ref: 1837dad072c1f2db2d8aef60f0cc555267607c14
parent: b6af21f31c9309e1ee031bb4c0cf51b213cf7f3c
author: Jean-Marc Valin <jmvalin@jmvalin.ca>
date: Tue Jul 24 13:52:33 EDT 2018
audio-domain version
--- /dev/null
+++ b/dnn/train_wavenet_audio.py
@@ -1,0 +1,70 @@
+#!/usr/bin/python3
+
+import wavenet
+import sys
+import numpy as np
+from keras.optimizers import Adam
+from keras.callbacks import ModelCheckpoint
+from ulaw import ulaw2lin, lin2ulaw
+import keras.backend as K
+import h5py
+
+#import tensorflow as tf
+#from keras.backend.tensorflow_backend import set_session
+#config = tf.ConfigProto()
+#config.gpu_options.per_process_gpu_memory_fraction = 0.44
+#set_session(tf.Session(config=config))
+
+nb_epochs = 40
+batch_size = 64
+
+model = wavenet.new_wavenet_model(fftnet=True)
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
+model.summary()
+
+pcmfile = sys.argv[1]
+feature_file = sys.argv[2]
+frame_size = 160
+nb_features = 54
+nb_used_features = wavenet.nb_used_features
+feature_chunk_size = 15
+pcm_chunk_size = frame_size*feature_chunk_size
+
+data = np.fromfile(pcmfile, dtype='int16')
+data = np.minimum(127, lin2ulaw(data[160:]/32768.))
+nb_frames = len(data)//pcm_chunk_size
+
+features = np.fromfile(feature_file, dtype='float32')
+
+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.;
+
+features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
+pitch = 1.*data
+pitch[:320] = 0
+for i in range(2, nb_frames*feature_chunk_size):
+ period = int(50*features[i,36]+100)
+ period = period - 4
+ pitch[i*frame_size:(i+1)*frame_size] = data[i*frame_size-period:(i+1)*frame_size-period]
+in_pitch = np.reshape(pitch/16., (nb_frames, pcm_chunk_size, 1))
+
+in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
+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))
+features = features[:, :, :nb_used_features]
+
+
+#in_data = np.concatenate([in_data, in_pitch], axis=-1)
+
+#with h5py.File('in_data.h5', 'w') as f:+# f.create_dataset('data', data=in_data[:50000, :, :])+# f.create_dataset('feat', data=features[:50000, :, :])+
+checkpoint = ModelCheckpoint('wavenet3c_{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'])
+model.fit([in_data, features], out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])
--- a/dnn/wavenet.py
+++ b/dnn/wavenet.py
@@ -39,15 +39,15 @@
for k in range(10):
res = tmp
dilation = 9-k if fftnet else k
- '''#tmp = Concatenate()([tmp, rfeat])
+ tmp = Concatenate()([tmp, rfeat])
c = GatedConv(units, 2, dilation_rate=2**dilation, activation='tanh', kernel_initializer=init)
- tmp = Dense(units, activation='relu')(c(tmp, cond=rfeat))'''
+ tmp = Dense(units, activation='relu')(c(tmp))
- tmp = Concatenate()([tmp, rfeat])
+ '''tmp = Concatenate()([tmp, rfeat])
c1 = CausalConv(units, 2, dilation_rate=2**dilation, activation='tanh')
c2 = CausalConv(units, 2, dilation_rate=2**dilation, activation='sigmoid')
tmp = Multiply()([c1(tmp), c2(tmp)])
- tmp = Dense(units, activation='relu')(tmp)
+ tmp = Dense(units, activation='relu')(tmp)'''
if k != 0:
tmp = Add()([tmp, res])
--
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