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
- Xet hash:
- c23380d63cc5a634690ed2afd2a0a21c1d86d8ee16b14fb97d6a344c91dde5ac
- Size of remote file:
- 3.86 GB
- SHA256:
- 773a37f11c447bab07c1b7e5565fe5aa5a63e2e470b6729888f829ea52c1978f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.