Instructions to use Talha/urdu-audio-emotions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Talha/urdu-audio-emotions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Talha/urdu-audio-emotions")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Talha/urdu-audio-emotions") model = AutoModelForAudioClassification.from_pretrained("Talha/urdu-audio-emotions") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0010614ec2268ae3c56af7d79a3263c731d52ed29b50546e9e2b69c82526fc3
- Size of remote file:
- 1.26 GB
- SHA256:
- 84976b642d80951f175a1bb2a1edccd502d9342f2a71dd2aea7be11d731137e8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.