Tencent open-sources AuK, a 1.5B model that edits speech like text
A quiet morning in the open-weights scene, and two releases that both point the same way: the tooling around voice AI is getting strong enough, and cheap enough, that the community no longer needs a closed API to use it.
Tencent open-sourced AuK, a 1.5B-parameter foundation model for speech generation and editing, alongside AuK-Flash, a distilled variant that produces audio in four inference steps. The weights, code and technical report went public under the MIT license, with the paper posted to arXiv on September 8. The model was trained on roughly 3.03 billion instruction-audio pairs and 1.95 million hours of effective supervision, and everything — zero-shot voice cloning from a reference clip, text-to-speech driven by a written voice description, rewriting what was said inside a recording, emotion and paralinguistic edits, noise removal, and separating mixed audio — runs through one natural-language instruction interface rather than five separate models. The Flash variant is where the practical interest sits: distilled down to four generation steps, it runs 4.5 times faster than the full model with no quality-class collapse. The strategic read: Tencent keeps shipping genuinely open weights in voice while most US labs gate their best audio behind paid APIs, and a permissively licensed model that can clone, edit and clean speech in one pass is raw material for dubbing, podcast and agent-voice pipelines that currently rent those features per minute. It follows the real-time audio race we tracked last month — Meta ships Muse Voice Transcribe, its first real-time audio model.
Bartowski — the uploader behind a huge share of the quantized model files people download to run AI locally — rebuilt how those files are made, replacing a fixed recipe with one learned per model. Quantization squeezes a model's weights into fewer bits so a 27B-parameter model fits on a consumer GPU; the catch is that not every layer loses quality equally when squeezed. Bartowski wrote a testing framework with an AI coding assistant, then ran roughly 96 hours of experiments producing and evaluating over 1,000 quantized variants to map which weight groups in a model are actually sensitive. The result, "per-tensor layout maps," bumps the delicate layers to higher precision and crushes the tolerant ones, and the same method validated across other model families. Two details matter beyond the marginal quality gain: a solver decided the layouts rather than a human rule, and every new architecture now faces an automated canary test — one model failed it, and the pipeline correctly fell back to the old method instead of shipping a bad quant. Below about 3 bits per weight, dense models land roughly where they always were, so this is a refinement, not a revolution — but it is what a healthy open-source toolchain looks like: one maintainer's multi-day experiment quietly upgrades everyone's downloads.
What to watch: whether AuK gets independent TTS Arena-style rankings, and how fast the rest of the quantization ecosystem adopts per-tensor layouts.
If a permissively licensed model could clone and edit any voice in four steps, where would you want that capability banned first — and who would enforce it? Tell us in the comments.
Sources: AuK (GitHub)
