---
pipeline_tag: image-text-to-text
datasets:
- openbmb/RLAIF-V-Dataset
library_name: transformers
language:
- multilingual
tags:
- minicpm-o
- omni
- vision
- ocr
- multi-image
- video
- custom_code
---

<h1>A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone</h1>

[GitHub](https://github.com/OpenBMB/MiniCPM-V) | Online Demo [US](https://minicpm-omni-webdemo-us.modelbest.cn)/[CN](https://minicpm-omni-webdemo.modelbest.cn)</a>


## MiniCPM-o 2.6

**MiniCPM-o 2.6** is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.6, and introduces new features for realtime speech conversation and multimodal live streaming. Notable features of MiniCPM-o 2.6 include:

- 🔥 **Leading Visual Capability.**
  MiniCPM-o 2.6 achieves an average score of 70.2 on OpenCompass, a comprehensive evaluation over 8 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-202405, Gemini 1.5 Pro, and Claude 3.5 Sonnet** for single image understanding. It also **outperforms GPT-4V and Claude 3.5 Sonnet** in mutli-image and video understanding, and shows promising in-context learning capability.

- 🎙 **State-of-the-art Speech Capability.** MiniCPM-o 2.6 supports **bilingual realtime speech conversation with configurable voices** in English and Chinese. It **outperforms GPT-4o-realtime on audio understanding tasks** such as ASR and STT translation, and shows **state-of-the-art performance on speech conversation in both semantic and acoustic evaluations in the open-source community**. It also allows for fun features such as emotion/speed/style control, voice cloning, role play, etc.

- 🎬 **Strong Multimodal Live Streaming Capability.** As a new feature, MiniCPM-o 2.6 can **accept continous video and audio streams independent of user queries, and support realtime speech interaction**. It **outperforms GPT-4o-realtime and Claude 3.5 Sonnet and shows state-of-art performance in open-source community on StreamingBench**, a comprehensive benchmark for real-time video understanding, omni-source (video & audio) understanding , and multimodal contextual understanding.										

- 💪 **Strong OCR Capability and Others.**
Advancing popular visual capabilites from MiniCPM-V series, MiniCPM-o 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344). It achieves **state-of-the-art performance on OCRBench for models under 25B, surpassing proprietary models such as GPT-4o-202405**.
  Based on the the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) and [VisCPM](https://github.com/OpenBMB/VisCPM) techniques, it features **trustworthy behaviors**, outperforming GPT-4o and Claude 3.5 Sonnet on MMHal-Bench, and supports **multilingual capabilities** on more than 30 languages.


- 🚀 **Superior Efficiency.**
  In addition to its friendly size, MiniCPM-o 2.6 also shows **state-of-the-art token density** (i.e., number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-o 2.6 can efficiently support **multimodal live streaming** on end-side devices such as iPad.

-  💫  **Easy Usage.**
MiniCPM-o 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [LLaMA-Factory](./docs/llamafactory_train.md), (5) quick local WebUI demo setup with [Gradio](#chat-with-our-demo-on-gradio), and (6) online web demo on [CN](https://minicpm-omni-webdemo.modelbest.cn/) server and [US](https://minicpm-omni-webdemo-us.modelbest.cn/) server.


**Model Architecture.**

- **End-to-end Omni-modal Architecture.** Different modality encoder/decoders are connected and trained in an **end-to-end** fashion to fully exploit rich multimodal knowledge.
- **Omni-modal Live Streaming Mechanism.** (1) We change the offline modality encoder/decoders into online ones for **streaminig inputs/outputs.** (2) We devise a **time-division multiplexing (TDM) mechanism** for omni-modality streaminig processing in the LLM backbone. It divides parallel omni-modality streams into sequential info within small periodic time slices. 
- **Configurable Speech Modeling Design.** We devise a multimodal system prompt, including traditional text system prompt, and **a new audio system prompt to determine the assistant voice**. This enables flexible voice configurations in inference time, and also facilitates voice cloning and description-based voice creation.

<div align="center">
<img src="https://github.com/OpenBMB/MiniCPM-V/blob/main/assets/minicpm-o-26-framework.png" , width=80%>
</div>

### Evaluation  <!-- omit in toc -->

<div align="center">
    <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/radar.png" width=66% />
</div>

<details>
<summary>Click to view visual understanding results.</summary>

**Image Understanding**

<div align="center">
<table style="margin: 0px auto;">
    <thead>
        <tr>
            <th align="left">Model</th>
            <th>Size</th>
            <th>Token Density<sup>+</sup></th>
            <th>OpenCompass</th>
            <th>OCRBench</th>
            <th>MathVista mini</th>
            <th>ChartQA</th>
            <th>MMVet</th>
            <th>MMStar</th>
            <th>MME</th>
            <th>MMB1.1 test</th>
            <th>AI2D</th>
            <th>MMMU val</th>
            <th>HallusionBench</th>
            <th>TextVQA val</th>
            <th>DocVQA test</th>
            <th>MathVerse mini</th>
            <th>MathVision</th>
            <th>MMHal Score</th>
        </tr>
    </thead>
    <tbody align="center">
        <tr>
            <td colspan="19" align="left"><strong>Proprietary</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">GPT-4o-20240513</td>
            <td>-</td>
            <td>1088</td>
            <td><u>69.9</u></td>
            <td>736</td>
            <td>61.3</td>
            <td>85.7</td>
            <td><strong>69.1</strong></td>
            <td>63.9</td>
            <td>2328.7</td>
            <td>82.2</td>
            <td>84.6</td>
            <td><strong>69.2</strong></td>
            <td><strong>55.0</strong></td>
            <td>-</td>
            <td>92.8</td>
            <td><strong>50.2</strong></td>
            <td><strong>30.4</strong></td>
            <td><u>3.6</u></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Claude3.5-Sonnet</td>
            <td>-</td>
            <td>750</td>
            <td>67.9</td>
            <td>788</td>
            <td>61.6</td>
            <td><strong>90.8</strong></td>
            <td>66.0</td>
            <td>62.2</td>
            <td>1920.0</td>
            <td>78.5</td>
            <td>80.2</td>
            <td><u>65.9</u></td>
            <td>49.9</td>
            <td>-</td>
            <td><strong>95.2</strong></td>
            <td>-</td>
            <td>-</td>
            <td>3.4</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Gemini-1.5-Pro</td>
            <td>-</td>
            <td>-</td>
            <td>64.4</td>
            <td>754</td>
            <td>57.7</td>
            <td>81.3</td>
            <td>64.0</td>
            <td>59.1</td>
            <td>2110.6</td>
            <td>73.9</td>
            <td>79.1</td>
            <td>60.6</td>
            <td>45.6</td>
            <td>73.5</td>
            <td>86.5</td>
            <td>-</td>
            <td>19.2</td>
            <td>-</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">GPT-4o-mini-20240718</td>
            <td>-</td>
            <td>1088</td>
            <td>64.1</td>
            <td>785</td>
            <td>52.4</td>
            <td>-</td>
            <td>66.9</td>
            <td>54.8</td>
            <td>2003.4</td>
            <td>76.0</td>
            <td>77.8</td>
            <td>60.0</td>
            <td>46.1</td>
            <td>-</td>
            <td>-</td>
            <td>-</td>
            <td>-</td>
            <td>3.3</td>
        </tr>
        <tr>
            <td colspan="19" align="left"><strong>Open Source</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Cambrian-34B</td>
            <td>34B</td>
            <td><u>1820</u></td>
            <td>58.3</td>
            <td>591</td>
            <td>50.3</td>
            <td>75.6</td>
            <td>53.2</td>
            <td>54.2</td>
            <td>2049.9</td>
            <td>77.8</td>
            <td>79.5</td>
            <td>50.4</td>
            <td>41.6</td>
            <td>76.7</td>
            <td>75.5</td>
            <td>-</td>
            <td>-</td>
            <td>-</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">GLM-4V-9B</td>
            <td>13B</td>
            <td>784</td>
            <td>59.1</td>
            <td>776</td>
            <td>51.1</td>
            <td>-</td>
            <td>58.0</td>
            <td>54.8</td>
            <td>2018.8</td>
            <td>67.9</td>
            <td>71.2</td>
            <td>46.9</td>
            <td>45.0</td>
            <td>-</td>
            <td>-</td>
            <td>-</td>
            <td>-</td>
            <td>-</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Pixtral-12B</td>
            <td>12B</td>
            <td>256</td>
            <td>61.0</td>
            <td>685</td>
            <td>56.9</td>
            <td>81.8</td>
            <td>58.5</td>
            <td>54.5</td>
            <td>-</td>
            <td>72.7</td>
            <td>79.0</td>
            <td>51.1</td>
            <td>47.0</td>
            <td>75.7</td>
            <td>90.7</td>
            <td>-</td>
            <td>-</td>
            <td>-</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">DeepSeek-VL2-27B (4B)</td>
            <td>27B</td>
            <td>672</td>
            <td>66.4</td>
            <td>809</td>
            <td>63.9</td>
            <td>86.0</td>
            <td>60.0</td>
            <td>61.9</td>
            <td>2253.0</td>
            <td>81.2</td>
            <td>83.8</td>
            <td>54.0</td>
            <td>45.3</td>
            <td><u>84.2</u></td>
            <td>93.3</td>
            <td>-</td>
            <td>-</td>
            <td>3.0</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Qwen2-VL-7B</td>
            <td>8B</td>
            <td>784</td>
            <td>67.1</td>
            <td><u>866</u></td>
            <td>58.2</td>
            <td>83.0</td>
            <td>62.0</td>
            <td>60.7</td>
            <td>2326.0</td>
            <td>81.8</td>
            <td>83.0</td>
            <td>54.1</td>
            <td>50.6</td>
            <td><strong>84.3</strong></td>
            <td><u>94.5</u></td>
            <td>31.9</td>
            <td>16.3</td>
            <td>3.2</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">LLaVA-OneVision-72B</td>
            <td>72B</td>
            <td>182</td>
            <td>68.1</td>
            <td>741</td>
            <td>67.5</td>
            <td>83.7</td>
            <td>60.6</td>
            <td><strong>65.8</strong></td>
            <td>2261.0</td>
            <td><strong>85.0</strong></td>
            <td><u>85.6</u></td>
            <td>56.8</td>
            <td>49.0</td>
            <td>80.5</td>
            <td>91.3</td>
            <td>39.1</td>
            <td>-</td>
            <td>3.5</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">InternVL-2.5-8B</td>
            <td>8B</td>
            <td>706</td>
            <td>68.3</td>
            <td>822</td>
            <td><u>64.4</u></td>
            <td>84.8</td>
            <td>62.8</td>
            <td>62.8</td>
            <td>2344.0</td>
            <td><u>83.6</u></td>
            <td>84.5</td>
            <td>56.0</td>
            <td>50.1</td>
            <td>79.1</td>
            <td>93.0</td>
            <td>39.5</td>
            <td>19.7</td>
            <td>3.4</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
            <td>8B</td>
            <td><strong>2822</strong></td>
            <td>65.2</td>
            <td>852*</td>
            <td>60.6</td>
            <td>79.4</td>
            <td>60.0</td>
            <td>57.5</td>
            <td><u>2348.4*</u></td>
            <td>78.0</td>
            <td>82.1</td>
            <td>49.8*</td>
            <td>48.1*</td>
            <td>80.1</td>
            <td>90.8</td>
            <td>25.7</td>
            <td>18.3</td>
            <td>3.6</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
            <td>8B</td>
            <td><strong>2822</strong></td>
            <td><strong>70.2</strong></td>
            <td><strong>897*</strong></td>
            <td><strong>71.9*</strong></td>
            <td><u>86.9*</u></td>
            <td><u>67.5</u></td>
            <td><u>64.0</u></td>
            <td><strong>2372.0*</strong></td>
            <td>80.5</td>
            <td><strong>85.8</strong></td>
            <td>50.4*</td>
            <td><u>51.9</u></td>
            <td>82.0</td>
            <td>93.5</td>
            <td><u>41.4*</u></td>
            <td><u>23.1*</u></td>
            <td><strong>3.8</strong></td>
        </tr>
    </tbody>
</table>
</div>
* We evaluate this benchmark using chain-of-thought prompting. Specifically, for MME, we used this technique only for the Cognition set.


<sup>+</sup> Token Density: number of pixels encoded into each visual token at maximum resolution, i.e., # pixels at maximum resolution / # visual tokens.

Note: For proprietary models, we calculate token density based on the image encoding charging strategy defined in the official API documentation, which provides an upper-bound estimation.


**Multi-image and Video Understanding**

<div align="center">
 
<table style="margin: 0px auto;">
    <thead>
        <tr>
            <th align="left">Model</th>
            <th>Size</th>
            <th>BLINK-val</th>
            <th>Mantis-Eval</th>
            <th>MIRB</th>
            <th>Video-MME (wo / w subs)</th>
        </tr>
    </thead>
    <tbody align="center">
        <tr>
            <td colspan="6" align="left"><strong>Proprietary</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">GPT-4o-20240513</td>
            <td>-</td>
            <td><strong>68</strong></td>
            <td>-</td>
            <td>-</td>
            <td><strong>71.9/77.2<strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">GPT4V</td>
            <td>-</td>
            <td>54.6</td>
            <td>62.7</td>
            <td>53.1</td>
            <td>59.9/63.3</td>
        </tr>
        <tr>
            <td colspan="6" align="left"><strong>Open-source</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">LLaVA-NeXT-Interleave 14B</td>
            <td>14B</td>
            <td>52.6</td>
            <td>66.4</td>
            <td>30.2</td>
            <td>-</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">LLaVA-One-Vision-72B</td>
            <td>72B</td>
            <td>55.4</td>
            <td><strong>77.6</strong></td>
            <td>-</td>
            <td><u>66.2/69.5</u></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">MANTIS 8B</td>
            <td>8B</td>
            <td>49.1</td>
            <td>59.5</td>
            <td>34.8</td>
            <td>-</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Qwen2-VL-7B</td>
            <td>8B</td>
            <td>53.2</td>
            <td>69.6*</td>
            <td><strong>67.6*</strong></td>
            <td>63.3/69.0</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">InternVL-2.5-8B</td>
            <td>8B</td>
            <td>54.8</td>
            <td>67.7</td>
            <td>52.5</td>
            <td>64.2/66.9</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
            <td>8B</td>
            <td>53</td>
            <td>69.1</td>
            <td>53.8</td>
            <td>60.9/63.6</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
            <td>8B</td>
            <td><u>56.7</u></td>
            <td><u>71.9</u></td>
            <td><u>58.6</u></td>
            <td>63.9/67.9</td>
        </tr>
    </tbody>
</table>

</div>
* We evaluate officially released checkpoints by ourselves.

</details>


<details>
<summary>Click to view audio understanding and speech conversation results.</summary>

**Audio Understanding**

<div align="center">
<table style="margin: 0px auto;">
    <thead>
        <tr>
            <th align="left">Task</th>
            <th>Size</th>
            <th colspan="3">ASR (zh)</th>
            <th colspan="3">ASR (en)</th>
            <th colspan="2">ASR</th>
            <th>Emotion</th>
        </tr>
        <tr>
            <th align="left">Metric</th>
            <td></td>
            <th colspan="3">CER↓</th>
            <th colspan="3">WER↓</th>
            <th colspan="2">BLEU↑</th>
            <th>ACC↑</th>
        </tr>
        <tr>
            <th align="left">Dataset</th>
            <td></td>
            <th>AISHELL-1</th>
            <th>Fleurs zh</th>
            <th>WenetSpeech test-net</th>
            <th>LibriSpeech test-clean</th>
            <th>GigaSpeech</th>
            <th>TED-LIUM</th>
            <th>CoVoST en2zh</th>
            <th>CoVoST zh2en</th>
            <th>MELD emotion</th>
        </tr>
    </thead>
    <tbody align="center">
        <tr>
            <td colspan="11" align="left"><strong>Proprietary</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">GPT-4o-Realtime</td>
            <td>-</td>
            <td>7.3*</td>
            <td><u>5.4*</u></td>
            <td>28.9*</td>
            <td>2.6*</td>
            <td>12.9*</td>
            <td>4.8*</td>
            <td>37.1*</td>
            <td>15.7*</td>
            <td>33.2*</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Gemini-1.5-Pro</td>
            <td>-</td>
            <td>4.5*</td>
            <td>5.9*</td>
            <td>14.3*</td>
            <td>2.9*</td>
            <td>10.6*</td>
            <td><strong>3.0*</strong></td>
            <td><u>47.3*</u></td>
            <td>22.6*</td>
            <td>48.4*</td>
        </tr>
        <tr>
            <td colspan="11" align="left"><strong>Open-Source</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Qwen2-Audio</td>
            <td>8B</td>
            <td>-</td>
            <td>7.5</td>
            <td>-</td>
            <td><strong>1.6</strong></td>
            <td>-</td>
            <td>-</td>
            <td>45.2</td>
            <td><u>24.4</u></td>
            <td><strong>55.3</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Qwen2-Audio-Instruction</td>
            <td>8B</td>
            <td>2.6*</td>
            <td>6.9*</td>
            <td><u>10.3*</u></td>
            <td>3.1*</td>
            <td><u>9.7</u>*</td>
            <td>5.9*</td>
            <td>39.5*</td>
            <td>22.9*</td>
            <td>17.4*</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">GLM-4-Voice-Base</td>
            <td>9B</td>
            <td><u>2.5</u></td>
            <td>-</td>
            <td>-</td>
            <td>2.8</td>
            <td>-</td>
            <td>-</td>
            <td>-</td>
            <td>-</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
            <td>8B</td>
            <td><strong>1.6</strong></td>
            <td><strong>4.4</strong></td>
            <td><strong>6.9</strong></td>
            <td><u>1.7</u></td>
            <td><strong>8.7</strong></td>
            <td><strong>3.0</strong></td>
            <td><strong>48.2</strong></td>
            <td><strong>27.2</strong></td>
            <td><u>52.4</u></td>
        </tr>
    </tbody>
</table>
</div>
* We evaluate officially released checkpoints by ourselves.<br><br>

**Speech Generation**

<div align="center">
<table style="margin: 0px auto;">
    <thead>
        <tr>
            <th align="left">Task</th>
            <th>Size</th>
            <th colspan="9">SpeechQA</th>
        </tr>
        <tr>
            <th align="left">Metric</th>
            <th></th>
            <th colspan="3">ACC↑</th>
            <th>G-Eval (10 point)↑</th>
            <th>Semantic ELO score↑</th>
            <th>Acoustic ELO score↑</th>
            <th>Overall ELO score↑</th>
            <th>UTMOS↑</th>
            <th>ASR-WER↓</th>
        </tr>
        <tr>
            <th align="left">Dataset</th>
            <th></th>
            <th>Speech Llama Q.</th>
            <th>Speech Web Q.</th>
            <th>Speech Trivia QA</th>
            <th>Speech AlpacaEval</th>
            <th colspan="5">AudioArena</th>
        </tr>
    </thead>
    <tbody align="center">
        <tr>
            <td colspan="11" align="left"><strong>Proprietary</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">GPT-4o-Realtime</td>
            <td></td>
            <td><strong>71.7</strong></td>
            <td><strong>51.6</strong></td>
            <td><strong>69.7</strong></td>
            <td><strong>7.4</strong></td>
            <td><strong>1157</strong></td>
            <td><strong>1203</strong></td>
            <td><strong>1200</strong></td>
            <td><strong>4.2</strong></td>
            <td><strong>2.3</strong></td>
        </tr>
        <tr>
            <td colspan="11" align="left"><strong>Open-Source</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">GLM-4-Voice</td>
            <td>9B</td>
            <td>50.0</td>
            <td>32.0</td>
            <td>36.4</td>
            <td><u>5.1</u></td>
            <td>999</td>
            <td>1147</td>
            <td>1035</td>
            <td><u>4.1</u></td>
            <td><u>11.7</u></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Llama-Omni</td>
            <td>8B</td>
            <td>45.3</td>
            <td>22.9</td>
            <td>10.7</td>
            <td>3.9</td>
            <td>960</td>
            <td>878</td>
            <td>897</td>
            <td>3.2</td>
            <td>24.3</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Moshi</td>
            <td>7B</td>
            <td>43.7</td>
            <td>23.8</td>
            <td>16.7</td>
            <td>2.4</td>
            <td>871</td>
            <td>808</td>
            <td>875</td>
            <td>2.8</td>
            <td>8.2</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Mini-Omni</td>
            <td>1B</td>
            <td>22.0</td>
            <td>12.8</td>
            <td>6.9</td>
            <td>2.5</td>
            <td>926</td>
            <td>803</td>
            <td>865</td>
            <td>3.4</td>
            <td>10.0</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
            <td>8B</td>
            <td><u>61.0</u></td>
            <td><u>40.0</u></td>
            <td><u>40.2</u></td>
            <td><u>5.1</u></td>
            <td><u>1088</u></td>
            <td><u>1163</u></td>
            <td><u>1131</u></td>
            <td><strong>4.2</strong></td>
            <td>9.8</td>
        </tr>
    </tbody>
</table>
</div>
All results are from AudioEvals, and the evaluation methods along with further details can be found in <a href="https://github.com/OpenBMB/UltraEval-Audio" target="_blank">AudioEvals</a>.<br><br>

**Voice Cloning**

<div align="center">
<table style="margin: 0px auto;">
    <thead>
        <tr>
            <th align="left">Task</th>
            <th colspan="2">Voice cloning</th>
        </tr>
        <tr>
            <th align="left">Metric</th>
            <th>SIMO↑</th>
            <th>SIMO↑</th>
        </tr>
        <tr>
            <th align="left">Dataset</th>
            <th>Seed-TTS test-zh</th>
            <th>Seed-TTS test-en</th>
        </tr>
    </thead>
    <tbody align="center">
        <tr>
            <td nowrap="nowrap" align="left">F5-TTS</td>
            <td><strong>76</strong></td>
            <td><strong>67</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">CosyVoice</td>
            <td><u>75</u></td>
            <td><u>64</u></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">FireRedTTS</td>
            <td>63</td>
            <td>46</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
            <td>57</td>
            <td>47</td>
        </tr>
    </tbody>
</table>
</div>
Note: Mimick Task: Takes audio input, and outputs both an ASR transcription and a voice imitation (TTS)

</details>

<details>
<summary>Click to view multimodal live streaming results.</summary>
  
**Multimodal Live Streaming**: results on StreamingBench

<table style="margin: 0px auto;">
    <thead>
        <tr>
            <th align="left">Model</th>
            <th>Size</th>
            <th>Real-Time Video Understanding</th>
            <th>Omni-Source Understanding</th>
            <th>Contextual Understanding</th>
            <th>Overall</th>
        </tr>
    </thead>
    <tbody align="center">
        <tr>
            <td colspan="7" align="left"><strong>Proprietary</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Gemini 1.5 Pro</td>
            <td>-</td>
            <td><u>77.4</u></td>
            <td><strong>67.8</strong></td>
            <td><strong>51.1</strong></td>
            <td><strong>70.3</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">GPT-4o</td>
            <td>-</td>
            <td>74.5</td>
            <td>51.0</td>
            <td><u>48.0</u></td>
            <td>64.1</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Claude-3.5-Sonnet</td>
            <td>-</td>
            <td>74.0</td>
            <td>41.4</td>
            <td>37.8</td>
            <td>59.7</td>
        </tr>
        <tr>
            <td colspan="9" align="left"><strong>Open-source</strong></td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">VILA-1.5</td>
            <td>8B</td>
            <td>61.5</td>
            <td>37.5</td>
            <td>26.7</td>
            <td>49.5</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">LongVA</td>
            <td>7B</td>
            <td>63.1</td>
            <td>35.9</td>
            <td>30.2</td>
            <td>50.7</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">LLaVA-Next-Video-34B</td>
            <td>34B</td>
            <td>69.8</td>
            <td>41.7</td>
            <td>34.3</td>
            <td>56.7</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">Qwen2-VL-7B</td>
            <td>8B</td>
            <td>71.2</td>
            <td>40.7</td>
            <td>33.1</td>
            <td>57.0</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">InternVL2-8B</td>
            <td>8B</td>
            <td>70.1</td>
            <td>42.7</td>
            <td>34.1</td>
            <td>57.0</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">VITA-1.5</td>
            <td>8B</td>
            <td>70.9</td>
            <td>40.8</td>
            <td>35.8</td>
            <td>57.4</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">LLaVA-OneVision-7B</td>
            <td>8B</td>
            <td>74.3</td>
            <td>40.8</td>
            <td>31.0</td>
            <td>58.4</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">InternLM-XC2.5-OL-7B</td>
            <td>8B</td>
            <td>75.4</td>
            <td>46.2</td>
            <td>33.6</td>
            <td>60.8</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
            <td>8B</td>
            <td>72.4</td>
            <td>40.2</td>
            <td>33.4</td>
            <td>57.7</td>
        </tr>
        <tr>
            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
            <td>8B</td>
            <td><strong>79.9</strong></td>
            <td><u>53.4</u></td>
            <td>38.5</td>
            <td><u>66.0</u></td>
        </tr>
    </tbody>
</table>

