Automatic Speech Recognition
Transformers
PyTorch
hubert
audio
speech
african-languages
multilingual
simba
low-resource
speech-recognition
asr
Instructions to use UBC-NLP/Simba-H with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/Simba-H with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UBC-NLP/Simba-H")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("UBC-NLP/Simba-H") model = AutoModelForCTC.from_pretrained("UBC-NLP/Simba-H") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b4502556b8a99b825b1dc3fda27a51da864691f900ddfbce29f34ef7152e6e6
- Size of remote file:
- 379 MB
- SHA256:
- 4bd01b633fc5af37d2d3c9d95ea9275fe8b0fe10d0b04bba04f42520b02d2c35
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