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--- |
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library_name: transformers |
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license: apache-2.0 |
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tags: [] |
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pipeline_tag: audio-text-to-text |
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--- |
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# R1-AQA --- Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question Answering |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Introduction |
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R1-AQA is a audio question answering (AQA) model based on `Qwen2-Audio-7B-Instruct`, optimized through reinforcement learning using the group relative policy optimization (GRPO) algorithm. |
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This implementation has achieved state-of-the-art performance on MMAU *Test-mini* benchmark with only 38k post-training samples. |
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For more details, please refer to our [Github](https://github.com/xiaomi-research/r1-aqa) and [Technical Report](https://arxiv.org/abs/2503.11197). |
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### Table: Accuracies (%) on MMAU Test-mini benchmark |
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| Model | Method | Sound | Music | Speech | Average | |
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|--------------------------------------------|-------------------------|--------|--------|--------|---------| |
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| \ | Human\* | 86.31 | 78.22 | 82.17 | 82.23 | |
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| Gemini Pro 2.0 Flash | Direct Inference\* | 56.46 | 58.68 | 51.65 | 55.60 | |
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| Audio Flamingo 2 | Direct Inference\* | 61.56 | **73.95** | 30.93 | 55.48 | |
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| GPT4o + Strong Cap. | Direct Inference\* | 57.35 | 49.70 | **64.86** | 57.30 | |
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| Llama-3-8B-Instruct + Strong Cap. | Direct Inference\* | 50.75 | 48.93 | 55.25 | 52.10 | |
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| Gemini Pro v1.5 | Direct Inference\* | 56.75 | 49.40 | 58.55 | 54.90 | |
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| Qwen2-Audio-7B-Instruct | Direct Inference\* | 54.95 | 50.98 | 42.04 | 49.20 | |
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| GPT4o + Weak Cap. | Direct Inference\* | 39.33 | 41.90 | 58.25 | 45.70 | |
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| Llama-3-8B-Instruct + Weak Cap. | Direct Inference\* | 34.23 | 38.02 | 54.05 | 42.10 | |
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| SALMONN | Direct Inference\* | 41.00 | 34.80 | 25.50 | 33.70 | |
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| Qwen2-Audio-7B-Instruct | CoTA \[1\] | 60.06 | 64.30 | 60.70 | 61.71 | |
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| Qwen2-Audio-7B-Instruct | Zero-Shot-CoT \[2\] | 61.86 | 56.29 | 55.26 | 57.80 | |
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| **Qwen2-Audio-7B-Instruct** | **GRPO (Ours)** | **69.37** | 66.77 | 57.36 | **64.50** | |
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#### Notes: |
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\* The data are sourced from the MMAU official website: [https://sakshi113.github.io/mmau_homepage/](https://sakshi113.github.io/mmau_homepage/) |
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\[1\] Xie, Zhifei, et al. "Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models." arXiv preprint arXiv:2503.02318 (2025). |
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\[2\] Ma, Ziyang, et al. "Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model." arXiv preprint arXiv:2501.07246 (2025). |
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## Inference |
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```python |
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import torch |
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import torchaudio |
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from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor |
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# Load model |
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model_name = "mispeech/r1-aqa" |
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processor = AutoProcessor.from_pretrained(model_name) |
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model = Qwen2AudioForConditionalGeneration.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") |
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# Load example audio |
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wav_path = "test-mini-audios/3fe64f3d-282c-4bc8-a753-68f8f6c35652.wav" # from MMAU dataset |
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waveform, sampling_rate = torchaudio.load(wav_path) |
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if sampling_rate != 16000: |
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waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)(waveform) |
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audios = [waveform[0].numpy()] |
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# Make prompt text |
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question = "Based on the given audio, identify the source of the speaking voice." |
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options = ["Man", "Woman", "Child", "Robot"] |
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prompt = f"{question} Please choose the answer from the following options: {str(options)}. Output the final answer in <answer> </answer>." |
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message = [ |
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{"role": "user", "content": [ |
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{"type": "audio", "audio_url": wav_path}, |
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{"type": "text", "text": prompt} |
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]} |
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] |
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texts = processor.apply_chat_template(message, add_generation_prompt=True, tokenize=False) |
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# Process |
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inputs = processor(text=texts, audios=audios, sampling_rate=16000, return_tensors="pt", padding=True).to(model.device) |
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generated_ids = model.generate(**inputs, max_new_tokens=256) |
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generated_ids = generated_ids[:, inputs.input_ids.size(1):] |
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response = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) |
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print(response) |
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``` |