</details>


### Examples <!-- omit in toc -->

We deploy MiniCPM-o 2.6 on end devices. The demo video is the raw screen recording on a iPad Pro without edition.


<div style="display: flex; flex-direction: column; align-items: center;">
  <img src="https://github.com/OpenBMB/MiniCPM-V/blob/main/assets/minicpmo2_6/minicpmo2_6_math_intersect.png" alt="math" style="margin-bottom: 5px;">
  <img src="https://github.com/OpenBMB/MiniCPM-V/blob/main/assets/minicpmo2_6/minicpmo2_6_diagram_train_NN.png" alt="diagram" style="margin-bottom: 5px;">
  <img src="https://github.com/OpenBMB/MiniCPM-V/blob/main/assets/minicpmo2_6/minicpmo2_6_multi-image_bike.png" alt="bike" style="margin-bottom: 5px;">
</div>




## Online Demo
Click here to try the online demo of **MiniCPM-o 2.6** on [CN](https://minicpm-omni-webdemo.modelbest.cn/) server and [US](https://minicpm-omni-webdemo-us.modelbest.cn) server.


## Usage
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10：
```
Pillow==10.1.0
torch==2.2.0
torchaudio==2.2.0
torchvision==0.17.0
transformers==4.44.2
librosa==0.9.0
soundfile==0.12.1
vector-quantize-pytorch==1.18.5
vocos==0.1.0
decord
moviepy
```


### Model initialization
```python

import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

# load omni model default, the default init_vision/init_audio/init_tts is True
# if load vision-only model, please set init_audio=False and init_tts=False
# if load audio-only model, please set init_vision=False
model = AutoModel.from_pretrained(
    'openbmb/MiniCPM-o-2_6',
    trust_remote_code=True,
    attn_implementation='sdpa', # sdpa or flash_attention_2
    torch_dtype=torch.bfloat16,
    init_vision=True,
    init_audio=True,
    init_tts=True
)


model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-o-2_6', trust_remote_code=True)

# In addition to vision-only mode, tts processor and vocos also needs to be initialized
model.init_tts()
model.tts.float()
```
### Omni mode
we provide two inference modes: chat and streaming

#### Chat inference
```python
import math
import numpy as np
from PIL import Image
from moviepy.editor import VideoFileClip
import tempfile
import librosa
import soundfile as sf

def get_video_chunk_content(video_path, flatten=True):
    video = VideoFileClip(video_path)
    print('video_duration:', video.duration)
    
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_audio_file:
        temp_audio_file_path = temp_audio_file.name
        video.audio.write_audiofile(temp_audio_file_path, codec="pcm_s16le", fps=16000)
        audio_np, sr = librosa.load(temp_audio_file_path, sr=16000, mono=True)
    num_units = math.ceil(video.duration)
    
    # 1 frame + 1s audio chunk
    contents= []
    for i in range(num_units):
        frame = video.get_frame(i+1)
        image = Image.fromarray((frame).astype(np.uint8))
        audio = audio_np[sr*i:sr*(i+1)]
        if flatten:
            contents.extend(["<unit>", image, audio])
        else:
            contents.append(["<unit>", image, audio])
    
    return contents

video_path="/path/to/video"
# if use voice clone prompt, please set ref_audio
ref_audio_path = 'assets/demo.wav'
ref_audio, _ = librosa.load(ref_audio_path, sr=16000, mono=True)
sys_msg = model.get_sys_prompt(ref_audio=ref_audio, mode='omni', language='en')
# or use default prompt
# sys_msg = model.get_sys_prompt(mode='omni', language='en')

contents = get_video_chunk_content(video_path)
msg = {"role":"user", "content": contents}
msgs = [sys_msg, msg]

# please set generate_audio=True and output_audio_path to save the tts result
generate_audio = True
output_audio_path = 'output.wav'

res = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True,
    temperature=0.5,
    max_new_tokens=4096,
    omni_input=True, # please set omni_input=True when omni inference
    use_tts_template=True,
    generate_audio=generate_audio,
    output_audio_path=output_audio_path,
    max_slice_nums=1,
    use_image_id=False,
    return_dict=True
)
print(res)
```
#### Streaming inference
```python
# a new conversation need reset session first, it will reset the kv-cache
model.reset_session()

contents = get_video_chunk_content(video_path, flatten=False)
session_id = '123'
generate_audio = True

# 1. prefill system prompt
res = model.streaming_prefill(
    session_id=session_id,
    msgs=[sys_msg], 
    tokenizer=tokenizer
)

# 2. prefill video/audio chunks
for content in contents:
    msgs = [{"role":"user", "content": content}]
    res = model.streaming_prefill(
        session_id=session_id,
        msgs=msgs, 
        tokenizer=tokenizer
    )

# 3. generate
res = model.streaming_generate(
    session_id=session_id,
    tokenizer=tokenizer,
    temperature=0.5,
    generate_audio=generate_audio
)

audios = []
text = ""

if generate_audio:
    for r in res:
        audio_wav = r.audio_wav
        sampling_rate = r.sampling_rate
        txt = r.text

        audios.append(audio_wav)
        text += txt
        
    res = np.concatenate(audios)
    sf.write("output.wav", res, samplerate=sampling_rate)
    print("text:", text)
    print("audio saved to output.wav")
else:
    for r in res:
        text += r['text']
    print("text:", text)

