shithub: opus

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Add input embedding

--- a/dnn/train_wavenet_audio.py

+++ b/dnn/train_wavenet_audio.py

@@ -31,7 +31,7 @@

 pcm_chunk_size = frame_size*feature_chunk_size

 data = np.fromfile(pcmfile, dtype='int16')

-data = np.minimum(127, lin2ulaw(data[160:]/32768.))

+data = np.minimum(127, lin2ulaw(data[80:]/32768.))

 nb_frames = len(data)//pcm_chunk_size

 features = np.fromfile(feature_file, dtype='float32')

@@ -39,7 +39,7 @@

 data = data[:nb_frames*pcm_chunk_size]

 features = features[:nb_frames*feature_chunk_size*nb_features]

-in_data = np.concatenate([data[0:1], data[:-1]])/16.;

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

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

 pitch = 1.*data

@@ -51,6 +51,7 @@

 in_pitch = np.reshape(pitch/16., (nb_frames, pcm_chunk_size, 1))

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

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

--- a/dnn/wavenet.py

+++ b/dnn/wavenet.py

@@ -4,6 +4,7 @@

 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 keras.initializers import Initializer

 from keras.initializers import VarianceScaling

 from mdense import MDense

 import numpy as np

@@ -17,7 +18,31 @@

 pcm_levels = 2**pcm_bits

 nb_used_features = 38

+class PCMInit(Initializer):

+    def __init__(self, gain=.1, seed=None):

+        self.gain = gain

+        self.seed = seed

+    def __call__(self, shape, dtype=None):

+        num_rows = 1

+        for dim in shape[:-1]:

+            num_rows *= dim

+        num_cols = shape[-1]

+        flat_shape = (num_rows, num_cols)

+        if self.seed is not None:

+            np.random.seed(self.seed)

+        a = np.random.uniform(-1.7321, 1.7321, flat_shape)

+        #a[:,0] = math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows

+        #a[:,1] = .5*a[:,0]*a[:,0]*a[:,0]

+        a = a + np.reshape(math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows, (num_rows, 1))

+        return self.gain * a

+

+    def get_config(self):

+        return {

+            'gain': self.gain,

+            'seed': self.seed

+        }

+

 def new_wavenet_model(fftnet=False):

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

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

@@ -34,7 +59,8 @@

     activation='tanh'

     rfeat = rep(cfeat)

     #tmp = Concatenate()([pcm, rfeat])

-    tmp = pcm

+    embed = Embedding(256, units, embeddings_initializer=PCMInit())

+    tmp = Reshape((-1, units))(embed(pcm))

     init = VarianceScaling(scale=1.5,mode='fan_avg',distribution='uniform')

     for k in range(10):

         res = tmp

-- 

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