README.md

---
license: apache-2.0
language:
- th
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- chat
- audio
---

# Pathumma-Audio

## Model Description
**Pathumma-llm-audio-1.0.0** is a 8 billion parameter Thai large language model designed for audio understanding tasks. The model can process multiple types of audio inputs including **speech, general audio, and music**, converting them into meaningful textual representations.

## Model Architecture
The model combines two key components:
- 1. Base Language Model: [OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B](https://huggingface.co/nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B) (Qwen2)
- 2. Base Speech Encoder: [Pathumma-whisper-th-large-v3](https://huggingface.co/nectec/Pathumma-whisper-th-large-v3) (Whisper)

## Quickstart
To load the model and generate responses using the Hugging Face Transformers library, follow the steps below.

#### 1. Install the required dependencies:
Make sure you have the necessary libraries installed by running:
```shell
pip install librosa torch transformers peft
```
#### 2. Load the model and generate a response:
You can load the model and use it to generate a response with the following code snippet:
```python
import torch
import librosa
from transformers import AutoModel

device = "cuda" 

model = AutoModel.from_pretrained(
    "nectec/Pathumma-llm-audio-1.0.0",
    torch_dtype=torch.bfloat16, 
    lora_infer_mode=True,
    init_from_scratch=True,    
    trust_remote_code=True
)
model = model.to(device)

prompt = "ช่วยถอดความเสียงนี้ให้หน่อย"
audio_path = "audio_path.wav"
audio, sr = librosa.load(audio_path, sr=16000)

model.eval()
with torch.no_grad():
  response = model.generate(
        raw_wave=audio,
        prompts=prompt,
        device=device,
        max_new_tokens=200,
        repetition_penalty=1.0,
)
print(response[0])
```
## Evaluation Performance
Additional details are required.

## Limitations and Future Work
At present, our model remains in the experimental research phase and is not yet fully suitable for practical applications as an assistant. Future work will focus on upgrading the language model to a newer version ([OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B](https://huggingface.co/nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B)), and curating more refined and robust datasets to improve performance. Additionally, we aim to address and prioritize the safety and reliability of the model's outputs.

## Citation
@misc{tipkasorn2024pathumma,
  Note = {\href{https://huggingface.co/nectec/Pathumma-llm-audio-1.0.0}{Pathumma-Audio}},
  author = {Pattara Tipkasorn and Wayupuk Sommuang and Oatsada Chatthong and Kwanchiva Thangthai},
  publisher = { Hugging Face },
  year = {2024},
}

## Acknowledgements
We are grateful to ThaiSC, also known as NSTDA Supercomputer Centre, for providing the LANTA that was utilised for model training and finetuning.  Additionally, we would like to express our gratitude to the SALMONN team for making their code publicly available, and to Typhoon Audio at SCB 10X for making available the huggingface project, source code, and technical paper, which served as a valuable guide for us. Many other open-source projects have contributed valuable information, code, data, and model weights; we are grateful to them all.

## Pathumma Audio Team
*Pattara Tipkasorn*, Wayupuk Sommuang, Oatsada Chatthong, *Kwanchiva Thangthai*