From 08b5fe6cdce594ece41a6c1cdd4273da33866b2a Mon Sep 17 00:00:00 2001 From: Jean-Marc Valin Date: Fri, 17 Aug 2018 00:31:27 -0400 Subject: [PATCH] working decoder --- dnn/test_wavenet_audio.py | 22 +++++++++------------- 1 file changed, 9 insertions(+), 13 deletions(-) diff --git a/dnn/test_wavenet_audio.py b/dnn/test_wavenet_audio.py index 2bd76309..420a48bc 100755 --- a/dnn/test_wavenet_audio.py +++ b/dnn/test_wavenet_audio.py @@ -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 = (out_data.astype('int16')+128).astype('uint8') 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)) -model.load_weights('wavenet3h12_30.h5') +model.load_weights('wavenet3h13_30.h5') order = 16 pcm = 0.*out_data -exc = out_data-0 -pitch = np.zeros((1, 1, 1), dtype='float32') -fexc = np.zeros((1, 1, 1), dtype='float32') +fexc = np.zeros((1, 1, 2), dtype='float32') iexc = np.zeros((1, 1, 1), dtype='int16') state = np.zeros((1, lpcnet.rnn_units), dtype='float32') for c in range(1, nb_frames): cfeat = enc.predict(features[c:c+1, :, :nb_used_features]) for fr in range(1, feature_chunk_size): f = c*feature_chunk_size + fr - a = features[c, fr, nb_used_features:] + a = features[c, fr, nb_features-order:] #print(a) gain = 1.; period = int(50*features[c, fr, 36]+100) period = period - 4 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] = in_data[f*frame_size + i, 0] - #print(cfeat.shape) + pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0]) + fexc[0, 0, 1] = np.minimum(127, lin2ulaw(pred/32768.)) + 128 + 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)) - #print(np.sum(p)) + 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) print(iexc[0, 0, 0], out_data[f*frame_size + i, 0], pcm[f*frame_size + i, 0])