Instructions to use rjac/whisper-medium-BTCv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjac/whisper-medium-BTCv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rjac/whisper-medium-BTCv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rjac/whisper-medium-BTCv2") model = AutoModelForSpeechSeq2Seq.from_pretrained("rjac/whisper-medium-BTCv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rjac/whisper-medium-BTCv2: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/rjac/whisper-medium-BTCv2/resolve/main/training_args.bin
- Command line
-
hf download hf://rjac/whisper-medium-BTCv2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rjac/whisper-medium-BTCv2/resolve/main/training_args.bin
3.77 kB
- Xet hash:
- e622b8857e2debdda7ab89bd30d532d0f1a804a45c21016634d8be267379dcee
- Size of remote file:
- 3.77 kB
- SHA256:
- 418bfaa0a27e2e56eded38c10adf6d13f58ee3df52ed0af31c447676fbf63342
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