MGM-Omni-32B

MGM-Omni-32B

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Introduction

MGM-Omni is an omni-chatbot capable of processing text, image, video, and speech inputs, and generating both text and speech responses. MGM-Omni is capable of long-form speech understanding and generation, as well as zero-shot voice cloning in both Chinese and English. MGM-Omni-32B is the MLLM component of MGM-Omni for omni modality perception. For the SpeechLM part, please refer MGM-Omni-TTS.

Main Properties

• Omni-modality supports: MGM-Omni supports audio, video, image, and text inputs, understands long contexts, and can generate both text and speech outputs, making it a truly versatile multi-modal AI assistant.
• Long-form Speech Understanding: Unlike most existing open-source multi-modal models, which typically fail with inputs longer than 15 minutes, MGM-Omni can handle hour-long speech inputs while delivering superior overall and detailed understanding and performance!
• Long-form Speech Generation: With a treasure trove of training data and smart Chunk-Based Decoding, MGM-Omni can generate over 10 minutes of smooth, natural speech for continuous storytelling.
• Streaming Generation: Thanks to the parallel decoding approach for speech tokens, MGM-Omni enables efficient and smooth streaming audio, making it suitable for live conversations.
• Zero-shot Voice Cloning: With MGM-Omni’s extensive and diverse audio training, you can create a customized voice clone by simply recording a short clip (around 10 seconds) and reviewing the results.
• Fully Open-source: All the code, models, and training data will be released.

Evaluation

Speech and Audio Understanding

This table presents WER and CER results on speech understanding. Here LS refers to LibriSpeech and CM refers to Common Voice.

This table presents evaluation results on AIR-Bench Chat (speech, sound, music, etc.).

Speech Generation

This table presents evaluation results on speech generation on seed-tts-eval. For Qwen2.5-Omni, model size refers to the size of the talker.

Citation

If you find this repo useful for your research, we would appreciate it if you could cite our work:

@misc{wang2025mgmomni,
  title={MGM-Omni: An Open-source Omni Chatbot},
  author={Wang, Chengyao and Zhong, Zhisheng and Peng, Bohao and Yang, Senqiao and Liu, Yuqi and Yu, Bei and Jia, Jiaya},
  year={2025},
  howpublished={\url{https://mgm-omni.notion.site}},
  note={Notion Blog}
}