def Playback(
self
): # similar to Go, but uses data from Load instead of collecting new data
self.Status = True
chunkSize = 8192
windowSize = 3
p = pyaudio.PyAudio()
audioStream = p.open(format=pyaudio.paInt16,
channels=1,
rate=self.fs,
input=True,
frames_per_buffer=chunkSize)
numSamples = len(self.Recording)
self.Formants = np.zeros((100, 5), dtype=np.float32)
self.FormantTime = np.zeros(100, dtype=np.float32)
self.Pitch = np.zeros(100, dtype=np.float32)
self.PitchTime = np.zeros(100, dtype=np.float32)
PitchCount = 0
FormantCount = 0
ax = self.RawPlot.figure.add_subplot(111)
f0ax = self.FundamentalFrequenncyPlot.figure.add_subplot(111)
f0ax.tick_params(axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
f0ax.set_position([0.35, 0.05, 0.6, 0.93])
tractAx = self.VocalTractPlot.figure.add_subplot(111)
tractAx.tick_params(axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
tractAx.set_position([0.35, 0.05, 0.6, 0.93])
tractAx.set_ylabel('Vocal Tract Length (cm)')
tractAx.set_ylim((0, 25))
tractAx.set_xlim((0, 0.8))
formantAx = self.FormantPlot.figure.add_subplot(111)
maxPitchLag = 3
maxVocalLag = 3
ds_rate = 3
c = 34300 # speed of sound in cm/s
Count = 0
t = 0
print('Beginning Playback')
time = np.linspace(0, numSamples / self.fs, numSamples)
try:
start = ti.time()
while t < numSamples - chunkSize and self.Status:
t += chunkSize
data = PyAudioTest.getChunk(chunkSize, audioStream, Random=0)
data = self.Recording[t - chunkSize:t]
data_ds = data[0:chunkSize:ds_rate]
# use yin implementation
data_hamming = data * np.hamming(chunkSize)
df = yin.differenceFunction(data_hamming, chunkSize,
self.fs / 75)
cmndf = yin.cumulativeMeanNormalizedDifferenceFunction(
df, len(df))
f0 = yin.getPitch(cmndf,
self.fs / 500,
self.fs / 75,
harmo_th=0.35)
if f0:
# store ot pitch and time
self.Pitch[PitchCount] = 1.0 * self.fs / f0
self.PitchTime[PitchCount] = 1.0 * (
t - chunkSize / 2) / self.fs
PitchCount += 1
# add space if needed
if PitchCount >= len(self.PitchTime):
self.Pitch = np.concatenate(
(self.Pitch, np.zeros(200, dtype=np.float32)))
self.PitchTime = np.concatenate(
(self.PitchTime, np.zeros(200, dtype=np.float32)))
RecentPitches = []
pitchIDX = PitchCount - 1
while self.PitchTime[pitchIDX] >= 1.0 * (
t - chunkSize /
2) / self.fs - maxPitchLag and pitchIDX >= 0:
RecentPitches.append(self.Pitch[pitchIDX])
pitchIDX -= 1
meanPitch = np.mean(RecentPitches)
if len(RecentPitches) == 1:
stdPitch = 25
else:
stdPitch = np.std(RecentPitches)
f0ax.bar([0], [2.0 * stdPitch],
bottom=[meanPitch - stdPitch])
f0ax.set_ylabel('Fundamental Frequency (Hz)')
f0ax.set_ylim((0, 500))
f0ax.set_xlim((0, 0.8))
self.FundamentalFrequenncyPlot.draw()
# use my terrible gaussian estimation formant finder
formantAx.clear()
formantAx.hold(True)
if f0:
fBins, PSD = sp.signal.periodogram(data_ds,
self.fs / ds_rate)
PSD = 20 * np.log10(PSD)
try:
Formants = FormantFinder.findFormantsLPC(
data_ds, self.fs / ds_rate)
for f in range(len(Formants)):
formantAx.plot([Formants[f], Formants[f]],
[-100, 75],
color='red')
formantAx.plot(fBins, PSD)
formantAx.set_title('Power Spectrum - Formants')
formantAx.set_xlabel('Frequency (Hz)')
formantAx.set_ylabel('Power (dB)')
formantAx.set_ylim((-90, 90))
formantAx.set_xlim((0, 5000))
'''
formantAx.bar(range(len(Formants)), Formants)
formantAx.set_xlabel('Formant number')
formantAx.set_ylabel('Frequency (Hz)')
formantAx.set_title('Formants Frequencies')
formantAx.set_xlim((0, 4.8))
formantAx.set_ylim((0, 5000))
formantAx.set_xticks([0.4, 1.4, 2.4, 3.4, 4.4])
formantAx.set_xticklabels(['F1', 'F2', 'F3', 'F4', 'F5'])
'''
self.FormantPlot.draw()
formantAx.hold(False)
if len(Formants) >= 5:
self.Formants[FormantCount, 0:5] = Formants[0:5]
else:
self.Formants[FormantCount,
0:len(Formants)] = Formants
self.FormantTime[FormantCount] = 1.0 * (
t - chunkSize / 2) / self.fs
FormantCount += 1
# add space if needed
if FormantCount >= len(self.FormantTime):
self.Formants = np.concatenate(
(self.Formants,
np.zeros((200, 5), dtype=np.float32)))
self.FormantTime = np.concatenate(
(self.FormantTime,
np.zeros(200, dtype=np.float32)))
RecentTractLength = []
tractIDX = FormantCount - 1
while self.FormantTime[tractIDX] >= 1.0 * (
t - chunkSize /
2) / self.fs - maxVocalLag and tractIDX >= 0:
RecentTractLength.append(
FormantFinder.getVocalTractLength(
self.Formants[tractIDX, :],
c,
method='lammert'))
tractIDX -= 1
meanTractLength = np.median(RecentTractLength)
if len(RecentTractLength) == 1:
stdTractLength = 2
else:
stdTractLength = np.std(RecentTractLength)
#TractLength = FormantFinder.getVocalTractLength(Formants, c)
tractAx.bar([0], [2 * stdTractLength],
bottom=[meanTractLength - stdTractLength])
#tractAx.bar([0], [TractLength])
tractAx.set_ylabel('Vocal Tract Length (cm)')
tractAx.set_ylim((0, 25))
tractAx.set_xlim((0, 0.8))
self.VocalTractPlot.draw()
except (RuntimeError):
Formants = np.zeros(3)
else:
fBins = np.linspace(0, self.fs / 2, 10)
PSD = np.zeros(10)
Count += 1
if t > windowSize * self.fs and Count % 3 == 0:
ax.plot(time[t - windowSize * self.fs:t],
self.Recording[t - windowSize * self.fs:t])
plt.xlim(t / self.fs - windowSize, t / self.fs + 1)
ax.set_xlabel('Time (s)')
ax.set_ylabel('amplitude')
ax.set_title('Raw Waveform')
self.RawPlot.draw()
QtCore.QCoreApplication.processEvents()
except (KeyboardInterrupt, SystemExit):
self.FormantPlot.draw()
self.RawPlot.draw()
self.FundamentalFrequenncyPlot.draw()
self.Pitch = self.Pitch[0:PitchCount]
self.PitchTime = self.PitchTime[0:PitchCount]
self.Formants = self.Formants[0:FormantCount, :]
self.FormantTime = self.FormantTime[0:FormantCount]
print('Recording Completed')
print('recorded time is')
print(1.0 * t / self.fs)
print('elapsed time is:')
print(ti.time() - start)
return True
self.Pitch = self.Pitch[0:PitchCount]
self.PitchTime = self.PitchTime[0:PitchCount]
self.Formants = self.Formants[0:FormantCount, :]
self.FormantTime = self.FormantTime[0:FormantCount]
print('Recording Completed')
print('recorded time is')
print(1.0 * t / self.fs)
print('elapsed time is:')
print(ti.time() - start)
def main():
# CONSTANTS
global min_peak_threshold
min_peak_threshold = 3000
global brightness
brightness = 200
global low_bright
low_bright = 75
global chill_threshold
chill_threshold = .25
global CHUNK
CHUNK = 2**11
global RATE
RATE = 44100
global basePath
basePath = r"http://192.168.1.135/api/xREOsUlYetInkIHuxDldgzqJYLZySU6xDIaobRsx/"
global lightArray
lightArray = [6, 1, 3, 2, 4]
#initalize audio stream
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16, channels=1, rate=RATE, input=True, frames_per_buffer=CHUNK)
#initialize peak variables
max_peak = 0
max_peak_time = datetime.datetime.now()
oldoldpeak = 0
oldpeak = 0
peak = 0
lastTriggered = datetime.datetime.now()
max_buffer_counter = 0
while True:
percent_string=""
data = np.fromstring(stream.read(CHUNK), dtype=np.int16)
peak = np.average(np.abs(data))*2
#max peak handling
if (peak >= max_peak) or ((datetime.datetime.now() - max_peak_time) > datetime.timedelta(seconds=30)):
if max_buffer_counter > 2:
max_peak = peak
max_peak_time = datetime.datetime.now()
min_peak_threshold = max_peak/10
max_buffer_counter = 0
print(" NEWMAX")
else:
max_buffer_counter += 1
currentTrigger = datetime.datetime.now()
#check for huge spike
if (oldpeak + min_peak_threshold * 5) < peak or (oldoldpeak + min_peak_threshold * 5) < peak:
if ((currentTrigger - lastTriggered) > datetime.timedelta(seconds=chill_threshold)):
lastTriggered = currentTrigger
pulseAll()
percent_string=" 50%"
else:
print("Chill")
#check for medium spike
elif (oldpeak + min_peak_threshold * 3) < peak or (oldoldpeak + min_peak_threshold * 3) < peak:
if ((currentTrigger - lastTriggered) > datetime.timedelta(seconds=chill_threshold)):
lastTriggered = currentTrigger
pulseEnds()
percent_string=" 30%"
else:
print("Chill")
#check for small spike
elif (oldpeak + min_peak_threshold) < peak or (oldoldpeak+min_peak_threshold) < peak:
if ((currentTrigger - lastTriggered) > datetime.timedelta(seconds=chill_threshold)):
lastTriggered = currentTrigger
pulseOne()
percent_string=" 10%"
else:
print("Chill")
#no spike
else:
percent_string=" 0%"
#render wavform
bars = "#" * int(200 * peak / 2**16)
print("MAX:%05d Peak:%05d Delta:%s %s" % (max_peak, peak, percent_string, bars))
oldoldpeak = oldpeak
oldpeak = peak
time.sleep(.1)
stream.stop_stream()
stream.close()
p.terminate()
def Run(C, R, mic, Plot):
CHUNK = 44100 # number of data points to read at a time 4096
CHUNK = C
# 4096 byte
# the number of frames
RATE = 44100 # 176400 # time resolution for reading device (Hz) 44100 samples/second
RATE = R
# sampling rate i.e the number of frames per second
serSignal = 'S'
KnockSignal = 'K'
Input_Device_Index = 2
Input_Device_Index = mic
plot = Plot
# Define the serial port
ser_port = "COM8" # for window computer, int must be used COM1 = 0,COM2=1 ...
baud_rate = 9600
count = 0
flag = False
signal = False
mlab = Matlab(executable=r"D:\MATLAB\bin\matlab.exe")
mlab.start()
p = pyaudio.PyAudio()
# while True:
# ser.write(serSignal.encode('utf-8'))
# if ser.readline().decode('utf-8') != "Spray":
# break
stream = p.open(format=pyaudio.paInt16,
channels=1,
rate=RATE,
input=True,
input_device_index=None,
frames_per_buffer=CHUNK)
ser = serial.Serial(ser_port, baud_rate)
print(ser.readline().decode("utf-8"))
print("Input delay is %f" % stream.get_input_latency())
while (True):
for i in range(int(3)): #only loop forA int(??) times
#if(count>1):
# sleep(1)
if (count == 1):
ser.write(KnockSignal.encode(
"utf-8")) # encode is used for string.encode()
sleep(.32) # **change here (0.1s per 5000samples)
flag = True
print("Must Knock Here")
# The input device id "2" => built-in microphone
# info = p.get_host_api_info_by_index(0)
# numdevices = info.get('deviceCount')
# for i in range(0, numdevices):
# if (p.get_device_info_by_host_api_device_index(0, i).get('maxInputChannels')) > 0:
# pass
#print('Input Device id', i, '-', p.get_device_info_by_host_api_device_index(0, i).get('name'))
# get the default device info
#print(p.get_default_input_device_info())
# create a numpy array holding a single read of audio data
#now = datetime.now()
if flag == True:
# if count ==1:
# sleep(.5)
np.set_printoptions(threshold=sys.maxsize)
data = np.fromstring(stream.read(CHUNK), dtype=np.short)
#print(stream)
time = np.arange(0, CHUNK)
#peak=np.average(np.abs(data))*21
#bars="#"*int(50*peak/2**16)
#print("%04d %s"%(i,data))
#print("%s %s" % (data/32768,now ))
#print("Input data is ", type(data))
# Test Matlab data 1
#res = mlab.run_func('jk.m', {'arg1': data})
#print("Output data is ", type(res['result']))
#data1 = res['result'] # The data in matlab is float64 (e.g for 64bit window) https://stackoverflow.com/questions/8855574/convert-ndarray-from-float64-to-integer
#M_data1 = data1[0] / 32768
#print("jk.m is",res)
# data1 = np.array(res['result'], dtype=np.float64).astype(np.int64)
# print(type(data1))
#Write data to text file before matlab
# with open("SignalTest1.txt", "wt") as file:
# file.write("%s" % (str(M_data1).lstrip('[').rstrip(']')))
# file.flush()
# file.close()
# # file.writelines("%s %04d %s\n"%(now,i,data))
# # close the stream gracefully
# max_val =np.amax(data)
# print(max_val)
# if max_val >30000:
#data/32768
#print(M_data1)
if count == 1:
print("Write")
with open("SignalTest.txt", "wt") as out_file:
out_file.writelines(
str(data)) #it can only write string
if plot == True and count == 2:
past = stream.get_time()
np.set_printoptions(threshold=sys.maxsize)
data = np.fromstring(stream.read(CHUNK), dtype=np.short)
present = stream.get_time()
delay = present - past
print("The delay is %f" % delay)
plt.title('AudioSample')
plt.plot(time, data)
plt.ylim(-40000, 40000)
plt.ylabel('Amplitude')
plt.xlabel('Sample Size')
#plt.pause(.0000000000000000000000000000000000000000000000000000000001)
#plt.clf()
#print(stream.get_time())
dataprocess = mlab.run_func(
'final_judge.m', {"arg1": data}) # ,{'arg1':data}
# print("The input data is ",M_data1)
print(np.amax(data))
print(dataprocess['result'])
d1 = dataprocess['result']
if d1 == 1:
ser.write(serSignal.encode(
"utf-8")) # encode is used for string.encode()
# print(ser.write(serSignal.encode("utf-8")))
#print(ser.readline().decode("utf-8"))
#d1 = 2
plt.show()
flag = False
count = 0
count += 1
#ser.reset_output_buffer()
mlab.stop()
out_file.close()
stream.stop_stream()
stream.close()
p.terminate()
sys.exit(0)
def play_rec(self,
out_file_name,
recode_second,
device_name='ReSpeaker 4 Mic Array (UAC1.0)',
CHUNK=1024,
input_file_name='./test_out.wav',
need_data=False,
order_index=None,
order_ch=None):
# file_name = '../_exp/Speaker_Sound/up_tsp_1num.wav'
wf = wave.open(out_file_name, 'rb')
sampling = wf.getframerate()
if order_index is not None:
index = order_index
channels = order_ch
else:
index, channels = self.get_index(device_name)
p = pyaudio.PyAudio()
stream1 = p.open(
format=pyaudio.paInt16,
channels=channels,
rate=sampling,
frames_per_buffer=CHUNK,
input=True,
input_device_index=index,
)
stream2 = p.open(format=pyaudio.paInt16,
channels=1,
rate=sampling,
frames_per_buffer=CHUNK,
output=True)
if sampling * recode_second < wf.getnframes():
print('Error recode time is not enough',
wf.getnframes() / sampling)
sys.exit()
elif sampling * recode_second > wf.getnframes() * 2:
print('Error recode time is too long')
sys.exit()
else:
out_data = wf.readframes(CHUNK)
in_data = stream1.read(CHUNK)
recoding_data = [in_data]
for i in range(0, int(sampling / CHUNK * recode_second)):
input_data = stream1.read(CHUNK)
recoding_data.append(input_data)
if out_data != b'':
stream2.write(out_data)
out_data = wf.readframes(CHUNK)
recoded_data = b''.join(recoding_data)
# print(type(recoded_data))
self.wave_save(recoded_data,
channels=channels,
sampling=sampling,
wave_file=input_file_name)
stream1.stop_stream()
stream2.stop_stream()
stream1.close()
stream2.close()
p.terminate()
if need_data:
# print('use data return data', np.frombuffer(np.array(recoding_data), dtype='int16').shape)
recoded_input_data = np.array(np.frombuffer(np.array(recoding_data), dtype='int16'))\
.reshape((channels, -1), order='F')
return recoded_input_data, sampling
#2019h1030124h
import pyaudio
import wave
# output file name
name = "recordedAud.mp3"
chunk = 1024
# sample format
FORMAT = pyaudio.paInt16
channels = 1
# 44100 samples per second
sample_rate = 44100
record_seconds = 10
# initialize PyAudio object
obj = pyaudio.PyAudio()
# open stream object as input & output
stream = obj.open(format=FORMAT,
channels=channels,
rate=sample_rate,
input=True,
output=True,
frames_per_buffer=chunk)
frames = []
print("Recording audio...")
for i in range(int(44100 / chunk * record_seconds)):
data = stream.read(chunk)
frames.append(data)
print("Finished recording.")
stream.stop_stream()
def recognize(self, args, userin, user_full_name, user_prefix):
with noalsaerr():
p = pyaudio.PyAudio() # Create a PyAudio session
# Create a stream
stream = p.open(
format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
output=True,
frames_per_buffer=CHUNK)
try:
data = stream.read(
CHUNK) # Get first data frame from the microphone
# Loop over the frames of the audio / data chunks
while data != '':
rms = audioop.rms(
data, 2) # Calculate Root Mean Square of current chunk
if rms >= THRESHOLD: # If Root Mean Square value is greater than THRESHOLD constant
self.decoder_pipeline.init_request(
"recognize",
"audio/x-raw, layout=(string)interleaved, rate=(int)16000, format=(string)S16LE, channels=(int)1"
)
self.decoder_pipeline.process_data(data)
silence_counter = 0 # Define silence counter
# While silence counter value less than SILENCE_DETECTION constant
while silence_counter < SILENCE_DETECTION:
data = stream.read(
CHUNK) # Read a new chunk from the stream
if LISTENING:
stream.write(data, CHUNK)
self.decoder_pipeline.process_data(data)
rms = audioop.rms(
data, 2
) # Calculate Root Mean Square of current chunk again
if rms < THRESHOLD: # If Root Mean Square value is less than THRESHOLD constant
silence_counter += 1 # Then increase silence counter
else: # Else
silence_counter = 0 # Assign zero value to silence counter
stream.stop_stream()
self.decoder_pipeline.end_request()
while not self.finished:
time.sleep(0.1)
stream.start_stream()
words = self.words
words = [x for x in words if x != '<#s>']
com = ' '.join(words)
her = VirtualAssistant(args, userin, user_full_name, user_prefix)
t = Thread(target=her.command, args=(com,))
t.start()
self.reset()
data = stream.read(CHUNK) # Read a new chunk from the stream
if LISTENING:
stream.write(data, CHUNK)
except KeyboardInterrupt:
stream.stop_stream()
stream.close()
p.terminate()
self.loop.quit()
raise KeyboardInterrupt
def __init__(self):
self.p = pyaudio.PyAudio()
self.stream = self.p.open(
format=pyaudio.paInt16, channels=1, rate=44100, input=True, frames_per_buffer=1024)
self.bitlist = [0]
def start(self,
detected_callback=play_audio_file,
interrupt_check=lambda: False,
sleep_time=0.03):
"""
Start the voice detector. For every `sleep_time` second it checks the
audio buffer for triggering keywords. If detected, then call
corresponding function in `detected_callback`, which can be a single
function (single model) or a list of callback functions (multiple
models). Every loop it also calls `interrupt_check` -- if it returns
True, then breaks from the loop and return.
