--- title: "MOSS-SoundEffect-v2.0" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b35de8d2-17f8-4bde-ae85-5190e005521b" updated: "2026-11-13" --- # MOSS-SoundEffect-v2.0 > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates sound effects from a text prompt, such as nature, city scenes, animals and human actions, up to 30 seconds at 48 kHz. For creators and developers who describe sounds in English or Chinese. MOSS-SoundEffect-v2.0 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 OpenMOSS-Team ## About ### What it is MOSS-SoundEffect v2.0 turns a written description into audio. You describe a sound in words, and it generates the clip. It uses a Diffusion Transformer trained with flow matching, together with an audio VAE and a Qwen3 text encoder. It belongs to the MOSS-TTS family, where it is the model for non-speech sound. Version 2.0 replaces the earlier token-by-token design with a continuous-latent one. ### Who it suits It suits creators and developers who need sound effects without recording them, and who can write a short description of the sound in English or Chinese. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Games | Sound designers | Draft ambient and creature sounds from text | Make a forest background loop | | Video | Editors | Add placeholder effects to a cut | Generate a city street ambience | | Software | Developers | Build sound generation into an app | Create effects on demand | | Research | Audio researchers | Study text-to-audio generation | Compare prompts and settings | If you need speech, a voice or full songs, you can skip it, since this model makes sound effects and short music-like clips. ### Where to use it The card shows a Python pipeline that runs on a CUDA GPU, on a machine where you can set up a Python 3.12 environment. - A Python script that loads the pipeline - A Gradio demo that comes with the project - Windows, macOS or Linux for fetching the files | Use case | Who | What they get back | | --- | --- | --- | | Ambience | Sound designers | Natural and urban environment sounds | | Creature and action sounds | Game teams | Animal, creature and human action effects | | Short clips | Creators | Short music-like or percussive clips | ### How to set it up You need conda or another way to make a Python 3.12 environment, a CUDA GPU, and the project's code from the MOSS-TTS repository on GitHub. - Python 3.12 in a clean environment - A CUDA GPU - The moss\_soundeffect\_v2 folder of the MOSS-TTS repository 1. Create and activate a clean Python 3.12 environment. 2. Get the MOSS-TTS repository from GitHub and open the moss\_soundeffect\_v2 folder. 3. Install it with pip, using the inference-only option if you do not plan to fine-tune. 4. Download the model files. ```bash conda create -n moss-soundeffect-v2 python=3.12 -y conda activate moss-soundeffect-v2 ``` ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-SoundEffect-v2.0 ``` ### How to use it You load the pipeline once, then call it with a text prompt and a length in seconds. It returns a waveform that you save as a WAV file. ```python import torch from moss_soundeffect_v2 import MossSoundEffectPipeline pipe = MossSoundEffectPipeline.from_pretrained( "OpenMOSS-Team/MOSS-SoundEffect-v2.0", # this repo, or a local dir torch_dtype=torch.bfloat16, device="cuda", ) audio = pipe( prompt="A dog barking loudly in a park.", seconds=10, num_inference_steps=100, cfg_scale=4.0, ) # (B, C, T) waveform tensor pipe.save_audio(audio, "out.wav") ``` | Setting | Default | What it controls | | --- | --- | --- | | num\_inference\_steps | 100 | How many solver steps the sampler takes | | cfg\_scale | 4.0 | How strongly the prompt guides the result | | sigma\_shift | 5.0 | A scheduler shift applied on each call | | seconds | 10.0 | The length of the clip, up to 30 | ### Example You write a prompt asking for rain on a metal roof and set the length to 10 seconds. The pipeline returns a waveform, and you save it as out.wav to listen to it. ### For newcomers and experts If you are new, run the sample script once with the default settings and a short prompt, then change the prompt and length. The first call can take a few minutes, because the model is compiled then. If you know