| --- |
| license: mit |
| tags: |
| - codec |
| - audio_tokenizer |
| - audio_codec |
| --- |
| |
| [](https://github.com/Respaired/Higgs_Codec_Extended) |
|
|
| This is an on-going project. it is a modified version of Higgs-Boson audio tokenizer, you can fully train it. all scripts have been tested. |
| a Few notes however: |
|
|
| - this is not backward compatible with the original checkpoint (I think you can tweak it to be, but you have to adhere to Boson community license if you do.) |
| |
| - I highly recommend you to pretrain the model without the mel and adversarial setup first. it saves you a significant amount of compute, time and speed-up your convergence. raise the batch size as much as you can before the adversarial phase. |
| |
| - for the semantic teacher, I am using ```utter-project/mHuBERT-147``` which has a good multilingual support. if you want the original setup you can change it in the config. |
| |
| - The loss weights and hyperparameters may not be ideal, feel free to play around with different values. |
| |
| I will train a checkpoint on a larger enough dataset one of these days after figuring out a few things first. but the setup is solid. |
|
|
| # Training |
|
|
| ```bash |
| python train_boson_mixed_precision.py --data_csv "yourdata.csv" \ # full path to your audio files, the format can be anything .mp3 .wav .ogg etc. |
| --config config.json --batch_size 42 \ |
| --use_mixed_precision \ |
| --use_discriminator |
| ``` |
|
|
| # Simple Inference |
|
|
| take a look at the notebook |
|
|
| # Batch inference |
| take a look at boson_codeit.py |
| |
| Happy using / training (~~inshallah~~). |
| |
