def load(self):
# for user in self.userdata:
# if self.userdata.index(user) == self.Userchooser.currentIndex() - 1:
# file_name = user[0]
# break
userindex = self.Userchooser.currentIndex() - 1
if (userindex > -1):
file_name = self.userdata[userindex][0]
#self.enroll_checklist()
if file_name == None:
self.status("no user")
return
fname = "gmms/" + file_name + ".model"
#fname = QFileDialog.getOpenFileName(self, "Open Data File:", "", "")
if fname:
if self.backend == None:
try:
self.backend = ModelInterface()
self.backend.load(fname, file_name)
except Exception as e:
self.warn(str(e))
else:
try:
self.backend.load(fname, file_name)
except Exception as e:
self.warn(str(e))
self.status("loaded model " + file_name)
else:
self.status("Please select user.")
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)
training_stats = []
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 {0}".format(d)
continue
print "Label '{0}' has files: {1}".format(label, ', '.join(wavs))
total_len = 0
for wav in wavs:
fs, signal = read_wav(wav)
print " File '{}' has frequency={} and length={}".format(
wav, fs, len(signal))
total_len += len(signal)
m.enroll(label, fs, signal)
training_stats.append((label, total_len))
print "--------------------------------------------"
for label, total_len in training_stats:
print "Total length of training data for '{}' is {}".format(
label, total_len)
print "For best accuracy, please make sure all labels have similar amount of training data!"
m.train()
m.dump(output_model)
def task_enroll(input_directory, output_model):
m = ModelInterface()
for k in input_directory.strip().split():
input_dirs = [os.path.expanduser(k)]
for d in input_directory:
dirs = itertools.chain(*(glob.glob(d)))
for d in dirs:
if os.path.isdir(d):
dirs = [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 {0}".format(d)
continue
print "Label {0} has files {1}".format(label, ','.join(wavs))
for wav in wavs:
fs, signal = read_wav(wav)
m.enroll(label, fs, signal)
m.train()
m.dump(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('/'))
#label =d
wavs = glob.glob(d + '/*.wav')
#print(wavs)
if len(wavs) == 0:
print ("No wav file found in {0}".format(d))
continue
print ("Label {0} has files {1}".format(label, ','.join(wavs)))
for wav in wavs:
fs, signal = read_wav(wav)
label=wav.split('/')[-1].split('_')[0]
#print(label)
m.enroll(label, fs, signal)
m.train()
m.dump(output_model)
def task_predict(input_file, input_model):
# FS = 8000
m = ModelInterface.load(input_model)
fs_noise, noise = read_wav("noise.wav")
m.init_noise(fs_noise, noise)
#for f in [input_files]:
try:
fs, signal = read_wav(input_file)
print("freq " + str(fs))
signal = m.filter(fs, signal)
print("len " + str(len(signal)))
if len(signal) < 50:
return None
print("AA")
label = m.predict(fs, signal)
print input_file, '->', label
return label
except:
print "Unexpected error:", sys.exc_info()[0]
def task_predict(input_files, input_model):
m = ModelInterface.load(input_model)
for f in glob.glob(os.path.expanduser(input_files)):
fs, signal = read_wav(f)
label = m.predict(fs, signal)
f = os.path.basename(f)
print f, '->', label
def task_predict(input_files, input_model):
m = ModelInterface.load(input_model)
for f in glob.glob(os.path.expanduser(input_files)):
wavs = glob.glob(f + '/*.wav')
for wav in wavs:
fs, signal = read_wav(wav)
label = m.predict(fs, signal)
print (wav.split('/')[-1].split('_')[0]+'|->|'+label)
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)
training_stats = []
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 {0}".format(d)
continue
print "Label '{0}' has files: {1}".format(label, ', '.join(wavs))
total_len = 0
for wav in wavs:
fs, signal = read_wav(wav)
print " File '{}' has frequency={} and length={}".format(wav, fs, len(signal))
total_len += len(signal)
m.enroll(label, fs, signal)
training_stats.append((label, total_len))
print "--------------------------------------------"
for label, total_len in training_stats:
print "Total length of training data for '{}' is {}".format(label, total_len)
print "For best accuracy, please make sure all labels have similar amount of training data!"
