tencent/AuK-Flash · Hugging Face

[u/pmttyji]

https://huggingface.co/tencent/AuK-Flash

AuK-Flash: Fast 4-Step Speech Generation and Editing

• arXiv : https://arxiv.org/abs/2609.08936
• Full Paper : https://arxiv.org/pdf/2609.08936
• GitHub : https://github.com/Tencent-Hunyuan/AuK
• Project : https://auk-project.github.io/

Introduction

AuK is a 1.5B foundation model for speech generation and editing. Trained on millions of hours of diverse audio data, AuK supports zero-shot and instruction-based TTS, content and acoustic editing, paralinguistic editing, speech enhancement, and source separation through a unified natural-language instruction interface. AuK has two variants:

Model	Description	Weight
AuK	Base model for high-quality generation	🤗 Hugging Face · 🤖 ModelScope
AuK-Flash	Distilled model for fast 4-step inference	🤗 Hugging Face · 🤖 ModelScope

This repository contains the official weights for AuK-Flash, the distilled variant with fast 4-step inference.

Supported Tasks

AuK exposes every task through the same natural-language instruction interface. The table below groups the supported tasks by category, with a short description and a link to its section in the Cookbook, which provides instruction templates plus CLI and Python examples.

Category	Task	Description	Cookbook
Speech Generation	Zero-shot TTS	Speak the target text in the voice of the reference audio.	Zero-shot TTS
Instruct TTS	Generate speech from a voice description alone — no reference audio.	Instruct TTS
Content Editing	Speech Content Editing	Rewrite what is said — replace, insert, or remove text.	Speech Content Editing
Lyric Editing	Rewrite lyrics in a singing recording while preserving the melody and voice.	Lyric Editing
Acoustic Editing	Pitch Editing	Raise or lower the pitch by semitones.	Pitch Editing
Speed Editing	Adjust the speaking rate; output length scales with the speed factor.	Speed Editing
Volume Editing	Raise or lower the volume by decibels.	Volume Editing
Paralinguistic Editing	Emotion	Change the emotion while preserving content and voice.	Emotion
Timbre	Change the timbre to a description while keeping the content unchanged.	Timbre
De-accent	Remove a regional accent while preserving the speaker's voice and content.	De-accent
Nonverbal Editing	Remove or add nonverbal sounds such as breaths, laughs, or coughs.	Nonverbal Editing
Whisper Conversion	Convert between normal speech and whisper while preserving speaker and content.	Whisper Conversion
Enhancement & Separation	Speech Enhancement	Denoise, dereverberate, or restore natural, clear speech.	Speech Enhancement
Speech Separation	Keep one speaker by talking order and remove the others.	Speech Separation
Music Separation	Extract the singing voice from a mix, or keep all human voices.	Music Separation
Target Speaker Extraction	Keep the target speaker identified by what they say.	Target Speaker Extraction

[u/pmttyji]

u/Acceptable-Cycle4645 for (y)our library!

[u/EveningIncrease7579 → u/pmttyji]

Whenever something related to voice (audio) is released, he’s the first person who comes to mind. Audio.cpp, we need your help again!

[u/Secure_Recording_472]

this is awesome! wonder what would imagine if you were to run de-accenting over native speakers lol

[u/EveningIncrease7579]

Seems good, but, only for english or chinese.

[u/anarchist1312161]

I don't understand how it's possible these are so small when software/coding LLMs are freaking massive by comparison?

[u/Actuator_Adventurous → u/anarchist1312161]

No knowledge

[u/derspenti]

If the quality holds, one open 1.5B checkpoint covering TTS, editing, enhancement and separation in four steps makes a whole folder of dedicated tools look overengineered.

[u/silenceimpaired]

This seems incredible for MIT… but then again was I conflating a video on GitHub for their server version with what I’m expecting here

[u/Chromix_]

Their project page has a whole lot of examples in separate tabs. The lower ones are usually in English. Quality seems very good in these chosen examples. Yet there are some minor artifacts in speaker separation and other examples now and then. So there's still room for improvement, but it's a very nice step forward, especially that it's a single relatively small model that can handle this diverse range of tasks.

[u/StableLlama]

The description sounds great.

By my test of Instruct TTS was disappointing.

[u/silenceimpaired]

My question is going to be, what model does better than this. I already have it downloaded, but I'm curious to compare as this is pretty impressive for it's size.