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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("UBC-NLP/Simba-X") model = AutoModelForCTC.from_pretrained("UBC-NLP/Simba-X", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from UBC-NLP/Simba-X: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/UBC-NLP/Simba-X/resolve/main/training_args.bin
- Command line
-
hf download hf://UBC-NLP/Simba-X/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/UBC-NLP/Simba-X/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- c510bcc0177348fce956c64fa929e6934cdef89d102fa638f8c5dfd5f9f2b161
- Size of remote file:
- 4.09 kB
- SHA256:
- 875cf88512c7fb59f5e0a63eea1c3c0b801f1d554bf17923c524c6414043ca0a
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