m.train()
m.dump(output_model)
def task_predict(input_model):
m = ModelInterface.load(input_model)
test_data = range(1,4)
for f in test_data:
wav = "./data/test/"+str(f)+".wav"
print wav
fs, signal = read_wav(wav)
label = m.predict(fs, signal)
print f, '->', label
def task_predict(input_model):
m = ModelInterface.load(input_model)
test_data = range(1, 4)
for f in test_data:
wav = "./data/test/" + str(f) + ".wav"
print wav
fs, signal = read_wav(wav)
label = m.predict(fs, signal)
print f, '->', label
def task_enroll(output_model):
m = ModelInterface()
train_data = range(1, 4)
for i in train_data:
wav = "./data/train/" + str(i) + ".wav"
print wav
label = str(i)
fs, signal = read_wav(wav)
m.enroll(label, fs, signal)
m.train()
m.dump(output_model)
def task_predict(input_files, input_model):
m = ModelInterface.load(input_model)
for f in glob.glob(os.path.expanduser(input_files)):
fs, signal = read_wav(f)
#label = m.predict(fs, signal)
(label, score) = m.predict_with_score(fs, signal)
if math.fabs(score) < 0.13:
print f, '->', label, ' score:', score
else:
print f, 'NO SPEAKER RECOGNIZED (', label, ' score:', score, ')'
def task_predict(input_files, input_model, index):
m = ModelInterface.load(input_model)
for f in glob.glob(os.path.expanduser(input_files)):
fs, signal = read_wav(f)
label = m.predict(fs, signal)
print f, '->', label
if label == index:
sys.exit(1)
else:
sys.exit(2)
def load_check_box(self, label):
fname = "gmms/" + label + ".model"
if fname == None:
self.status("no user")
return
#fname = QFileDialog.getOpenFileName(self, "Open Data File:", "", "")
if fname:
if self.backend == None:
try:
self.backend = ModelInterface()
self.backend.load(fname, label)
except Exception as e:
self.warn(str(e))
else:
try:
self.backend.load(fname, label)
except Exception as e:
self.warn(str(e))
self.status("loaded all")
def handle_speaker_rec_greeting_intent(self, message):
directory = "/tmp/mycroft_wake_words"
self.newest = max(glob.iglob(os.path.join(directory, '*.wav')),
key=os.path.getctime)
CWD_PATH = os.path.dirname(__file__)
input_model = os.path.join(CWD_PATH, "model.out")
m = ModelInterface.load(input_model)
input_files = self.newest
fs, signal = read_wav(input_files)
label = m.predict(fs, signal)
self.speak("Yes, I do recognize your voice, %s" % (label))
print(self.newest)
def task_enroll(output_model):
m = ModelInterface()
train_data = range(1,4)
for i in train_data:
wav = "./data/train/"+str(i)+".wav"
print wav
label = str(i)
fs, signal = read_wav(wav)
m.enroll(label, fs, signal)
m.train()
m.dump(output_model)
def task_predict(input_files, input_model):
m = ModelInterface.load(input_model)
ev = []
for f in glob.glob(os.path.expanduser(input_files)):
fs, signal = read_wav(f)
label = m.predict(fs, signal)
ev.append(label)
print f, '->', label
size = len(ev)
cnt = Counter(ev)
mostCommon = cnt.most_common(1)[0][1]
print(mostCommon)
