opus/dnn/torch/rdovae

Deep REDundancy (DRED) with RDO-VAE

This is a rate-distortion-optimized variational autoencoder (RDO-VAE) designed to coding redundancy information. Pre-trained models are provided as C code in the dnn/ directory with the corresponding model in dnn/models/ directory (name starts with rdovae_). If you don't want to train a new DRED model, you can skip straight to the Inference section.

Data preparation

First, fetch all the data from the datasets.txt file using:

./download_datasets.sh

Then concatenate and resample the data into a single 16-kHz file:

./process_speech.sh

The script will produce an all_speech.pcm speech file in raw 16-bit PCM format.

For data preparation you need to build Opus as detailed in the top-level README. You will need to use the --enable-dred configure option. The build will produce an executable named "dump_data". To prepare the training data, run:

./dump_data -train all_speech.pcm all_features.f32 /dev/null

Training

To perform training, run the following command:

python ./train_rdovae.py --sequence-length 400 --split-mode random_split --state-dim 80 --batch-size 512 --epochs 400 --lambda-max 0.04 --lr 0.003 --lr-decay-factor 0.0001 all_features.f32 output_dir

The final model will be in output_dir/checkpoints/chechpoint_400.pth.

The model can be converted to C using:

python export_rdovae_weights.py output_dir/checkpoints/chechpoint_400.pth dred_c_dir

which will create a number of C source and header files in the fargan_c_dir directory. Copy these files to the opus/dnn/ directory (replacing the existing ones) and recompile Opus.

Inference

DRED is integrated within the Opus codec and can be evaluated using the opus_demo executable. For example:

./opus_demo voip 16000 1 64000 -loss 50 -dred 100 -sim_loss 50 input.pcm output.pcm

Will tell the encoder to encode a 16 kHz raw audio file at 64 kb/s using up to 1 second of redundancy (units are based on 10-ms) and then simulate 50% loss. Refer to opus_demo --help for more details.