shithub: opus

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Training seems to work

--- a/dnn/lpcnet.py

+++ b/dnn/lpcnet.py

@@ -41,7 +41,7 @@

         }

 def new_wavernn_model():

-    pcm = Input(shape=(None, 1))

+    pcm = Input(shape=(None, 2))

     pitch = Input(shape=(None, 1))

     feat = Input(shape=(None, nb_used_features))

     dec_feat = Input(shape=(None, 32))

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

         cpitch = pitch

     embed = Embedding(256, 128, embeddings_initializer=PCMInit())

-    cpcm = Reshape((-1, 128))(embed(pcm))

+    cpcm = Reshape((-1, 128*2))(embed(pcm))

     cfeat = fconv2(fconv1(feat))

--- a/dnn/train_wavenet_audio.py

+++ b/dnn/train_wavenet_audio.py

@@ -51,8 +51,18 @@

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

 pred = pred[:nb_frames*pcm_chunk_size]

+

+pred_in = 32768.*ulaw2lin(data)

+for i in range(2, nb_frames*feature_chunk_size):

+    pred[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] - \

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

+

 pred = np.minimum(127, lin2ulaw(pred/32768.))

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

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

 pitch = 1.*data

@@ -72,7 +82,7 @@

 pred = np.reshape(pred, (nb_frames, pcm_chunk_size, 1))

 pred = (pred.astype('int16')+128).astype('uint8')

-#in_data = np.concatenate([in_data, pred], axis=-1)

+in_data = np.concatenate([in_data, pred], axis=-1)

 #in_data = np.concatenate([in_data, in_pitch], axis=-1)

-- 

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