:param detected_callback: a function or list of functions. The number of
items must match the number of models in
`decoder_model`.
:param interrupt_check: a function that returns True if the main loop
needs to stop.
:param float sleep_time: how much time in second every loop waits.
:return: None
"""
self.audio = pyaudio.PyAudio()
self.stream_in = self.audio.open(
input=True,
output=False,
format=self.audio.get_format_from_width(
self.detector.BitsPerSample() / 8),
channels=self.detector.NumChannels(),
rate=self.detector.SampleRate(),
frames_per_buffer=2048,
stream_callback=audio_callback)
if interrupt_check():
logger.debug("detect voice return")
return
tc = type(detected_callback)
if tc is not list:
detected_callback = [detected_callback]
if len(detected_callback) == 1 and self.num_hotwords > 1:
detected_callback *= self.num_hotwords
assert self.num_hotwords == len(detected_callback), \
"Error: hotwords in your models (%d) do not match the number of " \
"callbacks (%d)" % (self.num_hotwords, len(detected_callback))
logger.debug("detecting...")
while True:
if interrupt_check():
logger.debug("detect voice break")
break
data = self.ring_buffer.get()
if len(data) == 0:
time.sleep(sleep_time)
continue
ans = self.detector.RunDetection(data)
if ans == -1:
logger.warning(
"Error initializing streams or reading audio data")
elif ans > 0:
message = "Keyword " + str(ans) + " detected at time: "
message += time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime(time.time()))
logger.info(message)
callback = detected_callback[ans - 1]
if callback is not None:
callback()
logger.debug("finished.")
def listen():
# 変数
status = Status.WAIT
# SYN判定用のハミング符号分を含めた直近16ビットのバッファ
recent_bin_data = np.zeros(16, dtype=np.int8)
# # 検波したバイナリデータ
# bin_data = np.empty(0).astype(np.int8)
# メッセージ本文の2進数データ
input_bin_data = np.empty(0).astype(np.int8)
# PyAudioの初期化処理
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16,
channels=1,
rate=common.SR,
frames_per_buffer=CHUNK,
input=True)
# マイクから入力値を受け付ける
while stream.is_active():
raw_data = stream.read(CHUNK)
data = np.frombuffer(raw_data, dtype=np.int16) / np.iinfo(np.int16).max
signal = demodulation(data)
recent_bin_data = np.roll(recent_bin_data, -1)
recent_bin_data[-1] = signal
print("chunk", recent_bin_data)
# 待ち状態の場合
if status == Status.WAIT:
status_text.set("wait")
# 直近のデータがSYNコードと一致するか判定
if check_syn(recent_bin_data):
# SYNコードの場合入力受付状態に変更
status = Status.READY
# 入力受付状態の場合
elif status == Status.READY:
status_text.set("ready")
input_bin_data = np.r_[input_bin_data, signal]
print("input bin data", input_bin_data)
# 信号の受信に失敗した場合は状態を戻す
if np.all(recent_bin_data == -1):
button.configure(state=tk.NORMAL)
status_text.set("error")
break
# SYNコードか判定
if len(input_bin_data) % 16 == 0 and check_syn(recent_bin_data):
# 直近8ビットがsynコードなので入力データから除外
input_bin_data = input_bin_data[:-16]
# 成功処理
correct_data = correct_hamming_code(input_bin_data)
message = decode(correct_data)
end(message)
break
# PyAudioの終了処理
stream.stop_stream()
stream.close()
p.terminate()
def __init__(self, language_code='en-US', last_contexts=None):
"""Initialize all params and load data"""
""" Constants and params """
self.CHUNK = 4096
self.FORMAT = pyaudio.paInt16
self.CHANNELS = 1
self.RATE = 16000
self.USE_AUDIO_SERVER = rospy.get_param('/dialogflow_client/use_audio_server', False)
self.PLAY_AUDIO = rospy.get_param('/dialogflow_client/play_audio', True)
self.DEBUG = rospy.get_param('/dialogflow_client/debug', False)
# Register Ctrl-C sigint
signal.signal(signal.SIGINT, self._signal_handler)
""" Dialogflow setup """
# Get hints/clues
rp = rospkg.RosPack()
file_dir = rp.get_path('dialogflow_ros') + '/config/context.yaml'
with open(file_dir, 'r') as f:
try:
self.phrase_hints = load(f)
except YAMLError:
rospy.logwarn("DF_CLIENT: Unable to open phrase hints yaml file!")
self.phrase_hints = []
# Dialogflow params
project_id = rospy.get_param('/dialogflow_client/project_id', 'my-project-id')
session_id = str(uuid4()) # Random
self._language_code = language_code
self.last_contexts = last_contexts if last_contexts else []
# DF Audio Setup
audio_encoding = AudioEncoding.AUDIO_ENCODING_LINEAR_16
# Possibel models: video, phone_call, command_and_search, default
self._audio_config = InputAudioConfig(audio_encoding=audio_encoding,
language_code=self._language_code,
sample_rate_hertz=self.RATE,
phrase_hints=self.phrase_hints,
model='command_and_search')
self._output_audio_config = OutputAudioConfig(
audio_encoding=OutputAudioEncoding.OUTPUT_AUDIO_ENCODING_LINEAR_16
)
# Create a session
self._session_cli = dialogflow_v2beta1.SessionsClient()
self._session = self._session_cli.session_path(project_id, session_id)
rospy.logdebug("DF_CLIENT: Session Path: {}".format(self._session))
""" ROS Setup """
results_topic = rospy.get_param('/dialogflow_client/results_topic',
'/dialogflow_client/results')
requests_topic = rospy.get_param('/dialogflow_client/requests_topic',
'/dialogflow_client/requests')
text_req_topic = requests_topic + '/string_msg'
text_event_topic = requests_topic + '/string_event'
msg_req_topic = requests_topic + '/df_msg'
event_req_topic = requests_topic + '/df_event'
self._results_pub = rospy.Publisher(results_topic, DialogflowResult,
queue_size=10)
rospy.Subscriber(text_req_topic, String, self._text_request_cb)
rospy.Subscriber(text_event_topic, String, self._text_event_cb)
rospy.Subscriber(msg_req_topic, DialogflowRequest, self._msg_request_cb)
rospy.Subscriber(event_req_topic, DialogflowEvent, self._event_request_cb)
""" Audio setup """
# Mic stream input setup
self.audio = pyaudio.PyAudio()
self._server_name = rospy.get_param('/dialogflow_client/server_name',
'127.0.0.1')
self._port = rospy.get_param('/dialogflow_client/port', 4444)
if self.PLAY_AUDIO:
self._create_audio_output()
rospy.logdebug("DF_CLIENT: Last Contexts: {}".format(self.last_contexts))
rospy.loginfo("DF_CLIENT: Ready!")
