---
license: apache-2.0
library_name: transformers
tags: []
---

# R1-AQA 

<!-- Provide a quick summary of what the model is/does. -->

## Introduction

R1-AQA is based on `Qwen2-Audio-7B-Instruc`, but applied group relative policy optimization (GRPO) algorithm to the Audio Question Answering(AQA) task.  
For more details, please refer to our [Github](https://github.com/xiaomi/r1-aqa) and [Report]().


## Inference
```python
import torch
import torchaudio
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor


def _get_audio(wav_path):
    waveform, sample_rate = torchaudio.load(wav_path)
    if sample_rate != 16000:
        waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
    audio = waveform[0]
    return audio

model_name = "mispeech/r1-aqa"
audio_url = "test-mini-audios/3fe64f3d-282c-4bc8-a753-68f8f6c35652.wav"  # Copyied from MMAU dataset

processor = AutoProcessor.from_pretrained(model_name)
model = Qwen2AudioForConditionalGeneration.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")

message = [
    {"role": "user", "content": [
        {"type": "audio", "audio_url": audio_url},
        {"type": "text", "text": "Based on the given audio, identify the source of the speaking voice. Please choose the answer from the following options: ['Man', 'Woman', 'Child', 'Robot']. Output the final answer in <answer> </answer>."}
    ]}
]

audios = [_get_audio(audio_url).numpy()]
texts = processor.apply_chat_template(message, add_generation_prompt=True, tokenize=False)

inputs = processor(text=texts, audios=audios, sampling_rate=16000, return_tensors="pt", padding=True).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=256)
generated_ids = generated_ids[:, inputs.input_ids.size(1):]
response = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(f"response:{response}")
```