Instructions to use sengtha/whisper-tiny-khmer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sengtha/whisper-tiny-khmer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sengtha/whisper-tiny-khmer")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sengtha/whisper-tiny-khmer") model = AutoModelForSpeechSeq2Seq.from_pretrained("sengtha/whisper-tiny-khmer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c9fffc7bac8c651b5b0a18e904ef8ad7fa34cf9dcbc56f01c7bc1024f2b77f8
- Size of remote file:
- 118 MB
- SHA256:
- 0159579c414b4b46d2c76462891470715ba38b9904fdc1aa410f16811fb491ad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.