Automatic Speech Recognition
Transformers
Safetensors
Ikposo
whisper
african-languages
waxal
waxalnet
Instructions to use waxal-benchmarking/whisper-small-waxal-kpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waxal-benchmarking/whisper-small-waxal-kpo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="waxal-benchmarking/whisper-small-waxal-kpo")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("waxal-benchmarking/whisper-small-waxal-kpo") model = AutoModelForSpeechSeq2Seq.from_pretrained("waxal-benchmarking/whisper-small-waxal-kpo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cccc369521cee1e3af0f4023a72c123da7f2ae5c87c99caf8fdfe8287c01c148
- Size of remote file:
- 1.93 GB
- SHA256:
- b31ad5169d0c46675abb87fd3075893f9aafeefdb01016fe81fb10e4070b2b04
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.