# MOSS-Audio


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  <a href="./README.md">English</a> | <a href="./README_zh.md">简体中文</a>
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MOSS-Audio is an open-source **audio understanding model** from [MOSI.AI](https://mosi.cn/#hero), the [OpenMOSS team](https://www.open-moss.com/), and [Shanghai Innovation Institute](https://www.sii.edu.cn/). It performs unified modeling over complex real-world audio, supporting **speech understanding, environmental sound understanding, music understanding, audio captioning, time-aware QA, and complex reasoning**. In this release, we provide **four models**: **MOSS-Audio-4B-Instruct**, **MOSS-Audio-4B-Thinking**, **MOSS-Audio-8B-Instruct**, and **MOSS-Audio-8B-Thinking**. The Instruct variants are optimized for direct instruction following, while the Thinking variants provide stronger chain-of-thought reasoning capabilities.


## News
* 2026.6.1: We have released the [MOSS-Audio Technical Report](https://arxiv.org/pdf/2606.01802) on arXiv.
* 2026.4.20: We have added the MOSS-Audio fine-tuning code and documentation. See `finetune/FINETUNE.md` for LoRA and full-parameter training examples.
* 2026.4.13: 🎉🎉🎉 We have released [MOSS-Audio](https://huggingface.co/collections/OpenMOSS-Team/moss-audio). Blog coming soon!


## Contents

- [Introduction](#introduction)
- [Model Architecture](#model-architecture)
  - [DeepStack Cross-Layer Feature Injection](#deepstack-cross-layer-feature-injection)
  - [Time-Aware Representation](#time-aware-representation)
- [Released Models](#released-models)
- [Evaluation](#evaluation)
- [Quickstart](#quickstart)
  - [Environment Setup](#environment-setup)
  - [Basic Usage](#basic-usage)
  - [Fine-tuning](#fine-tuning)
  - [Gradio App](#gradio-app)
  - [SGLang Serving](#sglang-serving)
- [More Information](#more-information)
- [Citation](#citation)


## Introduction

<p align="center">
  <img src="./assets/moss-audio-image.png" width="95%" />
</p>



Understanding audio requires more than simply transcribing words — it demands the ability to perceive acoustic cues, recognize speakers and emotions, interpret environmental sounds, reason over temporal context, and handle complex multi-step inference. **MOSS-Audio** is built to unify these capabilities within a single model.

- **Speech & Content Understanding**: Accurately recognizes and transcribes spoken content from audio inputs, producing clean and well-structured text outputs. Supports both word-level and sentence-level timestamp alignment.
- **Speaker, Emotion & Event Analysis**: Identifies speaker characteristics, analyzes emotional states based on tone, timbre, and context, and detects key acoustic events within the audio.
- **Scene & Sound Cue Extraction**: Extracts meaningful cues from background sounds, environmental noise, music, and non-speech signals to infer scene context and atmosphere.
- **Music Understanding**: Analyzes musical style, emotional progression, instrumentation, and salient acoustic features in music segments.
- **Audio Question Answering & Summarization**: Answers questions and generates summaries about speech, podcasts, meetings, interviews, and environmental recordings, helping users efficiently extract key information.
- **Time-Aware QA**: Supports time-aware questions, including word-level and sentence-level timestamp ASR.
- **Complex Reasoning**: Performs multi-hop reasoning over audio content, powered by chain-of-thought training and reinforcement learning.

## Model Architecture

<p align="center">
  <img src="./assets/arc2.png" width="95%" />
</p>

MOSS-Audio follows a modular design comprising three components: an audio encoder, a modality adapter, and a large language model. Raw audio is first encoded by **MOSS-Audio-Encoder** into continuous temporal representations at **12.5 Hz**, which are then projected into the language model's embedding space through the adapter and finally consumed by the LLM for auto-regressive text generation. 

Rather than relying on off-the-shelf audio frontends, we train a dedicated encoder from scratch to obtain more robust speech representations, tighter temporal alignment, and better extensibility across acoustic domains.


