1#!/usr/bin/python3 2'''Copyright (c) 2021-2022 Amazon 3 Copyright (c) 2018-2019 Mozilla 4 5 Redistribution and use in source and binary forms, with or without 6 modification, are permitted provided that the following conditions 7 are met: 8 9 - Redistributions of source code must retain the above copyright 10 notice, this list of conditions and the following disclaimer. 11 12 - Redistributions in binary form must reproduce the above copyright 13 notice, this list of conditions and the following disclaimer in the 14 documentation and/or other materials provided with the distribution. 15 16 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 17 ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 18 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 19 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR 20 CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 21 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 22 PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 23 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 24 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 25 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 26 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27''' 28 29# Train an LPCNet model 30import tensorflow as tf 31strategy = tf.distribute.MultiWorkerMirroredStrategy() 32 33 34import argparse 35#from plc_loader import PLCLoader 36 37parser = argparse.ArgumentParser(description='Train a quantization model') 38 39parser.add_argument('features', metavar='<features file>', help='binary features file (float32)') 40parser.add_argument('output', metavar='<output>', help='trained model file (.h5)') 41parser.add_argument('--model', metavar='<model>', default='rdovae', help='PLC model python definition (without .py)') 42group1 = parser.add_mutually_exclusive_group() 43group1.add_argument('--quantize', metavar='<input weights>', help='quantize model') 44group1.add_argument('--retrain', metavar='<input weights>', help='continue training model') 45parser.add_argument('--cond-size', metavar='<units>', default=1024, type=int, help='number of units in conditioning network (default 1024)') 46parser.add_argument('--epochs', metavar='<epochs>', default=120, type=int, help='number of epochs to train for (default 120)') 47parser.add_argument('--batch-size', metavar='<batch size>', default=128, type=int, help='batch size to use (default 128)') 48parser.add_argument('--seq-length', metavar='<sequence length>', default=1000, type=int, help='sequence length to use (default 1000)') 49parser.add_argument('--lr', metavar='<learning rate>', type=float, help='learning rate') 50parser.add_argument('--decay', metavar='<decay>', type=float, help='learning rate decay') 51parser.add_argument('--logdir', metavar='<log dir>', help='directory for tensorboard log files') 52 53 54args = parser.parse_args() 55 56import importlib 57rdovae = importlib.import_module(args.model) 58 59import sys 60import numpy as np 61from tensorflow.keras.optimizers import Adam 62from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger 63import tensorflow.keras.backend as K 64import h5py 65 66#gpus = tf.config.experimental.list_physical_devices('GPU') 67#if gpus: 68# try: 69# tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)]) 70# except RuntimeError as e: 71# print(e) 72 73nb_epochs = args.epochs 74 75# Try reducing batch_size if you run out of memory on your GPU 76batch_size = args.batch_size 77 78quantize = args.quantize is not None 79retrain = args.retrain is not None 80 81if quantize: 82 lr = 0.00003 83 decay = 0 84 input_model = args.quantize 85else: 86 lr = 0.001 87 decay = 2.5e-5 88 89if args.lr is not None: 90 lr = args.lr 91 92if args.decay is not None: 93 decay = args.decay 94 95if retrain: 96 input_model = args.retrain 97 98 99opt = Adam(lr, decay=decay, beta_2=0.99) 100 101with strategy.scope(): 102 model, encoder, decoder, _ = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size, nb_quant=16) 103 model.compile(optimizer=opt, loss=[rdovae.feat_dist_loss, rdovae.feat_dist_loss, rdovae.sq1_rate_loss, rdovae.sq2_rate_loss], loss_weights=[.5, .5, 1., .1], metrics={'hard_bits':rdovae.sq_rate_metric}) 104 model.summary() 105 106lpc_order = 16 107 108feature_file = args.features 109nb_features = model.nb_used_features + lpc_order 110nb_used_features = model.nb_used_features 111sequence_size = args.seq_length 112 113# u for unquantised, load 16 bit PCM samples and convert to mu-law 114 115 116features = np.memmap(feature_file, dtype='float32', mode='r') 117nb_sequences = len(features)//(nb_features*sequence_size)//batch_size*batch_size 118features = features[:nb_sequences*sequence_size*nb_features] 119 120features = np.reshape(features, (nb_sequences, sequence_size, nb_features)) 121print(features.shape) 122features = features[:, :, :nb_used_features] 123 124#lambda_val = np.repeat(np.random.uniform(.0007, .002, (features.shape[0], 1, 1)), features.shape[1]//2, axis=1) 125#quant_id = np.round(10*np.log(lambda_val/.0007)).astype('int16') 126#quant_id = quant_id[:,:,0] 127quant_id = np.repeat(np.random.randint(16, size=(features.shape[0], 1, 1), dtype='int16'), features.shape[1]//2, axis=1) 128lambda_val = .0002*np.exp(quant_id/3.8) 129quant_id = quant_id[:,:,0] 130 131# dump models to disk as we go 132checkpoint = ModelCheckpoint('{}_{}_{}.h5'.format(args.output, args.cond_size, '{epoch:02d}')) 133 134if args.retrain is not None: 135 model.load_weights(args.retrain) 136 137if quantize or retrain: 138 #Adapting from an existing model 139 model.load_weights(input_model) 140 141model.save_weights('{}_{}_initial.h5'.format(args.output, args.cond_size)) 142 143callbacks = [checkpoint] 144#callbacks = [] 145 146if args.logdir is not None: 147 logdir = '{}/{}_{}_logs'.format(args.logdir, args.output, args.cond_size) 148 tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir) 149 callbacks.append(tensorboard_callback) 150 151model.fit([features, quant_id, lambda_val], [features, features, features, features], batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=callbacks) 152