Automatic Speech Recognition
Transformers
Safetensors
Yoruba
whisper
yoruba
africa
low-resource
speech-recognition
devblock
Eval Results (legacy)
Instructions to use devblockHQ/eti-yoruba-asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devblockHQ/eti-yoruba-asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devblockHQ/eti-yoruba-asr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devblockHQ/eti-yoruba-asr") model = AutoModelForSpeechSeq2Seq.from_pretrained("devblockHQ/eti-yoruba-asr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee2f1008b074bd9f6a06a477a2cefd6fe624422717523d6a818f2bfc8631e82d
- Size of remote file:
- 248 MB
- SHA256:
- 4c92575a19d5458c9f1ec12fce88f478e2ce24f8e9a7eeefa2d63924625a6d7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.