model_dir, c_batch_size_eval=batch_size,
# c_data_len=data_len)
#
# pred, label = Vd.main()

end_time = time()

time_taken = end_time - start_time
print(time_taken)

elif mode == 3:

sys.path.insert(0, os.path.abspath('./configure/LSTM'))

import config as cg

if is_default:
graph_list = sorted(glob.glob(model_dir + '/backup/backup_pb/frozen_model_LSTM.pb'))
norm_dir = model_dir + '/backup/backup_norm'
pred, label = graph_test.do_test(graph_list[-1], data_dir, norm_dir, data_len, is_default, mode)
else:
graph_list = sorted(glob.glob(model_dir + '/graph/LSTM/*.pb'))
print(graph_list)
pred, label = graph_test.do_test(graph_list[-1], data_dir, norm_dir, data_len, is_default, mode)

# Vl.test_config(c_test_dir=data_dir,
# c_norm_dir=norm_dir,
# c_initial_logs_dir=model_dir, c_batch_num=200, c_seq_size=20,
# c_data_len=data_len)
#
# pred, label = Vl.main()
pred, label = graph_test.do_test(graph_list[-1], data_dir, norm_dir, data_len, is_default, mode)
print('{} pred : {} label'.format(pred, label))
return pred, label


if __name__ == '__main__':

try:
opts, args = getopt.getopt(sys.argv[1:], 'hm:l:d:', ["data_dir=", "norm_dir=", "model_dir="])
except getopt.GetoptError as err:
print(str(err))
sys.exit(1)

if len(opts) != 6:
print("arguments are not enough.")
sys.exit(1)

for opt, arg in opts:
if opt == '-h':
sys.exit(0)
elif opt == '-m':
mode = int(arg)
elif opt == '-l':
data_len = int(arg)
elif opt == '-d':
is_default = int(arg)
elif opt == '--data_dir':
data_dir = str(arg)
elif opt == '--norm_dir':
norm_dir = str(arg)
elif opt == '--model_dir':
model_dir = str(arg)

pred, label = get_predictions(mode, data_len, is_default, data_dir, norm_dir, model_dir)

sio.savemat('./result/pred.mat', {'pred': pred})
sio.savemat('./result/label.mat', {'label': label})
sio.savemat('result/pred.mat', {'pred': pred})
sio.savemat('result/label.mat', {'label': label})
print("done")
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8 changes: 4 additions & 4 deletions lib/python/data_reader_bDNN_v2.py
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Original file line number Diff line number Diff line change
@@ -1,14 +1,14 @@
import numpy as np
import os
import glob
import utils

import numpy as np
import scipy.io as sio

from . import utils


class DataReader(object):

def __init__(self, input_dir, output_dir, norm_dir, w=19, u=9, name=None):
# print(name + " data reader initialization...")
self._input_dir = input_dir
self._output_dir = output_dir
self._norm_dir = norm_dir
Expand Down
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