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237245f815
Not sure why CuDNNGRU doesn't get used by default, but we need to explicitly use it to get things to run fast.
129 lines
4.5 KiB
Python
Executable file
129 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 = 128
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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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if adaptation:
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lr = 0.0001
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decay = 0
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else:
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lr = 0.001
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decay = 2.5e-5
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opt = Adam(lr, decay=decay, beta_2=0.99)
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strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
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with strategy.scope():
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model, _, _ = lpcnet.new_lpcnet_model(training=True)
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model.compile(optimizer=opt, 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)//batch_size*batch_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('lpcnet33e_384_{epoch:02d}.h5')
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if adaptation:
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#Adapting from an existing model
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model.load_weights('lpcnet33a_384_100.h5')
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sparsify = lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2))
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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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model.save_weights('lpcnet33e_384_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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