lucataco / qwen2.5-omni-7b

Qwen2.5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner.

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Run time and cost

This model costs approximately $0.00098 to run on Replicate, or 1020 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 1 seconds.

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Qwen2.5-Omni

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Introduction

Qwen2.5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner.

Key Features

  • Omni and Novel Architecture: We propose Thinker-Talker architecture, an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner. We propose a novel position embedding, named TMRoPE (Time-aligned Multimodal RoPE), to synchronize the timestamps of video inputs with audio.

  • Real-Time Voice and Video Chat: Architecture designed for fully real-time interactions, supporting chunked input and immediate output.

  • Natural and Robust Speech Generation: Surpassing many existing streaming and non-streaming alternatives, demonstrating superior robustness and naturalness in speech generation.

  • Strong Performance Across Modalities: Exhibiting exceptional performance across all modalities when benchmarked against similarly sized single-modality models. Qwen2.5-Omni outperforms the similarly sized Qwen2-Audio in audio capabilities and achieves comparable performance to Qwen2.5-VL-7B.

  • Excellent End-to-End Speech Instruction Following: Qwen2.5-Omni shows performance in end-to-end speech instruction following that rivals its effectiveness with text inputs, evidenced by benchmarks such as MMLU and GSM8K.

Model Architecture

### Performance We conducted a comprehensive evaluation of Qwen2.5-Omni, which demonstrates strong performance across all modalities when compared to similarly sized single-modality models and closed-source models like Qwen2.5-VL-7B, Qwen2-Audio, and Gemini-1.5-pro. In tasks requiring the integration of multiple modalities, such as OmniBench, Qwen2.5-Omni achieves state-of-the-art performance. Furthermore, in single-modality tasks, it excels in areas including speech recognition (Common Voice), translation (CoVoST2), audio understanding (MMAU), image reasoning (MMMU, MMStar), video understanding (MVBench), and speech generation (Seed-tts-eval and subjective naturalness).

