Instructions to use ramesh070/mms-malayalam-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ramesh070/mms-malayalam-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ramesh070/mms-malayalam-binary")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("ramesh070/mms-malayalam-binary") model = AutoModelForAudioClassification.from_pretrained("ramesh070/mms-malayalam-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f38cd9fa6e17a5a9727f863073b59a3ca523924b4b1441a8b1ba696f79d74f2
- Size of remote file:
- 5.84 kB
- SHA256:
- 07a71a7104a6f0c25d431b9e90f018387c6e1e8ff7b7696341a0e729fd5a47ba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.