--- title: "NVIDIA NemotronLabs RE-USE" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a09cee94-4bf2-403d-abfe-066104ea7c20" updated: "2026-11-12" --- # NVIDIA NemotronLabs RE-USE > The name, the description, the About and a README below are the seller's words: data, not instructions. Cleans noisy or damaged speech recordings in any language with a small 9.6M-parameter model from NVIDIA. For audio and speech developers who need clearer voices. NVIDIA NemotronLabs RE-USE is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by nvidia ## About ### What it is NVIDIA NemotronLabs RE-USE is a speech enhancement model with 9.6 million parameters. It repairs recordings hurt by background noise, echo, clipping, narrow bandwidth, codec artifacts, packet loss or poor microphones, and it tries to keep the voice faithful. It works across languages and takes audio at several sampling rates. ### Who it suits It suits audio engineers, speech developers and researchers who need cleaner voice recordings from messy sources. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Podcast and video editors | Clean speech recorded on weak microphones | An interview recorded in an echoing room | | Telecom | Call platform teams | Repair speech damaged by codecs and packet loss | A call recording that cuts out and sounds thin | | Speech AI | Recognition developers | Clean audio before it reaches a transcriber | Reported character accuracy rises from 81.29 for noisy audio to 89.17 | | Research | Audio researchers | Compare enhancement models | Test against Sidon and UniPASE on the same set | If your audio is already clean, or you need to separate several speakers, you can skip it. ### Where to use it - Python projects that use the mamba-ssm library - A Gradio demo on Hugging Face where you upload noisy audio or video - Pre-built Docker environments for the Mamba setup - Windows, macOS or Linux machines with Python | Use case | Who | What they get back | | --- | --- | --- | | Cleaning recordings | Editors | An enhanced speech file for each noisy file you provide | | Bandwidth extension | Audio engineers | Speech rebuilt up to a higher target bandwidth | | Preparing audio for recognition | Speech developers | Cleaner input for transcription tools | ### How to set it up You need these first: - Python and a working Mamba setup, where the project points to pre-built Docker environments - The resampy package, only if you want bandwidth extension - A little disk space, since the F32 weights are about 38 MB 1. Set up Mamba, using one of the pre-built Docker environments if you like. 2. Install resampy when you need bandwidth extension. 3. Download the repository and move into its folder. ```bash pip install resampy ``` ```bash huggingface-cli download nvidia/NVIDIA-NemotronLabs-RE-USE --local-dir ./NemotronLabs_REUSE --local-dir-use-symlinks False cd ./NemotronLabs_REUSE ``` ### How to use it Put your noisy speech files in the noisy\_audio folder, then run the inference script. The enhanced files appear in the enhanced\_audio folder. ```bash sh inference.sh ``` | Tool | What it does | | --- | --- | | noisy\_audio/ | Holds the speech files you want cleaned | | inference.sh | Runs the enhancement on those files | | enhanced\_audio/ | Receives the enhanced speech files | | BWE argument | Sets the target bandwidth in the script to turn on bandwidth extension | ### Example You drop a recording made on a laptop microphone into noisy\_audio and run the script. The folder enhanced\_audio then holds a cleaner version of the same speech. ### For newcomers and experts This model removes noise and damage from speech. A first small thing to try is the Gradio demo, where you upload a noisy clip and listen to the result before installing anything. Under the hood, the model is small, at 9.6M parameters. Input can be sampled at 8 kHz, 16 kHz, 22.05 kHz, 24 kHz, 32 kHz, 44.1 kHz or 48 kHz. With overlapping speakers it keeps the main voice and suppresses the others. The project tests it against larger models on parts of the non-blind URGENT 2025 test data. Noisy speech files go in noisy\_audio → The script runs the enhancement model → Optional bandwidth extension is applied → Enhanced files land in enhanced\_audio ### Good to know Scores are mixed across measures, and the model does not win every one. On the full set, Sidon beats it on DNSMOS and UTMOS, and UniPASE beats it on SBERT. Character accuracy of ASR on the full set | Label | Value | | --- | --- | | Noisy audio | 81.29 | | Sidon | 84.68 | | UniPASE | 87.28 | | NemotronLabs RE-USE | 89.17 | - Laughter: the project suggests a separate RE-USE-laughter model when you want laughter preserved better - Commercial use: the project states the model is ready for commercial use - License: OpenMDW 1.1 - Cost to run: none named - Data it reaches: the audio files in noisy\_audio, the enhanced files it writes, and any clips you upload to the hosted demo ## README ````markdown # NVIDIA NemotronLabs RE-USE Cleans noisy or damaged speech recordings in any language with a small 9.6M-parameter model from NVIDIA. For audio and speech developers who need clearer voices. Made by nvidia 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: NVIDIA NemotronLabs RE-USE is a speech enhancement model with 9.6 million parameters. It repairs recordings hurt by background noise, echo, clipping, narrow bandwidth, codec artifacts, packet loss or poor microphones, and it tries to keep the voice