### DeepStack Cross-Layer Feature Injection

Using only the encoder's top-layer features tends to lose low-level prosody, transient events, and local time-frequency structure. To address this, we design a **DeepStack**-inspired cross-layer injection module between the encoder and the language model: in addition to the encoder's final-layer output, features from earlier and intermediate layers are selected, independently projected, and injected into the language model's early layers, preserving multi-granularity information from low-level acoustic details to high-level semantic abstractions.

This design is especially well-suited for audio understanding tasks, as it helps retain rhythm, timbre, transients, and background structure — information that a single high-level representation cannot fully capture.

### Time-Aware Representation

Time is a critical dimension in audio understanding. To enhance explicit temporal awareness, we adopt a **time-marker insertion** strategy during pretraining: explicit time tokens are inserted between audio frame representations at fixed time intervals to indicate temporal positions. This design enables the model to learn "what happened when" within a unified text generation framework, naturally supporting timestamp ASR, event localization, time-based QA, and long-audio retrospection.


## Released Models


| Model | Audio Encoder | LLM Backbone | Total Size | Hugging Face | ModelScope |
|---|---|---|---:|---|---|
| **MOSS-Audio-4B-Instruct** | MOSS-Audio-Encoder | Qwen3-4B | ~4.6B | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-Audio-4B-Instruct) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-624AFF)](https://modelscope.cn/models/openmoss/MOSS-Audio-4B-Instruct) |
| **MOSS-Audio-4B-Thinking** | MOSS-Audio-Encoder | Qwen3-4B | ~4.6B | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-Audio-4B-Thinking) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-624AFF)](https://modelscope.cn/models/openmoss/MOSS-Audio-4B-Thinking) |
| **MOSS-Audio-8B-Instruct** | MOSS-Audio-Encoder | Qwen3-8B | ~8.6B | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-Audio-8B-Instruct) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-624AFF)](https://modelscope.cn/models/openmoss/MOSS-Audio-8B-Instruct) |
| **MOSS-Audio-8B-Thinking** | MOSS-Audio-Encoder | Qwen3-8B | ~8.6B | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-Audio-8B-Thinking) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-624AFF)](https://modelscope.cn/models/openmoss/MOSS-Audio-8B-Thinking) |

> More model families, sizes, and variants will be released in the future. Stay tuned!


## Evaluation

We evaluate MOSS-Audio on a comprehensive set of audio understanding benchmarks. Key results:

- **General Audio Understanding**: MOSS-Audio-8B-Thinking achieves an average accuracy of **71.08**, with **77.33** on MMAU, **64.92** on MMAU-Pro, **66.53** on MMAR, and **75.52** on MMSU, outperforming all open-source models.
- **Speech Captioning**: MOSS-Audio-Instruct variants lead across **11 out of 13** fine-grained speech description dimensions, with **MOSS-Audio-8B-Instruct** achieving the best overall average score (**3.7252**).
- **ASR**: On a diverse ASR benchmark suite spanning 12 evaluation dimensions, MOSS-Audio achieves the **lowest overall CER (11.30)**, with particular strength in health-condition, code-switching, dialect, singing, and non-speech scenarios.
- **Timestamp ASR**: MOSS-Audio-8B-Instruct achieves **35.77 AAS** on AISHELL-1 and **131.61 AAS** on LibriSpeech, dramatically outperforming Qwen3-Omni (833.66) and Gemini-3.1-Pro (708.24) in timestamp asr accuracy.

### General Audio Understanding (Accuracy↑)

<p align="center">
  <img src="./assets/general_audio_bar.svg" width="75%" />
</p>

