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125 lines
4.5 KiB
Python
Executable file
125 lines
4.5 KiB
Python
Executable file
#!/usr/bin/python3
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'''Copyright (c) 2018 Mozilla
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR
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CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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'''
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# Train a LPCNet model (note not a Wavenet model)
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import lpcnet
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import sys
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import numpy as np
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from tensorflow.keras.optimizers import Adam
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from tensorflow.keras.callbacks import ModelCheckpoint
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from ulaw import ulaw2lin, lin2ulaw
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import tensorflow.keras.backend as K
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import h5py
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import tensorflow as tf
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gpus = tf.config.experimental.list_physical_devices('GPU')
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if gpus:
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try:
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tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)])
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except RuntimeError as e:
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print(e)
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nb_epochs = 120
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# Try reducing batch_size if you run out of memory on your GPU
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batch_size = 64
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model, _, _ = lpcnet.new_lpcnet_model(training=True)
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
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model.summary()
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feature_file = sys.argv[1]
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pcm_file = sys.argv[2] # 16 bit unsigned short PCM samples
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frame_size = model.frame_size
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nb_features = 55
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nb_used_features = model.nb_used_features
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feature_chunk_size = 15
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pcm_chunk_size = frame_size*feature_chunk_size
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# u for unquantised, load 16 bit PCM samples and convert to mu-law
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data = np.fromfile(pcm_file, dtype='uint8')
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nb_frames = len(data)//(4*pcm_chunk_size)
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features = np.fromfile(feature_file, dtype='float32')
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# limit to discrete number of frames
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data = data[:nb_frames*4*pcm_chunk_size]
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features = features[:nb_frames*feature_chunk_size*nb_features]
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features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
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sig = np.reshape(data[0::4], (nb_frames, pcm_chunk_size, 1))
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pred = np.reshape(data[1::4], (nb_frames, pcm_chunk_size, 1))
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in_exc = np.reshape(data[2::4], (nb_frames, pcm_chunk_size, 1))
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out_exc = np.reshape(data[3::4], (nb_frames, pcm_chunk_size, 1))
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del data
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print("ulaw std = ", np.std(out_exc))
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features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
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features = features[:, :, :nb_used_features]
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features[:,:,18:36] = 0
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fpad1 = np.concatenate([features[0:1, 0:2, :], features[:-1, -2:, :]], axis=0)
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fpad2 = np.concatenate([features[1:, :2, :], features[0:1, -2:, :]], axis=0)
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features = np.concatenate([fpad1, features, fpad2], axis=1)
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periods = (.1 + 50*features[:,:,36:37]+100).astype('int16')
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#periods = np.minimum(periods, 255)
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in_data = np.concatenate([sig, pred, in_exc], axis=-1)
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del sig
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del pred
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del in_exc
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# dump models to disk as we go
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checkpoint = ModelCheckpoint('lpcnet32y_384_10_G16_{epoch:02d}.h5')
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#Set this to True to adapt an existing model (e.g. on new data)
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adaptation = False
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model.load_weights('lpcnet32v_384_10_G16_00.h5')
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if adaptation:
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#Adapting from an existing model
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model.load_weights('lpcnet32v_384_10_G16_100.h5')
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sparsify = lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2))
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lr = 0.0001
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decay = 0
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else:
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#Training from scratch
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sparsify = lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))
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lr = 0.001
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decay = 5e-5
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model.compile(optimizer=Adam(lr, decay=decay, beta_2=0.99), loss='sparse_categorical_crossentropy')
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model.save_weights('lpcnet32y_384_10_G16_00.h5');
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model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify])
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