Instructions to use devkyle/Akan-3-small-2000ms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/Akan-3-small-2000ms with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/Akan-3-small-2000ms")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/Akan-3-small-2000ms") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/Akan-3-small-2000ms", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9dde1328dbc611f1ae128721f5fb73fecbcf74c85573ec98e3ae75b10cf5216a
- Size of remote file:
- 5.43 kB
- SHA256:
- 1273ddf6d0b1b3d4b1172beff2466808ff57839188c05ec324d1bf662bb9d9d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.