def predict(modelPath, labelPath, wavPath):
lf = open(labelPath, 'r')
labelLn = lf.readlines()
# remove \n
label = []
for item in labelLn:
label.append(item.strip())
pred = Predictor()
pred.load(modelPath)
#70Hzと74Hzの区別がつく分解能が要る。
FR = 44100
T = 1.0 / FR
N = 16384
rate, dataAll = wavfile.read(wavPath, True)
if rate != FR:
print('Error: sample rate is not 44.1kHz!')
return
# get channel 0 (left channel in stereo)
if dataAll.ndim != 1:
dataAll = dataAll[:, 0]
print('Time Chord Confidence')
nPred = (int)(len(dataAll) / FR)
for t in range(0, nPred):
data = dataAll[t * FR:t * FR + N]
# 16bit int format -> float64
x = data.astype(np.float)
# normalize input time domain data to [-1 +1]
xs = normalizeArray(x)
w = scipy.signal.blackman(N)
xf = scipy.fftpack.fft(xs * w)
xfa = np.abs(xf)
#70Hzと4kHzの区間を見る。
startIdx = (int)(70 * N / FR)
endIdx = (int)(4000 * N / FR)
xfs = xfa[startIdx:endIdx]
# normalize frequency domain data to [0, +1]
xIn = normalizePositiveArray(xfs)
xInR = np.reshape(xIn, (1, -1))
(ypred, conf) = pred.predict(xInR)
ypredI = int(ypred)
#print('', ypredI)
if conf.max() >= 0:
print('%3d:%02d %10s %6.2f' %
((int)(t / 60), t % 60, label[ypredI], conf.max()))
