Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use MHaurel/whisper-tiny-english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MHaurel/whisper-tiny-english with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MHaurel/whisper-tiny-english")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("MHaurel/whisper-tiny-english") model = AutoModelForSpeechSeq2Seq.from_pretrained("MHaurel/whisper-tiny-english") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb37a1ee0e833ae1030206e3495e685730a57934d64f918fdc9e04ee02d43f4e
- Size of remote file:
- 5.37 kB
- SHA256:
- 1a05ae06f10ab6f38fb80495b1a775a7f242e8572b8dcc20bee434b9a7221f6c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.