Instructions to use oluwagbotty/mms_eng_yor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oluwagbotty/mms_eng_yor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="oluwagbotty/mms_eng_yor")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("oluwagbotty/mms_eng_yor") model = AutoModelForCTC.from_pretrained("oluwagbotty/mms_eng_yor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-300/optimizer.pt from oluwagbotty/mms_eng_yor: direct link, hf CLI and curl.
- Browser
- Download file 18.1 MB
-
https://huggingface.co/oluwagbotty/mms_eng_yor/resolve/864d5d791c390a94462701e845a135a24fa8589f/checkpoint-300/optimizer.pt
- Command line
-
hf download hf://oluwagbotty/mms_eng_yor@864d5d791c390a94462701e845a135a24fa8589f/checkpoint-300/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/oluwagbotty/mms_eng_yor/resolve/864d5d791c390a94462701e845a135a24fa8589f/checkpoint-300/optimizer.pt
18.1 MB
- Xet hash:
- c96b59219f2ad92fa86c8406235aa9ce1ee82e1db6154628894507083b8c1dc7
- Size of remote file:
- 18.1 MB
- SHA256:
- d2ef0416b7603904a8d5c0b81dd312c297a777823e112a59d357fe0a5ededfab
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