def train_and_dump(dirs, start, end, output_model, features_save):
m = ModelInterface()
#print("len(dirs[start:end]):", len(dirs[start:end]))
for d in dirs[start:end]:
label = os.path.basename(d.rstrip('/'))
wavs = glob.glob(d + '/*.wav')
if len(wavs) == 0:
print("No wav file found in %s" % (d))
continue
for wav in wavs:
try:
fs, signal = read_wav(wav)
m.enroll(label, fs, signal)
#print("wav %s has been enrolled" % (wav))
except Exception as e:
print(wav + " error %s" % (e))
print("The group wav files has been enrolled")
# 如果指定了mfcc特征文件保存路径，则保存mfcc特征文件
if features_save:
m.mfcc_dump(features_save)
print(
"The features of this group wav files has been pickle.dumped to %s"
% features_save)
m.train()
m.dump(output_model)
print("%s has been pickle.dumped\t" % output_model)
def task_enroll(input_dirs, output_model):
m = ModelInterface()
input_dirs = [os.path.expanduser(k) for k in input_dirs.strip().split()]
dirs = itertools.chain(*(glob.glob(d) for d in input_dirs))
dirs = [d for d in dirs if os.path.isdir(d)]
files = []
if len(dirs) == 0:
print ("No valid directory found!")
sys.exit(1)
for d in dirs:
label = os.path.basename(d.rstrip('/'))
wavs = glob.glob(d + '/*.wav')
if len(wavs) == 0:
print ("No wav file found in %s"%(d))
continue
for wav in wavs:
try:
fs, signal = read_wav(wav)
m.enroll(label, fs, signal)
print("wav %s has been enrolled"%(wav))
except Exception as e:
print(wav + " error %s"%(e))
m.train()
m.dump(output_model)
def train(train_data_dir, model_path):
m = ModelInterface()
files = [f for f in os.listdir(train_data_dir) if re.search(r"\.wav", f)]
for f in files:
label, _ = f.split("_")
file = os.path.join(train_data_dir, f)
try:
fs, signal = read_wav(file)
m.enroll(label, fs, signal)
logger.info("wav %s has been enrolled" % (file))
except Exception as e:
logger.info(file + " error %s" % (e))
m.train()
m.dump(model_path)
def train( ):
m = ModelInterface()
train_dir = 'data/train/'
train_class = ['FAML_S', 'FDHH_S', 'FEAB_S', 'FHRO_S',
'FJAZ_S', 'FMEL_S', 'FMEV_S', 'FSLJ_S', 'FTEJ_S',
'FUAN_S', 'MASM_S', 'MCBR_S', 'MFKC_S', 'MKBP_S',
'MLKH_S', 'MMLP_S', 'MMNA_S', 'MNHP_S', 'MOEW_S',
'MPRA_S', 'MREM_S', 'MTLS_S']
file_name = ['a.wav', 'b.wav', 'c.wav', 'd.wav', 'e.wav', 'f.wav', 'g.wav']
for c in train_class:
for n in file_name:
fs, signal = wavfile.read(train_dir + c + n)
m.enroll(c, fs, signal)
m.train()
m.dump('model/model.out')
def task_train_single(wav_url, person_id):
if os.path.exists(model):
m = ModelInterface.load(model)
else:
m = ModelInterface()
if person_id in m.features:
return 'fail', 'aleady exist'
#下载训练语音文件
dest_dir = train_voice_dir + person_id
if not os.path.exists(dest_dir):
os.makedirs(dest_dir)
current_time = time.strftime("%Y%m%d%H%I%S", time.localtime(time.time()))
dest_wav = dest_dir + '/' + current_time + '_' + person_id + '.wav'
print(wav_url)
print(dest_wav)
utils.download_file(wav_url, dest_wav)
#获取下载好的训练语音文件
wavs = glob.glob(dest_dir + '/*.wav')
if len(wavs) == 0:
return 'fail', 'no wav files under this dir'
#train the wavs
for wav in wavs:
try:
fs, signal = utils.read_wav(wav)
m.enroll(person_id, fs, signal)
print("wav %s has been enrolled" % (wav))
except Exception as e:
print(wav + " error %s" % (e))
m.train_single(person_id)
m.dump(model)
return 'success', ''
def task_enroll():
train_sounds_path = os.path.join(os.getcwd(), "trainSounds")
# print('inside enroll fun')
dirs = os.listdir(train_sounds_path)
wavs = []
if len(dirs) == 0:
print('Directory empty')
else:
for d in dirs:
ext = os.path.splitext(d)[-1].lower()
if ext == '.wav':
wavs.append(d)
m = ModelInterface()
for w in wavs:
sample_rate, signal = read_wav(os.path.join(train_sounds_path, w))
label = os.path.splitext(w)[0]
m.enroll(label, sample_rate, signal)
print(label + ' enrolled')
m.train()
m.dump('data.bin')
def task_train_full(input_dirs, output_model):
m = ModelInterface()
#get all the subdir
train_dir = []
for subdirs in os.walk(input_dirs):
train_dir.append(subdirs[0])
train_dir.remove(train_dir[0]) #去掉本身根目录
#input_dirs = [os.path.expanduser(k) for k in input_dirs.strip().split()]
#train_dir = itertools.chain(*(glob.glob(d) for d in input_dirs))
#train_dir = [d for d in train_dir if os.path.isdir(d)]
files = []
if len(train_dir) == 0:
print("No valid directory found!")
