Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use KoRiF/whisper-tiny-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoRiF/whisper-tiny-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KoRiF/whisper-tiny-en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KoRiF/whisper-tiny-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("KoRiF/whisper-tiny-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca4d0865df28be956d0b2b10d933c709ff5af1478ea32cd1a0b89e4f3e754493
- Size of remote file:
- 3.96 kB
- SHA256:
- 31f830ced4e37c94285c93ceb285649ff6e3ac3be09761146d890b1e2995ff94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.