print(mostCommon * 100 / size)
def task_enroll(input_dirs, output_model):
m = ModelInterface()
input_dirs = 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 = []
for d in dirs:
label = os.path.basename(d)
wavs = glob.glob(d + '/*.wav')
if len(wavs) == 0:
continue
print "Label {0} has files {1}".format(label, ','.join(wavs))
for wav in wavs:
fs, signal = wavfile.read(wav)
m.enroll(label, fs, signal)
m.train()
m.dump(output_model)
def task_predict(input_files, input_current, input_model):
m = ModelInterface.load(input_model)
with open('/host{0}/predictions.csv'.format(input_current), 'w') as pred:
ss = csv.writer(pred, delimiter=',')
ss.writerow(['score', 'label', 'file'])
for f in glob.glob(os.path.expanduser(input_files)):
fs, signal = read_wav(f)
scores = m.predict_scores(fs, signal)
y_scores = dict(zip(m.gmmset.y, scores))
i = 0
for label, score in sorted(y_scores.items(),
key=lambda o: o[1],
reverse=True):
if i == 0 and label in [
'Albert', 'Casado', 'Pedro', 'Iglesias'
]:
ss.writerow([score, label, f])
i += 1
def task_enroll(input_dirs, output_model):
m = ModelInterface()
input_dirs = 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 = []
for d in dirs:
label = os.path.basename(d)
wavs = glob.glob(d + '/*.wav')
if len(wavs) == 0:
continue
print "Label {0} has files {1}".format(label, ','.join(wavs))
for wav in wavs:
fs, signal = wavfile.read(wav)
m.enroll(label, fs, signal)
m.train()
m.dump(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 {0}".format(d)
continue
print "Label {0} has files {1}".format(label, ','.join(wavs))
for wav in wavs:
fs, signal = read_wav(wav)
m.enroll(label, fs, signal)
m.train()
m.dump(output_model)
def sendData(link, data, timestart):
global pause
global voiceMode
global fail
global rawYData
global e
time = datetime.datetime.now()
#print(time.strftime('%Y-%m-%d %H:%M:%S'))
jsonData = {
'wave': data.tolist(),
'sr': 16000,
'time_start': time.strftime('%Y-%m-%d %H:%M:%S'),
'user_id_id': 1
}
jsonData = json.dumps(jsonData, sort_keys=True)
r = requests.post(url=link, data=jsonData)
print("The response is:%s" % r.text)
trigger = json.loads(r.text)["Trigger"]
labelsound = str(json.loads(r.text)["Labels"])
#trigger = r.json()
#trigger = trigger[0]['Trigger']
print trigger
if voiceMode and trigger == True:
voiceMode = False
pause = True
print 'pause'
model = ModelInterface.load("m.out")
#fs,array = read_wav("start.mp3")
#sd.play(array,fs)
mixer.init(16000)
mixer.music.load('start.mp3')
mixer.music.play()
ti.sleep(2)
print "Recording Voice..."
samplerate = 44100 # Hertz
duration = 5 #8s
predictData = sd.rec(int(samplerate * duration),
samplerate=samplerate,
channels=1,
blocking=True)
signal = np.array(predictData).flatten()
#VAD filter
#-------
label = model.predict(samplerate, signal)
if label == 'unknown':
mixer.music.load('fail.mp3')
mixer.music.play()
ti.sleep(3)
print "Recording Voice..."