def __init__(self, API_KEY, URL, enviornment_id, collection_id,
NLU_API_KEY, NLU_URL, ASSISTANT_API_KEY, ASSISTANT_URL,
ASSISSTANT_ID, S2T_KEY, S2T_URL, SMMY_API_KEY):
'''
Initialize a hindsight chatbot
:param API_KEY: IBM Watson Discovery API Key
:param URL: IBM Watson Discovery base url
:param enviornment_id: IBM Enviornment id
:param collection_id: IBM document collection id
:return:
'''
self.chat_states = {'add_mode': 1, 'ask_mode': 2}
self.speech_mode_enabled = False
self.intents = {
'show_notes': 1,
'summarize_notes': 2,
'sentiment_notes': 3
}
self.state = self.chat_states['add_mode']
self.prompt = '>>> '
self.chatprompt = '\t~~~ '
self.state_prompt = 'Add a note: '
self.discovery = DiscoveryV1(version='2018-12-03',
iam_apikey=API_KEY,
url=URL)
self.nlu = NaturalLanguageUnderstandingV1(version='2018-11-16',
iam_apikey=NLU_API_KEY,
url=NLU_URL)
self.assistant = AssistantV2(version='2018-11-08',
iam_apikey=ASSISTANT_API_KEY,
url=ASSISTANT_URL)
self.session_id = self.assistant.create_session(
assistant_id=ASSISSTANT_ID).get_result()['session_id']
self.enviornment_id = enviornment_id
self.collection_id = collection_id
self.assistant_id = ASSISSTANT_ID
self.ROOT_PATH = sys.path[0]
self.METADATA_PATH = self.ROOT_PATH + '/notes_metadata'
if not os.path.exists(self.METADATA_PATH):
os.makedirs(self.METADATA_PATH)
self.GLOBAL_ENTITIES = self.ROOT_PATH + '/notes_metadata/global_entities.p'
if not os.path.exists(self.GLOBAL_ENTITIES):
t = {'NULL': 0}
pickle.dump(t, open(self.GLOBAL_ENTITIES, "wb"))
self.GLOBAL_DOC_IDS = self.ROOT_PATH + '/notes_metadata/global_doc_ids.p'
if not os.path.exists(self.GLOBAL_DOC_IDS):
t = {'NULL': '/'}
pickle.dump(t, open(self.GLOBAL_DOC_IDS, "wb"))
self.NOTES_PATH = self.ROOT_PATH + '/notes.html'
if not os.path.exists(self.NOTES_PATH):
os.makedirs(self.NOTES_PATH)
self.INTENT_LINES = []
if not os.path.exists(self.ROOT_PATH + '/intent_training_data.csv'):
print('!!! ERROR: ./scripts/intent_training_data.csv required')
quit()
lines = open(self.ROOT_PATH + '/intent_training_data.csv').readlines()
self.INTENT_LINES = [l.strip().split(',')[0] for l in lines]
self.S2T_KEY = S2T_KEY
self.S2T_URL = S2T_URL
self.pyAudio = pyaudio.PyAudio()
self.SMMY_API_KEY = SMMY_API_KEY
def recognize(self, args, userin, user_full_name, user_prefix):
with noalsaerr():
p = pyaudio.PyAudio() # Create a PyAudio session
# Create a stream
stream = p.open(
format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
output=True,
frames_per_buffer=CHUNK)
try:
data = stream.read(CHUNK) # Get first data frame from the microphone
# Loop over the frames of the audio / data chunks
audio = None
# print("START LISTENNING")
while data != '':
rms = audioop.rms(data, 2) # Calculate Root Mean Square of current chunk
if rms >= THRESHOLD: # If Root Mean Square value is greater than THRESHOLD constant
audio = data
silence_counter = 0 # Define silence counter
# While silence counter value less than SILENCE_DETECTION constant
while silence_counter < SILENCE_DETECTION:
data = stream.read(CHUNK) # Read a new chunk from the stream
if LISTENING:
stream.write(data, CHUNK)
audio = audio + data
rms = audioop.rms(data, 2) # Calculate Root Mean Square of current chunk again
if rms < THRESHOLD: # If Root Mean Square value is less than THRESHOLD constant
silence_counter += 1 # Then increase silence counter
else: # Else
silence_counter = 0 # Assign zero value to silence counter
# print("Analyzing...")
stream.stop_stream()
audio_data = sr.AudioData(audio, RATE, p.get_sample_size(FORMAT))
try:
com = self.recognizer.recognize_google(audio_data)
print(com)
her = VirtualAssistant(args, userin, user_full_name, user_prefix)
t = Thread(target=her.command, args=(com,))
t.start()
except sr.UnknownValueError:
# print("Google Speech Recognition could not understand audio")
pass
except sr.RequestError as e:
print("Could not request results from Google Speech Recognition service; {0}".format(e))
stream.start_stream()
self.reset()
data = stream.read(CHUNK) # Read a new chunk from the stream
if LISTENING:
stream.write(data, CHUNK)
except KeyboardInterrupt:
stream.stop_stream()
stream.close()
p.terminate()
# self.loop.quit()
raise KeyboardInterrupt
def recognize():
# Voice Authentication
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
CHUNK = 1024
RECORD_SECONDS = 4
FILENAME = "./test.wav"
audio = pyaudio.PyAudio()
# start Recording
stream = audio.open(format=FORMAT, channels=CHANNELS,
rate=RATE, input=True,
frames_per_buffer=CHUNK)
time.sleep(2.0)
print("recording...")
frames = []
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
data = stream.read(CHUNK)
frames.append(data)
print("finished recording")
# stop Recording
stream.stop_stream()
stream.close()
audio.terminate()
# saving wav file
waveFile = wave.open(FILENAME, 'wb')
waveFile.setnchannels(CHANNELS)
waveFile.setsampwidth(audio.get_sample_size(FORMAT))
waveFile.setframerate(RATE)
waveFile.writeframes(b''.join(frames))
waveFile.close()
modelpath = "./gmm_models/"
gmm_files = [os.path.join(modelpath,fname) for fname in
os.listdir(modelpath) if fname.endswith('.gmm')]
models = [pickle.load(open(fname,'rb')) for fname in gmm_files]
speakers = [fname.split("/")[-1].split(".gmm")[0] for fname
in gmm_files]
if len(models) == 0:
print("No Users in the Database!")
return
#read test file
sr,audio = read(FILENAME)
# extract mfcc features
vector = extract_features(audio,sr)
log_likelihood = np.zeros(len(models))
#checking with each model one by one
for i in range(len(models)):
gmm = models[i]
scores = np.array(gmm.score(vector))
log_likelihood[i] = scores.sum()
pred = np.argmax(log_likelihood)
identity = speakers[pred]
# if voice not recognized than terminate the process
if identity == 'unknown':
print("Not Recognized! Try again...")
return
print( "Recognized as - ", identity)
# face recognition
print("Keep Your face infront of the camera")
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)
cascade = cv2.CascadeClassifier('./haarcascades/haarcascade_frontalface_default.xml')
#loading the database
database = pickle.load(open('face_database/embeddings.pickle', "rb"))
time.sleep(1.0)
start_time = time.time()
while True:
curr_time = time.time()
_, frame = cap.read()
frame = cv2.flip(frame, 1, 0)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face = cascade.detectMultiScale(gray, 1.3, 5)
name = 'unknown'
if len(face) == 1:
for (x, y, w, h) in face:
roi = frame[y-10:y+h+10, x-10:x+w+10]
fh, fw = roi.shape[:2]
min_dist = 100
#make sure the face is of required height and width
if fh < 20 and fh < 20:
continue
#resizing image as required by the model
img = cv2.resize(roi, (96, 96))
#128 d encodings from pre-trained model
encoding = img_to_encoding(img)
# loop over all the recorded encodings in database
for knownName in database:
# find the similarity between the input encodings and recorded encodings in database using L2 norm
dist = np.linalg.norm(np.subtract(database[knownName], encoding) )
# check if minimum distance or not
if dist < min_dist:
min_dist = dist
name = knownName
# if min dist is less then threshold value and face and voice matched than unlock the door
if min_dist <= 0.4 and name == identity:
print ("Door Unlocked! Welcome " + str(name))
break
#open the cam for 3 seconds
if curr_time - start_time >= 3:
break
cv2.waitKey(1)
cv2.imshow('frame', frame)
cap.release()
cv2.destroyAllWindows()
if len(face) == 0:
print('There was no face found in the frame. Try again...')
elif len(face) > 1:
print("More than one faces found. Try again...")
elif min_dist > 0.4 or name != identity:
print("Not Recognized! Try again...")
port = 23333
addr = "239.192.0.233"
buf_size = 65536
pygame.init()
pygame.camera.init()
size = (128, 96)
cam = pygame.camera.Camera("/dev/video0", size)
NUM_SAMPLES = 2000
framerate = 8000
channels = 1
sampwidth = 2
sleep_time = 0.25
pin = pyaudio.PyAudio()
streamin = pin.open(format=pyaudio.paInt16,
channels=1,
rate=framerate,
input=True,
frames_per_buffer=NUM_SAMPLES)
pout = pyaudio.PyAudio()
streamout = pout.open(format=pyaudio.paInt16,
channels=1,
rate=framerate,
output=True)
TYPE = 3
def init():
def find_input_devices():
pa = pyaudio.PyAudio()
for i in range(pa.get_device_count()):
devinfo = pa.get_device_info_by_index(i)
print("Device %d: %s" % (i, devinfo["name"]))
def __init__(self, freq):
self.audio = pyaudio.PyAudio()
self.freq = freq
self.stream = audio.get_stream(self.audio, output=True)
self.ping_buffer = make_buffer_from_bit_pattern(
self.bitstream, self.freq, 0)
CHANNELS = 1
CHUNK_SIZE = int(FRAME_LEN * SAMPLE_RATE)
asr = FrameASR(model_definition={
'sample_rate': SAMPLE_RATE,
'AudioToMelSpectrogramPreprocessor': cfg.preprocessor.params,
'JasperEncoder': cfg.encoder.params,
'labels': cfg.decoder.params.vocabulary
},
frame_len=FRAME_LEN,
frame_overlap=2,
offset=4)
asr.reset()
p = pa.PyAudio()
#print('Available audio input devices:')
input_devices = []
for i in range(p.get_device_count()):
dev = p.get_device_info_by_index(i)
if dev.get('maxInputChannels'):
input_devices.append(i)
#print(i, dev.get('name'))
if len(input_devices):
dev_idx = -2
while dev_idx not in input_devices:
# print('Please type input device ID:')
dev_idx = 3 # 3 for virtal cable
empty_counter = 0
def main():
if len(sys.argv) != 4:
print("Error!!!!")
