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MiMo-V2.5-ASR
MiMo-V2.5-ASR turns audio into text. Xiaomi MiMo’s published examples cover Chinese dialects, Chinese–English conversations, sung words and overlapping speech.
Downloadable weights, a separate audio tokenizer and a Gradio interface provide a local transcription workflow. The repository also exposes the Python method used by that interface. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
Audio in, transcript out
A speech-recognition model with downloadable files and an asr_sft() interface that returns text.
Why it stands out
Mixed-language speech
The publisher recommends Auto language selection for code-switched speech; Chinese and English tags instead bias recognition toward the selected language.
Availability
Model plus audio tokenizer
Local setup uses two downloads. The model card links both components, while the repository supplies the demo and inference code.
Why it matters
What makes it useful
For a conversation that switches between Chinese and English, the README recommends leaving the language setting on Auto. Its examples include Wu, Cantonese, Hokkien and Sichuanese. Chinese and English are recognition biases rather than separate model downloads, so select the tag according to the audio rather than the language of the surrounding application.
What to know
Where it fits
The current asr_sft() implementation returns a transcript string. It does not return timestamped segments or speaker-labelled turns through that method. That output fits text collection; a subtitle editor or meeting application needing timings has another output requirement to solve. The demo’s separate status panel reports the input filename, language choice and elapsed processing time.
Notable points
What stands out
Xiaomi’s results table separates recognition scenarios: its reported English average word error rate is 5.73, while the AMI meeting result is 10.63. The page labels lower WER as better. Read the relevant scenario column when comparing a meeting workflow; the overall average and one meeting dataset describe different tests. These are publisher results, not LifeHubber measurements.
Before using
What to review
The setup guide specifies Linux, Python 3.12, CUDA 12.0 or later and flash-attn 2.7.4.post1. It offers a precompiled wheel when compilation takes too long. Download MiMo-V2.5-ASR and MiMo-Audio-Tokenizer separately, then supply both local paths. The demo supports initialization from command-line paths or its Model Configuration tab.
Reader fit
Who may find it relevant
When switching from an uploaded clip to the microphone in the demo, clear the previous input first. The current interface chooses the uploaded file when both audio fields contain a recording. Its Clear button resets both inputs, the language choice to Auto, the transcript and the status. The status filename identifies which recording was processed.
Editorial note
Why LifeHubber lists it
For Python integration, check the constructor before copying the model card’s example: it uses tokenizer_path, but the current MimoAudio class names that argument mimo_audio_tokenizer_path. The repository’s demo passes the model and tokenizer paths positionally. Match your call to the downloaded class signature; the two published examples do not use the same argument binding.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
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