Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Greek
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use M2LabOrg/whisper-small-el with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M2LabOrg/whisper-small-el with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="M2LabOrg/whisper-small-el")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("M2LabOrg/whisper-small-el") model = AutoModelForSpeechSeq2Seq.from_pretrained("M2LabOrg/whisper-small-el", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d0502e1eff4b95e630231c1719ce8f0527da5d836259473767bbd3f47e30f56
- Size of remote file:
- 967 MB
- SHA256:
- f464c54e28e8a10a62eb29179fe153494032f272ef8869d5aab7f136f7619cbe
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