Automatic Speech Recognition
Transformers
Safetensors
Wolaytta
whisper
african-languages
waxal
waxalnet
Instructions to use waxal-benchmarking/whisper-tiny-waxal-wal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waxal-benchmarking/whisper-tiny-waxal-wal 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-wal")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("waxal-benchmarking/whisper-tiny-waxal-wal") model = AutoModelForSpeechSeq2Seq.from_pretrained("waxal-benchmarking/whisper-tiny-waxal-wal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 976bda38e5aa05e344bd8ea04b3e00ffe8e6132ead3887e5b68d5dc90cb78f5f
- Size of remote file:
- 5.39 kB
- SHA256:
- 1a22cf75f7ff45cfc20411dfb056f87850da79e5faf72a0f05f850d77a7bcb1a
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