Instructions to use sulaimank/w2vbert-shona-sd2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sulaimank/w2vbert-shona-sd2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sulaimank/w2vbert-shona-sd2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sulaimank/w2vbert-shona-sd2") model = AutoModelForCTC.from_pretrained("sulaimank/w2vbert-shona-sd2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sulaimank/w2vbert-shona-sd2: direct link, hf CLI and curl.
- Browser
- Download file 5.33 kB
-
https://huggingface.co/sulaimank/w2vbert-shona-sd2/resolve/main/training_args.bin
- Command line
-
hf download hf://sulaimank/w2vbert-shona-sd2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sulaimank/w2vbert-shona-sd2/resolve/main/training_args.bin
5.33 kB
- Xet hash:
- fc8ee0d0d89ed0899750a02d4ff72355b8bb6cc02d021d8acadf77d40fb394d9
- Size of remote file:
- 5.33 kB
- SHA256:
- 9fa834b4d69b55146c287fb51e8ac9acdc7975077a0101f7ada1830c14ae40fc
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