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153 lines
6.5 KiB
Python
Executable file
153 lines
6.5 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 an LPCNet model
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import argparse
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parser = argparse.ArgumentParser(description='Train an LPCNet model')
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parser.add_argument('features', metavar='<features file>', help='binary features file (float32)')
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parser.add_argument('data', metavar='<audio data file>', help='binary audio data file (uint8)')
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parser.add_argument('output', metavar='<output>', help='trained model file (.h5)')
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parser.add_argument('--model', metavar='<model>', default='lpcnet', help='LPCNet model python definition (without .py)')
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parser.add_argument('--quantize', metavar='<input weights>', help='quantize model')
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parser.add_argument('--density', metavar='<global density>', type=float, help='average density of the recurrent weights (default 0.1)')
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parser.add_argument('--density-split', nargs=3, metavar=('<update>', '<reset>', '<state>'), type=float, help='density of each recurrent gate (default 0.05, 0.05, 0.2)')
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parser.add_argument('--grub-density', metavar='<global GRU B density>', type=float, help='average density of the recurrent weights (default 1.0)')
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parser.add_argument('--grub-density-split', nargs=3, metavar=('<update>', '<reset>', '<state>'), type=float, help='density of each GRU B input gate (default 1.0, 1.0, 1.0)')
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parser.add_argument('--grua-size', metavar='<units>', default=384, type=int, help='number of units in GRU A (default 384)')
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parser.add_argument('--grub-size', metavar='<units>', default=16, type=int, help='number of units in GRU B (default 16)')
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parser.add_argument('--epochs', metavar='<epochs>', default=120, type=int, help='number of epochs to train for (default 120)')
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parser.add_argument('--batch-size', metavar='<batch size>', default=128, type=int, help='batch size to use (default 128)')
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args = parser.parse_args()
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density = (0.05, 0.05, 0.2)
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if args.density_split is not None:
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density = args.density_split
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elif args.density is not None:
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density = [0.5*args.density, 0.5*args.density, 2.0*args.density];
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grub_density = (1., 1., 1.)
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if args.grub_density_split is not None:
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grub_density = args.grub_density_split
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elif args.grub_density is not None:
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grub_density = [0.5*args.grub_density, 0.5*args.grub_density, 2.0*args.grub_density];
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import importlib
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lpcnet = importlib.import_module(args.model)
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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 = args.epochs
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# Try reducing batch_size if you run out of memory on your GPU
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batch_size = args.batch_size
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quantize = args.quantize is not None
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if quantize:
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lr = 0.00003
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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(rnn_units1=args.grua_size, rnn_units2=args.grub_size, training=True, quantize=quantize)
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model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics='sparse_categorical_crossentropy')
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model.summary()
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feature_file = args.features
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pcm_file = args.data # 16 bit unsigned short PCM samples
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frame_size = model.frame_size
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nb_features = 36
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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.memmap(pcm_file, dtype='uint8', mode='r')
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nb_frames = (len(data)//(4*pcm_chunk_size)-1)//batch_size*batch_size
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features = np.memmap(feature_file, dtype='float32', mode='r')
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# limit to discrete number of frames
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data = data[4*2*frame_size:]
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data = data[:nb_frames*4*pcm_chunk_size]
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data = np.reshape(data, (nb_frames, pcm_chunk_size, 4))
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in_data = data[:,:,:3]
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out_exc = data[:,:,3:4]
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print("ulaw std = ", np.std(out_exc))
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sizeof = features.strides[-1]
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features = np.lib.stride_tricks.as_strided(features, shape=(nb_frames, feature_chunk_size+4, nb_features),
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strides=(feature_chunk_size*nb_features*sizeof, nb_features*sizeof, sizeof))
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features = features[:, :, :nb_used_features]
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periods = (.1 + 50*features[:,:,18:19]+100).astype('int16')
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#periods = np.minimum(periods, 255)
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# dump models to disk as we go
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checkpoint = ModelCheckpoint('{}_{}_{}.h5'.format(args.output, args.grua_size, '{epoch:02d}'))
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if quantize:
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#Adapting from an existing model
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model.load_weights(args.quantize)
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sparsify = lpcnet.Sparsify(0, 0, 1, density)
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grub_sparsify = lpcnet.SparsifyGRUB(0, 0, 1, args.grua_size, grub_density)
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else:
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#Training from scratch
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sparsify = lpcnet.Sparsify(2000, 40000, 400, density)
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grub_sparsify = lpcnet.SparsifyGRUB(2000, 40000, 400, args.grua_size, grub_density)
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model.save_weights('{}_{}_initial.h5'.format(args.output, args.grua_size))
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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, grub_sparsify])
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