predictData = sd.rec(int(samplerate * duration),
samplerate=samplerate,
channels=1,
blocking=True)
signal = np.array(predictData).flatten()
label1 = model.predict(samplerate, signal)
#print label1
if label1 == "unknown":
fail = True
mixer.music.load('fail.mp3')
mixer.music.play()
ti.sleep(3)
notifyFailer(labelsound)
if (fail == False):
mixer.music.load('success.mp3')
mixer.music.play()
ti.sleep(30)
#predictData = np.asarray(predictData,dtype="float32")
#write_wav("test.wav",samplerate,signal)
#sf.write("test.wav", signal, samplerate)
print "done"
rawYData = []
e = 0
fail = False
pause = False
voiceMode = True
from pandas import DataFrame, Series
from emitStack import EmitStack
import os
import numpy as np
import sys
sys.path.append(
os.path.join(os.path.dirname(os.path.realpath(__file__)), 'gui'))
from gui.interface import ModelInterface
modelName = "model.out"
model = ModelInterface(
) if not os.path.isfile(modelName) else ModelInterface.load(modelName)
modelDict = {}
train_second = 2
participantDict = {}
bufferDict = {}
stack = EmitStack(2)
train_stack = EmitStack(2)
dataPathDict = {}
room = []
OUTPUTPATH = 'Output/'
def handleAction(action):
if action['type'] == "AUDIO":
label = action['label']
sampleRate = action['sampleRate']
audio = action['audio']
length = action['length']
groupid = action['groupid']
participantid = action['participantid']
if groupid not in modelDict.keys():
modelDict[groupid] = ModelInterface()
if groupid not in bufferDict.keys():
bufferDict[groupid] = EmitStack(train_second)
if groupid not in participantDict.keys():
participantDict[groupid] = {}
if participantid not in participantDict[groupid].keys():
participantDict[groupid][participantid] = label
audioArr = np.empty(length)
for key, value in audio.iteritems():
audioArr[int(key)] = value
bufferDict[groupid].extend(audioArr, length)
if bufferDict[groupid].canEmit(sampleRate):
emitArr = bufferDict[groupid].emitLabel()
modelDict[groupid].enroll(participantid, sampleRate, emitArr)
print audioArr
elif action['type'] == "TRAIN_GROUP":
groupid = action['groupid']
if groupid in bufferDict.keys():
bufferDict[groupid].emitLabel()
if groupid in modelDict.keys():
modelDict[groupid].train()
print "Finish training"
else:
print "No model with group id {0}".format(groupid)
elif action['type'] == "STARTOVER":
groupid = action['groupid']
modelDict.pop(groupid, None)
bufferDict.pop(groupid, None)
participantDict.pop(groupid, None)
print "group id {0} starts over".format(groupid)
elif action['type'] == "FINISH":
groupid = action['groupid']
modelDict.pop(groupid, None)
bufferDict.pop(groupid, None)
participantDict.pop(groupid, None)
dataPathDict.pop(groupid, None)
print "group id {0} finish".format(groupid)
elif action['type'] == "PREDICT":
sampleRate = action['sampleRate']
audio = action['audio']
length = action['length']
groupid = action['groupid']
audioArr = np.empty(length)
for key, value in audio.iteritems():
audioArr[int(key)] = value
predict_label = "N/A"
if groupid in bufferDict.keys() and groupid in modelDict.keys(
) and groupid in participantDict.keys():
bufferDict[groupid].extend(audioArr, length)
if bufferDict[groupid].canEmit(sampleRate):
emitArr = bufferDict[groupid].emitLabel()
voiceThreshold = 0.02
if np.mean(emitArr[emitArr > 0]) > voiceThreshold:
label = modelDict[groupid].predict(sampleRate, emitArr)
emitSecond = stack.emitHeight
if label in participantDict[groupid].keys():
print participantDict[groupid][label]
predict_label = label
emit(
'data', {
'type': 'DATA',
'label': participantDict[groupid][label],
'second': emitSecond
})
else:
print "No one is speaking"
if not "csvFile" in dataPathDict[groupid].keys():
dataPathDict[groupid]["csvFile"] = DataFrame(
