Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
audio
speech
african-languages
multilingual
simba
low-resource
speech-recognition
asr
Instructions to use UBC-NLP/Simba-M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/Simba-M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UBC-NLP/Simba-M")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("UBC-NLP/Simba-M") model = AutoModelForCTC.from_pretrained("UBC-NLP/Simba-M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e65d49b6ca98ba6288adc13fea04e19eec1189667a9a23aa51da8f6a303d580e
- Size of remote file:
- 3.86 GB
- SHA256:
- f5670e00ab5a3e126af3608015a7b0b18ffe537e75d0f58d6453cf62488824c7
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