shithub: opus

Info  •  Files  •  Log  •  Branches

Fix input noise

--- 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('wavenet3h13_30.h5')

+model.load_weights('wavenet3h21_30.h5')

 order = 16

@@ -90,11 +90,13 @@

             fexc[0, 0, 1] = np.minimum(127, lin2ulaw(pred/32768.)) + 128

             p, state = dec.predict([fexc, cfeat[:, fr:fr+1, :], state])

+            #p = p*p

+            #p = p/(1e-18 + np.sum(p))

             p = np.maximum(p-0.001, 0)

             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)

-            print(iexc[0, 0, 0], out_data[f*frame_size + i, 0], pcm[f*frame_size + i, 0])

+            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

@@ -45,7 +45,9 @@

 features = features[:nb_frames*feature_chunk_size*nb_features]

 in_data = np.concatenate([data[0:1], data[:-1]]);

-in_data = in_data + np.random.randint(-1, 1, len(data))

+noise = np.concatenate([np.zeros((len(data)//3)), np.random.randint(-2, 2, len(data)//3), np.random.randint(-1, 1, len(data)//3)])

+in_data = in_data + noise

+in_data = np.maximum(-127, np.minimum(127, in_data))

 features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))

@@ -52,7 +54,7 @@

 pred = np.fromfile(pred_file, dtype='int16')

 pred = pred[:nb_frames*pcm_chunk_size]

-pred_in = 32768.*ulaw2lin(data)

+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

     if i % 100000 == 0:

@@ -59,7 +61,7 @@

         print(i)

     for k in range(16):

         pred[i*frame_size:(i+1)*frame_size] = pred[i*frame_size:(i+1)*frame_size] - \

-            pred_in[i*frame_size-k-1:(i+1)*frame_size-k-1]*features[i, nb_features-16+k]

+            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 = pred + np.random.randint(-1, 1, len(data))

@@ -90,7 +92,7 @@

 # f.create_dataset('data', data=in_data[:50000, :, :])

 # f.create_dataset('feat', data=features[:50000, :, :])

-checkpoint = ModelCheckpoint('wavenet3h13_{epoch:02d}.h5')

+checkpoint = ModelCheckpoint('wavenet3h21_{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'])

-- 

⑨