An open-weight 30B MoE with only ~4B active parameters makes speech-to-text with speaker diarisation and translation runnable on modest hardware, undercutting the usual cost of per-language ASR pipelines. Builders working on voice agents or meeting transcription can self-host one checkpoint instead of chaining multiple services. With 40-language coverage, it is a practical candidate for multilingual Southeast Asian speech workloads.
英伟达把转录、说话人区分和翻译打包成一个开源模型，40 种语言还能处理多人抢话，做会议纪要或字幕的可以试试。
NVIDIA 开源了个语音转写模型，MoE 架构只激活 4.3B 参数，能转 40 种语言还带说话人标签，英文错误率 4.71%，可以自己部署。
Combining transcription, speaker labelling, translation and audio Q&A in one self-hostable model reduces the need to chain multiple speech tools on a single GPU. Teams handling multilingual meetings or recorded calls across Southeast Asia get 40-language coverage plus translation into English from 31 languages. Compared with closed APIs from providers like OpenAI, it can run on your own NVIDIA hardware, keeping sensitive recordings in-house.
Nvidia 把语音转写、翻译和区分说话人打包进一个开源模型，参数还只有竞品的六分之一，做语音相关项目的可以直接拿来用。
Day-0 vLLM support means teams can self-host a multilingual transcription model immediately instead of routing audio through paid APIs. The NVFP4 quantised weights are notable for developers running inference on NVIDIA GPUs, cutting memory costs at scale. Teams building voice agents across Southeast Asian languages can compare it with Whisper-based pipelines on their target locales.
Efficiency, not just accuracy, is the story: matching top open-weight transcription quality at 4B active parameters cuts inference cost substantially for voice pipelines. Teams building voice agents should shortlist it, since it leads specifically on conversational agent audio (1.8% vs 2.3% for Voxtral Small). The open weights licence also lets companies self-host transcription instead of relying on API vendors.
NVIDIA 出了两个语音转写模型，能用提示词控制输出格式和多语言识别，做语音相关项目的可以看看。
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