Instructions to use thunderboltc/whisper-small-santali-sanlish-frozen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thunderboltc/whisper-small-santali-sanlish-frozen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="thunderboltc/whisper-small-santali-sanlish-frozen")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("thunderboltc/whisper-small-santali-sanlish-frozen") model = AutoModelForSpeechSeq2Seq.from_pretrained("thunderboltc/whisper-small-santali-sanlish-frozen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3445499a6201c9fd319d109234144e30589fb76f58c959055b80f987e4522f7e
- Size of remote file:
- 5.46 kB
- SHA256:
- 2c6e5304b46994e66d1a3f6876d5fff55e890e8f155532c01a65fc8d8522f541
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