shithub: opus

--- a/dnn/training_tf2/dump_lpcnet.py

+++ b/dnn/training_tf2/dump_lpcnet.py

@@ -229,12 +229,15 @@

     return False

 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.summary()

-model.load_weights(sys.argv[1])

+model.load_weights(filename)

 if len(sys.argv) > 2:

     cfile = sys.argv[2];

--- a/dnn/training_tf2/train_lpcnet.py

+++ b/dnn/training_tf2/train_lpcnet.py

@@ -25,9 +25,35 @@

    SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

'''

-# Train a LPCNet model (note not a Wavenet model)

+# Train an LPCNet model

-import lpcnet

+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 sys

 import numpy as np

 from tensorflow.keras.optimizers import Adam

@@ -44,16 +70,15 @@

 #  except RuntimeError as e:

 #    print(e)

-nb_epochs = 120

+nb_epochs = args.epochs

 # 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)

-adaptation = False

+quantize = args.quantize is not None

-if adaptation:

-    lr = 0.0001

+if quantize:

+    lr = 0.00003

     decay = 0

 else:

     lr = 0.001

@@ -63,12 +88,12 @@

 strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()

 with strategy.scope():

-    model, _, _ = lpcnet.new_lpcnet_model(training=True)

-    model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])

+    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_crossentropy')

     model.summary()

-feature_file = sys.argv[1]

-pcm_file = sys.argv[2]     # 16 bit unsigned short PCM samples

+feature_file = args.features

+pcm_file = args.data     # 16 bit unsigned short PCM samples

 frame_size = model.frame_size

 nb_features = 55

 nb_used_features = model.nb_used_features

@@ -115,15 +140,15 @@

 del in_exc

 # 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

-    model.load_weights('lpcnet33a_384_100.h5')

-    sparsify = lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2))

+    model.load_weights(args.quantize)

+    sparsify = lpcnet.Sparsify(0, 0, 1, density)

 else:

     #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])

--

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