columns=('group id', 'time', 'participant id',
'condition', 'meeting', 'date'))
tempDict = dataPathDict[groupid]
tempDict["csvFile"].loc[len(tempDict["csvFile"])] = [
groupid, tempDict['time'], predict_label,
tempDict['condition'], tempDict['meeting'],
tempDict['date']
]
tempDict['time'] = tempDict['time'] + train_second
else:
print "Group {0} is not registerd".format(groupid)
if not "soundFile" in dataPathDict[groupid].keys():
sound_format = Format('wav')
dataPathDict[groupid]["soundFile"] = Sndfile(
dataPathDict[groupid]["soundPath"], 'w', sound_format, 1,
sampleRate)
dataPathDict[groupid]["soundFile"].write_frames(audioArr)
elif action['type'] == "OPEN_MEETING":
groupid = action['groupid']
condition = action['condition']
meeting = action['meeting']
nowString = datetime.now().strftime('%Y%m%d%H%M%S')
nowFormat = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
if not os.path.exists(OUTPUTPATH):
os.mkdir(OUTPUTPATH)
groupPath = os.path.join(OUTPUTPATH, groupid + '/')
if not os.path.exists(groupPath):
os.mkdir(groupPath)
meetingPath = os.path.join(groupPath, meeting + '/')
if not os.path.exists(meetingPath):
os.mkdir(meetingPath)
filename = groupid + '-' + nowString
if groupid not in dataPathDict.keys():
dataPathDict[groupid] = {}
dataPathDict[groupid]['soundPath'] = os.path.join(
meetingPath, filename + '.wav')
dataPathDict[groupid]['csvPath'] = os.path.join(
meetingPath, filename + '.csv')
dataPathDict[groupid]['condition'] = condition
dataPathDict[groupid]['meeting'] = meeting
dataPathDict[groupid]['date'] = nowFormat
dataPathDict[groupid]['time'] = 0
elif action['type'] == "CLOSE_MEETING":
groupid = action['groupid']
dataPathDict[groupid]['soundFile'].close()
dataPathDict[groupid].pop('soundFile', None)
print "Sound file finish recorded"
dataPathDict[groupid]['csvFile'].to_csv(
path_or_buf=dataPathDict[groupid]['csvPath'])
dataPathDict[groupid].pop('csvFile', None)
print "CSV finish recorded"
elif action['type'] == "REGISTER_GROUP":
groupid = action['groupid']
join_room(groupid)
print "{0} is register".format(groupid)
elif action['type'] == "REGISTER":
user = action['user']
room.append(user)
join_room(user)
print "{0} is register".format(user)
elif action['type'] == "LEAVE":
user = action['user']
leave_room(user)
room.remove(user)
print "{0} leaves".format(user)
else:
print "This action is not handled yet"
def task_predict(input_files, input_model):
m = ModelInterface.load(input_model)
for f in glob.glob(input_files):
fs, signal = wavfile.read(f)
label = m.predict(fs, signal)
print f, '->', label
def task_predict(input_files, input_model):
m = ModelInterface.load(input_model)
for f in glob.glob(input_files):
fs, signal = wavfile.read(f)
label = m.predict(fs, signal)
print f, '->', label
from pandas import DataFrame, Series
from emitStack import EmitStack
import os
import numpy as np
import sys
sys.path.append(os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'gui'))
from gui.interface import ModelInterface
modelName = "model.out"
model = ModelInterface() if not os.path.isfile(modelName) else ModelInterface.load(modelName)
modelDict = {}
train_second = 2
participantDict = {}
bufferDict = {}
stack = EmitStack(2)
train_stack = EmitStack(2)
dataPathDict = {}
room = []
OUTPUTPATH = 'Output/'
class Main(QMainWindow):
CONV_INTERVAL = 0.2
CONV_DURATION = 0.5
SMALL_TRESHOLD = 0.6
CONV_FILTER_DURATION = CONV_DURATION
FS = 8000
TEST_DURATION = 3
def google_service_producer(self):
###speech to text
credentials = GoogleCredentials.get_application_default(
).create_scoped(['https://www.googleapis.com/auth/cloud-platform'])
http = httplib2.Http()
credentials.authorize(http)
return discovery.build('speech',
'v1beta1',
http=http,
discoveryServiceUrl=DISCOVERY_URL)
###
#checkbox
def show_checkbox(self):
self.available_to_enroll = QGroupBox("users")
self.vbox = QVBoxLayout()
for user in self.userdata:
print(user)
checkbox = QtGui.QCheckBox(str(user[0]))
checkbox.setGeometry(QtCore.QRect(50, 390, 71, 21))
self.vbox.addWidget(checkbox)
self.available_to_enroll.setLayout(self.vbox)
def __init__(self, parent=None):
QWidget.__init__(self, parent)
uic.loadUi("gui/edytor.ui", self)
self.last_none_start = 0
self.last_switch_user = 0
self.last_enter_none_handler = False
self.last_user_detected = 'None'
self.statusBar()
self.check_result = False
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.AddUser.clicked.connect(self.add_user)
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"gui/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"gui/image/a_hello.png")
self.Alading.setPixmap(self.aladingpic)
self.Alading_conv.setPixmap(self.aladingpic)
#default user image setting
self.avatarname = "gui/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('gui/avatar/')
#quick enroll
self.show_checkbox()
self.checkbox.setWidget(self.available_to_enroll)
self.LoadAll.clicked.connect(self.enroll_checklist)
#Conversation Mode Variables
self.conv_record = np.array([], dtype=NPDtype)
self.time_init = QTimer(self)
self.current_label = None
# 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.generateTranscript.clicked.connect(self.generate_transcript)
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
# def
##check enroll
def enroll_checklist(self):
all_check_box = self.available_to_enroll.findChildren(QtGui.QCheckBox)
for check_box in all_check_box:
# print(str(check_box.text())+" : "+str(check_box.isChecked()))
if (check_box.isChecked()):
self.load_check_box(str(check_box.text()))
def load_check_box(self, label):
fname = "gmms/" + label + ".model"
if fname == None:
self.status("no user")
return
#fname = QFileDialog.getOpenFileName(self, "Open Data File:", "", "")
if fname:
if self.backend == None:
try:
self.backend = ModelInterface()
self.backend.load(fname, label)
except Exception as e:
self.warn(str(e))
else:
try:
self.backend.load(fname, label)
except Exception as e:
self.warn(str(e))
self.status("loaded all")
############ RECORD
def start_record(self):
self.pyaudio = pyaudio.PyAudio()
self.status("Recording...")
self.movie.start()
self.Alading.setPixmap(QPixmap(u"gui/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 stopped")
############## conversation
def start_conv_record(self):
if not (self.check_result):
path = 'speech/'
queue = 'queue/'
files = glob.glob(path + queue + '*.wav')
for file in files:
os.remove(file)
progress = 'progress/'
files = glob.glob(path + progress + '*.wav')
for file in files:
os.remove(file)
done = 'done/'
files = glob.glob(path + done + '*.wav')
for file in files:
os.remove(file)
result = 'result/'
files = glob.glob(path + result + '*.wav')
for file in files:
os.remove(file)
self.conv_result_list = []
self.start_record()
self.recording_id = 0
self.recording_result = 0
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()
self.conv_threading = TranscriptThread(self)
self.conv_threading.start()
self.check_result = True
else:
pass
def stop_conv(self):
self.recording_id = 0
self.stop_record()
self.conv_timer.stop()
self.check_result = False
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)
# if ((label!=self.current_label)&(label!=None)):
# write_wav("try.wav",Main.FS,self.conv_record)
# self.conv_record=np.array([], dtype=NPDtype)
# self.current_label=label
# self.conv_record=np.concatenate((self.conv_record,signal),axis=0)
# print(self.conv_record.shape)
# else:
# self.conv_record=np.concatenate((self.conv_record,signal),axis=0)
except Exception as e:
