Update DRED REDME.md

Author: jmvalin

dnn/torch/fargan/README.md

@@ -1,6 +1,9 @@
 # Framewise Auto-Regressive GAN (FARGAN)
 
-Implementation of FARGAN, a low-complexity neural vocoder.
+Implementation of FARGAN, a low-complexity neural vocoder. Pre-trained models
+are provided as C code in the dnn/ directory with the corresponding model in
+dnn/models/ directory (name starts with fargan_). If you don't want to train
+a new FARGAN model, you can skip straight to the Inference section.
 
 ## Data preparation
 

dnn/torch/rdovae/README.md

@@ -1,24 +1,48 @@
-# Rate-Distortion-Optimized Variational Auto-Encoder
+# Deep REDundancy (DRED) with RDO-VAE
 
-## Setup
-The python code requires python >= 3.6 and has been tested with python 3.6 and python 3.10. To install requirements run
+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
+
+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:
 ```
-python -m pip install -r requirements.txt
+./dump_data -train in_speech.pcm out_features.f32 out_speech.pcm
 ```
+Where the in_speech.pcm speech file is a raw 16-bit PCM file sampled at 16 kHz.
+The speech data used for training the model can be found at:
+https://media.xiph.org/lpcnet/speech/tts_speech_negative_16k.sw
+The out_speech.pcm file isn't needed for DRED, but it is needed to train
+the FARGAN vocoder (see dnn/torch/fargan/ for details).
 
 ## Training
-To generate training data use dump date from the main LPCNet repo
+
+To perform training, run the following command:
 ```
-./dump_data -train 16khz_speech_input.s16 features.f32 data.s16
+python ./train_rdovae.py --cuda-visible-devices 0 --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 out_features.f32 output_dir
 ```
+The final model will be in output_dir/checkpoints/chechpoint_400.pth.
 
-To train the model, simply run
+The model can be converted to C using:
 ```
-python train_rdovae.py features.f32 output_folder
+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.
 
-To train on CUDA device add `--cuda-visible-devices idx`.
+## Inference
 
-
-## ToDo
-- Upload checkpoints and add URLs
+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.