<details> <summary>Multimodality -> Text</summary>

Datasets Model Performance
OmniBench
Speech | Sound Event | Music | Avg		 Gemini-1.5-Pro 42.67%|42.26%|46.23%|42.91%
MIO-Instruct 36.96%|33.58%|11.32%|33.80%
AnyGPT (7B) 17.77%|20.75%|13.21%|18.04%
video-SALMONN 34.11%|31.70%|56.60%|35.64%
UnifiedIO2-xlarge 39.56%|36.98%|29.25%|38.00%
UnifiedIO2-xxlarge 34.24%|36.98%|24.53%|33.98%
MiniCPM-o -|-|-|40.50%
Baichuan-Omni-1.5 -|-|-|42.90%
Qwen2.5-Omni-7B 55.25%|60.00%|52.83%|56.13%
</details> <details> <summary>Audio -> Text</summary>
Datasets Model Performance
ASR
Librispeech
dev-clean | dev other | test-clean | test-other		 SALMONN -|-|2.1|4.9
SpeechVerse -|-|2.1|4.4
Whisper-large-v3 -|-|1.8|3.6
Llama-3-8B -|-|-|3.4
Llama-3-70B -|-|-|3.1
Seed-ASR-Multilingual -|-|1.6|2.8
MiniCPM-o -|-|1.7|-
MinMo -|-|1.7|3.9
Qwen-Audio 1.8|4.0|2.0|4.2
Qwen2-Audio 1.3|3.4|1.6|3.6
Qwen2.5-Omni-7B 1.6|3.5|1.8|3.4
Common Voice 15
en | zh | yue | fr		 Whisper-large-v3 9.3|12.8|10.9|10.8
MinMo 7.9|6.3|6.4|8.5
Qwen2-Audio 8.6|6.9|5.9|9.6
Qwen2.5-Omni-7B 7.6|5.2|7.3|7.5
Fleurs
zh | en		 Whisper-large-v3 7.7|4.1
Seed-ASR-Multilingual -|3.4
Megrez-3B-Omni 10.8|-
MiniCPM-o 4.4|-
MinMo 3.0|3.8
Qwen2-Audio 7.5|-
Qwen2.5-Omni-7B 3.0|4.1
Wenetspeech
test-net | test-meeting		 Seed-ASR-Chinese 4.7|5.7
Megrez-3B-Omni -|16.4
MiniCPM-o 6.9|-
MinMo 6.8|7.4
Qwen2.5-Omni-7B 5.9|7.7
Voxpopuli-V1.0-en Llama-3-8B 6.2
Llama-3-70B 5.7
Qwen2.5-Omni-7B 5.8
S2TT
CoVoST2
en-de | de-en | en-zh | zh-en		 SALMONN 18.6|-|33.1|-
SpeechLLaMA -|27.1|-|12.3
BLSP 14.1|-|-|-
MiniCPM-o -|-|48.2|27.2
MinMo -|39.9|46.7|26.0
Qwen-Audio 25.1|33.9|41.5|15.7
Qwen2-Audio 29.9|35.2|45.2|24.4
Qwen2.5-Omni-7B 30.2|37.7|41.4|29.4
SER
Meld WavLM-large 0.542
MiniCPM-o 0.524
Qwen-Audio 0.557
Qwen2-Audio 0.553
Qwen2.5-Omni-7B 0.570
VSC
VocalSound CLAP 0.495
Pengi 0.604
Qwen-Audio 0.929
Qwen2-Audio 0.939
Qwen2.5-Omni-7B 0.939
Music
GiantSteps Tempo Llark-7B 0.86
Qwen2.5-Omni-7B 0.88
MusicCaps LP-MusicCaps 0.291|0.149|0.089|0.061|0.129|0.130
Qwen2.5-Omni-7B 0.328|0.162|0.090|0.055|0.127|0.225
Audio Reasoning
MMAU
Sound | Music | Speech | Avg		 Gemini-Pro-V1.5 56.75|49.40|58.55|54.90
Qwen2-Audio 54.95|50.98|42.04|49.20
Qwen2.5-Omni-7B 67.87|69.16|59.76|65.60
Voice Chatting
VoiceBench
AlpacaEval | CommonEval | SD-QA | MMSU		 Ultravox-v0.4.1-LLaMA-3.1-8B 4.55|3.90|53.35|47.17
MERaLiON 4.50|3.77|55.06|34.95
Megrez-3B-Omni 3.50|2.95|25.95|27.03
Lyra-Base 3.85|3.50|38.25|49.74
MiniCPM-o 4.42|4.15|50.72|54.78
Baichuan-Omni-1.5 4.50|4.05|43.40|57.25
Qwen2-Audio 3.74|3.43|35.71|35.72
Qwen2.5-Omni-7B 4.49|3.93|55.71|61.32
VoiceBench
OpenBookQA | IFEval | AdvBench | Avg		 Ultravox-v0.4.1-LLaMA-3.1-8B 65.27|66.88|98.46|71.45
MERaLiON 27.23|62.93|94.81|62.91
Megrez-3B-Omni 28.35|25.71|87.69|46.25
Lyra-Base 72.75|36.28|59.62|57.66
MiniCPM-o 78.02|49.25|97.69|71.69
Baichuan-Omni-1.5 74.51|54.54|97.31|71.14
Qwen2-Audio 49.45|26.33|96.73|55.35
Qwen2.5-Omni-7B 81.10|52.87|99.42|74.12
</details> <details> <summary>Image -> Text</summary> | Dataset | Qwen2.5-Omni-7B | Other Best | Qwen2.5-VL-7B | GPT-4o-mini | |--------------------------------|--------------|------------|---------------|-------------| | MMMUval | 59.2 | 53.9 | 58.6 | **60.0** | | MMMU-Prooverall | 36.6 | - | **38.3** | 37.6 | | MathVistatestmini | 67.9 | **71.9** | 68.2 | 52.5 | | MathVisionfull | 25.0 | 23.1 | **25.1** | - | | MMBench-V1.1-ENtest | 81.8 | 80.5 | **82.6** | 76.0 | | MMVetturbo | 66.8 | **67.5** | 67.1 | 66.9 | | MMStar | **64.0** | **64.0** | 63.9 | 54.8 | | MMEsum | 2340 | **2372** | 2347 | 2003 | | MuirBench | 59.2 | - | **59.2** | - | | CRPErelation | **76.5** | - | 76.4 | - | | RealWorldQAavg | 70.3 | **71.9** | 68.5 | - | | MME-RealWorlden | **61.6** | - | 57.4 | - | | MM-MT-Bench | 6.0 | - | **6.3** | - | | AI2D | 83.2 | **85.8** | 83.9 | - | | TextVQAval | 84.4 | 83.2 | **84.9** | - | | DocVQAtest | 95.2 | 93.5 | **95.7** | - | | ChartQAtest Avg | 85.3 | 84.9 | **87.3** | - | | OCRBench_V2en | **57.8** | - | 56.3 | - | | Dataset | Qwen2.5-Omni-7B | Qwen2.5-VL-7B | Grounding DINO | Gemini 1.5 Pro | |--------------------------|--------------|---------------|----------------|----------------| | Refcocoval | 90.5 | 90.0 | **90.6** | 73.2 | | RefcocotextA | **93.5** | 92.5 | 93.2 | 72.9 | | RefcocotextB | 86.6 | 85.4 | **88.2** | 74.6 | | Refcoco+val | 85.4 | 84.2 | **88.2** | 62.5 | | Refcoco+textA | **91.0** | 89.1 | 89.0 | 63.9 | | Refcoco+textB | **79.3** | 76.9 | 75.9 | 65.0 | | Refcocog+val | **87.4** | 87.2 | 86.1 | 75.2 | | Refcocog+test | **87.9** | 87.2 | 87.0 | 76.2 | | ODinW | 42.4 | 37.3 | **55.0** | 36.7 | | PointGrounding | 66.5 | **67.3** | - | - | </details> <details> <summary>Video(without audio) -> Text</summary> | Dataset | Qwen2.5-Omni-7B | Other Best | Qwen2.5-VL-7B | GPT-4o-mini | |-----------------------------|--------------|------------|---------------|-------------| | Video-MMEw/o sub | 64.3 | 63.9 | **65.1** | 64.8 | | Video-MMEw sub | **72.4** | 67.9 | 71.6 | - | | MVBench | **70.3** | 67.2 | 69.6 | - | | EgoSchematest | **68.6** | 63.2 | 65.0 | - | </details> <details> <summary>Zero-shot Speech Generation</summary>
Datasets Model Performance
Content Consistency
SEED
test-zh | test-en | test-hard 		Seed-TTS_ICL 1.11 | 2.24 | 7.58
Seed-TTS_RL 1.00 | 1.94 | 6.42
MaskGCT 2.27 | 2.62 | 10.27
E2_TTS 1.97 | 2.19 | -
F5-TTS 1.56 | 1.83 | 8.67
CosyVoice 2 1.45 | 2.57 | 6.83
CosyVoice 2-S 1.45 | 2.38 | 8.08
Qwen2.5-Omni-7B_ICL 1.70 | 2.72 | 7.97
Qwen2.5-Omni-7B_RL 1.42 | 2.32 | 6.54
Speaker Similarity
SEED
test-zh | test-en | test-hard 		Seed-TTS_ICL 0.796 | 0.762 | 0.776
Seed-TTS_RL 0.801 | 0.766 | 0.782
MaskGCT 0.774 | 0.714 | 0.748
E2_TTS 0.730 | 0.710 | -
F5-TTS 0.741 | 0.647 | 0.713
CosyVoice 2 0.748 | 0.652 | 0.724
CosyVoice 2-S 0.753 | 0.654 | 0.732
Qwen2.5-Omni-7B_ICL 0.752 | 0.632 | 0.747
Qwen2.5-Omni-7B_RL 0.754 | 0.641 | 0.752
</details> <details> <summary>Text -> Text</summary> | Dataset | Qwen2.5-Omni-7B | Qwen2.5-7B | Qwen2-7B | Llama3.1-8B | Gemma2-9B | |-----------------------------------|-----------|------------|----------|-------------|-----------| | MMLU-Pro | 47.0 | **56.3** | 44.1 | 48.3 | 52.1 | | MMLU-redux | 71.0 | **75.4** | 67.3 | 67.2 | 72.8 | | LiveBench0831 | 29.6 | **35.9** | 29.2 | 26.7 | 30.6 | | GPQA | 30.8 | **36.4** | 34.3 | 32.8 | 32.8 | | MATH | 71.5 | **75.5** | 52.9 | 51.9 | 44.3 | | GSM8K | 88.7 | **91.6** | 85.7 | 84.5 | 76.7 | | HumanEval | 78.7 | **84.8** | 79.9 | 72.6 | 68.9 | | MBPP | 73.2 | **79.2** | 67.2 | 69.6 | 74.9 | | MultiPL-E | 65.8 | **70.4** | 59.1 | 50.7 | 53.4 | | LiveCodeBench2305-2409 | 24.6 | **28.7** | 23.9 | 8.3 | 18.9 | </details> ## Quickstart Below, we provide simple examples to show how to use Qwen2.5-Omni with 🤗 Transformers. The codes of Qwen2.5-Omni on Hugging Face Transformers are in pull request stage and not merged into the main branch yet. We offer a toolkit to help you handle various types of audio and visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved audio, images and videos. You can install it using the following command and make sure your system has `ffmpeg` installed: If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-omni-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video. <details> <summary>Minimum