def estimate(valid, v_W, c_W, valid_v, valid_c, r, score_inf=None):
fn = 'est/0' + r
W = np.append(v_W, c_W, axis=1)
H = NMF.extractActivation(valid, W)
t_num = v_W.shape[1]
if score_inf is not None :
print('apply score inf')
H = NMF.cons_Activation(score_inf, H)
v_H = H[0:t_num]
c_H = H[t_num:H.shape[0]]
o_v, o_c = reconstruct(valid, v_W, v_H, c_W, c_H)
librosa.output.write_wav(fn + '_vio_est.wav', o_v, 44100)
librosa.output.write_wav(fn + '_cla_est.wav', o_c, 44100)
valid_v = valid_v[0:220160]
valid_c = valid_c[0:220160]
sdr, sir, sar, perm = basic.evalBSS(np.array([valid_v, valid_c]), np.array([o_v, o_c]))
print(sdr)
print(sir)
print(sar)
def R2(valid, W):
H = NMF.extractActivation(valid, W)
r = np.dot(W, H)
o = librosa.core.istft(r, win_length=NMF.d_w, hop_length=NMF.d_h)
"""showDicSpectr(W)
showActSpectr(H)
showReconSpectr(r)"""
librosa.output.write_wav('01_cla.wav', o, 44100)
def R4(v_W, c_W):
path = '../audio/test/'
fn = '../pred/'
test_clips = u.readClips(path)
W = np.append(v_W, c_W, axis=1)
for i in range(0, len(test_clips)):
p = fn + '0' + str(i + 6)
H = NMF.extractActivation(test_clips[i][0], W)
v_H = H[0:t_num]
c_H = H[t_num:H.shape[0]]
o_v, o_c = est.reconstruct(test_clips[i][0], v_W, v_H, c_W, c_H)
librosa.output.write_wav(p + '_vio_est.wav', o_v, 44100)
librosa.output.write_wav(p + '_cla_est.wav', o_c, 44100)
