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

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+# Opus Speech Coding Enhancement
+
+This folder hosts models for enhancing Opus SILK.
+
+## Environment setup
+The code is tested with python 3.11. Conda setup is done via
+
+
+`conda create -n osce python=3.11`
+
+`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. Data is taken from the datasets listed in dnn/datasets.txt and the exact list of items used for training and validation is
+located in dnn/torch/osce/resources.
+
+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.
+
+## 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`.
+
+## Inference
+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
+
+autogen.sh downloads pre-trained model weights to the subfolder dnn/models of the main repo.