return 'fail', 'No valid directory found!'
for d in train_dir:
label = os.path.basename(d.rstrip('/'))
wavs = glob.glob(d + '/*.wav')
if len(wavs) == 0:
print("No wav file found in %s" % (d))
continue
for wav in wavs:
try:
fs, signal = utils.read_wav(wav)
m.enroll(label, fs, signal)
print("wav %s has been enrolled" % (wav))
except Exception as e:
print(wav + " error %s" % (e))
m.train_full()
m.dump(output_model)
return 'success', ''
def task_enroll(input_dirs,
output_model,
features_save=None,
group_person_num=None):
m = ModelInterface()
# 把输入的多个目录字符串分离为目录列表
input_dirs = [os.path.expanduser(k) for k in input_dirs.strip().split()]
# 把各个目录下的子目录列表解压出来组合成一个迭代器
dirs = itertools.chain(*(glob.glob(d) for d in input_dirs))
# 生成包括所有子目录的列表
dirs = [d for d in dirs if os.path.isdir(d)]
for d in dirs:
label = os.path.basename(d.rstrip('/'))
wavs = glob.glob(d + '/*.wav')
if len(wavs) == 0:
print("No wav file found in %s" % (d))
continue
for wav in wavs:
try:
fs, signal = read_wav(wav)
m.enroll(label, fs, signal)
#print("wav %s has been enrolled" % (wav))
except Exception as e:
print(wav + " error %s" % (e))
print("The wav files has been enrolled")
# 如果指定了mfcc特征文件保存路径，则保存mfcc特征文件
if features_save:
m.mfcc_dump(features_save)
print(
"The features of this group wav files has been pickle.dumped to %s"
% features_save)
m.train()
m.dump(output_model)
print("%s has been pickle.dumped\t" % output_model)
class Main(QMainWindow):
CONV_INTERVAL = 0.4
CONV_DURATION = 1.5
CONV_FILTER_DURATION = CONV_DURATION
FS = 8000
TEST_DURATION = 3
def __init__(self, parent=None):
QWidget.__init__(self, parent)
uic.loadUi("edytor.ui", self)
self.statusBar()
self.timer = QTimer(self)
self.timer.timeout.connect(self.timer_callback)
self.noiseButton.clicked.connect(self.noise_clicked)
self.recording_noise = False
self.loadNoise.clicked.connect(self.load_noise)
self.enrollRecord.clicked.connect(self.start_enroll_record)
self.stopEnrollRecord.clicked.connect(self.stop_enroll_record)
self.enrollFile.clicked.connect(self.enroll_file)
self.enroll.clicked.connect(self.do_enroll)
self.startTrain.clicked.connect(self.start_train)
self.dumpBtn.clicked.connect(self.dump)
self.loadBtn.clicked.connect(self.load)
self.recoRecord.clicked.connect(self.start_reco_record)
self.stopRecoRecord.clicked.connect(self.stop_reco_record)
# self.newReco.clicked.connect(self.new_reco)
self.recoFile.clicked.connect(self.reco_file)
self.recoInputFiles.clicked.connect(self.reco_files)
#UI.init
self.userdata =[]
self.loadUsers()
self.Userchooser.currentIndexChanged.connect(self.showUserInfo)
self.ClearInfo.clicked.connect(self.clearUserInfo)
self.UpdateInfo.clicked.connect(self.updateUserInfo)
self.UploadImage.clicked.connect(self.upload_avatar)
#movie test
self.movie = QMovie(u"image/recording.gif")
self.movie.start()
self.movie.stop()
self.Animation.setMovie(self.movie)
self.Animation_2.setMovie(self.movie)
self.Animation_3.setMovie(self.movie)
self.aladingpic = QPixmap(u"image/a_hello.png")
self.Alading.setPixmap(self.aladingpic)
self.Alading_conv.setPixmap(self.aladingpic)
#default user image setting
self.avatarname = "image/nouser.jpg"
self.defaultimage = QPixmap(self.avatarname)
self.Userimage.setPixmap(self.defaultimage)
self.recoUserImage.setPixmap(self.defaultimage)
self.convUserImage.setPixmap(self.defaultimage)
self.load_avatar('avatar/')
# Graph Window init
self.graphwindow = GraphWindow()
self.newname = ""
self.lastname = ""
self.Graph_button.clicked.connect(self.graphwindow.show)
self.convRecord.clicked.connect(self.start_conv_record)
self.convStop.clicked.connect(self.stop_conv)
self.backend = ModelInterface()
# debug
QShortcut(QKeySequence("Ctrl+P"), self, self.printDebug)
#init
try:
fs, signal = read_wav("bg.wav")
self.backend.init_noise(fs, signal)
except:
pass
############ RECORD
def start_record(self):
self.pyaudio = pyaudio.PyAudio()
self.status("Recording...")
