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LPCNet
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Cleanup 

Signed-off-by: Jean-Marc Valin <jmvalin@jmvalin.ca>
pull/8/head
David Rowe 2018-10-25 16:19:45 -04:00 committed by Jean-Marc Valin
parent 52bccfabf1
commit 3c25449180
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2 changed files with 4 additions and 4 deletions
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4
README.md
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@ -29,14 +29,14 @@ always use ±5% or 10% resampling to augment your data).
1. Now that you have your files, you can do the training with:
```
./train_wavenet_audio.py exc.s8 features.f32 pred.s16 pcm.s16
./train_lpcnet.py exc.s8 features.f32 pred.s16 pcm.s16
```
and it will generate a wavenet*.h5 file for each iteration. If it stops with a
"Failed to allocate RNN reserve space" message try reducing the *batch\_size* variable in train_wavenet_audio.py.
1. You can synthesise speech with:
```
./test_wavenet_audio.py features.f32 > pcm.txt
./test_lpcnet.py features.f32 > pcm.txt
```
The output file pcm.txt contains ASCII PCM samples that need to be converted to WAV for playback

4
src/train_lpcnet.py
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@ -23,7 +23,7 @@ config.gpu_options.per_process_gpu_memory_fraction = 0.44
set_session(tf.Session(config=config))
nb_epochs = 40
nb_epochs = 120
# Try reducing batch_size if you run out of memory on your GPU
batch_size = 64
@ -120,4 +120,4 @@ checkpoint = ModelCheckpoint('lpcnet9_384_10_G16_{epoch:02d}.h5')
#model.load_weights('wavenet4f2_30.h5')
model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=120, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, 0.1)])
model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, 0.1)])