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:
- a27c88b894eeff893641ae36d76295cf999da197b48f206a558b8e8bb1d99fff
- Size of remote file:
- 1.47 kB
- SHA256:
- f639b07c233e516c88e74c81908fd1fdc3b5cc248e4ea1b0328c2b37705e02f1
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