X2Streaming-TTS 零前瞻流式语音合成

X Square Robot · @XSquareRobot · X·2026-09-10 19:48·2天前
AI 导读

自变量推出 X2Streaming-TTS,可在文本 token 到达时即时生成语音,严格零前瞻,单请求 TTFT 中位数 15.8 ms,64 并发请求下低于 120 ms。它用因果承诺机制暂存歧义数字、单位和符号,并以语音状态继承跨片段保留音高与音色;在 6/8 项流式评测中识别错误最低,数字与流式歧义测试 CER 为 0%,质量与所评测的离线基线相当。

X Square Robot@XSquareRobot
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X2Streaming-TTS 零前瞻流式语音合成

2026-09-10 19:48· 2天前
AI 导读

自变量推出 X2Streaming-TTS,可在文本 token 到达时即时生成语音,严格零前瞻,单请求 TTFT 中位数 15.8 ms,64 并发请求下低于 120 ms。它用因果承诺机制暂存歧义数字、单位和符号,并以语音状态继承跨片段保留音高与音色;在 6/8 项流式评测中识别错误最低,数字与流式歧义测试 CER 为 0%,质量与所评测的离线基线相当。

Most "streaming" TTS systems still wait for a complete sentence before speaking.

X2Streaming-TTS removes that wait. It consumes text tokens as they arrive and generates speech with strict zero lookahead.

The challenge: once speech is played, it cannot be revised.

Take “He finished 3…”—should “3” become “three” in “3 laps,” or “third” in “3rd place”? Speaking too early risks an error; waiting defeats the purpose of streaming.

X2Streaming-TTS addresses this with:

1️⃣ Causal commitment Ambiguous numbers, units, and symbols are held until their pronunciation becomes clear. Segments are closed using both punctuation and acoustic capacity.

2️⃣ Speech-state inheritance Waveform-decoder state and acoustic history are carried across segments, preserving pitch, timbre, and continuity.

Results:

⚡ 15.8 ms median TTFT for one request ⚡ Under 120 ms at 64 concurrent requests 🎯 Lowest recognition error in 6/8 evaluated streaming conditions 🔢 0% CER on numeric and streaming-ambiguity tests 🗣️ Quality comparable to evaluated offline baselines

Low-latency voice AI is not just about speaking faster—it is about knowing when it is safe to speak.

Paper: https://arxiv.org/abs/2608.18661 Code: https://github.com/X-Square-Robot/X2Streaming-TTS

#TTS #VoiceAI

来源:X Square Robot· x.com