k2-fsa's OmniVoice Clones Any Voice Across 600 Languages at 40x Real-Time Speed

The k2-fsa team , the research group behind Kaldi and k2, with Daniel Povey as a core contributor , has open-sourced OmniVoice, a zero-shot text-to-speech model that covers more than 600 languages. That is not a typo. Most production TTS systems top out at a few dozen languages. OmniVoice blows past that ceiling and does it without needing any fine-tuning on a target voice , just a short audio clip.

The problem with existing multilingual TTS

Most competitive TTS models today , including XTTS v2 , support somewhere between 17 and 30 languages. Scaling further has historically meant either training separate models per language or accepting severe quality degradation on low-resource languages. The standard architecture compounds this: a two-stage pipeline that first converts text to semantic tokens, then converts those to audio, introduces error propagation at every step.

At its core, OmniVoice uses a novel diffusion language model-style discrete non-autoregressive (NAR) architecture. Unlike conventional discrete NAR models that suffer from performance bottlenecks in complex two-stage pipelines, OmniVoice directly maps text to multi-codebook acoustic tokens. Cutting out the intermediate semantic stage removes a major source of quality loss at scale.

How it actually works

OmniVoice uses a diffusion language model architecture. Unlike traditional autoregressive TTS, this approach uses a Qwen LLM backbone to perform iterative unmasking/diffusion over multi-codebook discrete audio tokens. Think of it like BERT-style masked language modeling, but applied to audio tokens instead of text , the model learns to predict masked audio tokens from context, then iteratively refines the full sequence at inference time.