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 pytorch_model.bin from FrancescoBonzi/whisper-tiny-en: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/FrancescoBonzi/whisper-tiny-en/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://FrancescoBonzi/whisper-tiny-en/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/FrancescoBonzi/whisper-tiny-en/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- 00d23f14bd72d1738b29fcfd71331638f47536fb1975c7c2f17671bdee4d43a6
- Size of remote file:
- 151 MB
- SHA256:
- d8f28f5308c8354c3f078f19bbd82bf7696670e08974c73e4b4932e17b7c6f66
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