Instructions to use CIIL/Anuvani-LID with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIIL/Anuvani-LID with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="CIIL/Anuvani-LID", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("CIIL/Anuvani-LID", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Demo for this model on Spaces
Hi @CIIL 🤗
I'm Apolinario, from the open-source team at Hugging Face. Congrats and thanks for open-sourcing CIIL/Anuvani-LID on the Hub! We were excited about this work and built with an agent an interactive demo app of it on Hugging Face Spaces, running on a free ZeroGPU infrastructure.
Here's a link to the demo: https://huggingface.co/spaces/hugging-apps/anuvani-lid
We would love to transfer this demo to you or your organization. Would you like this demo to live under your own account or organization? If so just let me know here which username to transfer to, and we'll transfer the Space over to you, we hope it can give your work more visibility, discoverability and allows folks to try it out.
(If you have any questions or just want to chat more about this, you can find me on Twitter, LinkedIn or apolinario @ huggingface.co)
Cheers,
Poli
Hello, @multimodalart thanks for building the demo app of it on HF spaces, your support and efforts in promoting our app means a lot.
given that at the moment we are a newly registered org at huggingface we barely have any visibility, i guess the space being hosted under hugging-apps means we would have more visibility than having it under our org. however we would really love a shout out on X and LinkedIn in all ways possible. the following is a link to the post on LinkedIn https://lnkd.in/p/d4DBxVZ7
in addition to this we have also released a far stronger Diarization model CIIL/Anuvani-Diarization, which is currently ranked #1 opensource model on Indian Diarization benchmark as well as very competitive on global Diarization benchmarks, all of this while being merely 75M parameters. we also opensourced the whole triton inference server kits where the inference backend is Rust accelerated, yielding upto 700x RTFx you can check more about it here: https://lnkd.in/p/dBTZuEBa
let me know if you want to chat more about this.