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updated osce readme

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# Opus Speech Coding Enhancement
This folder hosts models for enhancing Opus SILK. See related Opus repo https://gitlab.xiph.org/xiph/opus/-/tree/exp-neural-silk-enhancement
for feature generation.
This folder hosts models for enhancing Opus SILK.
## Environment setup
The code is tested with python 3.11. Conda setup is done via
@ -12,3 +11,53 @@ The code is tested with python 3.11. Conda setup is done via
`conda activate osce`
`python -m pip install -r requirements.txt`
## Generating training data
First step is to convert all training items to 16 kHz and 16 bit pcm and then concatenate them. A convenient way to do this is to create a file list and then run
`python scripts/concatenator.py filelist 16000 dataset/clean.s16 --db_min -40 --db_max 0`
which on top provides some random scaling.
Second step is to run a patched version of opus_demo in the dataset folder, which will produce the coded output and add feature files. To build the patched opus_demo binary, check out the exp-neural-silk-enhancement branch and build opus_demo the usual way. Then run
`cd dataset && <path_to_patched_opus_demo>/opus_demo voip 16000 1 9000 -silk_random_switching 249 clean.s16 coded.s16 `
The argument to -silk_random_switching specifies the number of frames after which parameters are switched randomly.
## Generating inference data
Generating inference data is analogous to generating training data. Given an item 'item1.wav' run
`mkdir item1.se && sox item1.wav -r 16000 -e signed-integer -b 16 item1.raw && cd item1.se && <path_to_patched_opus_demo>/opus_demo voip 16000 1 <bitrate> ../item1.raw noisy.s16`
The folder item1.se then serves as input for the test_model.py script or for the --testdata argument of train_model.py resp. adv_train_model.py
## Regression loss based training
Create a default setup for LACE or NoLACE via
`python make_default_setup.py model.yml --model lace/nolace --path2dataset <path2dataset>`
Then run
`python train_model.py model.yml <output folder> --no-redirect`
for running the training script in foreground or
`nohup python train_model.py model.yml <output folder> &`
to run it in background. In the latter case the output is written to `<output folder>/out.txt`.
## Adversarial training (NoLACE only)
Create a default setup for NoLACE via
`python make_default_setup.py nolace_adv.yml --model nolace --adversarial --path2dataset <path2dataset>`
Then run
`python adv_train_model.py nolace_adv.yml <output folder> --no-redirect`
for running the training script in foreground or
`nohup python adv_train_model.py nolace_adv.yml <output folder> &`
to run it in background. In the latter case the output is written to `<output folder>/out.txt`.