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LatestXiaomiV2.5-ASR
XiaomiSpeech → Text

Xiaomi Releases MiMo Model for Speech Recognition

The new open-source model from the Chinese tech giant offers automatic speech recognition for Mandarin, Cantonese, and English under a permissive MIT license.

Apr 23, 2026
NotableMIT
MiMo-V2.5-ASR

Chinese technology company Xiaomi has released MiMo-V2.5-ASR, a new model for automatic speech recognition (ASR) available to the open-source community. The model is designed to perform speech-to-text tasks in three languages: Mandarin Chinese, English, and Cantonese.

While Xiaomi has not provided extensive details on the model's architecture or training dataset, its focused multilingual capability makes it a potentially valuable tool for developers building applications for these specific language markets. The inclusion of Cantonese is particularly notable, as it is often less supported than Mandarin in large-scale ASR systems.

Permissive and Practical

The release is significant as it comes from a major global electronics manufacturer, signaling a continued interest from large corporations in contributing to the open AI ecosystem. The model's utility is enhanced by its licensing terms.

Xiaomi has released MiMo-V2.5-ASR under the MIT license, one of the most permissive open-source licenses available. This allows for unrestricted use, modification, and distribution, including for commercial purposes, removing a common barrier to adoption for many businesses and independent developers. The model and its usage instructions are available on its Hugging Face repository.

Sources

  • XiaomiMiMo/MiMo-V2.5-ASR

    Hugging Face

    Visit
OlderLLaDA2.0-Uni: A Unified MoE for Vision TasksinclusionAI · Any-to-Any · 6 months agoNewerGoogle Releases 2B Multimodal Gemma 4 Assistant ModelGoogle DeepMind · Any-to-Any · 6 months ago

Get the model

Hugging Face

Specs

LanguagesChinese, English, Cantonese
Size32.1 GB
PrecisionFP32
ArchitectureMiMoV2ASRForCausalLM
LicenseMIT
Downloads3.9K
Likes114

Can you run it?

Runs on a laptop — about 8.6 GB at FP8.

  • FP32 (as published)

    48 GB GPU (RTX 6000) or 2×24 GB · 64 GB Mac

    33.1 GB
  • 16-bit

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    16.2 GB
  • FP8

    12 GB GPU (RTX 3060 / 4070) · 16 GB Mac

    8.6 GB
Your machine

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Speech → Text

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