LPCNet

Low complexity implementation of the WaveRNN-based LPCNet algorithm, as described in:

J.-M. Valin, J. Skoglund, LPCNet: Improving Neural Speech Synthesis Through Linear Prediction, Submitted for ICASSP 2019, arXiv:1810.11846.

Introduction

Work in progress software for researching low CPU complexity algorithms for speech synthesis and compression by applying Linear Prediction techniques to WaveRNN. High quality speech can be synthesised on regular CPUs (around 3 GFLOP) with SIMD support (AVX, AVX2/FMA, NEON currently supported).

The BSD licensed software is written in C and Python/Keras. For training, a GTX 1080 Ti or better is recommended.

This software is an open source starting point for WaveRNN-based speech synthesis and coding.

NOTE: The repo aims to work with Tacotron2.

Quickstart

Set up a Keras system with GPU.
Generate training data:
```
make dump_data
./dump_data -train input.s16 features.f32 data.u8
```
where the first file contains 16 kHz 16-bit raw PCM audio (no header) and the other files are output files. This program makes several passes over the data with different filters to generate a large amount of training data.
Now that you have your files, train with:
```
./train_lpcnet.py features.f32 data.u8
```
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.
You can synthesise speech with Python and your GPU card:
```
./dump_data -test test_input.s16 test_features.f32
./test_lpcnet.py test_features.f32 test.s16
```
Note the .h5 is hard coded in test_lpcnet.py, modify for your .h file.

Or with C on a CPU: First extract the model files nnet_data.h and nnet_data.c

./dump_lpcnet.py lpcnet15_384_10_G16_64.h5

Then you can make the C synthesiser and try synthesising from a test feature file:

make test_lpcnet
./dump_data -test test_input.s16 test_features.f32
./test_lpcnet test_features.f32 test.s16
ffmpeg -f s16le -ar 16k -ac 1 -i test.s16 test-out.wav

Speech Material for Training LPCNet

Suitable training material can be obtained from the McGill University Telecommunications & Signal Processing Laboratory. Download the ISO and extract the 16k-LP7 directory, the src/concat.sh script can be used to generate a headerless file of training samples.

cd 16k-LP7
sh /path/to/concat.sh

Speech Material for Training Tacotron2

Although the model has 55 dims features when training LPCNet, there are 20 features to be used as input features when inferring the audio. You should enble TACOTRON2 Macro in Makefile to get the features for Training Tacotron2. You also should generate indepent features for every audio when training Tacotron2 other than concatate all features into one file when training LPCNet.

#preprocessing
./header_removal.sh
make dump_data taco=1   # Define TACOTRON2 macro
./feature_extract.sh

#synthesis
make test_lpcnet taco=1 # Define TACOTRON2 macro
./test_lpcnet test_features.f32 test.s16
ffmpeg -f s16le -ar 16k -ac 1 -i test.s16 test-out.wav

Reading Further

LPCNet: DSP-Boosted Neural Speech Synthesis
Sample model files: https://jmvalin.ca/misc_stuff/lpcnet_models/