self.movie.start()
self.Alading.setPixmap(QPixmap(u"image/a_thinking.png"))
self.recordData = []
self.stream = self.pyaudio.open(format=FORMAT, channels=1, rate=Main.FS,
input=True, frames_per_buffer=1)
self.stopped = False
self.reco_th = RecorderThread(self)
self.reco_th.start()
self.timer.start(1000)
self.record_time = 0
self.update_all_timer()
def add_record_data(self, i):
self.recordData.append(i)
return self.stopped
def timer_callback(self):
self.record_time += 1
self.status("Recording..." + time_str(self.record_time))
self.update_all_timer()
def stop_record(self):
self.movie.stop()
self.stopped = True
self.reco_th.wait()
self.timer.stop()
self.stream.stop_stream()
self.stream.close()
self.pyaudio.terminate()
self.status("Record stopeed")
############## conversation
def start_conv_record(self):
self.conv_result_list = []
self.start_record()
self.conv_now_pos = 0
self.conv_timer = QTimer(self)
self.conv_timer.timeout.connect(self.do_conversation)
self.conv_timer.start(Main.CONV_INTERVAL * 1000)
#reset
self.graphwindow.wid.reset()
def stop_conv(self):
self.stop_record()
self.conv_timer.stop()
def do_conversation(self):
interval_len = int(Main.CONV_INTERVAL * Main.FS)
segment_len = int(Main.CONV_DURATION * Main.FS)
self.conv_now_pos += interval_len
to_filter = self.recordData[max([self.conv_now_pos - segment_len, 0]):
self.conv_now_pos]
signal = np.array(to_filter, dtype=NPDtype)
label = None
try:
signal = self.backend.filter(Main.FS, signal)
if len(signal) > 50:
label = self.backend.predict(Main.FS, signal, True)
except Exception as e:
print traceback.format_exc()
print str(e)
global last_label_to_show
label_to_show = label
if label and self.conv_result_list:
last_label = self.conv_result_list[-1]
if last_label and last_label != label:
label_to_show = last_label_to_show
self.conv_result_list.append(label)
print label_to_show, "label to show"
last_label_to_show = label_to_show
#ADD FOR GRAPH
if label_to_show is None:
label_to_show = 'Nobody'
if len(NAMELIST) and NAMELIST[-1] != label_to_show:
NAMELIST.append(label_to_show)
self.convUsername.setText(label_to_show)
self.Alading_conv.setPixmap(QPixmap(u"image/a_result.png"))
self.convUserImage.setPixmap(self.get_avatar(label_to_show))
###### RECOGNIZE
def start_reco_record(self):
self.Alading.setPixmap(QPixmap(u"image/a_hello"))
self.recoRecordData = np.array((), dtype=NPDtype)
self.start_record()
def stop_reco_record(self):
self.stop_record()
signal = np.array(self.recordData, dtype=NPDtype)
self.reco_remove_update(Main.FS, signal)
def reco_do_predict(self, fs, signal):
label = self.backend.predict(fs, signal)
if not label:
label = "Nobody"
print label
self.recoUsername.setText(label)
self.Alading.setPixmap(QPixmap(u"image/a_result.png"))
self.recoUserImage.setPixmap(self.get_avatar(label))
# TODO To Delete
write_wav('reco.wav', fs, signal)
def reco_remove_update(self, fs, signal):
new_signal = self.backend.filter(fs, signal)
print "After removed: {0} -> {1}".format(len(signal), len(new_signal))
self.recoRecordData = np.concatenate((self.recoRecordData, new_signal))
real_len = float(len(self.recoRecordData)) / Main.FS / Main.TEST_DURATION * 100
if real_len > 100:
real_len = 100
self.reco_do_predict(fs, self.recoRecordData)
def reco_file(self):
fname = QFileDialog.getOpenFileName(self, "Open Wav File", "", "Files (*.wav)")
print 'reco_file'
if not fname:
return
self.status(fname)
fs, signal = read_wav(fname)
self.reco_do_predict(fs, signal)
def reco_files(self):
fnames = QFileDialog.getOpenFileNames(self, "Select Wav Files", "", "Files (*.wav)")
print 'reco_files'
for f in fnames:
fs, sig = read_wav(f)
newsig = self.backend.filter(fs, sig)
label = self.backend.predict(fs, newsig)
print f, label
########## ENROLL