exit()
ip = sys.argv[1] # Server's ip ---argv:argumento
port = sys.argv[2] # Server's port
identity = sys.argv[3].encode('ascii')
connected = False
context = zmq.Context()
s = context.socket(zmq.DEALER)
s.identity = identity
s.connect("tcp://{}:{}".format(
ip,
port)) #corchetes por que se enviaron los parametros el ip y el port
print("Started client with id {}".format(identity))
poller = zmq.Poller()
poller.register(sys.stdin, zmq.POLLIN)
poller.register(s, zmq.POLLIN)
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
CHUNK = 1024
RECORD_SECONDS = 0.3
p = pyaudio.PyAudio()
first = True
global queue
print("\n----Menu----")
print("- 'bring' {id de usuario} ......... Invitar a sesion (sin llaves)")
print("- 'exit' ......... Salir del programa")
threads = []
while True:
socks = dict(poller.poll())
if s in socks:
op, *msg = s.recv_multipart()
if op.decode() == "connect":
connected = True
elif op.decode() == "play":
#RECIBIENDO FRAMES
if msg[0] in queue:
queue[msg[0]].append(msg[1:])
else:
queue[msg[0]] = []
queue[msg[0]].append(msg[1:])
threads.append(
threading.Thread(target=play, args=(msg[0], )))
threads[-1].start()
if sys.stdin.fileno() in socks:
command = input()
command = command.split()
if command[0] == "bring":
s.send_multipart(
[bytes(command[0], 'ascii'),
bytes(command[1], 'ascii')])
connected = True
elif command[0] == "exit":
s.send_multipart(
[bytes(command[0], 'ascii'),
bytes("NA", 'ascii')])
break
else:
print(' Operacion no soportada')
if connected:
if first:
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
output=False,
frames_per_buffer=CHUNK)
first = False
frames = [bytes('send', 'ascii')]
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
frames.append(stream.read(CHUNK))
#ENVIANDO FRAMES
s.send_multipart(frames)
p.terminate()
stream.stop_stream()
stream.close()
def main():
mac_addr = open('/sys/class/net/wlan0/address').readline()
# Connect to db
con = MySQLdb.Connection(host=HOST,
port=PORT,
user=USER,
passwd=PASSWORD,
db=DB)
c = con.cursor()
c.execute(
'''CREATE TABLE IF NOT EXISTS zeroData(temp FLOAT, pres FLOAT, hum FLOAT, gas FLOAT, lux INTEGER, db FLOAT, dt DATETIME)'''
)
# Initialize db
parser = argparse.ArgumentParser()
parser.add_argument("db", help="zeroData")
parser.add_argument("token", help="35d4aa441b94cdbae7404050edd3fad6")
args = parser.parse_args()
corlysis_params = {
"db": args.db,
"u": "token",
"p": args.token,
"precision": "ms"
}
# Initialize sensor
bme = bme680.BME680(i2c_addr=0x77)
bme.set_humidity_oversample(bme680.OS_2X)
bme.set_pressure_oversample(bme680.OS_4X)
bme.set_temperature_oversample(bme680.OS_8X)
bme.set_filter(bme680.FILTER_SIZE_3)
bme.set_gas_status(bme680.ENABLE_GAS_MEAS)
# Initialize USB mic
pyaud = pyaudio.PyAudio()
stream = pyaud.open(format=pyaudio.paInt16,
channels=1,
rate=32000,
input_device_index=2,
input=True)
payload = ""
counter = 1
problem_counter = 0
now = time.strftime('%Y-%m-%d %H:%M:%S')
print("Readings began " + now)
print("Press ctrl+c to end readings and close connection.")
animation = "|/-\\"
aniCount = 0
# Main loop
while (True):
try:
# Get time for corlysis and db
unix_time_ms = int(time.time() * 1000)
now = time.strftime('%Y-%m-%d %H:%M:%S')
# Read from BME
bme.get_sensor_data()
tempCelcius = float("{0:.2f}".format(bme.data.temperature))
# Convert the above variable to fahrenheit
temperature = float(tempCelcius * (9 / 5) + 32)
pressure = float("{0:.2f}".format(bme.data.pressure))
humidity = float("{0:.2f}".format(bme.data.humidity))
gas = float("{0:.2f}".format(bme.data.gas_resistance))
# Read from lux sensor
tsl = TSL2561(debug=True)
luxVal = tsl.lux()
# Read from USB mic
rawsamps = stream.read(2048, exception_on_overflow=False)
samps = numpy.fromstring(rawsamps, dtype=numpy.int16)
deciVal = analyse.loudness(samps) + 65
line = "sensors_data temperature={},pressure={},humidity={},luxVal={},decib={} {}\n".format(
temperature, pressure, humidity, luxVal, deciVal, unix_time_ms)
payload += line
if counter % SENDING_PERIOD == 0:
try:
# try to send data to cloud
r = requests.post(URL,
params=corlysis_params,
data=payload)
if r.status_code != 204:
raise Exception("data not written")
payload = ""
except:
problem_counter += 1
print('cannot write to InfluxDB')
if problem_counter == MAX_LINES_HISTORY:
problem_counter = 0
payload = ""
counter += 1
# Print animation
sys.stdout.write("\rCollecting data... " + animation[aniCount])
sys.stdout.flush()
aniCount += 1
if (aniCount == 4):
aniCount = 0
time_diff_ms = int(time.time() * 1000) - unix_time_ms
# print(time_diff_ms)
if time_diff_ms < READING_DATA_PERIOD_MS:
time.sleep((READING_DATA_PERIOD_MS - time_diff_ms) / 1000.0)
values = (mac_addr, temperature, pressure, humidity, gas, luxVal,
deciVal, now)
add_val = ("INSERT INTO data "
"(mac, temp, pres, hum, gas, lux, db, dt)"
"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)")
c.execute(add_val, values)
con.commit()
except KeyboardInterrupt:
con.close()
break
except Exception as e:
pass
print(e)
__author__ = 'Victor'
import sys
import math
import wave
import struct
import curses
import pyaudio
import numpy as np
import matplotlib.pyplot as plt
standard = curses.initscr()
standard.nodelay(True)
curses.noecho()
curses.cbreak()
pythonAudioObject = pyaudio.PyAudio()
MODE = sys.argv[1]
FOLD = 1
SAMPLE_RATE = 44100
CHANNELS = 2
WIDTH = 2
try:
IterationsN = int(sys.argv[3])
except (ValueError, IndexError):
print('The second argument has to be a number.')
sys.exit()
def main():
standard.addstr('Noise-cancelling live')
def arduino_soundlight():
chunk = 2**11 # Change if too fast/slow, never less than 2**11
scale = 50 # Change if too dim/bright
exponent = 5 # Change if too little/too much difference between loud and quiet sounds
samplerate = 44100
# CHANGE THIS TO CORRECT INPUT DEVICE
# Enable stereo mixing in your sound card
# to make you sound output an input
# Use list_devices() to list all your input devices
device = 3
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16,
channels=1,
rate=44100,
input=True,
frames_per_buffer=chunk,
input_device_index=device)
print "Starting, use Ctrl+C to stop"
try:
#ser = serial.Serial(
#port='com3',
#timeout=1
#)
while True:
data = stream.read(chunk)
'''
# Old RMS code, will only show the volume
rms = audioop.rms(data, 2)
#level = min(rms / (2.0 ** 11) * scale, 1.0)
level = max(min(rms / scale, 1.0), 0.0)
level = level**exponent
level = int(level * 255)
print level
#ser.write(chr(level))
'''
# Do FFT
levels = calculate_levels(data, chunk, samplerate)
algo = []
# Make it look better and send to serial
for level in levels:
level = max(min(level / scale, 1.0), 0.0)
level = level**exponent
level = int(level * 255)
algo.append(level)
#print '>' * level,
#sys.stdout.flush()
#ser.write(chr(level))
'''print '>' * algo[0] + '\r'
print '>' * algo[1] + '\r'
print '>' * algo[2] + '\r'
print '>' * algo[3] + '\r'
print '>' * algo[4] + '\r'
print '>' * algo[5] + '\r',
sys.stdout.flush()'''
#s = ser.read(6)
if (((algo[2] + algo[3] + algo[0] + algo[1]) / 2) >= 5):
para.setData(2)
else:
para.setData(0)
except KeyboardInterrupt:
pass
finally:
print "\nStopping"
stream.close()
p.terminate()
def worker(self):
audio = pyaudio.PyAudio()
print('\n*******************************************')
print('RHAPSODY MODULE-I INPUT')
print('*******************************************\n')
print('\n===========================================')
print('STARTED RECORDING')
print('===========================================\n')
for i in range(1, 4):
print('\n===========================================')
print(str(i) + '...')
