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Adding option to change frame rate network size

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This commit is contained in:
Jean-Marc Valin 2021-10-13 02:44:51 -04:00
parent fe7b54c0e8
commit b24e53fdfa
3 changed files with 10 additions and 8 deletions
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3
dnn/training_tf2/dump_lpcnet.py
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@ -249,8 +249,9 @@ filename = sys.argv[1]
with h5py.File(filename, "r") as f:
units = min(f['model_weights']['gru_a']['gru_a']['recurrent_kernel:0'].shape)
units2 = min(f['model_weights']['gru_b']['gru_b']['recurrent_kernel:0'].shape)
cond_size = min(f['model_weights']['feature_dense1']['feature_dense1']['kernel:0'].shape)
model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=units, rnn_units2=units2, flag_e2e = flag_e2e)
model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=units, rnn_units2=units2, flag_e2e = flag_e2e, cond_size=cond_size)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
#model.summary()

12
dnn/training_tf2/lpcnet.py
View file

@ -230,24 +230,24 @@ class WeightClip(Constraint):
constraint = WeightClip(0.992)
def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_size=128, training=False, adaptation=False, quantize=False, flag_e2e = False):
def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_size=128, training=False, adaptation=False, quantize=False, flag_e2e = False, cond_size=128):
pcm = Input(shape=(None, 3), batch_size=batch_size)
feat = Input(shape=(None, nb_used_features), batch_size=batch_size)
pitch = Input(shape=(None, 1), batch_size=batch_size)
dec_feat = Input(shape=(None, 128))
dec_feat = Input(shape=(None, cond_size))
dec_state1 = Input(shape=(rnn_units1,))
dec_state2 = Input(shape=(rnn_units2,))
padding = 'valid' if training else 'same'
fconv1 = Conv1D(128, 3, padding=padding, activation='tanh', name='feature_conv1')
fconv2 = Conv1D(128, 3, padding=padding, activation='tanh', name='feature_conv2')
fconv1 = Conv1D(cond_size, 3, padding=padding, activation='tanh', name='feature_conv1')
fconv2 = Conv1D(cond_size, 3, padding=padding, activation='tanh', name='feature_conv2')
pembed = Embedding(256, 64, name='embed_pitch')
cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))])
cfeat = fconv2(fconv1(cat_feat))
fdense1 = Dense(128, activation='tanh', name='feature_dense1')
fdense2 = Dense(128, activation='tanh', name='feature_dense2')
fdense1 = Dense(cond_size, activation='tanh', name='feature_dense1')
fdense2 = Dense(cond_size, activation='tanh', name='feature_dense2')
if flag_e2e and quantize:
fconv1.trainable = False

3
dnn/training_tf2/train_lpcnet.py
View file

@ -45,6 +45,7 @@ parser.add_argument('--grub-density', metavar='<global GRU B density>', type=flo
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')
@ -121,7 +122,7 @@ opt = Adam(lr, decay=decay, beta_2=0.99)
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)
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)
if not flag_e2e:
model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics='sparse_categorical_crossentropy')
else: