Instructions to use alphaedge-ai/whisper-tiny-slk-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/whisper-tiny-slk-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-slk-32768")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-tiny-slk-32768") model = AutoModelForSpeechSeq2Seq.from_pretrained("alphaedge-ai/whisper-tiny-slk-32768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- beca03f9cefa3082f1f5cab28fc044511d811f1b8de666d4571e3fe6cb71e24c
- Size of remote file:
- 60.9 MB
- SHA256:
- fc0539474a790e491314fc394848629ad5fbf1b40c4b6dc4526a5d9a6c0bb018
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