Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use FrancescoBonzi/whisper-tiny-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FrancescoBonzi/whisper-tiny-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="FrancescoBonzi/whisper-tiny-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("FrancescoBonzi/whisper-tiny-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("FrancescoBonzi/whisper-tiny-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from FrancescoBonzi/whisper-tiny-en: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/FrancescoBonzi/whisper-tiny-en/resolve/main/training_args.bin
- Command line
-
hf download hf://FrancescoBonzi/whisper-tiny-en/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/FrancescoBonzi/whisper-tiny-en/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- abde6ff15dbdcba8310b6b9867bf03609a8f7ffb57643c15566bf6a4e4b3a2ec
- Size of remote file:
- 4.09 kB
- SHA256:
- 3e5580d23578c51b792f68adf66fbcebfb8d4377cc309c3c2a04e47db4abb49a
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