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managed_components/78__esp-opus/dnn/training_tf2/train_lpcnet.py

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+#!/usr/bin/python3
+'''Copyright (c) 2018 Mozilla
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+   - Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+   - Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE FOUNDATION OR
+   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+'''
+
+# Train an LPCNet model
+
+import argparse
+import os
+
+from dataloader import LPCNetLoader
+
+parser = argparse.ArgumentParser(description='Train an LPCNet model')
+
+parser.add_argument('features', metavar='<features file>', help='binary features file (float32)')
+parser.add_argument('data', metavar='<audio data file>', help='binary audio data file (uint8)')
+parser.add_argument('output', metavar='<output>', help='trained model file (.h5)')
+parser.add_argument('--model', metavar='<model>', default='lpcnet', help='LPCNet model python definition (without .py)')
+group1 = parser.add_mutually_exclusive_group()
+group1.add_argument('--quantize', metavar='<input weights>', help='quantize model')
+group1.add_argument('--retrain', metavar='<input weights>', help='continue training model')
+parser.add_argument('--density', metavar='<global density>', type=float, help='average density of the recurrent weights (default 0.1)')
+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)')
+parser.add_argument('--grub-density', metavar='<global GRU B density>', type=float, help='average density of the recurrent weights (default 1.0)')
+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)')
+parser.add_argument('--grua-size', metavar='<units>', default=384, type=int, help='number of units in GRU A (default 384)')
+parser.add_argument('--grub-size', metavar='<units>', default=16, type=int, help='number of units in GRU B (default 16)')
+parser.add_argument('--cond-size', metavar='<units>', default=128, type=int, help='number of units in conditioning network, aka frame rate network (default 128)')
+parser.add_argument('--epochs', metavar='<epochs>', default=120, type=int, help='number of epochs to train for (default 120)')
+parser.add_argument('--batch-size', metavar='<batch size>', default=128, type=int, help='batch size to use (default 128)')
+parser.add_argument('--end2end', dest='flag_e2e', action='store_true', help='Enable end-to-end training (with differentiable LPC computation')
+parser.add_argument('--lr', metavar='<learning rate>', type=float, help='learning rate')
+parser.add_argument('--decay', metavar='<decay>', type=float, help='learning rate decay')
+parser.add_argument('--gamma', metavar='<gamma>', type=float, help='adjust u-law compensation (default 2.0, should not be less than 1.0)')
+parser.add_argument('--lookahead', metavar='<nb frames>', default=2, type=int, help='Number of look-ahead frames (default 2)')
+parser.add_argument('--logdir', metavar='<log dir>', help='directory for tensorboard log files')
+parser.add_argument('--lpc-gamma', type=float, default=1, help='gamma for LPC weighting')
+parser.add_argument('--cuda-devices', metavar='<cuda devices>', type=str, default=None, help='string with comma separated cuda device ids')
+
+args = parser.parse_args()
+
+# set visible cuda devices
+if args.cuda_devices != None:
+    os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_devices
+
+density = (0.05, 0.05, 0.2)
+if args.density_split is not None:
+    density = args.density_split
+elif args.density is not None:
+    density = [0.5*args.density, 0.5*args.density, 2.0*args.density];
+
+grub_density = (1., 1., 1.)
