ref: d9df94947de8a754af8ab35b9ca1808d43a26f9e
dir: /dnn/torch/rdovae/README.md/
# 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.