精选88Rohan Paul模型发布/更新
Meta发布Muse语音转录模型，实时转写错误率低至3.1%
Love this, another huge release from Meta.
推荐理由
实时语音转写是Agent落地的关键瓶颈，Muse的低延迟与流式决策机制提供了新的工程范式，建议关注其API接入与性能表现。
Love this, another huge release from Meta.
非常喜欢，这是 Meta 的又一次重大发布。
Lunched Muse Voice Transcribe for real-time voice dictation, with the lowest 3.1% final-transcription word error rate with adaptive delay. which is significantly ahead of competing models.
推出了 Muse Voice Transcribe，用于实时语音听写，其最终转录词错误率低至 3.1%，并具备自适应延迟。 这显著领先于竞争模型。
- The big deal is that Muse does something beyond just transcribing speech; it learns when to wait, when to commit a word, when a speaker changes, and when a turn is actually over, all inside the same streaming model. That makes it much closer to a real-time perception layer for voice agents than a conventional speech-to-text API.
- Muse processes audio in 80ms chunks and chooses after each chunk whether to emit text or keep listening.
- 关键在于，Muse 所做的不仅仅是转录语音；它能在同一流式模型中判断何时等待、何时确认一个词、何时说话人发生变化以及何时发言真正结束。这使得它比传统的语音转文本 API 更接近于语音代理的实时感知层。
- Muse 以 80 毫秒为块处理音频，并在每个块处理后决定是输出文本还是继续监听。
Meta made it available through Meta Model API, Meta AI for Mac, and Muse Code
Meta 通过 Meta Model API、Mac 版 Meta AI 和 Muse Code 提供了该功能。
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