Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use bayerasif/whisper-tiny-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bayerasif/whisper-tiny-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bayerasif/whisper-tiny-en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bayerasif/whisper-tiny-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("bayerasif/whisper-tiny-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b821627c33c7d24641a51401166e1e462a507eef1070a43f3be8b3e2fb19c56
- Size of remote file:
- 151 MB
- SHA256:
- 9f90bac6bf0303fc117d3c879ce151f759544ed792e8c5dc50404a2821ba3415
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