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Adding command-line options to training script

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This commit is contained in:
Jean-Marc Valin 2021-07-13 03:09:04 -04:00
parent 1edf5d7986
commit 5a51e2eed1
2 changed files with 48 additions and 20 deletions
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7
dnn/training_tf2/dump_lpcnet.py
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@ -229,12 +229,15 @@ def dump_embedding_layer(self, f, hf):
return False return False
Embedding.dump_layer = dump_embedding_layer Embedding.dump_layer = dump_embedding_layer
filename = sys.argv[1]
with h5py.File(filename, "r") as f:
units = min(f['model_weights']['gru_a']['gru_a']['recurrent_kernel:0'].shape)
model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=384) model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=units)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy']) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
#model.summary() #model.summary()
model.load_weights(sys.argv[1]) model.load_weights(filename)
if len(sys.argv) > 2: if len(sys.argv) > 2:
cfile = sys.argv[2]; cfile = sys.argv[2];

61
dnn/training_tf2/train_lpcnet.py
View file

@ -25,9 +25,35 @@
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
''' '''
# Train a LPCNet model (note not a Wavenet model) # Train an LPCNet model
import argparse
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)')
parser.add_argument('--quantize', metavar='<input weights>', help='quantize 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('--grua-size', metavar='<units>', default=384, type=int, help='number of units in GRU A (default 384)')
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)')
args = parser.parse_args()
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];
import importlib
lpcnet = importlib.import_module(args.model)
import lpcnet
import sys import sys
import numpy as np import numpy as np
from tensorflow.keras.optimizers import Adam from tensorflow.keras.optimizers import Adam
@ -44,16 +70,15 @@ import tensorflow as tf
# except RuntimeError as e: # except RuntimeError as e:
# print(e) # print(e)
nb_epochs = 120 nb_epochs = args.epochs
# Try reducing batch_size if you run out of memory on your GPU # Try reducing batch_size if you run out of memory on your GPU
batch_size = 128 batch_size = args.batch_size
#Set this to True to adapt an existing model (e.g. on new data) quantize = args.quantize is not None
adaptation = False
if adaptation: if quantize:
lr = 0.0001 lr = 0.00003
decay = 0 decay = 0
else: else:
lr = 0.001 lr = 0.001
@ -63,12 +88,12 @@ opt = Adam(lr, decay=decay, beta_2=0.99)
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy() strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
with strategy.scope(): with strategy.scope():
model, _, _ = lpcnet.new_lpcnet_model(training=True) model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=args.grua_size, training=True, quantize=quantize)
model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy']) model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics='sparse_categorical_crossentropy')
model.summary() model.summary()
feature_file = sys.argv[1] feature_file = args.features
pcm_file = sys.argv[2] # 16 bit unsigned short PCM samples pcm_file = args.data # 16 bit unsigned short PCM samples
frame_size = model.frame_size frame_size = model.frame_size
nb_features = 55 nb_features = 55
nb_used_features = model.nb_used_features nb_used_features = model.nb_used_features
@ -115,15 +140,15 @@ del pred
del in_exc del in_exc
# dump models to disk as we go # dump models to disk as we go
checkpoint = ModelCheckpoint('lpcnet33e_384_{epoch:02d}.h5') checkpoint = ModelCheckpoint('{}_{}_{}.h5'.format(args.output, args.grua_size, '{epoch:02d}'))
if adaptation: if quantize:
#Adapting from an existing model #Adapting from an existing model
model.load_weights('lpcnet33a_384_100.h5') model.load_weights(args.quantize)
sparsify = lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2)) sparsify = lpcnet.Sparsify(0, 0, 1, density)
else: else:
#Training from scratch #Training from scratch
sparsify = lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2)) sparsify = lpcnet.Sparsify(2000, 40000, 400, density)
model.save_weights('lpcnet33e_384_00.h5'); model.save_weights('{}_{}_initial.h5'.format(args.output, args.grua_size))
model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify]) model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify])