--- title: "LongCat-AudioDiT-3.5B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5d44eecc-c813-4828-a8f1-4a318fa42243" updated: "2026-11-13" --- # LongCat-AudioDiT-3.5B > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns Chinese or English text into speech with a diffusion model that works directly on the waveform latent space, and can clone a voice from a reference sample you have the right to use. LongCat-AudioDiT-3.5B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by meituan-longcat ## About ### What it is LongCat-AudioDiT-3.5B is a text-to-speech model from Meituan's LongCat team. It generates audio with a diffusion backbone that works on a waveform latent space, so it skips intermediate steps such as mel-spectrograms. The pipeline needs only two parts, a waveform variational autoencoder and the diffusion backbone. The card lists Chinese and English as its languages and reports zero-shot voice cloning results. ### Who it suits Speech researchers and developers who want an open text-to-speech model with published code and weights. Voice cloning is a feature here, so use only voices you have the right to use. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Speech researchers | Study diffusion text-to-speech in a waveform latent space | Compare its scores with Seed-TTS on the Seed benchmark | | Software | App developers | Add synthetic voices in Chinese or English | Read short app messages aloud | | Media | Content producers | Narrate scripts without recording every line | Turn a written script into spoken audio | People who want a ready-made voice app can skip it, since the page offers model files and research code, not an app. ### Where to use it - Python scripts, using the Hugging Face compatible implementation in the repository - Windows, macOS or Linux with Python, for downloading the files - Chinese and English speech work | Use case | Who | What they get back | | --- | --- | --- | | Generate speech from text | Developers | Spoken audio in Chinese or English | | Clone a voice from a reference | Researchers and producers | Speech in a similar voice, if you hold the rights to it | | Benchmark text-to-speech | Researchers | Speaker similarity figures to compare with other systems | ### How to set it up Plan for a large download first, since the weights are about 15.3 GB in F32. - Python and pip, for the Hugging Face download tools - Disk space for the model files, about 15.3 GB in F32 by Hugging Face's count - The repository's inference scripts, which the card says come with the model 1. Install the huggingface\_hub package. 2. Download the model files with the hf command. 3. Follow the card's inference scripts to generate speech. ```bash pip install -U huggingface_hub hf download meituan-longcat/LongCat-AudioDiT-3.5B ``` ### How to use it The card says the repository provides the model definition, weight conversion and inference scripts. Run those scripts with your text. For voice cloning you also give a reference recording of the speaker. The usage part of the card is cut off here, so check the model page for the exact script names. ### Example You give it a short Chinese or English sentence and, for cloning, a reference recording you have permission to use. It returns an audio file of the sentence spoken in a similar voice. ### For newcomers and experts Think of it as a model that writes a voice recording from text. A good first try is to download the files, open the card and run one of its inference scripts on a single sentence. Underneath, a waveform VAE compresses audio into a latent space and a diffusion backbone generates in it, without autoregressive decoding. The authors fixed a training-inference mismatch and replaced classifier-free guidance with adaptive projection guidance. They also report that a better VAE reconstruction does not always give better speech. Text and a reference voice go in → The diffusion backbone works in latent space → The waveform VAE decodes the latent → Speech audio comes out ### Good to know The figures below are the authors' own results on the Seed benchmark, comparing speaker similarity with Seed-TTS. Speaker similarity, higher is better | Label | Value | | --- | --- | | Seed-TTS on Seed-ZH | 0.809 SIM | | LongCat-TTS-3.5B on Seed-ZH | 0.818 SIM | | Seed-TTS on Seed-Hard | 0.776 SIM | | LongCat-TTS-3.5B on Seed-Hard | 0.797 SIM | - Size: the name says 3.5B, and Hugging Face's record counts 3.8B parameters in Safetensors - Variants: the card also links a smaller 1B version, and Hugging Face shows 2 quantized copies by other people - License: MIT - Cost to run: no API key or paid service is named - Data it reaches: the text and reference audio you give it, plus the model files you download from Hugging Face - Not for: the card names no restrictions beyond its license, but cloning a voice needs the speaker's permission ## README ````markdown # LongCat-AudioDiT-3.5B Turns Chinese or English text into speech with a diffusion model that works directly on the waveform latent space, and can clone a voice from a reference sample you have the right to use. Made by meituan-longcat on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: LongCat-AudioDiT-3.5B is a text-to-speech model from Meituan's LongCat team. It generates audio with a diffusion backbone that works on a waveform latent space, so it skips intermediate steps such as mel-spectrograms. The pipeline needs only two parts, a waveform variational autoencoder and the diffusion backbone. The card lists Chinese and English as its languages and reports zero-shot voice cloning results. Who it suits: Speech researchers and developers who want an open text-to-speech model with published code and weights. Voice cloning is a feature here, so use only voices you have the right to use. ```table Industry | Who uses it | How they use it | Example Research | Speech researchers | Study diffusion text-to-speech in a waveform latent space | Compare its scores with Seed-TTS on the Seed benchmark Software | App developers | Add synthetic voices in Chinese or English | Read short app messages aloud Media | Content producers | Narrate scripts without recording every line | Turn a written script into spoken audio ``` People who want a ready-made voice app can skip it, since the page offers model files and research code, not an app. Where to use it: - Python scripts, using the Hugging Face compatible implementation in the repository - Windows, macOS or Linux with Python, for downloading the files - Chinese and English speech work ```table Use case | Who | What they get back Generate speech from text | Developers | Spoken audio in Chinese or English Clone a voice from a reference | Researchers and producers | Speech in a similar voice, if you hold the rights to it Benchmark text-to-speech | Researchers | Speaker similarity figures to compare with other systems ``` How to set it up: Plan for a large download first, since the weights are about 15.3 GB in F32. - Python and pip, for the Hugging Face download tools - Disk space for the model files, about 15.3 GB in F32 by Hugging Face's count - The repository's inference scripts, which the card says come with the model 1. Install the huggingface_hub package. 2. Download the model files with the hf command. 3. Follow the card's inference scripts to generate speech. ```bash pip install -U huggingface_hub hf download meituan-longcat/LongCat-AudioDiT-3.5B ``` How to use it: The card says the repository provides the model definition, weight conversion and inference scripts. Run those scripts with your text. For voice cloning you also give a reference recording of the speaker. The usage part of the card is cut off here, so check the model page for the exact script names. Example: You give it a short Chinese or English sentence and, for cloning, a reference recording you have permission to use. It returns an audio file of the sentence spoken in a similar voice. For newcomers and experts: Think of it as a model that writes a voice recording from text. A good first try is to download the files, open the card and run one of its inference scripts on a single sentence. Underneath, a waveform VAE compresses audio into a latent space and a diffusion backbone generates in it, without autoregressive decoding. The authors fixed a training-inference mismatch and replaced classifier-free guidance with adaptive projection guidance. They also report that a better VAE reconstruction does not always give better speech. ```flow Text and a reference voice go in The diffusion backbone works in latent space The waveform VAE decodes the latent Speech audio comes out ``` Good to know: The figures below are the authors' own results on the Seed benchmark, comparing speaker similarity with Seed-TTS. ```chart Title: Speaker similarity, higher is better Seed-TTS on Seed-ZH: 0.809 SIM LongCat-TTS-3.5B on Seed-ZH: 0.818 SIM Seed-TTS on Seed-Hard: 0.776 SIM LongCat-TTS-3.5B on Seed-Hard: 0.797 SIM ``` - Size: the name says 3.5B, and Hugging Face's record counts 3.8B parameters in Safetensors - Variants: the card also links a smaller 1B version, and Hugging Face shows 2 quantized copies by other people - License: MIT - Cost to run: no API key or paid service is named - Data it reaches: the text and reference audio you give it, plus the model files you download from Hugging Face - Not for: the card names no restrictions beyond its license, but cloning a voice needs the speaker's permission ## On Hugging Face, 13 November 2026 - Parameters: 3.8B - Weights: about 15.3 GB in F32; about 1.9 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,026 - Likes: 83 - License: mit - Languages: zh, en - Quantized versions: 2 - Tags: safetensors, audiodit ## Links - Model: https://huggingface.co/meituan-longcat/LongCat-AudioDiT-3.5B - Files: https://huggingface.co/meituan-longcat/LongCat-AudioDiT-3.5B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download meituan-longcat/LongCat-AudioDiT-3.5B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [meituan-longcat/LongCat-AudioDiT-3.5B on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-AudioDiT-3.5B) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | LongCat-AudioDiT-3.5B (this page) | 1,026 | 83 | 3.8B | mit | 2026-04-03 | | [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 | | [bge-small-en-v1.5](https://mindket.com/p/c288dc31-39c2-4e3e-85b8-0fec1d2b32b5) | 45,750,548 | 671 | 33.4M | mit | 2024-02-22 | | [GPT-2](https://mindket.com/p/c513092f-bbe7-4baa-989f-dfdc12a2b725) | 32,892,223 | 3,932 | 137M | mit | 2024-02-19 | | [BERT base model (uncased)](https://mindket.com/p/d5887db0-760c-4639-8457-d0da6a3a2699) | 31,346,528 | 3,071 | 110M | apache-2.0 | 2024-02-19 | | [paraphrase-multilingual-MiniLM-L12-v2](https://mindket.com/p/7babf7ff-2f8f-4358-a71b-a36915eab8d5) | 26,540,125 | 1,505 | 118M | apache-2.0 | 2026-01-28 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · 猫国防 · 2026-04-11](https://bsky.app/profile/maoguofang.bsky.social/post/3mj7ggtubds2r): LongCat-AudioDiT: High-Fidelity Diffusion Text-to-Speech in the Waveform Latent Space 目前最顶尖的声音生成/克隆模型 项目： aria-k-alethia.github.io/LongCat-Audi... GitHub: github.com/meituan-long... Hugging Face: 1B: huggingface.co/meituan-long... 3.5B: huggingface.co/meituan-long... ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-04-03 - First published: 2026-03-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open meituan-longcat/LongCat-AudioDiT-3.5B on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-AudioDiT-3.5B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5d44eecc-c813-4828-a8f1-4a318fa42243) - [Its page on Mindket](https://mindket.com/p/5d44eecc-c813-4828-a8f1-4a318fa42243)