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Hard quantization for training

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Also, using stateful GRU to randomize initialization
This commit is contained in:
Jean-Marc Valin 2021-10-04 02:53:46 -04:00
parent 3b8d64d746
commit c5a17a0716
3 changed files with 77 additions and 28 deletions
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18
dnn/training_tf2/train_lpcnet.py
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@ -28,6 +28,7 @@
# Train an LPCNet model
import argparse
from dataloader import LPCNetLoader
parser = argparse.ArgumentParser(description='Train an LPCNet model')
@ -148,10 +149,10 @@ data = data[:nb_frames*4*pcm_chunk_size]
data = np.reshape(data, (nb_frames, pcm_chunk_size, 4))
in_data = data[:,:,:3]
out_exc = data[:,:,3:4]
#in_data = data[:,:,:3]
#out_exc = data[:,:,3:4]
print("ulaw std = ", np.std(out_exc))
#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),
@ -171,8 +172,12 @@ if args.retrain is not None:
if quantize or retrain:
#Adapting from an existing model
model.load_weights(input_model)
sparsify = lpcnet.Sparsify(0, 0, 1, density)
grub_sparsify = lpcnet.SparsifyGRUB(0, 0, 1, args.grua_size, grub_density)
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, 40000, 400, density)
@ -180,4 +185,5 @@ else:
model.save_weights('{}_{}_initial.h5'.format(args.output, args.grua_size))
csv_logger = CSVLogger('training_vals.log')
model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify, grub_sparsify, csv_logger])
loader = LPCNetLoader(data, features, periods, batch_size)
model.fit(loader, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify, grub_sparsify, csv_logger])