def start_enroll_record(self):
self.enrollWav = None
self.enrollFileName.setText("")
self.start_record()
def enroll_file(self):
fname = QFileDialog.getOpenFileName(self, "Open Wav File", "", "Files (*.wav)")
if not fname:
return
self.status(fname)
self.enrollFileName.setText(fname)
fs, signal = read_wav(fname)
signal = monophonic(signal)
self.enrollWav = (fs, signal)
def stop_enroll_record(self):
self.stop_record()
print self.recordData[:300]
signal = np.array(self.recordData, dtype=NPDtype)
self.enrollWav = (Main.FS, signal)
# TODO To Delete
write_wav('enroll.wav', *self.enrollWav)
def do_enroll(self):
name = self.Username.text().trimmed()
if not name:
self.warn("Please Input Your Name")
return
# self.addUserInfo()
new_signal = self.backend.filter(*self.enrollWav)
print "After removed: {0} -> {1}".format(len(self.enrollWav[1]), len(new_signal))
print "Enroll: {:.4f} seconds".format(float(len(new_signal)) / Main.FS)
if len(new_signal) == 0:
print "Error! Input is silent! Please enroll again"
return
self.backend.enroll(name, Main.FS, new_signal)
def start_train(self):
self.status("Training...")
self.backend.train()
self.status("Training Done.")
####### UI related
def getWidget(self, splash):
t = QtCore.QElapsedTimer()
t.start()
while (t.elapsed() < 800):
str = QtCore.QString("times = ") + QtCore.QString.number(t.elapsed())
splash.showMessage(str)
QtCore.QCoreApplication.processEvents()
def upload_avatar(self):
fname = QFileDialog.getOpenFileName(self, "Open JPG File", "", "File (*.jpg)")
if not fname:
return
self.avatarname = fname
self.Userimage.setPixmap(QPixmap(fname))
def loadUsers(self):
with open("avatar/metainfo.txt") as db:
for line in db:
tmp = line.split()
self.userdata.append(tmp)
self.Userchooser.addItem(tmp[0])
def showUserInfo(self):
for user in self.userdata:
if self.userdata.index(user) == self.Userchooser.currentIndex() - 1:
self.Username.setText(user[0])
self.Userage.setValue(int(user[1]))
if user[2] == 'F':
self.Usersex.setCurrentIndex(1)
else:
self.Usersex.setCurrentIndex(0)
self.Userimage.setPixmap(self.get_avatar(user[0]))
def updateUserInfo(self):
userindex = self.Userchooser.currentIndex() - 1
u = self.serdata[userindex]
u[0] = unicode(self.Username.displayText())
u[1] = self.Userage.value()
if self.Usersex.currentIndex():
u[2] = 'F'
else:
u[2] = 'M'
with open("avatar/metainfo.txt","w") as db:
for user in self.userdata:
for i in range(3):
db.write(str(user[i]) + " ")
db.write("\n")
def writeuserdata(self):
with open("avatar/metainfo.txt","w") as db:
for user in self.userdata:
for i in range (0,4):
db.write(str(user[i]) + " ")
db.write("\n")
def clearUserInfo(self):
self.Username.setText("")
self.Userage.setValue(0)
self.Usersex.setCurrentIndex(0)
self.Userimage.setPixmap(self.defaultimage)
def addUserInfo(self):
for user in self.userdata:
if user[0] == unicode(self.Username.displayText()):
return
newuser = []
newuser.append(unicode(self.Username.displayText()))
newuser.append(self.Userage.value())
if self.Usersex.currentIndex():
newuser.append('F')
else:
newuser.append('M')
if self.avatarname:
shutil.copy(self.avatarname, 'avatar/' + user[0] + '.jpg')
self.userdata.append(newuser)
self.writeuserdata()
self.Userchooser.addItem(unicode(self.Username.displayText()))
############# UTILS
def warn(self, s):
QMessageBox.warning(self, "Warning", s)
def status(self, s=""):
self.statusBar().showMessage(s)
def update_all_timer(self):
s = time_str(self.record_time)
self.enrollTime.setText(s)
self.recoTime.setText(s)
self.convTime.setText(s)
def dump(self):
fname = QFileDialog.getSaveFileName(self, "Save Data to:", "", "")
if fname:
try:
self.backend.dump(fname)
except Exception as e:
self.warn(str(e))
else:
self.status("Dumped to file: " + fname)
