Automatic Speech Recognition
Transformers
Safetensors
Yoruba
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use EYEDOL/whisper-tiny-yoruba3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EYEDOL/whisper-tiny-yoruba3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EYEDOL/whisper-tiny-yoruba3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EYEDOL/whisper-tiny-yoruba3") model = AutoModelForSpeechSeq2Seq.from_pretrained("EYEDOL/whisper-tiny-yoruba3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8b06f3222370b8cb04767f3a3682ca1cbcfdc4332f39d47917bbb5101ce6861
- Size of remote file:
- 5.39 kB
- SHA256:
- dc528eea5d95c4fda3ec15b2a3067fad7040c9720be9c96a610b347f7c8a0529
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.