shithub: opus

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restructured osce readme

--- a/dnn/torch/osce/README.md

+++ b/dnn/torch/osce/README.md

@@ -26,14 +26,6 @@

 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

-

-Checkpoints of pre-trained models are located here https://media.xiph.org/lpcnet/models/lace-20231019.tar.gz.

-

 ## Regression loss based training

 Create a default setup for LACE or NoLACE via

@@ -62,4 +54,12 @@

 `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`.

\ No newline at end of file

+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

+

+Checkpoints of pre-trained models are located here: https://media.xiph.org/lpcnet/models/lace-20231019.tar.gz

\ No newline at end of file

-- 

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