Automatic Speech Recognition
Transformers
Safetensors
hubert
audio-classification
audio
speech
african-languages
multilingual
simba
low-resource
speech-recognition
asr
spoken-language-identification
language-identification
Instructions to use UBC-NLP/Simba-SLID-49 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/Simba-SLID-49 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UBC-NLP/Simba-SLID-49")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("UBC-NLP/Simba-SLID-49") model = AutoModelForAudioClassification.from_pretrained("UBC-NLP/Simba-SLID-49") - Notebooks
- Google Colab
- Kaggle
Ctrl+K