Instructions to use devkyle/Akan-tiny-2000ms-1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/Akan-tiny-2000ms-1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/Akan-tiny-2000ms-1k")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/Akan-tiny-2000ms-1k") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/Akan-tiny-2000ms-1k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63c6b1b5f2b9c7c6a633b64adbc0eddfd07be6dadea273e7217a6f4b71c8cdc8
- Size of remote file:
- 151 MB
- SHA256:
- e077e70d9014c8d88a2bf4c20c2903d1aa78a1d4f19add20d3ed220cf4e66cb6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.