Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
English
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use J3/whisper-tiny-en-US with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use J3/whisper-tiny-en-US with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="J3/whisper-tiny-en-US")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("J3/whisper-tiny-en-US") model = AutoModelForSpeechSeq2Seq.from_pretrained("J3/whisper-tiny-en-US") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6fd27e126969cb17eae48644ce9c5bc7223a6f404e90f8ad43ffdbf6eeb41de5
- Size of remote file:
- 4.09 kB
- SHA256:
- 430198a86e0a05f2fef3148dbe3cc81d577eb53fff3f6e1a49ce8a6443085834
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