<table>
  <thead>
    <tr>
      <th>Model</th>
      <th>Model Size</th>
      <th>MMAU</th>
      <th>MMAU-Pro</th>
      <th>MMAR</th>
      <th>MMSU</th>
      <th>Avg</th>
    </tr>
  </thead>
  <tbody>
    <tr><td colspan="7"><em><strong>Open Source (small)</strong></em></td></tr>
    <tr>
      <td>Kimi-Audio</td><td>7B</td><td>72.41</td><td>56.58</td><td>60.82</td><td>54.74</td><td>61.14</td>
    </tr>
    <tr>
      <td>Qwen2.5-Omni</td><td>7B</td><td>65.60</td><td>52.20</td><td>56.70</td><td>61.32</td><td>58.96</td>
    </tr>
    <tr>
      <td>Audio Flamingo 3</td><td>7B</td><td>61.23</td><td>51.70</td><td>57.96</td><td>60.04</td><td>57.73</td>
    </tr>
    <tr>
      <td>Audio Flamingo Next</td><td>8B</td><td>76.10</td><td>56.34</td><td>51.01</td><td>57.20</td><td>60.16</td>
    </tr>
    <tr>
      <td>MiMo-Audio-7B</td><td>7B</td><td>74.90</td><td>53.35</td><td>61.70</td><td>61.94</td><td>62.97</td>
    </tr>
    <tr>
      <td>MiniCPM-o-4.5</td><td>9B</td><td>70.97</td><td>39.65</td><td>55.75</td><td>60.96</td><td>56.83</td>
    </tr>
    <tr>
      <td><strong>MOSS-Audio-4B-Instruct</strong></td><td><strong>4B</strong></td><td>75.79</td><td>58.16</td><td>62.54</td><td>59.68</td><td>64.04</td>
    </tr>
    <tr>
      <td><strong>MOSS-Audio-4B-Thinking</strong></td><td><strong>4B</strong></td><td><strong>77.64</strong></td><td>60.75</td><td>63.91</td><td>71.20</td><td>68.37</td>
    </tr>
    <tr>
      <td><strong>MOSS-Audio-8B-Instruct</strong></td><td><strong>8B</strong></td><td>77.03</td><td>57.48</td><td>64.42</td><td>66.36</td><td>66.32</td>
    </tr>
    <tr>
      <td><strong>MOSS-Audio-8B-Thinking</strong></td><td><strong>8B</strong></td><td><strong>77.33</strong></td><td><strong>64.92</strong></td><td><strong>66.53</strong></td><td><strong>75.52</strong></td><td><strong>71.08</strong></td>
    </tr>
    <tr><td colspan="7"><em><strong>Open Source (large)</strong></em></td></tr>
    <tr>
      <td>Qwen3-Omni-30B-A3B-Instruct</td><td>30B</td><td>75.00</td><td><strong>61.22</strong></td><td>66.40</td><td>69.00</td><td>67.91</td>
    </tr>
    <tr>
      <td>Step-Audio-R1.1</td><td>33B</td><td>72.18</td><td>60.80</td><td>68.75</td><td>64.18</td><td>66.48</td>
    </tr>
    <tr>
      <td>Step-Audio-R1</td><td>33B</td><td><strong>78.67</strong></td><td>59.68</td><td><strong>69.15</strong></td><td><strong>75.18</strong></td><td><strong>70.67</strong></td>
    </tr>
    <tr><td colspan="7"><em><strong>Closed Source</strong></em></td></tr>
    <tr>
      <td>GPT4o-Audio</td><td>-</td><td>65.66</td><td>52.30</td><td>59.78</td><td>58.76</td><td>59.13</td>
    </tr>
    <tr>
      <td>Gemini-3-Pro</td><td>-</td><td>80.15</td><td>68.28</td><td>81.73</td><td>81.28</td><td>77.86</td>
    </tr>
    <tr>
      <td>Gemini-3.1-Pro</td><td>-</td><td><strong>81.10</strong></td><td><strong>73.47</strong></td><td><strong>83.70</strong></td><td><strong>81.30</strong></td><td><strong>79.89</strong></td>
    </tr>
  </tbody>
</table>

### Speech Captioning (LLM-as-a-Judge Score↑)

<p align="center">
  <img src="./assets/speech_caption_radar.png" width="70%" />
</p>

<details>
<summary><strong>Speech Captioning (click to expand)</strong></summary>


| Model | gender | age | accent | pitch | volume | speed | texture | clarity | fluency | emotion | tone | personality | summary | Avg |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| Audio-Flamingo-Next | 4.617 | 3.461 | 3.160 | 2.679 | 2.391 | 2.818 | 1.941 | 2.839 | 2.788 | 2.056 | 2.025 | 1.940 | 2.157 | 2.6825 |
| Qwen3-Omni-30B-A3B-Instruct | 4.436 | 3.936 | 4.356 | 3.590 | 3.682 | 3.614 | 3.093 | 3.521 | 3.531 | **3.328** | 3.224 | 3.292 | 3.179 | 3.5986 |
| Qwen3-Omni-30B-A3B-Thinking | 4.419 | 4.026 | 4.327 | 3.610 | 3.577 | 3.610 | 3.179 | 3.403 | 3.526 | 3.232 | 3.154 | 3.197 | 3.107 | 3.5667 |
| Gemini-3-Pro | 4.191 | 3.835 | 4.181 | 3.392 | 3.254 | 3.320 | 2.998 | 3.347 | 3.524 | 3.055 | 2.997 | 3.023 | 2.775 | 3.3763 |
| Gemini-3.1-Pro | 4.347 | **4.030** | 4.310 | 3.474 | 3.580 | **3.687** | 3.134 | 3.559 | 3.720 | 3.231 | 3.245 | 3.158 | 3.028 | 3.5772 |
| MOSS-Audio-4B-Instruct | **4.697** | 3.980 | 4.497 | 3.628 | **3.722** | 3.564 | **3.407** | 3.841 | 3.744 | 3.311 | **3.282** | **3.305** | 3.259 | 3.7105 |
| MOSS-Audio-8B-Instruct | 4.683 | 3.979 | **4.572** | **3.682** | 3.709 | 3.638 | 3.403 | **3.869** | **3.747** | 3.314 | 3.253 | 3.272 | **3.307** | **3.7252** |

</details>

### ASR 

| Model | Overall | Health Condition | Dialect | Singing | Non-Speech Vocalizations | Code-Switching | Acoustic Environment (Clean) | Acoustic Environment (Noisy) | Acoustic Characteristics: Whisper | Acoustic Characteristics: Far-Field / Near-Field | Multi-Speaker | Age | Semantic Content |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| Paraformer-Large | 15.77 | 22.18 | 43.45 | 32.34 | 4.95 | 12.65 | 3.11 | 4.67 | 5.02 | 17.46 | 20.33 | 14.96 | 7.14 |
| GLM-ASR-Nano | 17.29 | 24.49 | 22.39 | 51.95 | 4.65 | 11.88 | 3.68 | 5.02 | 4.94 | 27.51 | 28.02 | 17.19 | 7.32 |
| Fun-ASR-Nano | 12.04 | 21.99 | **7.80** | 19.35 | 4.76 | 11.23 | 2.98 | 3.46 | 3.78 | 18.38 | 19.82 | 14.95 | 6.08 |
| SenseVoice-Small | 14.50 | 24.04 | 8.89 | 23.79 | 4.92 | 13.90 | 4.13 | 4.93 | 5.57 | 26.66 | 24.06 | 17.63 | 7.55 |
| Kimi-Audio-7B-Instruct | 14.12 | 21.11 | 29.34 | 21.76 | 4.68 | 16.38 | **2.20** | **2.15** | 2.66 | 21.02 | 20.61 | 16.74 | 6.12 |
| Audio-Flamingo-Next | 30.19 | 36.13 | 25.85 | 25.80 | 8.25 | 34.53 | 8.64 | 38.85 | 12.84 | 42.90 | 62.13 | 38.30 | 30.18 |
| Qwen2.5-Omni-3B | 15.26 | 24.65 | 33.87 | 24.24 | 5.54 | 11.66 | 2.76 | 3.56 | 4.32 | 22.15 | 22.91 | 15.17 | 7.24 |
| Qwen2.5-Omni-7B | 15.05 | 23.85 | 31.91 | 22.69 | 4.56 | 12.97 | 2.52 | 3.16 | 3.64 | 25.38 | 21.01 | 16.13 | 6.78 |
| Qwen3-Omni-30B-A3B-Instruct | 11.39 | 20.73 | 15.63 | 16.01 | 4.73 | 11.30 | 2.23 | 2.47 | **1.90** | **17.08** | **18.15** | **11.46** | **5.74** |
| **MOSS-Audio-4B-Instruct** | 11.58 | 21.11 | 11.84 | 10.79 | **4.01** | **10.11** | 3.11 | 3.72 | 3.29 | 18.48 | 20.33 | 15.09 | 8.15 |
| **MOSS-Audio-8B-Instruct** | **11.30** | **19.18** | 8.76 | **9.81** | 4.31 | 10.18 | 2.70 | 3.20 | 2.75 | 24.04 | 24.36 | 15.26 | 7.69 |

