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working decoder

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This commit is contained in:
Jean-Marc Valin 2018-08-17 00:31:27 -04:00
parent 87cd75f6f4
commit 08b5fe6cdc
1 changed files with 9 additions and 13 deletions
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22
dnn/test_wavenet_audio.py
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@ -58,7 +58,7 @@ in_data = (in_data.astype('int16')+128).astype('uint8')
out_data = np.reshape(data, (nb_frames, pcm_chunk_size, 1)) out_data = np.reshape(data, (nb_frames, pcm_chunk_size, 1))
out_data = (out_data.astype('int16')+128).astype('uint8') out_data = (out_data.astype('int16')+128).astype('uint8')
features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features)) features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
features = features[:, :, :nb_used_features] features = features[:, :, :]
@ -66,38 +66,34 @@ in_data = np.reshape(in_data, (nb_frames*pcm_chunk_size, 1))
out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1)) out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1))
model.load_weights('wavenet3h12_30.h5') model.load_weights('wavenet3h13_30.h5')
order = 16 order = 16
pcm = 0.*out_data pcm = 0.*out_data
exc = out_data-0 fexc = np.zeros((1, 1, 2), dtype='float32')
pitch = np.zeros((1, 1, 1), dtype='float32')
fexc = np.zeros((1, 1, 1), dtype='float32')
iexc = np.zeros((1, 1, 1), dtype='int16') iexc = np.zeros((1, 1, 1), dtype='int16')
state = np.zeros((1, lpcnet.rnn_units), dtype='float32') state = np.zeros((1, lpcnet.rnn_units), dtype='float32')
for c in range(1, nb_frames): for c in range(1, nb_frames):
cfeat = enc.predict(features[c:c+1, :, :nb_used_features]) cfeat = enc.predict(features[c:c+1, :, :nb_used_features])
for fr in range(1, feature_chunk_size): for fr in range(1, feature_chunk_size):
f = c*feature_chunk_size + fr f = c*feature_chunk_size + fr
a = features[c, fr, nb_used_features:] a = features[c, fr, nb_features-order:]
#print(a) #print(a)
gain = 1.; gain = 1.;
period = int(50*features[c, fr, 36]+100) period = int(50*features[c, fr, 36]+100)
period = period - 4 period = period - 4
for i in range(frame_size): for i in range(frame_size):
pitch[0, 0, 0] = exc[f*frame_size + i - period, 0]
fexc[0, 0, 0] = iexc + 128 fexc[0, 0, 0] = iexc + 128
#fexc[0, 0, 0] = in_data[f*frame_size + i, 0] pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0])
#print(cfeat.shape) fexc[0, 0, 1] = np.minimum(127, lin2ulaw(pred/32768.)) + 128
p, state = dec.predict([fexc, cfeat[:, fr:fr+1, :], state]) p, state = dec.predict([fexc, cfeat[:, fr:fr+1, :], state])
p = np.maximum(p-0.0003, 0) p = np.maximum(p-0.001, 0)
p = p/(1e-5 + np.sum(p)) p = p/(1e-5 + np.sum(p))
#print(np.sum(p))
iexc[0, 0, 0] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))-128 iexc[0, 0, 0] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))-128
exc[f*frame_size + i] = iexc[0, 0, 0]/16.
#out_data[f*frame_size + i, 0] = iexc[0, 0, 0]
pcm[f*frame_size + i, 0] = 32768*ulaw2lin(iexc[0, 0, 0]*1.0) pcm[f*frame_size + i, 0] = 32768*ulaw2lin(iexc[0, 0, 0]*1.0)
print(iexc[0, 0, 0], out_data[f*frame_size + i, 0], pcm[f*frame_size + i, 0]) print(iexc[0, 0, 0], out_data[f*frame_size + i, 0], pcm[f*frame_size + i, 0])