the field, the Diffusion Transformer is trained with a flow matching objective and sampled with a flow-match solver. The card notes it is wrapped with torch.compile and Triton CUDA Graph, and suggests setting TORCHDYNAMO\_DISABLE=1 if compiler errors appear. A text prompt is encoded by Qwen3 → The Diffusion Transformer samples a latent → The audio VAE decodes it → A waveform is saved as WAV ### Good to know Clips run up to 30 seconds at 48 kHz, and the model was trained on both English and Chinese captions. The card points to a project README for the Gradio demo and fine-tuning recipes. - License: apache-2.0 - Cost to run: none named, but it needs a CUDA GPU - Data it reaches: files you download, and the prompts you write - Not for: speech or voice generation, since it makes non-speech sound ## README ````markdown # MOSS-SoundEffect-v2.0 Generates sound effects from a text prompt, such as nature, city scenes, animals and human actions, up to 30 seconds at 48 kHz. For creators and developers who describe sounds in English or Chinese. Made by OpenMOSS-Team 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: MOSS-SoundEffect v2.0 turns a written description into audio. You describe a sound in words, and it generates the clip. It uses a Diffusion Transformer trained with flow matching, together with an audio VAE and a Qwen3 text encoder. It belongs to the MOSS-TTS family, where it is the model for non-speech sound. Version 2.0 replaces the earlier token-by-token design with a continuous-latent one. Who it suits: It suits creators and developers who need sound effects without recording them, and who can write a short description of the sound in English or Chinese. ```table Industry | Who uses it | How they use it | Example Games | Sound designers | Draft ambient and creature sounds from text | Make a forest background loop Video | Editors | Add placeholder effects to a cut | Generate a city street ambience Software | Developers | Build sound generation into an app | Create effects on demand Research | Audio researchers | Study text-to-audio generation | Compare prompts and settings ``` If you need speech, a voice or full songs, you can skip it, since this model makes sound effects and short music-like clips. Where to use it: The card shows a Python pipeline that runs on a CUDA GPU, on a machine where you can set up a Python 3.12 environment. - A Python script that loads the pipeline - A Gradio demo that comes with the project - Windows, macOS or Linux for fetching the files ```table Use case | Who | What they get back Ambience | Sound designers | Natural and urban environment sounds Creature and action sounds | Game teams | Animal, creature and human action effects Short clips | Creators | Short music-like or percussive clips ``` How to set it up: You need conda or another way to make a Python 3.12 environment, a CUDA GPU, and the project's code from the MOSS-TTS repository on GitHub. - Python 3.12 in a clean environment - A CUDA GPU - The moss_soundeffect_v2 folder of the MOSS-TTS repository 1. Create and activate a clean Python 3.12 environment. 2. Get the MOSS-TTS repository from GitHub and open the moss_soundeffect_v2 folder. 3. Install it with pip, using the inference-only option if you do not plan to fine-tune. 4. Download the model files. ```bash conda create -n moss-soundeffect-v2 python=3.12 -y conda activate moss-soundeffect-v2 ``` ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-SoundEffect-v2.0 ``` How to use it: You load the pipeline once, then call it with a text prompt and a length in seconds. It returns a waveform that you save as a WAV file. ```python import torch from moss_soundeffect_v2 import MossSoundEffectPipeline pipe = MossSoundEffectPipeline.from_pretrained( "OpenMOSS-Team/MOSS-SoundEffect-v2.0", # this repo, or a local dir torch_dtype=torch.bfloat16, device="cuda", ) audio = pipe( prompt="A dog barking loudly in a park.", seconds=10, num_inference_steps=100, cfg_scale=4.0, ) # (B, C, T) waveform tensor pipe.save_audio(audio, "out.wav") ``` ```table Setting | Default | What it controls num_inference_steps | 100 | How many solver steps the sampler takes cfg_scale | 4.0 | How strongly the prompt guides the result sigma_shift | 