<details>
<summary><strong>Detailed ASR Results (click to expand)</strong></summary>

<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="3">Acoustic Environment (Clean)</th>
    <th colspan="1">Acoustic Environment (Noisy)</th>
    <th colspan="1">Acoustic Characteristics: Whisper</th>
    <th colspan="1">Acoustic Characteristics: Far-Field / Near-Field</th>
    <th colspan="1">Multi-Speaker</th>
    <th colspan="2">Age</th>
    <th colspan="2">Health Condition</th>
    <th colspan="2">Semantic Content</th>
    <th colspan="3">Code-Switching</th>
    <th colspan="2">Dialect</th>
    <th colspan="2">Singing</th>
    <th colspan="1">Non-Speech Vocalizations</th>
  </tr>
  <tr>
    <th>AISHELL-1<br><em>test</em></th>
    <th>AISHELL-2<br><em>Android | IOS | Mic</em></th>
    <th>THCHS-30<br><em>test</em></th>
    <th>MAGICDATA-READ<br><em>test</em></th>
    <th>AISHELL6-Whisper<br><em>normal | whisper</em></th>
    <th>AliMeeting<br><em>Test_Ali_far | Test_Ali_near</em></th>
    <th>AISHELL-4<br><em>test</em></th>
    <th>SeniorTalk<br><em>sentence</em></th>
    <th>ChildMandarin<br><em>test</em></th>
    <th>AISHELL-6A<br><em>mild | moderate | severe | StutteringSpeech</em></th>
    <th>AISHELL_6B<br><em>LRDWWS | Uncontrol</em></th>
    <th>WenetSpeech<br><em>test-meeting</em></th>
    <th>Fleurs<br><em>cmn_hans_cn</em></th>
    <th>CS-Dialogue<br><em>test</em></th>
    <th>TALCS<br><em>test</em></th>
    <th>ASCEND<br><em>test</em></th>
    <th>KeSpeech<br><em>test</em></th>
    <th>WSYue-ASR-eval<br><em>short</em></th>
    <th>MIR-1K<br><em>test</em></th>
    <th>openc-pop<br><em>test</em></th>
    <th>MNV_17</th>
  </tr>
  <tr>
    <td>Paraformer-Large</td>
    <td>1.98</td>
    <td>3.28 | 3.21 | 3.00</td>
    <td>4.07</td>
    <td>4.67</td>
    <td>1.11 | 8.92</td>
    <td><strong>25.64</strong> | 9.27</td>
    <td>20.33</td>
    <td>17.31</td>
    <td>12.60</td>
    <td>6.98 | 9.30 | 13.34 | 10.74</td>
    <td>47.59 | 45.08</td>
    <td>7.88</td>
    <td>6.40</td>
    <td>10.64</td>
    <td>10.77</td>
    <td>16.55</td>
    <td>11.48</td>
    <td>75.42</td>
    <td>57.70</td>
    <td>6.98</td>
    <td>4.95</td>
  </tr>
  <tr>
    <td>GLM-ASR-Nano</td>
    <td>2.89</td>
    <td>3.75 | 3.73 | 3.78</td>
    <td>4.23</td>
    <td>5.02</td>
    <td>0.83 | 9.06</td>
    <td>40.27 | 14.76</td>
    <td>28.02</td>
    <td>20.33</td>
    <td>14.06</td>
    <td>8.74 | 12.11 | 14.38 | 12.29</td>
    <td>50.34 | 49.09</td>
    <td>9.70</td>
    <td>4.94</td>
    <td>11.06</td>
    <td>11.07</td>
