Convert training code to Tensorflow 2

dnn/training_tf2/train_lpcnet.py

@@ -0,0 +1,124 @@
+#!/usr/bin/python3
+'''Copyright (c) 2018 Mozilla
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+   - Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+   - Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE FOUNDATION OR
+   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+'''
+
+# Train a LPCNet model (note not a Wavenet model)
+
+import lpcnet
+import sys
+import numpy as np
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import ModelCheckpoint
+from ulaw import ulaw2lin, lin2ulaw
+import tensorflow.keras.backend as K
+import h5py
+
+import tensorflow as tf
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+  try:
+    tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)])
+  except RuntimeError as e:
+    print(e)
+
+nb_epochs = 120
+
+# Try reducing batch_size if you run out of memory on your GPU
+batch_size = 64
+
+model, _, _ = lpcnet.new_lpcnet_model(training=True)
+
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
+model.summary()
+
+feature_file = sys.argv[1]
+pcm_file = sys.argv[2]     # 16 bit unsigned short PCM samples
+frame_size = model.frame_size
+nb_features = 55
+nb_used_features = model.nb_used_features
+feature_chunk_size = 15
+pcm_chunk_size = frame_size*feature_chunk_size
+
+# u for unquantised, load 16 bit PCM samples and convert to mu-law
+
+data = np.fromfile(pcm_file, dtype='uint8')
+nb_frames = len(data)//(4*pcm_chunk_size)
+
+features = np.fromfile(feature_file, dtype='float32')
+
+# limit to discrete number of frames
+data = data[:nb_frames*4*pcm_chunk_size]
+features = features[:nb_frames*feature_chunk_size*nb_features]
+
+features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
+
+sig = np.reshape(data[0::4], (nb_frames, pcm_chunk_size, 1))
+pred = np.reshape(data[1::4], (nb_frames, pcm_chunk_size, 1))
+in_exc = np.reshape(data[2::4], (nb_frames, pcm_chunk_size, 1))
+out_exc = np.reshape(data[3::4], (nb_frames, pcm_chunk_size, 1))
+del data
+
+print("ulaw std = ", np.std(out_exc))
+
+features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
+features = features[:, :, :nb_used_features]
+features[:,:,18:36] = 0
+
+fpad1 = np.concatenate([features[0:1, 0:2, :], features[:-1, -2:, :]], axis=0)
+fpad2 = np.concatenate([features[1:, :2, :], features[0:1, -2:, :]], axis=0)
+features = np.concatenate([fpad1, features, fpad2], axis=1)
+
+
+periods = (.1 + 50*features[:,:,36:37]+100).astype('int16')
+#periods = np.minimum(periods, 255)
+
+in_data = np.concatenate([sig, pred, in_exc], axis=-1)
+
+del sig
+del pred
+del in_exc
+
+# dump models to disk as we go
+checkpoint = ModelCheckpoint('lpcnet32c_384_10_G16_{epoch:02d}.h5')
+
+#Set this to True to adapt an existing model (e.g. on new data)
+adaptation = False
+
+if adaptation:
+    #Adapting from an existing model
+    model.load_weights('lpcnet24c_384_10_G16_120.h5')
+    sparsify = lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2))
+    lr = 0.0001
+    decay = 0
+else:
+    #Training from scratch
+    sparsify = lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))
+    lr = 0.001
+    decay = 5e-5
+
+model.compile(optimizer=Adam(lr, decay=decay, beta_2=0.99), loss='sparse_categorical_crossentropy')
+model.save_weights('lpcnet32c_384_10_G16_00.h5');
+model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify])