Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use sumet/whisper-tiny-en-US with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumet/whisper-tiny-en-US with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sumet/whisper-tiny-en-US")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sumet/whisper-tiny-en-US") model = AutoModelForSpeechSeq2Seq.from_pretrained("sumet/whisper-tiny-en-US") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d08bcf2cecc5fa342ce6c9b6c351faa1354b4a15f49d432971bdc42c0fd32413
- Size of remote file:
- 151 MB
- SHA256:
- 193b8bc3e9084bca78915937c56a4d62488eb6a90fd6ab4a37bb9f7ed6b050ca
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