first wavenet implementation

dnn/wavenet.py

@@ -0,0 +1,50 @@
+#!/usr/bin/python3
+
+import math
+from keras.models import Model
+from keras.layers import Input, LSTM, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Add, Multiply, Bidirectional, MaxPooling1D, Activation
+from keras import backend as K
+from mdense import MDense
+import numpy as np
+import h5py
+import sys
+from causalconv import CausalConv
+
+units=128
+pcm_bits = 8
+pcm_levels = 2**pcm_bits
+nb_used_features = 38
+
+
+def new_wavenet_model():
+    pcm = Input(shape=(None, 1))
+    pitch = Input(shape=(None, 1))
+    feat = Input(shape=(None, nb_used_features))
+    dec_feat = Input(shape=(None, 32))
+
+    fconv1 = Conv1D(128, 3, padding='same', activation='tanh')
+    fconv2 = Conv1D(32, 3, padding='same', activation='tanh')
+
+    cfeat = fconv2(fconv1(feat))
+
+    rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))
+
+    activation='tanh'
+    rfeat = rep(cfeat)
+    #tmp = Concatenate()([pcm, rfeat])
+    tmp = pcm
+    for k in range(10):
+        res = tmp
+        tmp = Concatenate()([tmp, rfeat])
+        c1 = CausalConv(units, 2, dilation_rate=2**k, activation='tanh')
+        c2 = CausalConv(units, 2, dilation_rate=2**k, activation='sigmoid')
+        tmp = Multiply()([c1(tmp), c2(tmp)])
+        tmp = Dense(units, activation='relu')(tmp)
+        if k != 0:
+            tmp = Add()([tmp, res])
+
+    md = MDense(pcm_levels, activation='softmax')
+    ulaw_prob = md(tmp)
+    
+    model = Model([pcm, feat], ulaw_prob)
+    return model