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).

Multimodality -> Text

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%

Audio -> Text

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

Image -> Text

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 - -

Video(without audio) -> Text

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 -

Zero-shot Speech Generation

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

Text -> Text

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

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 to get decord used when loading video.

Minimum GPU memory requirements

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.

Video ULR resource usage

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

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}
}