ref: 8a276fb44a56a362e27336be0a69064c3747401b
parent: a922f83cca2b67d1a947e6673c304f99ba9b0230
author: Jean-Marc Valin <jmvalin@jmvalin.ca>
date: Tue Aug 21 15:17:27 EDT 2018
predicting excitation
--- a/dnn/test_wavenet_audio.py
+++ b/dnn/test_wavenet_audio.py
@@ -66,7 +66,7 @@
out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1))
-model.load_weights('wavenet3h21_30.h5')+model.load_weights('wavenet4a1_13.h5')order = 16
@@ -96,7 +96,8 @@
p = p/(1e-5 + np.sum(p))
iexc[0, 0, 0] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))-128
- pcm[f*frame_size + i, 0] = 32768*ulaw2lin(iexc[0, 0, 0]*1.0)
+ pcm[f*frame_size + i, 0] = pred + 32768*ulaw2lin(iexc[0, 0, 0]*1.0)
+ iexc[0, 0, 0] = lin2ulaw(pcm[f*frame_size + i, 0]/32768)
print(iexc[0, 0, 0], 32768*ulaw2lin(out_data[f*frame_size + i, 0]), pcm[f*frame_size + i, 0], pred)
--- a/dnn/train_wavenet_audio.py
+++ b/dnn/train_wavenet_audio.py
@@ -51,19 +51,19 @@
features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
-pred = np.fromfile(pred_file, dtype='int16')
-pred = pred[:nb_frames*pcm_chunk_size]
+upred = np.fromfile(pred_file, dtype='int16')
+upred = upred[:nb_frames*pcm_chunk_size]
pred_in = 32768.*ulaw2lin(in_data)
for i in range(2, nb_frames*feature_chunk_size):
- pred[i*frame_size:(i+1)*frame_size] = 0
+ upred[i*frame_size:(i+1)*frame_size] = 0
if i % 100000 == 0:
print(i)
for k in range(16):
- pred[i*frame_size:(i+1)*frame_size] = pred[i*frame_size:(i+1)*frame_size] - \
+ upred[i*frame_size:(i+1)*frame_size] = upred[i*frame_size:(i+1)*frame_size] - \
pred_in[i*frame_size-k:(i+1)*frame_size-k]*features[i, nb_features-16+k]
-pred = np.minimum(127, lin2ulaw(pred/32768.))
+pred = np.minimum(127, lin2ulaw(upred/32768.))
#pred = pred + np.random.randint(-1, 1, len(data))
@@ -77,7 +77,8 @@
in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
in_data = (in_data.astype('int16')+128).astype('uint8')-out_data = np.reshape(data, (nb_frames, pcm_chunk_size, 1))
+out_data = np.reshape(lin2ulaw((32768*ulaw2lin(data)-upred)/32768), (nb_frames, pcm_chunk_size, 1))
+out_data = np.maximum(-127, np.minimum(127, out_data))
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]
@@ -92,7 +93,7 @@
# f.create_dataset('data', data=in_data[:50000, :, :]) # f.create_dataset('feat', data=features[:50000, :, :])-checkpoint = ModelCheckpoint('wavenet3h21_{epoch:02d}.h5')+checkpoint = ModelCheckpoint('wavenet4a1_{epoch:02d}.h5') #model.load_weights('wavernn1c_01.h5')model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
--
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