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managed_components/78__esp-opus/dnn/torch/rdovae/README.md

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+# 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.