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:
- 9891f9e7e5f6818cc93d6b5062d308d8b135e0b3b9f89fede9c6eb2ae7488b83
- Size of remote file:
- 967 MB
- SHA256:
- 11d7d6386daca5864f47365f80a27aaa00d53af37fac7c0280bb69fab07cb547
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