5.0 | A scheduler shift applied on each call seconds | 10.0 | The length of the clip, up to 30 ``` Example: You write a prompt asking for rain on a metal roof and set the length to 10 seconds. The pipeline returns a waveform, and you save it as out.wav to listen to it. For newcomers and experts: If you are new, run the sample script once with the default settings and a short prompt, then change the prompt and length. The first call can take a few minutes, because the model is compiled then. If you know the field, the Diffusion Transformer is trained with a flow matching objective and sampled with a flow-match solver. The card notes it is wrapped with torch.compile and Triton CUDA Graph, and suggests setting TORCHDYNAMO_DISABLE=1 if compiler errors appear. ```flow A text prompt is encoded by Qwen3 The Diffusion Transformer samples a latent The audio VAE decodes it A waveform is saved as WAV ``` Good to know: Clips run up to 30 seconds at 48 kHz, and the model was trained on both English and Chinese captions. The card points to a project README for the Gradio demo and fine-tuning recipes. - License: apache-2.0 - Cost to run: none named, but it needs a CUDA GPU - Data it reaches: files you download, and the prompts you write - Not for: speech or voice generation, since it makes non-speech sound ## On Hugging Face, 13 November 2026 - Task: text to audio - Library: diffusers - Parameters: 1.4B - Weights: about 5.7 GB in F32; about 708 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 6,096 - Likes: 158 - License: apache-2.0 - Languages: en, zh - Based on: Qwen/Qwen3-1.7B - Quantized versions: 1 - Fine-tunes: 3 - Tags: diffusers, safetensors, text-to-audio, diffusion, flow-matching, sound-effects, audio-generation ## Links - Model: https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect-v2.0 - Files: https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect-v2.0/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-SoundEffect-v2.0 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [OpenMOSS-Team/MOSS-SoundEffect-v2.0 on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect-v2.0) ## 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 | | --- | --- | --- | --- | --- | --- | | MOSS-SoundEffect-v2.0 (this page) | 6,096 | 158 | 1.4B | apache-2.0 | 2026-05-26 | | [Iris-3B by speridlabs](https://mindket.com/p/0abd1bcc-dae6-497c-b706-d6bc43cde2df) | 997 | 350 | 3B | apache-2.0 | 2026-10-12 | | [ACE-Step 1.5](https://mindket.com/p/9bcfe983-0ebe-44d7-811e-8cdfbec7b965) | 98,086 | 952 | — | mit | 2026-02-03 | | [AuroraIMG2-100M](https://mindket.com/p/9d6ca80f-0572-4791-870c-4ca37c86c8c8) | 179 | 24 | 101M | apache-2.0 | 2026-11-04 | | [AuroraIMG2-50M-Preview](https://mindket.com/p/5efb5977-dd94-4ae6-8694-b99a4907e67b) | 159 | 19 | 50.4M | apache-2.0 | 2026-10-30 | | [Maya-1](https://mindket.com/p/2db39d10-1520-4a3d-acee-a641a560d62e) | 8,798 | 943 | 3.3B | apache-2.0 | 2026-10-13 | 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 · AI без границ · 2026-07-31](https://bsky.app/profile/aibiznes.bsky.social/post/3mrwtnx7qu72j): Генерация звуков по тексту за секунды Нейросеть MOSS-SoundEffect создаёт аудио до 30 секунд по текстовому описанию. Работает быстро и не требует мощного железа. Что можно сделать: звуки природы, эффекты для игр и видео, фоновые... https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect-v2.0 - [Bluesky · AI без границ · 2026-07-21](https://bsky.app/profile/aibiznes.bsky.social/post/3mr57xmgx2k2j): Новая лёгкая модель генерирует звуки по тексту за 30 секунд Появилась компактная модель с 1 млрд параметров, которая создаёт аудио по описанию — от скрипов дверей до криков птиц и коротких музыкальных джинглов. MOSS-SoundEffect-v2.0... https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect-v2.0 ## 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: 2026-05-26 - First published: 2026-05-25 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open OpenMOSS-Team/MOSS-SoundEffect-v2.0 on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect-v2.0) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b35de8d2-17f8-4bde-ae85-5190e005521b) - [Its page on Mindket](https://mindket.com/p/b35de8d2-17f8-4bde-ae85-5190e005521b)