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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 968bbf7fa2826dfaf4d309facd89a47805afb795e4df1d88029d5f2efc2c42cd
- Size of remote file:
- 151 MB
- SHA256:
- 3cf069b57cb6958ef4821bbbb48847066d9aa332e99d55b1767172da3bf69e08
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.