---
license: llama3
language:
- en
- hi
---

`Shuka v1` is a language model which natively understands audio in Indic languages. It is an encoder-decoder model built by combining two models:
- Our state-of-the-art, in-house, audio encoder: Saaras v1
- Meta’s Llama3-8B-Instruct as the decoder

The encoder and decoder are connected by a small projector with ~60M parameters. During training, only the projector weights are finetuned while the rest of the network is frozen. Following our tradition of training models frugally, we train `Shuka v1` on less than 100 hours of audio.

Though we only finetune the projector on English and Hindi data, the multilingual nature of our encoder makes `Shuka v1` perform well on zero-shot QA in other Indic languages as well. We have tested on the model on Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, and Telugu.

See what `Shuka v1` can do in this [demo video](https://www.youtube.com/watch?v=VgJhjCPbORs), and get started by using huggingface pipeline, as follows:

```
# install libraries
# pip install transformers==4.41.2 peft==0.11.1 librosa==0.10.2

import transformers
import librosa

# load the model pipeline on gpu:0
pipe = transformers.pipeline(model='sarvamai/shuka_v1', trust_remote_code=True, device=0, torch_dtype='bfloat16')

# get a sample audio
# wget https://huggingface.co/sarvamai/shuka_v1/resolve/main/hi-question.webm

audio, sr = librosa.load("./hi-question.webm", sr=16000)
turns = [
          {'role': 'system', 'content': 'Respond naturally and informatively.'},
          {'role': 'user', 'content': '<|audio|>'}
        ]

pipe({'audio': audio, 'turns': turns, 'sampling_rate': sr}, max_new_tokens=512)
```

For more details, please see our blog (link coming soon).