print('===========================================\n')
sleep(1)
stream = audio.open(format=self.FORMAT,
channels=self.CHANNELS,
rate=self.RATE,
input=True,
frames_per_buffer=self.CHUNK)
f = []
for i in range(0, int(self.RATE / self.CHUNK * self.RECORD_SECONDS)):
data = stream.read(self.CHUNK)
f.append(data)
print('\n===========================================')
print('DONE RECORDING')
print('===========================================\n')
stream.stop_stream()
stream.close()
audio.terminate()
wf = wave.open(self.WAVE_OUTPUT_FILENAME, 'wb')
wf.setnchannels(self.CHANNELS)
wf.setsampwidth(audio.get_sample_size(self.FORMAT))
wf.setframerate(self.RATE)
wf.writeframes(b''.join(f))
wf.close()
"""""" """""" """""" """""" """""" """""" """
1 - Loading File
""" """""" """""" """""" """""" """""" """"""
filename = self.WAVE_OUTPUT_FILENAME
y, sr = librosa.load(filename)
"""""" """""" """""" """""" """""" """""" """
2 - Get Tempo == bpm
""" """""" """""" """""" """""" """""" """"""
tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
print('\n===========================================')
print('Estimated tempo: {:.2f} beats per minute'.format(tempo))
print('===========================================\n')
# generate csv files with beat times
#CSV_FILENAME = self.WAVE_OUTPUT_FILENAME_NO_EXTENSION + ".csv"
beat_times = librosa.frames_to_time(beat_frames, sr=sr)
CSV_FILENAME = os.path.abspath(
os.path.join(os.path.dirname(__file__), "Recordings",
self.final + ".csv"))
librosa.output.times_csv(CSV_FILENAME, beat_times)
# WRITING A FILE WITH THE TEMPO
#TEXT_FILENAME = self.WAVE_OUTPUT_FILENAME_NO_EXTENSION + ".txt"
TEXT_FILENAME = os.path.abspath(
os.path.join(os.path.dirname(__file__), "Recordings",
self.final + ".txt"))
bpm_value = open(TEXT_FILENAME, 'w')
tempo_text = str(tempo) + '\n'
bpm_value.write(tempo_text)
"""""" """""" """""" """""" """""" """""" """
3 - Get Notes
""" """""" """""" """""" """""" """""" """"""
hz = librosa.feature.chroma_cqt(y=y, sr=sr)
## GET STRONGEST OCTAVE
strongestOctave = 0
strongestOctave_sum = 0
for octave in range(len(hz)):
sum = 0
for frame in hz[octave]:
sum = sum + frame
if sum > strongestOctave_sum:
strongestOctave_sum = sum
strongestOctave = octave
## GET HEIGHEST HZ FOR EACH TIME FRAME
strongestHz = []
for i in range(len(hz[0])):
strongestHz.append(0)
notes = []
for i in range(len(hz[0])):
notes.append(0)
for frame_i in range(len(hz[0])):
strongest_temp = 0
for octave_i in range(len(hz)):
if hz[octave_i][frame_i] > strongest_temp:
strongest_temp = hz[octave_i][frame_i]
strongestHz[frame_i] = octave_i + 1
notes[frame_i] = librosa.hz_to_note(hz[octave_i][frame_i])
# C C# D D# E F F# G G# A A# B
# 1 2 3 4 5 6 7 8 9 10 11 12
strongestHz_sum = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
for note in strongestHz:
strongestHz_sum[note - 1] = strongestHz_sum[note - 1] + 1
for i in range(len(strongestHz_sum)):
strongestHz_sum[i] = float(strongestHz_sum[i]) / len(strongestHz)
noteSorted = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
for num in range(len(noteSorted)):
biggest = strongestHz_sum.index(max(strongestHz_sum))
noteSorted[num] = biggest + 1
strongestHz_sum[biggest] = strongestHz_sum[biggest] - 0.25
for note in noteSorted:
noteString = str(note) + '\n'
bpm_value.write(noteString)
bpm_value.close()
print('\n===========================================')
print('RECORDING ANALYSIS COMPLETED SUCCESSFULLY!!!')
print('===========================================\n')
self.finished.emit()
def run(self):
"""
Creates an input audio stream, initializes wake word detection (Porcupine) and speech to intent (Rhino)
engines, and monitors the audio stream for occurrences of the wake word and then infers the intent from speech
command that follows.
"""
porcupine = None
rhino = None
pa = None
audio_stream = None
wake_phrase_detected = False
intent_extraction_is_finalized = False
try:
porcupine = Porcupine(
library_path=self._porcupine_library_path,
model_file_path=self._porcupine_model_file_path,
keyword_file_paths=[self._porcupine_keyword_file_path],
sensitivities=[self._porcupine_sensitivity])
rhino = Rhino(
library_path=self._rhino_library_path,
model_file_path=self._rhino_model_file_path,
context_file_path=self._rhino_context_file_path)
print()
print('****************************** context ******************************')
print(rhino.context_expressions)
print('*********************************************************************')
print()
pa = pyaudio.PyAudio()
audio_stream = pa.open(
rate=porcupine.sample_rate,
channels=1,
format=pyaudio.paInt16,
input=True,
frames_per_buffer=porcupine.frame_length,
input_device_index=self._input_device_index)
# NOTE: This is true now and will be correct possibly forever. If it changes the logic below need to change.
assert porcupine.frame_length == rhino.frame_length
while True:
pcm = audio_stream.read(porcupine.frame_length)
pcm = struct.unpack_from("h" * porcupine.frame_length, pcm)
if self._output_path is not None:
self._recorded_frames.append(pcm)
if not wake_phrase_detected:
wake_phrase_detected = porcupine.process(pcm)
if wake_phrase_detected:
print('detected wake phrase')
elif not intent_extraction_is_finalized:
intent_extraction_is_finalized = rhino.process(pcm)
else:
if rhino.is_understood():
intent, slot_values = rhino.get_intent()
print()
print('intent: %s' % intent)
print('---')
for slot, value in slot_values.items():
print('%s: %s' % (slot, value))
print()
else:
print("didn't understand the command")
rhino.reset()
wake_phrase_detected = False
intent_extraction_is_finalized = False
except KeyboardInterrupt:
print('stopping ...')
finally:
if porcupine is not None:
porcupine.delete()
if rhino is not None:
rhino.delete()
if audio_stream is not None:
audio_stream.close()
if pa is not None:
pa.terminate()
if self._output_path is not None and len(self._recorded_frames) > 0:
recorded_audio = np.concatenate(self._recorded_frames, axis=0).astype(np.int16)
soundfile.write(self._output_path, recorded_audio, samplerate=porcupine.sample_rate, subtype='PCM_16')
def play_the_recording(file_path):
p = vlc.MediaPlayer(output_file)
p.play()
if __name__ == '__main__':
filename = 'sample.wav'
while True:
print()
command = input('Enter q to stop. Otherwise, press any key: ')
if command == 'q':
break
player = pyaudio.PyAudio()
record_to_file(filename, player)
player.terminate()
# Speech-to-text: Uzbek language
texts = speech_to_text(filename, 'uz')
# Translations Uzbek -> English
textEn = translate_text(texts[0], 'uz', 'en')
# Translation English -> Uzbek
textUz = translate_text(textEn, 'en', 'uz')
# Text-to-Speech: Uzbek language is not supported. Using English instead.
output_file = text_to_speech(textUz, 'en')
def localize():
global switch_beamforming
global DO_BEAMFORM
# Setup search space
source_plane = OrientedSourcePlane(SOURCE_PLANE_NORMAL, SOURCE_PLANE_UP,
SOURCE_PLANE_OFFSET)
space = SearchSpace(MIC_LOC, CAMERA_LOC, [source_plane])
# Setup pyaudio instances
pa = pyaudio.PyAudio()
helper = AudioHelper(pa)
localizer = KalmanTrackingLocalizer(mic_positions=mic_layout,
search_space=space,
mic_forward=MIC_FORWARD,
mic_above=MIC_ABOVE,
trans_mat=STATE_TRANSITION_MAT,
state_cov=STATE_TRANSITION_MAT,
emission_mat=EMISSION_MAT,
emission_cov=EMISSION_COV,
dft_len=FFT_LENGTH,
sample_rate=SAMPLE_RATE,
n_theta=N_THETA,
n_phi=N_PHI)
beamformer = BeamFormer(mic_layout, SAMPLE_RATE)
# Setup STFT object
stft = StftManager(dft_length=FFT_LENGTH,
window_length=WINDOW_LENGTH,
hop_length=HOP_LENGTH,
use_window_fcn=True,
n_channels=NUM_CHANNELS_IN,
dtype=DATA_TYPE)
# Setup devices
in_device = helper.get_input_device_from_user()
if PLAY_AUDIO:
out_device = helper.get_output_device_from_user()
else:
out_device = helper.get_default_output_device_info()
# Setup streams
in_stream = pa.open(rate=SAMPLE_RATE,
channels=NUM_CHANNELS_IN,
format=SAMPLE_TYPE,
frames_per_buffer=FRAMES_PER_BUF,
input=True,
input_device_index=int(in_device['index']),
stream_callback=read_in_data)
out_stream = pa.open(rate=SAMPLE_RATE,
channels=NUM_CHANNELS_OUT,
format=SAMPLE_TYPE,
output=True,
frames_per_buffer=FRAMES_PER_BUF,
output_device_index=int(out_device['index']),
stream_callback=write_out_data)
# Start recording/playing back
in_stream.start_stream()
out_stream.start_stream()
# Start thread to check for user quit
quit_thread = threading.Thread(target=check_for_quit)
quit_thread.start()
# Setup directions and alignment matrices
direcs = localizer.get_directions()
align_mats = localizer.get_pos_align_mat()
# Plotting setup
if PLOT_POLAR:
fig = plt.figure()
ax = fig.add_subplot(111, projection='polar')
ax.set_rlim(0, 1)
plt.show(block=False)
# Setup space for plotting in new coordinates
spher_coords = localizer.get_spher_directions()
theta = spher_coords[1, :]
pol_plot, = plt.plot(theta, np.ones(theta.shape))
post_plot, = plt.plot(theta, np.ones(theta.shape), 'green')
ax.set_ylim(0, 1)
if DO_BEAMFORM:
pol_beam_plot, = plt.plot(theta, np.ones(theta.shape), 'red')
if PLOT_CARTES:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
plt.show(block=False)
x = localizer.to_spher_grid(direcs[0, :])
y = localizer.to_spher_grid(direcs[1, :])
z = localizer.to_spher_grid(direcs[2, :])
#scat = ax.scatter(x, y, z, s=100)
if EXTERNAL_PLOT:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.show(block=False)
count = 0
try:
global done
while in_stream.is_active() or out_stream.is_active():
data_available = in_buf.wait_for_read(WINDOW_LENGTH, TIMEOUT)
if data_available:
if switch_beamforming:
DO_BEAMFORM = not DO_BEAMFORM
switch_beamforming = False
# Get data from the circular buffer
data = in_buf.read_samples(WINDOW_LENGTH)
# Perform an stft
stft.performStft(data)
# Process dfts from windowed segments of input
dfts = stft.getDFTs()
rffts = mat.to_all_real_matlab_format(dfts)
d, energy = localizer.get_distribution_real(
rffts[:, :, 0], 'gcc') # Use first hop
post = localizer.get_distribution(rffts[:, :, 0])
ind = np.argmax(post)
u = 1.5 * direcs[:, ind] # Direction of arrival
#if energy < 500:
# continue
# Do beam forming
if DO_BEAMFORM:
align_mat = align_mats[:, :, ind]
