| --- |
| license: other |
| license_name: nvidia-oneway-noncommercial |
| license_link: https://huggingface.co/nvidia/audio-flamingo-next-think-hf |
| base_model: nvidia/audio-flamingo-next-think-hf |
| library_name: peft |
| pipeline_tag: audio-text-to-text |
| tags: |
| - audio |
| - audio-reasoning |
| - reinforcement-learning |
| - grpo |
| - self-supervised-learning |
| - lora |
| --- |
| |
| # Audio-R1 — Audio Flamingo Next Think (LoRA) |
|
|
| LoRA adapter for [nvidia/audio-flamingo-next-think-hf](https://huggingface.co/nvidia/audio-flamingo-next-think-hf) trained with **Audio-R1**, an RL post-training framework that uses audio self-supervised tasks as a source of verifiable rewards. |
|
|
| - **Paper:** [Audio-R1: Audio Reasoning through Reinforcement Learning on Self-Supervised Tasks](https://arxiv.org/abs/2610.34609) |
| - **Project page:** https://ijihoon98.github.io/Audio-R1/ |
|
|
| ## Training Details |
|
|
| | Hyperparameter | Value | |
| | :--- | :--- | |
| | Method | GRPO, 1 epoch, LoRA | |
| | Reward | accuracy (1.0 if the chosen letter is correct) + format (0.2) | |
| | Format reward | non-empty `<think>` (the model answers without an `<answer>` tag) | |
| | Optimizer / LR schedule | AdamW, cosine | |
| | Learning rate / warmup ratio | 5e-6 / 0.03 | |
| | Rollouts per prompt | 8 | |
| | Sampling temperature / top-p | 0.8 / 0.9 | |
| | KL coefficient β | 0.04 | |
| | Clip ε | 0.2 | |
| | Per-device batch × grad. accumulation | 8 × 4 | |
| | GPUs | 4 | |
| | LoRA rank / α / dropout | 8 / 32 / 0.05 | |
| | LoRA targets | all linear layers; audio encoder frozen | |
| | Precision / seed | bf16 / 42 | |
|
|
| ## Usage |
|
|
| ```python |
| import librosa, torch |
| from peft import PeftModel |
| from transformers import AutoProcessor, MusicFlamingoForConditionalGeneration |
| |
| base = "nvidia/audio-flamingo-next-think-hf" |
| processor = AutoProcessor.from_pretrained(base) |
| model = MusicFlamingoForConditionalGeneration.from_pretrained(base, dtype=torch.float32, device_map="auto") |
| model = PeftModel.from_pretrained(model, "NearAndDear98/Audio-R1-AF-Next-Think").eval() |
| |
| SYSTEM = ( |
| "Your response MUST contain two parts in this exact order: " |
| "1. Your reasoning enclosed within <think> </think> tags. " |
| "2. The final answer — a single letter for the correct option — enclosed " |
| "within <answer> </answer> tags." |
| ) |
| question = "<sound>\nQuestion ...\n(A) ...\n(B) ...\n(C) ...\n(D) ..." # one <sound> per audio |
| prompt = processor.apply_chat_template( |
| [{"role": "system", "content": SYSTEM}, {"role": "user", "content": question}], |
| tokenize=False, add_generation_prompt=True, |
| ) |
| audios = [librosa.load("example.wav", sr=16000, mono=True)[0]] |
| inputs = processor(text=[prompt], audio=audios, return_tensors="pt").to(model.device) |
| out = model.generate(**inputs, max_new_tokens=512, do_sample=False) |
| print(processor.batch_decode(out[:, inputs["input_ids"].shape[-1]:], skip_special_tokens=True)[0]) |
| ``` |
|
|
| ## License |
|
|
| The base model is released under the NVIDIA OneWay Noncommercial License; this adapter inherits those terms and is for **non-commercial research use only**. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{jeong2026audior1, |
| title = {Audio-R1: Audio Reasoning through Reinforcement Learning on Self-Supervised Tasks}, |
| author = {Jeong, Jihoon and Ravanelli, Mirco and Wang, Zhepei and Subakan, Cem}, |
| journal = {arXiv preprint arXiv:2610.34609}, |
| year = {2026} |
| } |
| ``` |
|
|
