class AudioHandler(object):
def __init__(self):
self.FORMAT = pyaudio.paFloat32
self.CHANNELS = 1
self.RATE = 44100
self.CHUNK = 1024 * 2
self.p = None
self.stream = None
self.chunksRead = None
self.predictor = Predictor()
def start(self):
self.p = pyaudio.PyAudio()
self.stream = self.p.open(format=self.FORMAT,
channels=self.CHANNELS,
rate=self.RATE,
input=True,
output=False,
stream_callback=self.callback,
frames_per_buffer=self.CHUNK)
def stop(self):
self.stream.close()
self.p.terminate()
def callback(self, in_data, frame_count, time_info, flag):
numpy_array = np.frombuffer(in_data, dtype=np.float32)
# # librosa.feature.mfcc(numpy_array)
self.chunksRead = in_data
return None, pyaudio.paContinue
def mainloop(self):
while (self.stream.is_active()): # if using button you can set self.stream to 0 (self.stream = 0), otherwise you can use a stop condition
if self.chunksRead != None:
numpy_array = np.frombuffer(self.chunksRead, dtype=np.float32)
data = numpy_array
y = data
Fs = self.RATE
n = len(y) # length of the signal
k = np.arange(n)
T = n / Fs
frq = k / T # two sides frequency range
frq = frq[1: int(n / 2)] # one side frequency range (sliced the frq in half)
Y = np.fft.fft(y) / n # fft computing and normalization
Y = Y[1: int(n / 2)]
Y2 = Y
Y = Y*100
Y = abs(Y)
#get the peaks
indexes = peakutils.indexes(Y, thres=0.01, min_dist=10)
#print('first indexes:', indexes)
fundamental = 0
overtone_series = [0, 0, 0, 0, 0, 0, 0, 0, 0]
if len(indexes) >= 7 and Y[indexes[1]] > 0.05:
fundamental = indexes[1]
indexes = peakutils.indexes(Y, thres=0.01, min_dist=fundamental-5)
#print('new indexes:', indexes)
if len(indexes) > 7:
overtones = indexes[1:8]
if Y[indexes[1]] != 0:
overtone_series = [0]*700
temp = Y[overtones] / Y[indexes[1]]
for i in range(0,700,100):
for j in range(20):
overtone_series[i+j] = temp[int(i/100)]
li.set_xdata(np.arange(len(overtone_series)))
li.set_ydata(overtone_series)
ax[0].get_xaxis().set_visible(False)
li2.set_xdata(frq)
li2.set_ydata(abs(Y2)*10)
plt.pause(0.01)
rms = librosa.feature.rms(numpy_array)[0]
for text in ax[0].texts:
text.set_visible(False)
ax[0].text(300, 2, 'None', style='italic',
bbox={'facecolor': 'white', 'alpha': 1, 'pad': 10})
ax[0].texts[len(ax[0].texts)-1].set_visible(True)
if min(rms) < 0.01:
continue
print('peaks:',indexes[:8])
# print('fundamental_frequqncy: ~',fundamental*21.5)
for text in ax[0].texts:
text.set_visible(False)
ax[0].text(300, 2, self.predictor.getPrediction(numpy_array)[0].upper(), style='italic',
bbox={'facecolor': 'green', 'alpha': 1, 'pad': 10})
ax[0].texts[len(ax[0].texts)-1].set_visible(True)