filtered = beamformer.filter_real(rffts, align_mat)
mat.set_dfts_real(dfts, filtered, n_channels=2)
# Take care of plotting
if count % 1 == 0:
if PLOT_CARTES:
ax.cla()
ax.grid(False)
d = localizer.to_spher_grid(
post / (np.max(post) + consts.EPS))
#d = localizer.to_spher_grid(d / (np.max(d) + consts.EPS))
ax.scatter(x, y, z, c=d, s=40)
#ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolor=plt.cm.gist_heat(d))
ax.plot([0, u[0]], [0, u[1]], [0, u[2]],
c='black',
linewidth=3)
if DO_BEAMFORM:
if np.max(np.abs(response)) > 1:
response /= np.max(np.abs(response))
X = response * x
Y = response * y
Z = response * z
ax.plot_surface(X,
Y,
Z,
rstride=1,
cstride=1,
color='white')
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_zlim(0, 1)
#ax.view_init(90, -90)
fig.canvas.draw()
if PLOT_2D:
# Get unconditional distribution
dist = localizer.to_spher_grid(d)
dist -= np.min(dist)
dist /= (np.sum(dist) + consts.EPS)
sample_mat[:, :-1] = sample_mat[:, 1:]
sample_mat[:, -1] = dist
# Get kalman estimate
maxind = np.argmax(post)
estimate_mat[:-1] = estimate_mat[1:]
estimate_mat[-1] = maxind
plot_2d.set_array(sample_mat)
state_est_plot.set_ydata(estimate_mat)
plt.draw()
count += 1
# Get the istft of the processed data
if PLAY_AUDIO or RECORD_AUDIO:
new_data = stft.performIStft()
new_data = out_buf.reduce_channels(new_data,
NUM_CHANNELS_IN,
NUM_CHANNELS_OUT)
# Write out the new, altered data
if PLAY_AUDIO:
if out_buf.get_available_write() >= WINDOW_LENGTH:
out_buf.write_samples(new_data)
if RECORD_AUDIO:
if record_buf.get_available_write() >= WINDOW_LENGTH:
record_buf.write_samples(new_data)
except KeyboardInterrupt:
print "Program interrupted"
done = True
print "Cleaning up"
in_stream.stop_stream()
in_stream.close()
out_stream.stop_stream()
out_stream.close()
pa.terminate()
# Take care of output file
if RECORD_AUDIO:
print "Writing output file"
make_wav()
print "Done"
def UploadSignal(self):
self.ui.GVOriginal.clear()
filePaths = QtWidgets.QFileDialog.getOpenFileNames(
self, 'Open File', "~/Desktop/sigViews", '*.wav')
for filePath in filePaths:
for self.f in filePath:
if self.f == '*':
break
p = pyaudio.PyAudio()
self.waveFile = wave.open(self.f, 'rb')
# wav1 = wave.open(f,'rb')
# self.ywav1=wav1.readframes(-1)
# self.ywav1 =np.fromstring(self.ywav1,'Int16')
# fs=wav1.getframerate()
# self.xwav1=np.linspace(0,len(self.ywav1)/fs,num=len(self.ywav1))
# print("length of signal")
# print(len(self.xwav1))
# self.ui.GVOriginal.plot(self.xwav1,self.ywav1, pen='b')
self.format = p.get_format_from_width(
self.waveFile.getsampwidth())
channel = self.waveFile.getnchannels()
self.rate = self.waveFile.getframerate()
self.frame = self.waveFile.getnframes()
self.stream = p.open(
format=self.format, # DATA needed for streaming
channels=channel,
rate=self.rate,
output=True)
#durationF = self.frame / float(self.rate)
self.data_int = self.waveFile.readframes(self.frame)
self.data_plot = np.fromstring(self.data_int, 'Int16')
self.data_plot.shape = -1, 2
self.data_plot = self.data_plot.T # Y-axis
self.ywav1 = self.data_plot
print('original data', self.ywav1)
self.time = np.arange(0, self.frame) * (1.0 / self.rate
) #X-axis
self.xwav1 = self.time
#fft_frame = np.fft.rfft(current_frame)
self.ywav1min = np.nanmin(self.ywav1[1])
self.ywav1max = np.nanmax(self.ywav1[1])
self.ui.GVOriginal.setXRange(self.xwav1[0], self.xwav1[-1])
self.ui.GVOriginal.plotItem.getViewBox().setLimits(
xMin=self.xwav1[0],
xMax=self.xwav1[-1],
yMin=self.ywav1min - self.ywav1min * 0.1,
yMax=self.ywav1max + self.ywav1max * 0.1)
self.ui.GVOriginal.plot(self.xwav1, self.ywav1[1], pen='b')
#===============================================Fourier Transform===============================================#
self.fs_rate, self.spf = wavfile.read(self.f)
# print("araay",self.spf.shape)
print("Frequency sampling", self.fs_rate)
l_audio = len(self.spf.shape)
print("Channels", l_audio)
if l_audio == 2:
self.spf = self.spf.mean(
axis=1) # To make it a mono signal, 1 channel only
N = self.spf.shape[0] # Give number of rows
print("complete Sampling N", N)
secs = N / float(self.fs_rate)
print("secs", secs)
Ts = 1.0 / self.fs_rate # sampling interval in time
print("Timestep between Ts", Ts)
t = scipy.arange(0, secs, Ts)
self.FFT = abs(scipy.fft(self.spf))
self.freqs = scipy.fftpack.fftfreq(
self.spf.size, t[1] - t[0]
) # Return the Discrete Fourier Transform sample frequencies. t[1]-t[0] is the sample spacing
FFT_side = self.FFT[range(
N // 2
)] # one side FFT range, remove the negative part (starts from zero)
self.FFT_sideArr = np.array(FFT_side)
self.bands = np.array_split(self.FFT_sideArr, 10)
# self.bands=np.array_split(self.FFT,20)
print('lehgth of band', int(len(self.bands[1])))
self.BandSize = int(len(self.FFT_sideArr) / 10)
self.phase = np.angle(scipy.fft(
self.spf)) #phase, we will use it later
freqs_side = self.freqs[range(N // 2)]
self.fft_freqs_side = np.array(freqs_side)
self.ui.GVFourier.plot(self.freqs, self.FFT, pen='g')
QtCore.QCoreApplication.processEvents()
print('Press Ctrl-C to quit.')
last_touched = cap.touched()
record_state = True
is_recording = [
False, False, False, False, False, False, False, False, False, False,
False, False
]
is_playing = [
False, False, False, False, False, False, False, False, False, False,
False, False
]
my_thread_record = None
my_thread_play = None
is_pygame_init = False
my_thread_init = None
audio = pyaudio.PyAudio() # create pyaudio instantiation
def loop_record2(x):
form_1 = pyaudio.paInt16 # 16-bit resolution
chans = 1 # 1 channel
samp_rate = 44100 # 44.1kHz sampling rate
chunk = 4096 # 2^12 samples for buffer
record_secs = 120 # seconds to record
dev_index = 2 # device index found by p.get_device_info_by_index(ii)
file_array = [
'test0.wav', 'test1.wav', 'test2.wav', 'test3.wav', 'test4.wav',
'test5.wav', 'test6.wav', 'test7.wav', 'test8.wav', 'test9.wav',
'test10.wav'
]
wav_output_filename = file_array[x] # name of .wav file
def speakerRecog():
#Recording Phase
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
RECORD_SECONDS = 5
WAVE_OUTPUT_FILENAME = "./SR/samples/test.wav"
p = pyaudio.PyAudio()
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
print("* recording")
frames = []
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
data = stream.read(CHUNK)
frames.append(data)
print("* done recording")
stream.stop_stream()
stream.close()
p.terminate()
wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))
wf.close()
#Now the recording is stored in test.wav
#We will now test the recording with the gmm models
source = "./SR/samples/"
modelpath = "./SR/gmm_models/"
test_file = "./SR/testing_sample_list.txt"
file_paths = open(test_file, 'r')
gmm_files = [
os.path.join(modelpath, fname) for fname in os.listdir(modelpath)
if fname.endswith(".gmm")
]
print(gmm_files)
#Load the Gaussian gender Models
models = [cPickle.load(open(fname, 'rb')) for fname in gmm_files]
speakers = [fname.split("/")[-1].split(".gmm")[0] for fname in gmm_files]
# Read the test directory and get the list of test audio files
for path in file_paths:
path = path.strip()
print(path)
sr, audio = read(source + path)
vector = extract_features(audio, sr)
log_likelihood = np.zeros(len(models))
for i in range(len(models)):
gmm = models[i] #checking with each model one by one
scores = np.array(gmm.score(vector))
log_likelihood[i] = scores.sum()
winner = np.argmax(log_likelihood)
print("\tdetected as - ", speakers[winner])
return speakers[winner]
time.sleep(1.0)
max_fps = 60
width_of_col = 1
scale = 1
skip_under = 0
file_path = "./File0161.wav"
#Init
pygame.init()
screen = pygame.display.set_mode(size)
pygame.display.set_caption("AV")
done = False
clock = pygame.time.Clock()
wf = wave.open(file_path, "rb")
p = pyaudio.PyAudio()
stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),
channels=wf.getnchannels(),
rate=wf.getframerate(),
output=True,
input=True)
while not done:
# --- Main event loop
for event in pygame.event.get():
if event.type == pygame.QUIT:
done = True
temp = wf.readframes(chunk)
if len(temp) < 4096:
def GoRun(self): #main button callback for collecting new data
self.Status = True
self.fs = 44100 #set sample rate, default to 44100
iters = 1000 # (mostly) deprecated
chunkSize = 8192 #number of samples to read in at once
windowSize = 3 #number of seconds to plot at once
numSamples = iters * chunkSize
#set up an audio stream
p = pyaudio.PyAudio()
audioStream = p.open(format=pyaudio.paInt16,
channels=1,
rate=self.fs,
input=True,
frames_per_buffer=chunkSize)
#empty out the recording
self.Recording = np.zeros(numSamples, dtype=np.int16)
self.Formants = np.zeros((100, 5), dtype=np.float32)
self.FormantTime = np.zeros(100, dtype=np.float32)
self.Pitch = np.zeros(100, dtype=np.float32)
self.PitchTime = np.zeros(100, dtype=np.float32)
FormantCount = 0
PitchCount = 0
#set up our axes
ax = self.RawPlot.figure.add_subplot(111)
f0ax = self.FundamentalFrequenncyPlot.figure.add_subplot(111)
f0ax.tick_params(axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
f0ax.set_position([0.35, 0.05, 0.6, 0.93])
formantAx = self.FormantPlot.figure.add_subplot(111)
tractAx = self.VocalTractPlot.figure.add_subplot(111)
tractAx.tick_params(axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
tractAx.set_position([0.35, 0.05, 0.6, 0.93])
tractAx.set_ylabel('Vocal Tract Length (cm)')
tractAx.set_ylim((0, 25))
tractAx.set_xlim((0, 0.8))
c = 34300 # speed of sound in cm/s
maxPitchLag = 3
maxVocalLag = 3
ds_rate = 3
#set up time vector
print('Beginning New Recording')
time = np.linspace(0, numSamples / self.fs, numSamples)
i = 0
try: #using try/except to enable keyboard interrupt
start = ti.time()
while self.Status: #keep going forever, or until keyboard interrupt
t = (i + 1) * chunkSize
if t > len(self.Recording
): # add space to the recording in necessary
extraSpace = np.zeros(numSamples, dtype=np.int16)
self.Recording = np.concatenate(
[self.Recording, extraSpace], axis=None)
time = np.linspace(0,
len(self.Recording) / self.fs,
len(self.Recording))
# pull a chunk from our audio stream
data = PyAudioTest.getChunk(chunkSize, audioStream, Random=0)
