Instructions to use alphaedge-ai/whisper-tiny-mon-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/whisper-tiny-mon-32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alphaedge-ai/whisper-tiny-mon-32768")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-tiny-mon-32768") model = AutoModelForSpeechSeq2Seq.from_pretrained("alphaedge-ai/whisper-tiny-mon-32768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 978cd8a551748c336738819f36843e5867b5ed01330f907b3fdd6b7418b30e5c
- Size of remote file:
- 60.9 MB
- SHA256:
- c603b69d289a1cfd7fe86d50528d37a2d1362ec4dfd26b702438b25a946e8d2b
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