```

### Audio-Only mode
#### Mimick
```python
mimick_prompt = "Please repeat each user's speech, including voice style and speech content."
audio_input, _ = librosa.load('xxx.wav', sr=16000, mono=True)
msgs = [{'role': 'user', 'content': [mimick_prompt,audio_input]}]

res = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True,
    max_new_tokens=128,
    use_tts_template=True,
    temperature=0.3,
    generate_audio=True,
    output_audio_path='output.wav', # save the tts result to output_audio_path
)
```

#### General Speech Conversation with Configurable Voices
<details> <summary>Click to view the Python code for enabling MiniCPM-o 2.6 to interact with you in a specified voice.</summary>

```python
ref_audio, _ = librosa.load('assets/demo.wav', sr=16000, mono=True) # load the reference audio

# Audio RolePlay:  # With this mode, model will role-play the character based on the audio prompt.
sys_prompt = model.get_sys_prompt(ref_audio=ref_audio, mode='audio_roleplay', language='en')
user_question = {'role': 'user', 'content': [librosa.load('xxx.wav', sr=16000, mono=True)[0]]}

# Audio Assistant: # With this mode, model will speak with the voice in ref_audio as a AI assistant.
# sys_prompt = model.get_sys_prompt(ref_audio=ref_audio, mode='audio_assistant', language='en') 
# user_question = {'role': 'user', 'content': [librosa.load('xxx.wav', sr=16000, mono=True)[0]]} # Try to ask something!
```
```python
msgs = [sys_prompt, user_question]
res = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True,
    max_new_tokens=128,
    use_tts_template=True,
    generate_audio=True,
    temperature=0.3,
    output_audio_path='result.wav',
)

# round two
history = msgs.append({'role': 'assistant', 'content': res})
user_question = {'role': 'user', 'content': [librosa.load('xxx.wav', sr=16000, mono=True)[0]]}
msgs = history.append(user_question)
res = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True,
    max_new_tokens=128,
    use_tts_template=True,
    generate_audio=True,
    temperature=0.3,
    output_audio_path='result_round_2.wav',
)
print(res)
```

</details>

#### Addressing various audio tasks
<details>
<summary> Click to show Python code running MiniCPM-o 2.6 with specific audioQA task. </summary>

```python
'''
Audio Understanding Task Prompt:
Speech:
    ASR with ZH(same as AST en2zh): 请仔细听这段音频片段，并将其内容逐字记录。
    ASR with EN(same as AST zh2en): Please listen to the audio snippet carefully and transcribe the content.
    Speaker Analysis: Based on the speaker's content, speculate on their gender, condition, age range, and health status.
General Audio:
    Audio Caption: Summarize the main content of the audio.
    Sound Scene Tagging: Utilize one keyword to convey the audio's content or the associated scene.
'''
task_prompt = "\n"
audio_input, _ = librosa.load('xxx.wav', sr=16000, mono=True)

msgs = [{'role': 'user', 'content': [task_prompt,audio_input]}]

res = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True,
    max_new_tokens=128,
    use_tts_template=True,
    generate_audio=True,
    temperature=0.3,
    output_audio_path='result.wav',
)
print(res)
```
```python
'''
Speech Generation Task Prompt:
    Human Instruction-to-Speech: see https://voxinstruct.github.io/VoxInstruct/
    Example:
        # 在新闻中，一个年轻男性兴致勃勃地说：“祝福亲爱的祖国母亲美丽富强！”他用低音调和低音量，慢慢地说出了这句话。
        # Delighting in a surprised tone, an adult male with low pitch and low volume comments:"One even gave my little dog a biscuit" This dialogue takes place at a leisurely pace, delivering a sense of excitement and surprise in the context. 

    Voice Cloning or Voice Creation: With this mode, model will act like a TTS model. 
'''
# Human Instruction-to-Speech:
task_prompt = '' #Try to make some Human Instruction-to-Speech prompt
msgs = [{'role': 'user', 'content': [task_prompt]}] # you can try to use the same audio question

# Voice Cloning mode: With this mode, model will act like a TTS model. 
# sys_prompt = model.get_sys_prompt(ref_audio=ref_audio, mode='voice_cloning', language='en')
# text_prompt = f"Please read the text below."
# user_question = {'role': 'user', 'content': [text_prompt, "content that you want to read"]} # using same voice in sys_prompt to read the text. (Voice Cloning)
# user_question = {'role': 'user', 'content': [text_prompt, librosa.load('xxx.wav', sr=16000, mono=True)[0]]} # using same voice in sys_prompt to read 'xxx.wav'. (Voice Creation)

msgs = [sys_prompt, user_question]
res = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True,
    max_new_tokens=128,
    use_tts_template=True,
    generate_audio=True,
    temperature=0.3,
    output_audio_path='result.wav',
)


