Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Khmer
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use ksoky/whisper-small-km with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ksoky/whisper-small-km with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ksoky/whisper-small-km")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ksoky/whisper-small-km") model = AutoModelForMultimodalLM.from_pretrained("ksoky/whisper-small-km") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 420281d611e7ae5750a7fd8b479a0e49cfc16abbd0f8d0f1a508a6c3a43ece66
- Size of remote file:
- 3.5 kB
- SHA256:
- 671a0afc21a0128fb18c81a108deeb4fa9564c48f385914d2ad021d2e0de0a83
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.