Instructions to use mitchelldehaven/whisper-large-v2-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mitchelldehaven/whisper-large-v2-ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mitchelldehaven/whisper-large-v2-ru")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mitchelldehaven/whisper-large-v2-ru") model = AutoModelForSpeechSeq2Seq.from_pretrained("mitchelldehaven/whisper-large-v2-ru") - Notebooks
- Google Colab
- Kaggle
translation of the model for use in faster-whisper.
Hello! I think you are familiar with faster-whisper algorithms. The developer of this software has his own site on GitHub, where he uses the same large-v2 and other models, only converts them first, and even attached software in Python, with which you can convert ordinary whisper ones.As a Russian user, I really liked your work, your model! but I can’t translate it for use in faster-whisper (for my work in law, it is preferable to work with this version). As the developer of your Russian model, can you try to translate your model for faster-whisper? for you as a developer, it may be an interesting experience and you will help me out a lot! Thank you. here is his github page
https://github.com/guillaumekln/faster-whisper#model-conversion
I myself transformed your model for use in faster whisper, but after that it worked poorly.
I'm sorry, I'm not really familiar with faster-whisper, so I'm not sure how to do the conversion process.
I would suggest simply using the v3 model once that is converted (which I assume it likely is), as that will likely have better Russian performance.