Automatic Speech Recognition
Transformers
Safetensors
Lingala
vits
text-to-audio
audio
speech
african-languages
multilingual
simba
low-resource
speech-recognition
asr
Instructions to use UBC-NLP/Simba-TTS-lin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/Simba-TTS-lin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UBC-NLP/Simba-TTS-lin")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/Simba-TTS-lin") model = AutoModelForTextToWaveform.from_pretrained("UBC-NLP/Simba-TTS-lin") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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[](https://aclanthology.org/2025.emnlp-main.559/)
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[](https://africa.dlnlp.ai/simba/)
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[](https://huggingface.co/spaces/UBC-NLP/SimbaBench)
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[](https://aclanthology.org/2025.emnlp-main.559/)
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[](https://africa.dlnlp.ai/simba/)
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[](https://huggingface.co/spaces/UBC-NLP/SimbaBench)
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[](https://github.com/UBC-NLP/simba)
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[](https://huggingface.co/collections/UBC-NLP/simba-speech-series)
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[](https://huggingface.co/datasets/UBC-NLP/SimbaBench_dataset)
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</div>
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