Instructions to use alphaedge-ai/whisper-tiny-san-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/whisper-tiny-san-16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alphaedge-ai/whisper-tiny-san-16384")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-tiny-san-16384") model = AutoModelForSpeechSeq2Seq.from_pretrained("alphaedge-ai/whisper-tiny-san-16384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fed39148c224aedb603b6a178fb4dc634ae30f111704ff3303a63186b554589
- Size of remote file:
- 48.3 MB
- SHA256:
- ed6979091c0be756f65a02fe3354e594b43765f63cf63e5eee65d857b0d1324f
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