Instructions to use alphaedge-ai/whisper-small-mkd-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/whisper-small-mkd-32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alphaedge-ai/whisper-small-mkd-32768")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-small-mkd-32768") model = AutoModelForSpeechSeq2Seq.from_pretrained("alphaedge-ai/whisper-small-mkd-32768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05a168834e538a2fa55878d0795cf18d457e41a465ddba766935a93999a1e70d
- Size of remote file:
- 454 MB
- SHA256:
- 902865a6c32c16c84d3c90260cead1894b8b45370413468363c627674f03a798
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