Instructions to use emonidi/whisper-tiny-order with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emonidi/whisper-tiny-order with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="emonidi/whisper-tiny-order")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("emonidi/whisper-tiny-order") model = AutoModelForSpeechSeq2Seq.from_pretrained("emonidi/whisper-tiny-order", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e164cf11cd8e3dffe74f3acb4a1cc8baa7f3ec4d51856660294a13ae674eb368
- Size of remote file:
- 5.05 kB
- SHA256:
- 5694683483cff3c9c7a0f623bd2331a82b9bf74979fdc4876065f257a2991b8e
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