    <td>13.50</td>
    <td>9.72</td>
    <td>35.07</td>
    <td>95.87</td>
    <td>8.03</td>
    <td>4.65</td>
  </tr>
  <tr>
    <td>Fun-ASR-Nano</td>
    <td>2.16</td>
    <td>3.04 | 2.99 | 3.07</td>
    <td>3.65</td>
    <td>3.46</td>
    <td>0.81 | 6.76</td>
    <td>27.21 | 9.55</td>
    <td>19.82</td>
    <td>16.96</td>
    <td>12.94</td>
    <td>6.60 | <strong>8.81</strong> | 12.98 | 10.30</td>
    <td>47.42 | 45.84</td>
    <td>7.39</td>
    <td><strong>4.76</strong></td>
    <td>10.47</td>
    <td><strong>8.09</strong></td>
    <td>15.13</td>
    <td>7.43</td>
    <td>8.17</td>
    <td>35.85</td>
    <td>2.84</td>
    <td>4.76</td>
  </tr>
  <tr>
    <td>SenseVoice-Small</td>
    <td>3.23</td>
    <td>4.16 | 4.02 | 3.96</td>
    <td>5.26</td>
    <td>4.93</td>
    <td>1.25 | 9.88</td>
    <td>37.01 | 16.31</td>
    <td>24.06</td>
    <td>21.07</td>
    <td>14.18</td>
    <td>7.62 | 9.85 | 14.39 | 11.47</td>
    <td>52.92 | 47.97</td>
    <td>8.35</td>
    <td>6.75</td>
    <td>12.81</td>
    <td>10.52</td>
    <td>18.38</td>
    <td>10.45</td>
    <td><strong>7.34</strong></td>
    <td>39.51</td>
    <td>8.07</td>
    <td>4.92</td>
  </tr>
  <tr>
    <td>Kimi-Audio-7B-Instruct</td>
    <td><strong>0.79</strong></td>
    <td>2.91 | 3.03 | 2.88</td>
    <td><strong>1.39</strong></td>
    <td><strong>2.15</strong></td>
    <td>0.69 | 4.63</td>
    <td>28.22 | 13.82</td>
    <td>20.61</td>
    <td>19.70</td>
    <td>13.79</td>
    <td>7.00 | 9.34 | 12.56 | 10.75</td>
    <td>44.44 | 42.57</td>
    <td>7.15</td>
    <td>5.10</td>
    <td>14.56</td>
    <td>12.74</td>
    <td>21.83</td>
    <td><strong>5.51</strong></td>
    <td>53.17</td>
    <td>38.35</td>
    <td>5.17</td>
    <td>4.68</td>
  </tr>
  <tr>
    <td>Audio Flamingo Next</td>
    <td>6.85</td>
    <td>7.22 | 8.12 | 8.62</td>
    <td>12.41</td>
    <td>6.24</td>
    <td>6.48 | 19.20</td>
    <td>60.68 | 25.12</td>
    <td>62.13</td>
    <td>39.44</td>
    <td>23.06</td>
    <td>24.13 | 29.40 | 35.69 | 21.90</td>
    <td>56.17 | 49.52</td>
    <td>54.46</td>
    <td>12.86</td>
    <td>30.52</td>
    <td>40.42</td>
    <td>32.63</td>
    <td>16.97</td>
    <td>34.74</td>
    <td>40.06</td>
    <td>11.54</td>
    <td>8.25</td>
  </tr>
  <tr>
    <td>Qwen2.5-Omni-3B</td>
    <td>1.51</td>
    <td>3.10 | 2.94 | 2.93</td>
    <td>3.32</td>
    <td>3.56</td>
    <td>0.82 | 7.82</td>
    <td>32.14 | 12.16</td>
    <td>22.91</td>
    <td>17.38</td>
    <td>12.96</td>
    <td>6.87 | 10.55 | 14.57 | 11.33</td>
    <td>54.54 | 50.03</td>
    <td>9.04</td>