GPU memory requirements</summary> | Precision | 15(s) Video | 30(s) Video | 60(s) Video | |-----------| ------------- | --------- | -------------- | | FP32 | 93.56 GB | Not Recommend | Not Recommend | | BF16 | 31.11 GB | 41.85 GB | 60.19 GB | Note: The table above presents the theoretical minimum memory requirements for inference with `transformers` and `BF16` is test with `attn_implementation="flash_attention_2"`; however, in practice, the actual memory usage is typically at least 1.2 times higher. For more information, see the linked resource [here](https://huggingface.co/docs/accelerate/main/en/usage_guides/model_size_estimator). </details> <details> <summary>Video ULR resource usage</summary> Video URL compatibility largely depends on the third-party library version. The details are in the table below. Change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one. | Backend | HTTP | HTTPS | |-------------|------|-------| | torchvision >= 0.19.0 | ✅ | ✅ | | torchvision < 0.19.0 | ❌ | ❌ | | decord | ✅ | ❌ | </details> ### Usage Tips #### Prompt for audio output If users need audio output, the system prompt must be set as "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.", otherwise the audio output may not work as expected. 
{
    "role": "system",
    "content": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.",
}
#### Use audio in video In the process of multimodal interaction, the videos provided by users are often accompanied by audio (such as questions about the content in the video, or sounds generated by certain events in the video). This information is conducive to the model providing a better interactive experience. So we provide the following options for users to decide whether to use audio in video. It is worth noting that during a multi-round conversation, the `use_audio_in_video` parameter in these places must be set to the same, otherwise unexpected results will occur. #### Use audio output or not The model supports both text and audio outputs, if users do not need audio outputs, they can set `enable_audio_output=False` in the `from_pretrained` function. This option will save about `~2GB` of GPU memory but the `return_audio` option for `generate` function will only allow to be set at `False`. In order to obtain a flexible experience, we recommend that users set `enable_audio_output` at `True` when initializing the model through `from_pretrained` function, and then decide whether to return audio when `generate` function is called. When `return_audio` is set to `False`, the model will only return text outputs to get text responses faster. #### Change voice type of output audio Qwen2.5-Omni supports the ability to change the voice of the output audio. The `"Qwen/Qwen2.5-Omni-7B"` checkpoint support two voice types as follow: | Voice Type | Gender | Description | |------------|--------|-------------| | Chelsie | Female | A honeyed, velvety voice that carries a gentle warmth and luminous clarity.| | Ethan | Male | A bright, upbeat voice with infectious energy and a warm, approachable vibe.| Users can use the `spk` parameter of `generate` function to specify the voice type. By default, if `spk` is not specified, the default voice type is `Chelsie`. ## Citation If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: :) 

@article{Qwen2.5-Omni,
  title={Qwen2.5-Omni Technical Report},
  author={Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, Bin Zhang, Xiong Wang, Yunfei Chu, Junyang Lin},
  journal={arXiv preprint arXiv:2503.20215},
  year={2025}
}