## How to get the models### For Indian LanguagesStore the models and the respective dictionary files in the folder `asr/1/models`. The languages are listed on the website `https://asr.iitm.ac.in/models`The base url for the model is `wget https://asr.iitm.ac.in/SPRING_INX/models/fine_tuned/SPRING_INX_data2vec_aqc_Bengali.pt`For dictionar is `wget https://asr.iitm.ac.in/SPRING_INX/models/dictionaries/SPRING_INX_Urdu_dict.txt`### For english whisperPlace the model files in `asr/1/whisper_models`* Install `faster_whisper`* In a python interpreter import `faster_whisper` and run `model = faster_whisper.WhisperModel('large-v3', device='cuda', compute_type="int8", device_index=1, download_root='/path/to/repo/asr/1/whisper_models')`* That will store the model files in that custom location## To create the environment* Install conda pack* Create a new python 3.10 environment* Clone `https://github.com/Speech-Lab-IITM/data2vec-aqc/tree/master` and `git apply` the patch `aqc.patch`* You can now create the wheel using `python setup.py bdist_wheel`* Do the same for `https://github.com/Spijkervet/torchaudio-augmentations`* Do the same for `https://github.com/flashlight/sequence`* And do `pip install git+https://github.com/kpu/kenlm.git fast_pytorch_kmeans tensorboardX flashlight-text soundfile torchaudio data2vec-aqc/dist/fairseq-0.12.2-cp310-cp310-linux_x86_64.whl sequence/dist/flashlight_sequence-0.0.0+91e2b0f.d20240210-cp310-cp310-linux_x86_64.whl torchaudio-augmentations/dist/torchaudio_augmentations-0.2.4-py3-none-any.whl faster-whisper`* Finally use conda pack to save the env.
