Rename the current files to use the LPCNet name since they're no longer WaveNet

dnn/train_lpcnet.py

@@ -0,0 +1,127 @@
+#!/usr/bin/python3
+# train_wavenet_audio.py
+# Jean-Marc Valin
+#
+# Train a CELPNet model (note not a Wavenet model)
+
+import wavenet
+import lpcnet
+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()
+
+# use this option to reserve GPU memory, e.g. for running more than
+# one thing at a time.  Best to disable for GPUs with small memory
+config.gpu_options.per_process_gpu_memory_fraction = 0.44
+
+set_session(tf.Session(config=config))
+
+nb_epochs = 40
+
+# Try reducing batch_size if you run out of memory on your GPU
+batch_size = 64
+
+# Note we are creating a CELPNet model
+
+#model = wavenet.new_wavenet_model(fftnet=True)
+model, _, _ = lpcnet.new_wavernn_model()
+
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
+model.summary()
+
+exc_file = sys.argv[1]     # not used at present
+feature_file = sys.argv[2]
+pred_file = sys.argv[3]    # LPC predictor samples. Not used at present, see below
+pcm_file = sys.argv[4]     # 16 bit unsigned short PCM samples
+frame_size = 160
+nb_features = 55
+nb_used_features = lpcnet.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
+
+udata = np.fromfile(pcm_file, dtype='int16')
+data = lin2ulaw(udata)
+nb_frames = len(data)//pcm_chunk_size
+
+features = np.fromfile(feature_file, dtype='float32')
+
+# limit to discrete number of frames
+data = data[:nb_frames*pcm_chunk_size]
+udata = udata[:nb_frames*pcm_chunk_size]
+features = features[:nb_frames*feature_chunk_size*nb_features]
+
+# Noise injection: the idea is that the real system is going to be
+# predicting samples based on previously predicted samples rather than
+# from the original. Since the previously predicted samples aren't
+# expected to be so good, I add noise to the training data.  Exactly
+# how the noise is added makes a huge difference
+
+in_data = np.concatenate([data[0:1], data[:-1]]);
+noise = np.concatenate([np.zeros((len(data)*1//5)), np.random.randint(-3, 3, len(data)*1//5), np.random.randint(-2, 2, len(data)*1//5), np.random.randint(-1, 1, len(data)*2//5)])
+#noise = np.round(np.concatenate([np.zeros((len(data)*1//5)), np.random.laplace(0, 1.2, len(data)*1//5), np.random.laplace(0, .77, len(data)*1//5), np.random.laplace(0, .33, len(data)*1//5), np.random.randint(-1, 1, len(data)*1//5)]))
+in_data = in_data + noise
+in_data = np.clip(in_data, 0, 255)
+
+features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
+
+# Note: the LPC predictor output is now calculated by the loop below, this code was
+# for an ealier version that implemented the prediction filter in C
+
+upred = np.fromfile(pred_file, dtype='int16')
+upred = upred[:nb_frames*pcm_chunk_size]
+
+# Use 16th order LPC to generate LPC prediction output upred[] and (in
+# mu-law form) pred[]
+
+pred_in = ulaw2lin(in_data)
+for i in range(2, nb_frames*feature_chunk_size):
+    upred[i*frame_size:(i+1)*frame_size] = 0
+    for k in range(16):
+        upred[i*frame_size:(i+1)*frame_size] = upred[i*frame_size:(i+1)*frame_size] - \
+            pred_in[i*frame_size-k:(i+1)*frame_size-k]*features[i, nb_features-16+k]
+
+pred = lin2ulaw(upred)
+
+in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
+in_data = in_data.astype('uint8')
+
+# LPC residual, which is the difference between the input speech and
+# the predictor output, with a slight time shift this is also the
+# ideal excitation in_exc
+
+out_data = lin2ulaw(udata-upred)
+in_exc = np.concatenate([out_data[0:1], out_data[:-1]]);
+
+out_data = np.reshape(out_data, (nb_frames, pcm_chunk_size, 1))
+out_data = out_data.astype('uint8')
+
+in_exc = np.reshape(in_exc, (nb_frames, pcm_chunk_size, 1))
+in_exc = in_exc.astype('uint8')
+
+
+features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
+features = features[:, :, :nb_used_features]
+features[:,:,18:36] = 0
+pred = np.reshape(pred, (nb_frames, pcm_chunk_size, 1))
+pred = pred.astype('uint8')
+
+periods = (50*features[:,:,36:37]+100).astype('int16')
+
+in_data = np.concatenate([in_data, pred], axis=-1)
+
+# dump models to disk as we go
+checkpoint = ModelCheckpoint('lpcnet9_384_10_G16_{epoch:02d}.h5')
+
+#model.load_weights('wavenet4f2_30.h5')
+model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
+model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=120, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, 0.1)])