Nari Labs Ships Qwen3-Based TTS and ASR Models

Nari Labs has released a pair of speech models built on Qwen3: a text-to-speech (TTS) system and an automatic speech recognition (ASR) system. The company frames the launch around three practical goals that matter to anyone building voice applications — accuracy, low latency, and cost — rather than raw novelty.

According to Nari Labs, the models lead on Coval voice AI benchmarks, a suite that evaluates conversational voice performance. By offering both synthesis and recognition, Nari is targeting the full round-trip of a voice agent: understanding what a user says and responding in natural speech.

Why it matters

Voice pipelines have historically stitched together separate vendors for ASR and TTS, each adding latency and per-minute costs that compound in real-time settings. A single provider tuning both ends on a common foundation — here, Alibaba's Qwen3 family — can smooth those seams.

• Accuracy: framed as competitive on Coval benchmarks
• Latency: a priority for real-time, interactive use
• Cost: positioned as a differentiator against incumbent APIs

Nari Labs is best known for Dia, its earlier open TTS work, so a Qwen3-based expansion into ASR signals broader ambitions in the voice stack. The release leaves some specifics — parameter counts, licensing terms, and language coverage — worth watching as more details surface.