print(traceback.format_exc())
print(str(e))
global last_label_to_show
label_to_show = label
directory = 'speech/queue/'
if label and len(self.conv_result_list) != 0:
last_label = self.conv_result_list[-1]
if last_label and last_label != label:
label_to_show = last_label_to_show
if (last_label != label) and (last_label != 'None'):
file_name = str(
self.recording_id) + '_' + last_label + '_' + str(
self.last_switch_user / Main.FS) + '_' + str(
self.conv_now_pos / Main.FS) + '.wav'
print(file_name)
write_wav(
directory + file_name, Main.FS,
self.backend.filter(
Main.FS,
np.array(
self.recordData[self.last_switch_user -
segment_len:self.conv_now_pos],
dtype=NPDtype)))
threading.Thread(target=SpeechToText,
args=(
file_name,
'speech',
'queue',
'done',
'progress',
'result',
self.google_service_producer(),
)).start()
self.recording_id += 1
if (last_label != label):
self.last_switch_user = self.conv_now_pos
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"gui/image/a_result.png"))
self.convUserImage.setPixmap(self.get_avatar(label_to_show))
# print to transcript area
# if (last_label!=label) and (label!='None'):
# self.TranscriptArea.append(str(label))
def generate_transcript(self):
fname = QFileDialog.getSaveFileName(self, "Save Transcript Result", "")
if not fname:
return
f = open(fname, 'w')
# print('halo'+self.TranscriptArea.toPlainText())
f.write(self.TranscriptArea.toPlainText())
###### RECOGNIZE
def start_reco_record(self):
self.Alading.setPixmap(QPixmap(u"gui/image/a_hello.png"))
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"gui/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)
new_signal = self.backend.filter(*self.enrollWav)
if (not len(new_signal) == 0):
userindex = self.Userchooser.currentIndex() - 1
if (userindex > -1):
u = self.userdata[userindex]
i = 1
while (os.path.exists('suara/' + u[0] + '/' + str(i) +
'.wav')):
i = i + 1
shutil.copy2('enroll.wav',
'suara/' + u[0] + '/' + str(i) + '.wav')
else:
self.status("Please select user.")
else:
self.status("Input is silent.")
def do_enroll(self):
name = self.Username.text()
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
path = 'suara/' + str(name) + '/'
wavs = glob.glob(path + '*.wav')
for wav in wavs:
fs, signal = read_wav(wav)
new_signal = self.backend.filter(fs, signal)
self.backend.enroll(name, 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):
showing = "times = " + str(t.elapsed())
splash.showMessage(showing)
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))
userindex = self.Userchooser.currentIndex() - 1
if (userindex > -1):
u = self.userdata[userindex]
shutil.copy2(fname, 'gui/avatar/' + u[0] + '.jpg')
else:
self.status("Please select user.")
def loadUsers(self):
with open("gui/avatar/metainfo.txt") as db:
for line in db:
tmp = line.split()
self.userdata.append(tmp)
self.Userchooser.addItem(tmp[0])
newpath = 'suara/' + tmp[0]
if not (os.path.exists(newpath)):
os.makedirs(newpath)
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)
if (os.path.exists('gui/avatar/' + str(user[0]) + '.jpg')):
self.Userimage.setPixmap(
QPixmap('gui/avatar/' + str(user[0]) + '.jpg'))
else:
self.Userimage.setPixmap(QPixmap("gui/image/nouser.jpg"))
def updateUserInfo(self):
userindex = self.Userchooser.currentIndex() - 1
if (userindex > -1):
u = self.userdata[userindex]
u[0] = str(self.Username.displayText())
u[1] = self.Userage.value()
if self.Usersex.currentIndex():
u[2] = 'F'
else:
u[2] = 'M'
with open("gui/avatar/metainfo.txt", "w") as db:
for user in self.userdata:
for i in range(3):
db.write(str(user[i]) + " ")
db.write("\n")
else:
self.status("Please select user.")
