add some code

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

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+#!/usr/bin/python3
+'''Copyright (c) 2021-2022 Amazon
+   Copyright (c) 2018-2019 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
+#from plc_loader import PLCLoader
+
+parser = argparse.ArgumentParser(description='Train a PLC model')
+
+parser.add_argument('bits', metavar='<bits file>', help='binary features file (int16)')
+parser.add_argument('output', metavar='<output>', help='output features')
+parser.add_argument('--model', metavar='<model>', default='rdovae', help='PLC model python definition (without .py)')
+group1 = parser.add_mutually_exclusive_group()
+group1.add_argument('--weights', metavar='<input weights>', help='model weights')
+parser.add_argument('--cond-size', metavar='<units>', default=1024, type=int, help='number of units in conditioning network (default 1024)')
+parser.add_argument('--batch-size', metavar='<batch size>', default=1, type=int, help='batch size to use (default 128)')
+parser.add_argument('--seq-length', metavar='<sequence length>', default=1000, type=int, help='sequence length to use (default 1000)')
+
+
+args = parser.parse_args()
+
+import importlib
+rdovae = importlib.import_module(args.model)
+
+import sys
+import numpy as np
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger
+import tensorflow.keras.backend as K
+import h5py
+
+import tensorflow as tf
+from rdovae import pvq_quantize
+from rdovae import apply_dead_zone
+
+# Try reducing batch_size if you run out of memory on your GPU
+batch_size = args.batch_size
+
+model, encoder, decoder, qembedding = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size)
+model.load_weights(args.weights)
+
+lpc_order = 16
+nbits=80
+
+
+bits_file = args.bits
+sequence_size = args.seq_length
+
+# u for unquantised, load 16 bit PCM samples and convert to mu-law
+
+
+bits = np.memmap(bits_file + "-syms.f32", dtype='float32', mode='r')
+nb_sequences = len(bits)//(40*sequence_size)//batch_size*batch_size
+bits = bits[:nb_sequences*sequence_size*40]
+
+bits = np.reshape(bits, (nb_sequences, sequence_size//2, 20*4))
+print(bits.shape)
+
+lambda_val = 0.001 * np.ones((nb_sequences, sequence_size//2, 1))
+quant_id = np.round(3.8*np.log(lambda_val/.0002)).astype('int16')
+quant_id = quant_id[:,:,0]
+quant_embed = qembedding(quant_id)
+quant_scale = tf.math.softplus(quant_embed[:,:,:nbits])
+dead_zone = tf.math.softplus(quant_embed[:, :, nbits : 2 * nbits])
+
+bits = bits*quant_scale
+bits = np.round(apply_dead_zone([bits, dead_zone]).numpy())
+bits = bits/quant_scale
+
+
+state = np.memmap(bits_file + "-state.f32", dtype='float32', mode='r')
+
+state = np.reshape(state, (nb_sequences, sequence_size//2, 24))
+state = state[:,-1,:]
+state = pvq_quantize(state, 82)
+#state = state/(1e-15+tf.norm(state, axis=-1,keepdims=True))
+
+print("shapes are:")
+print(bits.shape)
+print(state.shape)
+
+bits = bits[:,1::2,:]
+features = decoder.predict([bits, state], batch_size=batch_size)
+
+features.astype('float32').tofile(args.output)