    <td>5.45</td>
    <td>10.78</td>
    <td>10.94</td>
    <td>13.25</td>
    <td>7.67</td>
    <td>60.06</td>
    <td>45.00</td>
    <td>3.47</td>
    <td>5.54</td>
  </tr>
  <tr>
    <td>Qwen2.5-Omni-7B</td>
    <td>1.16</td>
    <td>2.88 | 2.77 | 2.73</td>
    <td>3.06</td>
    <td>3.16</td>
    <td>0.71 | 6.57</td>
    <td>32.03 | 18.73</td>
    <td>21.01</td>
    <td>19.96</td>
    <td>12.29</td>
    <td>7.27 | 10.94 | 12.92 | 10.53</td>
    <td>51.99 | 49.45</td>
    <td>8.43</td>
    <td>5.13</td>
    <td>14.02</td>
    <td>10.46</td>
    <td>14.42</td>
    <td>6.40</td>
    <td>57.43</td>
    <td>42.62</td>
    <td>2.75</td>
    <td>4.56</td>
  </tr>
  <tr>
    <td>Qwen3-Omni-30B-A3B-Instruct</td>
    <td>0.95</td>
    <td><strong>2.70</strong> | <strong>2.72</strong> | <strong>2.57</strong></td>
    <td>2.21</td>
    <td>2.47</td>
    <td><strong>0.59</strong> | <strong>3.22</strong></td>
    <td>25.72 | <strong>8.44</strong></td>
    <td><strong>18.15</strong></td>
    <td><strong>14.13</strong></td>
    <td><strong>8.79</strong></td>
    <td>6.20 | 8.88 | 11.59 | 10.25</td>
    <td>45.80 | 41.65</td>
    <td><strong>6.64</strong></td>
    <td>4.84</td>
    <td>12.94</td>
    <td>8.33</td>
    <td><strong>12.64</strong></td>
    <td>5.87</td>
    <td>25.39</td>
    <td>30.81</td>
    <td><strong>1.21</strong></td>
    <td>4.73</td>
  </tr>
  <tr>
    <td><strong>MOSS-Audio-4B-Instruct</strong></td>
    <td>2.26</td>
    <td>3.22 | 3.20 | 3.33</td>
    <td>3.53</td>
    <td>3.72</td>
    <td>0.73 | 5.86</td>
    <td>27.27 | 9.68</td>
    <td>20.33</td>
    <td>16.93</td>
    <td>13.25</td>
    <td>6.36 | 9.77 | 12.68 | 10.28</td>
    <td>43.35 | 44.25</td>
    <td>8.17</td>
    <td>8.13</td>
    <td>9.14</td>
    <td>8.37</td>
    <td>12.83</td>
    <td>14.65</td>
    <td>9.04</td>
    <td>18.47</td>
    <td>3.10</td>
    <td><strong>4.01</strong></td>
  </tr>
  <tr>
    <td><strong>MOSS-Audio-8B-Instruct</strong></td>
    <td>1.82</td>
    <td>2.97 | 2.95 | 2.91</td>
    <td>2.82</td>
    <td>3.20</td>
    <td>0.69 | 4.80</td>
    <td>36.82 | 11.25</td>
    <td>24.36</td>
    <td>17.42</td>
    <td>13.10</td>
    <td><strong>5.84</strong> | 8.94 | <strong>11.52</strong> | <strong>9.72</strong></td>
    <td><strong>39.76</strong> | <strong>39.27</strong></td>
    <td>7.86</td>
    <td>7.52</td>
    <td><strong>9.07</strong></td>
    <td>8.22</td>
    <td>13.26</td>
    <td>9.18</td>
    <td>8.33</td>
    <td><strong>17.24</strong></td>
    <td>2.39</td>
    <td>4.31</td>
  </tr>
</table>

</details>


### Timestamp ASR (AAS↓)

| Model | AISHELL-1(zh)  | LibriSpeech(en) |
|---|---:|---:|
| Audio-Flamingo-Next | -- | 211.15 |
| Qwen3-Omni-30B-A3B-Instruct | 833.66 | 646.95 |
| Gemini-3.1-Pro| 708.24 | 871.19 |
| MOSS-Audio-4B-Instruct | 76.96 | 358.13 |
| **MOSS-Audio-8B-Instruct** | **35.77** | **131.61** |


## Quickstart

### Environment Setup

We recommend Python 3.12 with a clean Conda environment. The commands below are enough for local inference.

#### Recommended setup

```bash
git clone https://github.com/OpenMOSS/MOSS-Audio.git
cd MOSS-Audio

conda create -n moss-audio python=3.12 -y
conda activate moss-audio

conda install -c conda-forge "ffmpeg=7" -y
pip install --extra-index-url https://download.pytorch.org/whl/cu128 -e ".[torch-runtime]"
```

#### Optional: FlashAttention 2

If your GPU supports FlashAttention 2, you can replace the last install command with:

```bash
pip install --extra-index-url https://download.pytorch.org/whl/cu128 -e ".[torch-runtime,flash-attn]"
```


### Basic Usage

Download the model first:

```bash
hf  download OpenMOSS-Team/MOSS-Audio-4B-Instruct --local-dir ./weights/MOSS-Audio-4B-Instruct 
hf  download OpenMOSS-Team/MOSS-Audio-4B-Thinking --local-dir ./weights/MOSS-Audio-4B-Thinking 
hf  download OpenMOSS-Team/MOSS-Audio-8B-Instruct --local-dir ./weights/MOSS-Audio-8B-Instruct 
hf  download OpenMOSS-Team/MOSS-Audio-8B-Thinking --local-dir ./weights/MOSS-Audio-8B-Thinking 

```

Then edit `MODEL_PATH` / `AUDIO_PATH` in `infer.py` as needed, and run:

```bash
python infer.py
```

The default prompt in `infer.py` is `Describe this audio.` You can directly edit that line if you want to try transcription, audio QA, or speech captioning.

<a id="fine-tuning"></a>

### Fine-tuning

We now provide an official fine-tuning script in `finetune/finetune.py`, with full instructions in `finetune/FINETUNE.md`.

Install the extra dependencies needed for training:

```bash
pip install librosa peft
```

Minimal example for LoRA fine-tuning:

```bash
accelerate launch finetune/finetune.py \
    --model_dir ./weights/MOSS-Audio-4B-Instruct \
    --data_path train.jsonl \
    --output_dir ./output/lora \
    --use_lora \
    --bf16
```

The training data should be a JSONL file containing audio-text conversations. For data format, supported arguments, multi-GPU examples, and full-parameter fine-tuning, see `finetune/FINETUNE.md`.

### Gradio App

Start the Gradio demo with:

```bash
python app.py
```



### SGLang Serving

If you want to serve MOSS-Audio with SGLang, see the full guide in `moss_audio_usage_guide.md`.

The shortest setup is:

```bash
git clone -b moss-audio https://github.com/OpenMOSS/sglang.git
cd sglang
pip install -e "python[all]"
pip install nvidia-cudnn-cu12==9.16.0.29
cd ..
sglang serve --model-path ./weights/MOSS-Audio --trust-remote-code
```

If you use the default `torch==2.9.1+cu128` runtime, installing `nvidia-cudnn-cu12==9.16.0.29` is recommended before starting `sglang serve`.


<a id="more-information"></a>

## More Information
- **MOSI.AI**: [https://mosi.cn](https://mosi.cn)
- **OpenMOSS**: [https://www.open-moss.com](https://www.open-moss.com)


## LICENSE

Models in MOSS-Audio are licensed under the Apache License 2.0.


## Citation

```bibtex
@misc{yang2026mossaudiotechnicalreport,
      title={MOSS-Audio Technical Report}, 
      author={Chen Yang and Chufan Yu and Hanfu Chen and Jie Zhu and Jingqi Chen and Ke Chen and Wenxuan Wang and Yang Wang and Yaozhou Jiang and Yi Jiang and Zhengyuan Lin and Ziqi Chen and Zhaoye Fei and Chenghao Liu and Jun Zhan and Kang Yu and Kexin Huang and Mingshu Chen and Qinyuan Cheng and Ruixiao Li and Shimin Li and Songlin Wang and Yang Gao and Yiyang Zhang and Xipeng Qiu},
      year={2026},
      eprint={2606.01802},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2606.01802}, 
}
```

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=OpenMOSS/MOSS-Audio&type=date&legend=top-left)](https://www.star-history.com/#OpenMOSS/MOSS-Audio&type=date&legend=top-left)