Instructions to use devkyle/Akan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/Akan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/Akan")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/Akan") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/Akan") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af7a591ba04aa66a18f8700a34d7fa7e3e5201ab77ad46710990024a96a97338
- Size of remote file:
- 151 MB
- SHA256:
- b10cccc62098d4778a5349b114bee6274f7e275518378503178b95ac86d72bc5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.