```

</details>

### Vision-Only mode

`MiniCPM-o-2_6` has the same inference methods as `MiniCPM-V-2_6`

#### Chat with single image
```python
# test.py
image = Image.open('xx.jpg').convert('RGB')
question = 'What is in the image?'
msgs = [{'role': 'user', 'content': [image, question]}]
res = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer
)
print(res)

## if you want to use streaming, please make sure sampling=True and stream=True
## the model.chat will return a generator
res = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True,
    stream=True
)
generated_text = ""
for new_text in res:
    generated_text += new_text
    print(new_text, flush=True, end='')
```

#### Chat with multiple images
<details>
<summary> Click to show Python code running MiniCPM-o 2.6 with multiple images input. </summary>
  
```python
image1 = Image.open('image1.jpg').convert('RGB')
image2 = Image.open('image2.jpg').convert('RGB')
question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.'
msgs = [{'role': 'user', 'content': [image1, image2, question]}]
answer = model.chat(
    msgs=msgs,
    tokenizer=tokenizer
)
print(answer)
```
</details>

#### In-context few-shot learning
<details>
<summary> Click to view Python code running MiniCPM-o 2.6 with few-shot input. </summary>

```python
question = "production date" 
image1 = Image.open('example1.jpg').convert('RGB')
answer1 = "2023.08.04"
image2 = Image.open('example2.jpg').convert('RGB')
answer2 = "2007.04.24"
image_test = Image.open('test.jpg').convert('RGB')
msgs = [
    {'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]},
    {'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]},
    {'role': 'user', 'content': [image_test, question]}
]
answer = model.chat(
    msgs=msgs,
    tokenizer=tokenizer
)
print(answer)
```
</details>

#### Chat with video
<details>
<summary> Click to view Python code running MiniCPM-o 2.6 with video input. </summary>

```python
MAX_NUM_FRAMES=64 # if cuda OOM set a smaller number
def encode_video(video_path):
    def uniform_sample(l, n):
        gap = len(l) / n
        idxs = [int(i * gap + gap / 2) for i in range(n)]
        return [l[i] for i in idxs]
    vr = VideoReader(video_path, ctx=cpu(0))
    sample_fps = round(vr.get_avg_fps() / 1)  # FPS
    frame_idx = [i for i in range(0, len(vr), sample_fps)]
    if len(frame_idx) > MAX_NUM_FRAMES:
        frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)
    frames = vr.get_batch(frame_idx).asnumpy()
    frames = [Image.fromarray(v.astype('uint8')) for v in frames]
    print('num frames:', len(frames))
    return frames
video_path ="video_test.mp4"
frames = encode_video(video_path)
question = "Describe the video"
msgs = [
    {'role': 'user', 'content': frames + [question]}, 
]
# Set decode params for video
params={}
params["use_image_id"] = False
params["max_slice_nums"] = 2 # use 1 if cuda OOM and video resolution >  448*448
answer = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    **params
)
print(answer)
```
</details>

Please look at [GitHub](https://github.com/OpenBMB/MiniCPM-V) for more detail about usage.


## Inference with llama.cpp<a id="llamacpp"></a>
MiniCPM-o 2.6 can run with llama.cpp. See our fork of [llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpm-v2.5/examples/minicpmv) for more detail.


## Int4 quantized version
Download the int4 quantized version for lower GPU memory (7GB) usage:  [MiniCPM-o-2_6-int4](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4).


## License
#### Model License
* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 
* The usage of MiniCPM-o and MiniCPM-V series model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
* The models and weights of MiniCPM are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-o 2.6 weights are also available for free commercial use.


#### Statement
* As an LMM, MiniCPM-o 2.6 generates contents by learning a large mount of multimodal corpora, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-o 2.6 does not represent the views and positions of the model developers
* We will not be liable for any problems arising from the use of the MinCPM-V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.

## Key Techniques and Other Multimodal Projects

👏 Welcome to explore key techniques of MiniCPM-o 2.6 and other multimodal projects of our team:

[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD)  | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V)

## Citation

If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！

```bib
@article{yao2024minicpm,
  title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
  author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others},
  journal={arXiv preprint arXiv:2408.01800},
  year={2024}
}
```