Instructions to use Sagicc/whisper-medium-sr-yodas with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sagicc/whisper-medium-sr-yodas with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sagicc/whisper-medium-sr-yodas")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sagicc/whisper-medium-sr-yodas") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sagicc/whisper-medium-sr-yodas", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Sagicc/whisper-medium-sr-yodas: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/Sagicc/whisper-medium-sr-yodas/resolve/main/training_args.bin
- Command line
-
hf download hf://Sagicc/whisper-medium-sr-yodas/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sagicc/whisper-medium-sr-yodas/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- afd7d7988eb69cc5987f5d0c08b29636041ce074ef401401267c226ff605fdb6
- Size of remote file:
- 4.6 kB
- SHA256:
- aa4b9309059f631f2d4a232a129ffc6aa49c3dfa785b965dbc24e4c985cb3285
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