data_ds = data[0:chunkSize:ds_rate] # downsample of data
# its generally a good idea to lowpass filter before downsampling,
# but to save computational time this is skipped here.
# our data is ~mostly~ band-limited, so I don't expect this to be huge problem
# add chunk to our recording
self.Recording[i * chunkSize:(i + 1) * chunkSize] = data
# get f0 and update f0 plot
# use my hack method for getting f0
#clipData = PyAudioTest.centerClip(data)
#acf = PyAudioTest.autocorr(clipData)
#f0 = PyAudioTest.getF0(acf, self.fs)
# use yin implementation instead
# yin's original implementation called for filtering,
# which we have not yet implemented for computational reasons
data_hamming = data * np.hamming(chunkSize)
df = yin.differenceFunction(data_hamming, chunkSize,
self.fs / 75)
cmndf = yin.cumulativeMeanNormalizedDifferenceFunction(
df, len(df))
f0 = yin.getPitch(cmndf,
self.fs / 500,
self.fs / 75,
harmo_th=0.35)
if f0: # if f0 is detected, update our graph
# store ot pitch and time
self.Pitch[PitchCount] = 1.0 * self.fs / f0
self.PitchTime[PitchCount] = 1.0 * (
t - chunkSize / 2) / self.fs
PitchCount += 1
# add space if needed
if PitchCount >= len(self.PitchTime):
self.Pitch = np.concatenate(
(self.Pitch, np.zeros(200, dtype=np.float32)))
self.PitchTime = np.concatenate(
(self.PitchTime, np.zeros(200, dtype=np.float32)))
#get pitches from the last 3 seconds
RecentPitches = []
pitchIDX = PitchCount - 1
while self.PitchTime[pitchIDX] >= 1.0 * (
t - chunkSize /
2) / self.fs - maxPitchLag and pitchIDX >= 0:
RecentPitches.append(self.Pitch[pitchIDX])
pitchIDX -= 1
#get mean and std
meanPitch = np.mean(RecentPitches)
if len(RecentPitches) == 1:
stdPitch = 25
else:
stdPitch = np.std(RecentPitches)
#plot
f0ax.bar([0], [2.0 * stdPitch],
bottom=[meanPitch - stdPitch])
f0ax.set_ylabel('Fundamental Frequency (Hz)')
f0ax.set_ylim((0, 500))
f0ax.set_xlim((0, 0.8))
self.FundamentalFrequenncyPlot.draw()
formantAx.clear()
formantAx.hold(True)
if f0: # if f0 is detected search for formants
#make PSD
fBins, PSD = sp.signal.periodogram(data_ds,
self.fs / ds_rate)
PSD = 20 * np.log10(PSD) #convert to dB
try:
Formants = FormantFinder.findFormantsLPC(
data_ds, self.fs /
ds_rate) # look for formants using LPC method
for f in range(
len(Formants
)): # plot the formants as vertical lines
formantAx.plot([Formants[f], Formants[f]],
[-100, 75],
color='red')
formantAx.plot(fBins, PSD)
formantAx.set_title('Power Spectrum - Formants')
formantAx.set_xlabel('Frequency (Hz)')
formantAx.set_ylabel('Power (dB)')
formantAx.set_ylim((-90, 90))
formantAx.set_xlim((0, 5000))
'''
formantAx.bar(range(len(Formants)), Formants)
formantAx.set_xlabel('Formant number')
formantAx.set_ylabel('Frequency (Hz)')
formantAx.set_title('Formants Frequencies')
formantAx.set_xlim((0, 4.8))
formantAx.set_ylim((0, 5000))
formantAx.set_xticks([0.4, 1.4, 2.4, 3.4, 4.4])
formantAx.set_xticklabels(['F1', 'F2', 'F3', 'F4', 'F5'])
'''
self.FormantPlot.draw()
formantAx.hold(False)
#store Formants
if len(Formants) >= 5:
self.Formants[FormantCount, 0:5] = Formants[0:5]
else:
self.Formants[FormantCount,
0:len(Formants)] = Formants
self.FormantTime[FormantCount] = 1.0 * (
t - chunkSize / 2) / self.fs
FormantCount += 1
# add space if needed
if FormantCount >= len(self.FormantTime):
self.Formants = np.concatenate(
(self.Formants,
np.zeros((200, 5), dtype=np.float32)))
self.FormantTime = np.concatenate(
(self.FormantTime,
np.zeros(200, dtype=np.float32)))
#detect recent vocal tract lengths
RecentTractLength = []
tractIDX = FormantCount - 1
while self.FormantTime[tractIDX] >= 1.0 * (
t - chunkSize /
2) / self.fs - maxVocalLag and tractIDX >= 0:
RecentTractLength.append(
FormantFinder.getVocalTractLength(
self.Formants[tractIDX, :],
c,
method='lammert'))
tractIDX -= 1
# get mean, std
meanTractLength = np.median(RecentTractLength)
if len(RecentTractLength) == 1:
stdTractLength = 2
else:
stdTractLength = np.std(RecentTractLength)
# plot bar
tractAx.bar([0], [2 * stdTractLength],
bottom=[meanTractLength - stdTractLength])
tractAx.set_ylabel('Vocal Tract Length (cm)')
tractAx.set_ylim((0, 25))
tractAx.set_xlim((0, 0.8))
self.VocalTractPlot.draw()
except (RuntimeError
): #formant detection can throw errors sometimes
Formants = np.zeros(3)
else: # if no f0, basically do nothing
fBins = np.linspace(0, self.fs / 2, 10)
PSD = np.zeros(10)
#update our raw data plot, but only everyother chunk, because its time consuming
if t > windowSize * self.fs and i % 3 == 0:
ax.plot(time[t - windowSize * self.fs:t],
self.Recording[t - windowSize * self.fs:t])
ax.set_title('Raw Waveform')
ax.set_xlabel('Time (s)')
ax.set_ylabel('amplitude')
self.RawPlot.draw()
i += 1
#check for incoming button clicks i.e. stop button
QtCore.QCoreApplication.processEvents()
except (
KeyboardInterrupt, SystemExit
): # in case of a keyboard interrupt or system exit, clean house
self.FormantPlot.draw()
self.RawPlot.draw()
self.FundamentalFrequenncyPlot.draw()
self.Pitch = self.Pitch[0:PitchCount]
self.PitchTime = self.PitchTime[0:PitchCount]
self.Formants = self.Formants[0:FormantCount, :]
self.FormantTime = self.FormantTime[0:FormantCount]
print('Recording Completed')
self.Recording = self.Recording[0:t]
print('recorded time is')
print(1.0 * t / self.fs)
print('elapsed time is:')
print(ti.time() - start)
return True
self.Pitch = self.Pitch[0:PitchCount]
self.PitchTime = self.PitchTime[0:PitchCount]
self.Formants = self.Formants[0:FormantCount, :]
self.FormantTime = self.FormantTime[0:FormantCount]
print('Recording Completed')
self.Recording = self.Recording[0:t]
print('recorded time is')
print(1.0 * t / self.fs)
print('elapsed time is:')
print(ti.time() - start)
return True