def writeuserdata(self):
with open("gui/avatar/metainfo.txt", "w") as db:
for user in self.userdata:
print(len(user))
for i in range(0, len(user)):
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)
# try:
print(self.Username.displayText())
for user, index in zip(self.userdata, range(0, len(self.userdata))):
print(user)
if self.userdata[index][0] == self.Username.displayText():
self.userdata.pop(index)
self.Userchooser.removeItem(index + 1)
self.vbox.removeWidget(index)
break
self.writeuserdata()
# checkbox = QtGui.QCheckBox(str(newuser[0]))
# checkbox.setGeometry(QtCore.QRect(50,390,71,21))
# self.vbox.addWidget(checkbox)
# self.available_to_enroll.setLayout(self.vbox)
self.status('not found user')
return
# self.userdata.remove(self.Username.displayText())
# except:
# self.status('the user is not exist')
def addUserInfo(self):
for user in self.userdata:
if user[0] == str(self.Username.displayText()):
return
if len(self.userdata) < 10:
newuser = []
newuser.append(str(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, 'gui/avatar/' + user[0] + '.jpg')
self.userdata.append(newuser)
self.writeuserdata()
self.Userchooser.addItem(str(self.Username.displayText()))
# checkbox = QtGui.QCheckBox(str(newuser[0]))
# checkbox.setGeometry(QtCore.QRect(50,390,71,21))
# self.vbox.addWidget(checkbox)
# self.available_to_enroll.setLayout(self.vbox)
# self.show_checkbox()
else:
self.status('you cannot add more user')
return
############# 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:", "", "")
fname = "gmms/"
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):
# for user in self.userdata:
# if self.userdata.index(user) == self.Userchooser.currentIndex() - 1:
# file_name = user[0]
# break
userindex = self.Userchooser.currentIndex() - 1
if (userindex > -1):
file_name = self.userdata[userindex][0]
#self.enroll_checklist()
if file_name == None:
self.status("no user")
return
fname = "gmms/" + file_name + ".model"
#fname = QFileDialog.getOpenFileName(self, "Open Data File:", "", "")
if fname:
if self.backend == None:
try:
self.backend = ModelInterface()
self.backend.load(fname, file_name)
except Exception as e:
self.warn(str(e))
else:
try:
self.backend.load(fname, file_name)
except Exception as e:
self.warn(str(e))
self.status("loaded model " + file_name)
else:
self.status("Please select user.")
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 add_user(self):
u = []
u.append(str(self.Username.displayText()))
u.append(self.Userage.value())
if self.Usersex.currentIndex():
u.append('F')
else:
u.append('M')
valid = True
for user in self.userdata:
if (user[0] == u[0]):
valid = False
if ((u[0] != '') & (u[1] != 0)):
if (valid):
with open("gui/avatar/metainfo.txt", "a") as db:
for i in range(3):
db.write(str(u[i]) + " ")
db.write("\n")
newpath = 'suara/' + u[0]
if not (os.path.exists(newpath)):
os.makedirs(newpath)
self.userdata.append(u)
self.Userchooser.addItem(u[0])
else:
self.status("The username has already been taken.")
else:
self.status("Please fill the form.")
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):
fname = 'gui/avatar/' + username + '.jpg'
if (fname != None):
return QPixmap(fname)
else:
return QPixmap(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))
'''
parser.add_argument('-t', '--task',
help='Task to do. Either "enroll" or "predict"',
required=True)
parser.add_argument('-i', '--input',
help='Input Files(to predict) or Directories(to enroll)',
required=True)
parser.add_argument('-m', '--model',
help='Model file to save(in enroll) or use(in predict)',
required=True)
ret = parser.parse_args()
return ret
m_enroll = ModelInterface()
def task_enroll(input_dirs, output_model):
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:
def __init__(self, parent=None):
QWidget.__init__(self, parent)
uic.loadUi("gui/edytor.ui", self)
self.last_none_start = 0
self.last_switch_user = 0
self.last_enter_none_handler = False
self.last_user_detected = 'None'
self.statusBar()
self.check_result = False
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.AddUser.clicked.connect(self.add_user)
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"gui/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"gui/image/a_hello.png")
self.Alading.setPixmap(self.aladingpic)
self.Alading_conv.setPixmap(self.aladingpic)
#default user image setting
self.avatarname = "gui/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('gui/avatar/')
#quick enroll
self.show_checkbox()
self.checkbox.setWidget(self.available_to_enroll)
self.LoadAll.clicked.connect(self.enroll_checklist)
#Conversation Mode Variables
self.conv_record = np.array([], dtype=NPDtype)
self.time_init = QTimer(self)
self.current_label = None
# 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.generateTranscript.clicked.connect(self.generate_transcript)
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
