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@ -4,6 +4,7 @@ Low complexity implementation of the WaveRNN-based LPCNet algorithm, as describe
- J.-M. Valin, J. Skoglund, [A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet](https://jmvalin.ca/papers/lpcnet_codec.pdf), *Submitted for INTERSPEECH 2019*.
- J.-M. Valin, J. Skoglund, [LPCNet: Improving Neural Speech Synthesis Through Linear Prediction](https://jmvalin.ca/papers/lpcnet_icassp2019.pdf), *Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, arXiv:1810.11846, 2019.
- J. Skoglund, J.-M. Valin, [Improving Opus Low Bit Rate Quality with Neural Speech Synthesis](https://jmvalin.ca/papers/opusnet.pdf), *Proc. INTERSPEECH*, arxiv:1905.04628, 2020.
# Introduction
@ -23,7 +24,9 @@ You can build the code using:
make
```
Note that the autogen.sh script is used when building from Git and will automatically download the latest model
(models are too large to put in Git).
(models are too large to put in Git). By default, LPCNet will attempt to use 8-bit dot product instructions on AVX*/Neon to
speed up inference. To disable that (e.g. to avoid quantization effects when retraining), add --disable-dot-product to the
configure script.
It is highly recommended to set the CFLAGS environment variable to enable AVX or NEON *prior* to running configure, otherwise
no vectorization will take place and the code will be very slow. On a recent x86 CPU, something like
@ -69,14 +72,14 @@ This codebase is also meant for research and it is possible to train new models.
and it will generate an lpcnet*.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_lpcnet.py.
1. You can synthesise speech with Python and your GPU card:
1. You can synthesise speech with Python and your GPU card (very slow):
```
./dump_data -test test_input.s16 test_features.f32
./src/test_lpcnet.py test_features.f32 test.s16
```
Note the .h5 is hard coded in test_lpcnet.py, modify for your .h5 file.
1. Or with C on a CPU:
1. Or with C on a CPU (C inference is much faster):
First extract the model files nnet_data.h and nnet_data.c
```
./dump_lpcnet.py lpcnet15_384_10_G16_64.h5
@ -95,6 +98,6 @@ sh /path/to/concat.sh
# Reading Further
1. [LPCNet: DSP-Boosted Neural Speech Synthesis](https://people.xiph.org/~jm/demo/lpcnet/)
1. Sample model files:
https://jmvalin.ca/misc_stuff/lpcnet_models/
1. [A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet](https://people.xiph.org/~jm/demo/lpcnet_codec/)
1. Sample model files (check compatibility): https://media.xiph.org/lpcnet/data/