def load(self):
fname = QFileDialog.getOpenFileName(self, "Open Data File:", "", "")
if fname:
try:
self.backend = ModelInterface.load(fname)
except Exception as e:
self.warn(str(e))
else:
self.status("Loaded from file: " + fname)
def noise_clicked(self):
self.recording_noise = not self.recording_noise
if self.recording_noise:
self.noiseButton.setText('Stop Recording Noise')
self.start_record()
else:
self.noiseButton.setText('Recording Background Noise')
self.stop_record()
signal = np.array(self.recordData, dtype=NPDtype)
wavfile.write("bg.wav", Main.FS, signal)
self.backend.init_noise(Main.FS, signal)
def load_noise(self):
fname = QFileDialog.getOpenFileName(self, "Open Data File:", "", "Wav File (*.wav)")
if fname:
fs, signal = read_wav(fname)
self.backend.init_noise(fs, signal)
def load_avatar(self, dirname):
self.avatars = {}
for f in glob.glob(dirname + '/*.jpg'):
name = os.path.basename(f).split('.')[0]
print f, name
self.avatars[name] = QPixmap(f)
def get_avatar(self, username):
p = self.avatars.get(str(username), None)
if p:
return p
else:
return self.defaultimage
def printDebug(self):
for name, feat in self.backend.features.iteritems():
print name, len(feat)
print "GMMs",
print len(self.backend.gmmset.gmms)
'''
class Main(QMainWindow):
CONV_INTERVAL = 0.4
CONV_DURATION = 1.5
CONV_FILTER_DURATION = CONV_DURATION
FS = 8000
TEST_DURATION = 3
def __init__(self, parent=None):
QWidget.__init__(self, parent)
uic.loadUi("edytor.ui", self)
self.statusBar()
self.timer = QTimer(self)
self.timer.timeout.connect(self.timer_callback)
self.noiseButton.clicked.connect(self.noise_clicked)
self.recording_noise = False
self.loadNoise.clicked.connect(self.load_noise)
self.enrollRecord.clicked.connect(self.start_enroll_record)
self.stopEnrollRecord.clicked.connect(self.stop_enroll_record)
self.enrollFile.clicked.connect(self.enroll_file)
self.enroll.clicked.connect(self.do_enroll)
self.startTrain.clicked.connect(self.start_train)
self.dumpBtn.clicked.connect(self.dump)
self.loadBtn.clicked.connect(self.load)
self.recoRecord.clicked.connect(self.start_reco_record)
self.stopRecoRecord.clicked.connect(self.stop_reco_record)
# self.newReco.clicked.connect(self.new_reco)
self.recoFile.clicked.connect(self.reco_file)
self.recoInputFiles.clicked.connect(self.reco_files)
#UI.init
self.userdata = []
self.loadUsers()
self.Userchooser.currentIndexChanged.connect(self.showUserInfo)
self.ClearInfo.clicked.connect(self.clearUserInfo)
self.UpdateInfo.clicked.connect(self.updateUserInfo)
self.UploadImage.clicked.connect(self.upload_avatar)
#movie test
self.movie = QMovie(u"image/recording.gif")
self.movie.start()
self.movie.stop()
self.Animation.setMovie(self.movie)
self.Animation_2.setMovie(self.movie)
self.Animation_3.setMovie(self.movie)
self.aladingpic = QPixmap(u"image/a_hello.png")
self.Alading.setPixmap(self.aladingpic)
self.Alading_conv.setPixmap(self.aladingpic)
#default user image setting
self.avatarname = "image/nouser.jpg"
self.defaultimage = QPixmap(self.avatarname)
self.Userimage.setPixmap(self.defaultimage)
self.recoUserImage.setPixmap(self.defaultimage)
self.convUserImage.setPixmap(self.defaultimage)
self.load_avatar('avatar/')
# Graph Window init
self.graphwindow = GraphWindow()
self.newname = ""
self.lastname = ""
self.Graph_button.clicked.connect(self.graphwindow.show)
self.convRecord.clicked.connect(self.start_conv_record)
self.convStop.clicked.connect(self.stop_conv)
self.backend = ModelInterface()
# debug
QShortcut(QKeySequence("Ctrl+P"), self, self.printDebug)
#init
try:
fs, signal = wavfile.read("bg.wav")
self.backend.init_noise(fs, signal)
except:
pass
############ RECORD
def start_record(self):
self.pyaudio = pyaudio.PyAudio()
self.status("Recording...")
self.movie.start()
self.Alading.setPixmap(QPixmap(u"image/a_thinking.png"))
self.recordData = []
self.stream = self.pyaudio.open(format=FORMAT,
channels=1,
rate=Main.FS,
input=True,
frames_per_buffer=1)
self.stopped = False
self.reco_th = RecorderThread(self)
self.reco_th.start()
self.timer.start(1000)
self.record_time = 0
self.update_all_timer()
def add_record_data(self, i):
self.recordData.append(i)
return self.stopped
def timer_callback(self):
self.record_time += 1
self.status("Recording..." + time_str(self.record_time))
self.update_all_timer()
def stop_record(self):
self.movie.stop()
self.stopped = True
self.reco_th.wait()
self.timer.stop()
self.stream.stop_stream()
self.stream.close()
self.pyaudio.terminate()
self.status("Record stopeed")
############## conversation
def start_conv_record(self):
self.conv_result_list = []
self.start_record()
self.conv_now_pos = 0
self.conv_timer = QTimer(self)
self.conv_timer.timeout.connect(self.do_conversation)
self.conv_timer.start(Main.CONV_INTERVAL * 1000)
#reset
self.graphwindow.wid.reset()
def stop_conv(self):
self.stop_record()
self.conv_timer.stop()
def do_conversation(self):
interval_len = int(Main.CONV_INTERVAL * Main.FS)
segment_len = int(Main.CONV_DURATION * Main.FS)
self.conv_now_pos += interval_len
to_filter = self.recordData[max([self.conv_now_pos -
segment_len, 0]):self.conv_now_pos]
signal = np.array(to_filter, dtype=NPDtype)
label = None
try:
signal = self.backend.filter(Main.FS, signal)
if len(signal) > 50:
label = self.backend.predict(Main.FS, signal, True)
except Exception as e:
print traceback.format_exc()
print str(e)
global last_label_to_show
label_to_show = label
if label and self.conv_result_list:
last_label = self.conv_result_list[-1]
if last_label and last_label != label:
label_to_show = last_label_to_show
self.conv_result_list.append(label)
print label_to_show, "label to show"
last_label_to_show = label_to_show
#ADD FOR GRAPH
if label_to_show is None:
label_to_show = 'Nobody'
if len(NAMELIST) and NAMELIST[-1] != label_to_show:
NAMELIST.append(label_to_show)
self.convUsername.setText(label_to_show)
self.Alading_conv.setPixmap(QPixmap(u"image/a_result.png"))
self.convUserImage.setPixmap(self.get_avatar(label_to_show))
###### RECOGNIZE
def start_reco_record(self):
self.Alading.setPixmap(QPixmap(u"image/a_hello"))
self.recoRecordData = np.array((), dtype=NPDtype)
self.start_record()
def stop_reco_record(self):
self.stop_record()
signal = np.array(self.recordData, dtype=NPDtype)
self.reco_remove_update(Main.FS, signal)
def reco_do_predict(self, fs, signal):
label = self.backend.predict(fs, signal)
if not label:
label = "Nobody"
print label
self.recoUsername.setText(label)
self.Alading.setPixmap(QPixmap(u"image/a_result.png"))
self.recoUserImage.setPixmap(self.get_avatar(label))
# TODO To Delete
write_wav('reco.wav', fs, signal)
def reco_remove_update(self, fs, signal):
new_signal = self.backend.filter(fs, signal)
print "After removed: {0} -> {1}".format(len(signal), len(new_signal))
self.recoRecordData = np.concatenate((self.recoRecordData, new_signal))
real_len = float(len(
self.recoRecordData)) / Main.FS / Main.TEST_DURATION * 100
if real_len > 100:
real_len = 100
self.reco_do_predict(fs, self.recoRecordData)
def reco_file(self):
fname = QFileDialog.getOpenFileName(self, "Open Wav File", "",
"Files (*.wav)")
print 'reco_file'
if not fname:
return
self.status(fname)
fs, signal = wavfile.read(fname)
self.reco_do_predict(fs, signal)
def reco_files(self):
fnames = QFileDialog.getOpenFileNames(self, "Select Wav Files", "",
"Files (*.wav)")
print 'reco_files'
for f in fnames:
fs, sig = wavfile.read(f)
newsig = self.backend.filter(fs, sig)
label = self.backend.predict(fs, newsig)
print f, label
########## ENROLL
def start_enroll_record(self):
self.enrollWav = None
self.enrollFileName.setText("")
self.start_record()
def enroll_file(self):
fname = QFileDialog.getOpenFileName(self, "Open Wav File", "",
"Files (*.wav)")
if not fname:
return
self.status(fname)
self.enrollFileName.setText(fname)
fs, signal = wavfile.read(fname)
signal = monophonic(signal)
self.enrollWav = (fs, signal)
def stop_enroll_record(self):
self.stop_record()
print self.recordData[:300]
signal = np.array(self.recordData, dtype=NPDtype)
self.enrollWav = (Main.FS, signal)
# TODO To Delete
write_wav('enroll.wav', *self.enrollWav)
def do_enroll(self):
name = self.Username.text().trimmed()
if not name:
self.warn("Please Input Your Name")
return
# self.addUserInfo()
new_signal = self.backend.filter(*self.enrollWav)
print "After removed: {0} -> {1}".format(len(self.enrollWav[1]),
len(new_signal))
print "Enroll: {:.4f} seconds".format(float(len(new_signal)) / Main.FS)
self.backend.enroll(name, Main.FS, new_signal)
def start_train(self):
self.status("Training...")
self.backend.train()
self.status("Training Done.")
####### UI related
def getWidget(self, splash):
t = QtCore.QElapsedTimer()
t.start()
while (t.elapsed() < 800):
str = QtCore.QString("times = ") + QtCore.QString.number(
t.elapsed())
splash.showMessage(str)
QtCore.QCoreApplication.processEvents()
def upload_avatar(self):
fname = QFileDialog.getOpenFileName(self, "Open JPG File", "",
"File (*.jpg)")
if not fname:
return
self.avatarname = fname
self.Userimage.setPixmap(QPixmap(fname))
def loadUsers(self):
with open("avatar/metainfo.txt") as db:
for line in db:
tmp = line.split()
self.userdata.append(tmp)
self.Userchooser.addItem(tmp[0])
def showUserInfo(self):
for user in self.userdata:
if self.userdata.index(
user) == self.Userchooser.currentIndex() - 1:
self.Username.setText(user[0])
self.Userage.setValue(int(user[1]))
if user[2] == 'F':
self.Usersex.setCurrentIndex(1)
else:
self.Usersex.setCurrentIndex(0)
self.Userimage.setPixmap(self.get_avatar(user[0]))
def updateUserInfo(self):
userindex = self.Userchooser.currentIndex() - 1
u = self.serdata[userindex]
u[0] = unicode(self.Username.displayText())
u[1] = self.Userage.value()
if self.Usersex.currentIndex():
u[2] = 'F'
else:
u[2] = 'M'
with open("avatar/metainfo.txt", "w") as db:
for user in self.userdata:
for i in range(3):
db.write(str(user[i]) + " ")
db.write("\n")
def writeuserdata(self):
with open("avatar/metainfo.txt", "w") as db:
for user in self.userdata:
for i in range(0, 4):
db.write(str(user[i]) + " ")
db.write("\n")
def clearUserInfo(self):
self.Username.setText("")
self.Userage.setValue(0)
self.Usersex.setCurrentIndex(0)
self.Userimage.setPixmap(self.defaultimage)
def addUserInfo(self):
for user in self.userdata:
if user[0] == unicode(self.Username.displayText()):
return
newuser = []
newuser.append(unicode(self.Username.displayText()))
newuser.append(self.Userage.value())
if self.Usersex.currentIndex():
newuser.append('F')
else:
newuser.append('M')
if self.avatarname:
shutil.copy(self.avatarname, 'avatar/' + user[0] + '.jpg')
self.userdata.append(newuser)
self.writeuserdata()
self.Userchooser.addItem(unicode(self.Username.displayText()))
############# UTILS
def warn(self, s):
QMessageBox.warning(self, "Warning", s)
def status(self, s=""):
self.statusBar().showMessage(s)
def update_all_timer(self):
s = time_str(self.record_time)
self.enrollTime.setText(s)
self.recoTime.setText(s)
self.convTime.setText(s)
def dump(self):
fname = QFileDialog.getSaveFileName(self, "Save Data to:", "", "")
if fname:
try:
self.backend.dump(fname)
except Exception as e:
self.warn(str(e))
else:
self.status("Dumped to file: " + fname)
def load(self):
fname = QFileDialog.getOpenFileName(self, "Open Data File:", "", "")
if fname:
try:
self.backend = ModelInterface.load(fname)
except Exception as e:
self.warn(str(e))
else:
self.status("Loaded from file: " + fname)
def noise_clicked(self):
self.recording_noise = not self.recording_noise
if self.recording_noise:
self.noiseButton.setText('Stop Recording Noise')
self.start_record()
else:
self.noiseButton.setText('Recording Background Noise')
self.stop_record()
signal = np.array(self.recordData, dtype=NPDtype)
wavfile.write("bg.wav", Main.FS, signal)
self.backend.init_noise(Main.FS, signal)
def load_noise(self):
fname = QFileDialog.getOpenFileName(self, "Open Data File:", "",
"Wav File (*.wav)")
if fname:
fs, signal = wavfile.read(fname)
self.backend.init_noise(fs, signal)
def load_avatar(self, dirname):
self.avatars = {}
for f in glob.glob(dirname + '/*.jpg'):
name = os.path.basename(f).split('.')[0]
print f, name
self.avatars[name] = QPixmap(f)
def get_avatar(self, username):
p = self.avatars.get(str(username), None)
if p:
return p
else:
return self.defaultimage
def printDebug(self):
for name, feat in self.backend.features.iteritems():
print name, len(feat)
print "GMMs",
print len(self.backend.gmmset.gmms)
'''
name = name.strip()
print(
f"Hello {name}. Please input your voice {args.num_samples} times")
with tempfile.TemporaryDirectory() as tempdir:
i = 1
while i <= args.num_samples:
with sr.Microphone() as source:
audio = r.listen(source)
# Generate random filename
filename = os.path.join(
tempdir, name + "_" + str(uuid.uuid1()) + ".wav")
with open(filename, "wb") as file:
file.write(audio.get_wav_data(convert_rate=16000))
# enroll a file
fs, signal = read_wav(filename)
model.enroll(name, fs, signal)
logger.info("wav file %s has been enrolled" % (filename))
i += 1
model.train()
model.dump(args.model_path)
else:
model = ModelInterface.load(args.model_path)
print("Please input your voice: ")
with tempfile.TemporaryDirectory() as tempdir:
with sr.Microphone() as source:
audio = r.listen(source)
filename = os.path.join(tempdir, str(uuid.uuid1()) + ".wav")
with open(filename, "wb") as file:
file.write(audio.get_wav_data(convert_rate=16000))
fs, signal = read_wav(filename)
def task_enroll(
input_dirs, output_model, isDynamic
): # Possible improvement : Store VAD signal of each .wav file instead of calculate it twice for dynamic threshold (useless for static)
"""
Enroll the speaker inside the GMM model.
Args:
input_dirs (string): directory of the database
output_model (string): path of the output model
"""
# Creates the model object #
m = ModelInterface()
# Extracts the absolute path from 'input_dirs' #
# If input_dirs is an array of directory, it is adapted by the second line #
print(input_dirs)
input_dirs = [os.path.expanduser(k) for k in input_dirs.strip().split()]
dirs = itertools.chain(*(glob.glob(d) for d in input_dirs))
dirs = [d for d in dirs if os.path.isdir(d)]
if len(dirs) == 0:
print('No valid directory found!')
sys.exit(1)
# Starts the enrollment of the valid directories #
start_time = time.time()
print('Starting enrollment')
for d in dirs:
print(d)
# Retrieves the label of the current directory name and loads .wav files are stored #
label = os.path.basename(d.rstrip('/'))
print(label)
wavs = glob.glob(d + '/*.wav')
if len(wavs) == 0:
print('No wav file found in %s' % (d))
continue
for wav in wavs:
# Audio processing of the .wav file #
# Retrieves sampling rate (fs), signal values #
# VAD removes silence inside the signal #
# Enrolls the cleared signal and its label inside the model #
try:
fs, signal = read_wav(wav)
signal = signal / max(abs(signal))
m.enroll(label, fs, VAD_process(signal))
except Exception as e:
print(wav + ' error %s' % (e))
# Starts the training of the model using the enrolled signals #
print('Enrollment finished\nTraining started')
m.train()
print('Training finished')
# Starts a dynamic threshold computation #
# /!\ Warning : computation takes a lot of time /!\ #
if (isDynamic):
print('Dynamic Threshold started')
i = 0
for d in dirs:
# Loads the .wav files #
# Each .wav file will be used to compute a score for each existing label after training #
if len(wavs) == 0:
print('No wav file found in %s' % (d))
continue
wavs = glob.glob(d + '/*.wav')
for wav in wavs:
try:
fs, signal = read_wav(wav)
signal = signal / max(abs(signal))
m.dynamic_threshold(fs, VAD_process(signal))
except Exception as e:
print(wav + ' error %s' % (e))
i += 1
for j in range(0, 100, 10):
if (i == int(len(dirs) * j * 0.01)):
print('%i percent done.' % (j))
# Keeps only the mean from all scores for a given label as dynamic threshold #
try:
m.dynamic_mean()
except Exception as e:
print('Error for dyanmic threshold : error %e' % (e))
print('Dynamic threshold finished')
print(time.time() - start_time, " seconds")
# Saves the model at the specified path 'output_model' #
m.dump(output_model)
