Automatic Speech Recognition
Transformers
Safetensors
Oromo
whisper
african-languages
waxal
waxalnet
Instructions to use waxal-benchmarking/whisper-tiny-waxal-orm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waxal-benchmarking/whisper-tiny-waxal-orm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="waxal-benchmarking/whisper-tiny-waxal-orm")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("waxal-benchmarking/whisper-tiny-waxal-orm") model = AutoModelForSpeechSeq2Seq.from_pretrained("waxal-benchmarking/whisper-tiny-waxal-orm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- acce929fa6374145890aba872cdd23664e89d9c39ab4724d9409fdeac0e0f01b
- Size of remote file:
- 302 MB
- SHA256:
- 8d2d525d881213eae962826e4de4a242bff6548533a979e112716fed5e88b6d4
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