+if args.grub_density_split is not None:
+    grub_density = args.grub_density_split
+elif args.grub_density is not None:
+    grub_density = [0.5*args.grub_density, 0.5*args.grub_density, 2.0*args.grub_density];
+
+gamma = 2.0 if args.gamma is None else args.gamma
+
+import importlib
+lpcnet = importlib.import_module(args.model)
+
+import sys
+import numpy as np
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger
+from ulaw import ulaw2lin, lin2ulaw
+import tensorflow.keras.backend as K
+import h5py
+
+import tensorflow as tf
+from tf_funcs import *
+from lossfuncs import *
+#gpus = tf.config.experimental.list_physical_devices('GPU')
+#if gpus:
+#  try:
+#    tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)])
+#  except RuntimeError as e:
+#    print(e)
+
+nb_epochs = args.epochs
+
+# Try reducing batch_size if you run out of memory on your GPU
+batch_size = args.batch_size
+
+quantize = args.quantize is not None
+retrain = args.retrain is not None
+
+lpc_order = 16
+
+if quantize:
+    lr = 0.00003
+    decay = 0
+    input_model = args.quantize
+else:
+    lr = 0.001
+    decay = 5e-5
+
+if args.lr is not None:
+    lr = args.lr
+
+if args.decay is not None:
+    decay = args.decay
+
+if retrain:
+    input_model = args.retrain
+
+flag_e2e = args.flag_e2e
+
+opt = Adam(lr, decay=decay, beta_1=0.5, beta_2=0.8)
+strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
+
+with strategy.scope():
+    model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=args.grua_size,
+                                          rnn_units2=args.grub_size,
+                                          batch_size=batch_size, training=True,
+                                          quantize=quantize,
+                                          flag_e2e=flag_e2e,
+                                          cond_size=args.cond_size,
+                                          lpc_gamma=args.lpc_gamma,
+                                          lookahead=args.lookahead
+                                          )
+    if not flag_e2e:
+        model.compile(optimizer=opt, loss=metric_cel, metrics=metric_cel)
+    else:
+        model.compile(optimizer=opt, loss = [interp_mulaw(gamma=gamma), loss_matchlar()], loss_weights = [1.0, 2.0], metrics={'pdf':[metric_cel,metric_icel,metric_exc_sd,metric_oginterploss]})
+    model.summary()
+
+feature_file = args.features
+pcm_file = args.data     # 16 bit unsigned short PCM samples
+frame_size = model.frame_size
+nb_features = model.nb_used_features + lpc_order
+nb_used_features = model.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
+
+data = np.memmap(pcm_file, dtype='int16', mode='r')
+nb_frames = (len(data)//(2*pcm_chunk_size)-1)//batch_size*batch_size
+
+features = np.memmap(feature_file, dtype='float32', mode='r')
+
+# limit to discrete number of frames
+data = data[(4-args.lookahead)*2*frame_size:]
+data = data[:nb_frames*2*pcm_chunk_size]
+
+
+data = np.reshape(data, (nb_frames, pcm_chunk_size, 2))
+
+#print("ulaw std = ", np.std(out_exc))
+
+sizeof = features.strides[-1]
+features = np.lib.stride_tricks.as_strided(features, shape=(nb_frames, feature_chunk_size+4, nb_features),
+                                           strides=(feature_chunk_size*nb_features*sizeof, nb_features*sizeof, sizeof))
+#features = features[:, :, :nb_used_features]
+
+
+periods = (.1 + 50*features[:,:,nb_used_features-2:nb_used_features-1]+100).astype('int16')
+#periods = np.minimum(periods, 255)
+
+# dump models to disk as we go
+checkpoint = ModelCheckpoint('{}_{}_{}.h5'.format(args.output, args.grua_size, '{epoch:02d}'))
+
+if args.retrain is not None:
+    model.load_weights(args.retrain)
+
+if quantize or retrain:
+    #Adapting from an existing model
+    model.load_weights(input_model)
+    if quantize:
+        sparsify = lpcnet.Sparsify(10000, 30000, 100, density, quantize=True)
+        grub_sparsify = lpcnet.SparsifyGRUB(10000, 30000, 100, args.grua_size, grub_density, quantize=True)
+    else:
+        sparsify = lpcnet.Sparsify(0, 0, 1, density)
+        grub_sparsify = lpcnet.SparsifyGRUB(0, 0, 1, args.grua_size, grub_density)
+else:
+    #Training from scratch
+    sparsify = lpcnet.Sparsify(2000, 20000, 400, density)
+    grub_sparsify = lpcnet.SparsifyGRUB(2000, 40000, 400, args.grua_size, grub_density)
+
+model.save_weights('{}_{}_initial.h5'.format(args.output, args.grua_size))
+
+loader = LPCNetLoader(data, features, periods, batch_size, e2e=flag_e2e, lookahead=args.lookahead)
+
+callbacks = [checkpoint, sparsify, grub_sparsify]
+if args.logdir is not None:
+    logdir = '{}/{}_{}_logs'.format(args.logdir, args.output, args.grua_size)
+    tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)
+    callbacks.append(tensorboard_callback)
+
+model.fit(loader, epochs=nb_epochs, validation_split=0.0, callbacks=callbacks)