Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
audio
speech
african-languages
multilingual
simba
low-resource
speech-recognition
asr
Instructions to use UBC-NLP/Simba-X with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/Simba-X with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UBC-NLP/Simba-X")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("UBC-NLP/Simba-X") model = AutoModelForCTC.from_pretrained("UBC-NLP/Simba-X") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9bfdf18916e7094e2d6f44e80c970938171cc9c964887e08a880e36b705cc9a
- Size of remote file:
- 7.67 GB
- SHA256:
- d9bd31d869d450ac8af0153e3c27448134fbbe0a16ad61600ee8ec42a6d547b4
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