Tencent Hunyuan open-sources Hy-SimulMT, a 30B-A3B speech-to-speech interpreter with about 3 seconds of lag

Tencent Hunyuan has open-sourced Hy-SimulMT, a simultaneous interpretation model that reports about 3 seconds of translation lag on the long-form RealSI benchmark. It converts Chinese speech to English speech, and English to Chinese, while the speaker is still talking, according to Pandaily, which cites IT Home and the project's GitHub and Hugging Face pages.

The model clones the speaker's voice in real time, so listeners hear the translation in a voice close to the original. It is available first in the Hy Translate app, which is free to use.

Latency and quality

Tencent measures lag with StreamLAAL, a standard metric for streaming translation delay. It reports about 3 seconds on RealSI, the lowest among the systems it compared. The project notes that StreamLAAL is not the same as the time until the first translated speech is heard. IT Home puts the typical lag of professional human interpreters at 4 to 5 seconds.

On quality, Tencent says Hy-SimulMT's GEMBA score on long-form speech beat the open models in the comparison and four of the five commercial APIs tested. On short-form sets it scored:

• FLEURS: 90.3
• CoVoST2: 75.5
• ACL6060: 81.2
• RealSI-Sentence: 84.7, the highest of every listed system

Comparison with human interpreters

The team also compared the model with human interpreters on 18 recordings of about six minutes each, recorded from September 2026 onward across 10 domains. Six interpreters took part: three holding China's top CATTI Level 1 interpreting certificate and three holding Level 2. The best result from each level was used. Tencent calls this a small-scale assessment.

Wang Weiwei, vice dean of the School of Interpreting and Translation Studies at Guangdong University of Foreign Studies, said in remarks published with the release that the system speaks first and corrects itself afterward, much like a human interpreter. He also said AI interpreting is moving from helping people understand toward usable communication in low-risk settings.

Model and tooling

• Architecture: 30B-A3B mixture-of-experts
• Builds: FP8 and GGUF, plus an Interleave variant that also outputs the source transcript
• License: Apache 2.0, as listed on the Hugging Face card
• SimulInfer: a serving stack built on vLLM, with a web interface, a WebSocket API and optional text-to-speech
• SimulTrain: a tool for further training on domain data
• llama.cpp: support has not yet been merged

Context window and API pricing are not yet disclosed. The Hy Translate app is free.

The release follows Hunyuan's earlier open translation models and moves the line from text into live speech.

What this means

The headline figure is lag, but it needs careful reading. StreamLAAL measures streaming translation delay, not the wait before the first translated speech is heard, so the 3-second figure does not describe that initial pause.

The Apache 2.0 license, FP8 and GGUF builds, and the SimulInfer and SimulTrain tools mean the weights, a serving stack and a fine-tuning path are all included. Teams can host the model themselves and adapt it to domain data. The missing llama.cpp support is a practical limit for anyone planning to run the GGUF builds through that runtime.

The human comparison covers 18 recordings and six interpreters, and Tencent itself describes it as small-scale. Wang Weiwei's framing points to low-risk settings as the realistic target for this kind of system.