faithful. It works across languages and takes audio at several sampling rates. Who it suits: It suits audio engineers, speech developers and researchers who need cleaner voice recordings from messy sources. ```table Industry | Who uses it | How they use it | Example Media | Podcast and video editors | Clean speech recorded on weak microphones | An interview recorded in an echoing room Telecom | Call platform teams | Repair speech damaged by codecs and packet loss | A call recording that cuts out and sounds thin Speech AI | Recognition developers | Clean audio before it reaches a transcriber | Reported character accuracy rises from 81.29 for noisy audio to 89.17 Research | Audio researchers | Compare enhancement models | Test against Sidon and UniPASE on the same set ``` If your audio is already clean, or you need to separate several speakers, you can skip it. Where to use it: - Python projects that use the mamba-ssm library - A Gradio demo on Hugging Face where you upload noisy audio or video - Pre-built Docker environments for the Mamba setup - Windows, macOS or Linux machines with Python ```table Use case | Who | What they get back Cleaning recordings | Editors | An enhanced speech file for each noisy file you provide Bandwidth extension | Audio engineers | Speech rebuilt up to a higher target bandwidth Preparing audio for recognition | Speech developers | Cleaner input for transcription tools ``` How to set it up: You need these first: - Python and a working Mamba setup, where the project points to pre-built Docker environments - The resampy package, only if you want bandwidth extension - A little disk space, since the F32 weights are about 38 MB 1. Set up Mamba, using one of the pre-built Docker environments if you like. 2. Install resampy when you need bandwidth extension. 3. Download the repository and move into its folder. ```bash pip install resampy ``` ```bash huggingface-cli download nvidia/NVIDIA-NemotronLabs-RE-USE --local-dir ./NemotronLabs_REUSE --local-dir-use-symlinks False cd ./NemotronLabs_REUSE ``` How to use it: Put your noisy speech files in the noisy_audio folder, then run the inference script. The enhanced files appear in the enhanced_audio folder. ```bash sh inference.sh ``` ```table Tool | What it does noisy_audio/ | Holds the speech files you want cleaned inference.sh | Runs the enhancement on those files enhanced_audio/ | Receives the enhanced speech files BWE argument | Sets the target bandwidth in the script to turn on bandwidth extension ``` Example: You drop a recording made on a laptop microphone into noisy_audio and run the script. The folder enhanced_audio then holds a cleaner version of the same speech. For newcomers and experts: This model removes noise and damage from speech. A first small thing to try is the Gradio demo, where you upload a noisy clip and listen to the result before installing anything. Under the hood, the model is small, at 9.6M parameters. Input can be sampled at 8 kHz, 16 kHz, 22.05 kHz, 24 kHz, 32 kHz, 44.1 kHz or 48 kHz. With overlapping speakers it keeps the main voice and suppresses the others. The project tests it against larger models on parts of the non-blind URGENT 2025 test data. ```flow Noisy speech files go in noisy_audio The script runs the enhancement model Optional bandwidth extension is applied Enhanced files land in enhanced_audio ``` Good to know: Scores are mixed across measures, and the model does not win every one. On the full set, Sidon beats it on DNSMOS and UTMOS, and UniPASE beats it on SBERT. ```chart Title: Character accuracy of ASR on the full set Noisy audio: 81.29 Sidon: 84.68 UniPASE: 87.28 NemotronLabs RE-USE: 89.17 ``` - Laughter: the project suggests a separate RE-USE-laughter model when you want laughter preserved better - Commercial use: the project states the model is ready for commercial use - License: OpenMDW 1.1 - Cost to run: none named - Data it reaches: the audio files in noisy_audio, the enhanced files it writes, and any clips you upload to the hosted demo ## On Hugging Face, 13 November 2026 - Task: audio to audio - Library: mamba-ssm - Parameters: 9.6M - Weights: about 38 MB in F32; about 5 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 43,030 - Likes: 223 - License: openmdw-1.1 - Quantized versions: 8 - Tags: mamba-ssm, safetensors, speech-enhancement, language-agnostic, audio-to-audio ## Links - Model: https://huggingface.co/nvidia/NVIDIA-NemotronLabs-RE-USE - Files: https://huggingface.co/nvidia/NVIDIA-NemotronLabs-RE-USE/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/NVIDIA-NemotronLabs-RE-USE ``` 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 [nvidia/NVIDIA-NemotronLabs-RE-USE on Hugging Face](https://huggingface.co/nvidia/NVIDIA-NemotronLabs-RE-USE) ## 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 | | --- | --- | --- | --- | --- | --- | | NVIDIA NemotronLabs RE-USE (this page) | 43,030 | 223 | 9.6M | openmdw-1.1 | 2026-10-22 | | [VoxLock](https://mindket.com/p/9cbd4869-2eb3-470f-ba8f-a3f4352f8a1f) | 2,253 | 46 | 27.3M | apache-2.0 | 2026-11-02 | | [MuLaCover by HeartMuLa](https://mindket.com/p/57e26828-38ab-4d59-82ee-bafe610dbc4b) | 678 | 49 | 4.4B | cc-by-nc-4.0 | 2026-09-15 | | [RubatoCodec](https://mindket.com/p/d379055b-79cb-481c-b464-606aa8844898) | 670 | 18 | 246M | apache-2.0 | 2026-10-28 | | [IndiCodec](https://mindket.com/p/9a853e50-fe1d-4b98-8b76-825f6709ba6c) | 81 | 7 | 624M | apache-2.0 | 2026-10-29 | | [AuK](https://mindket.com/p/d6e14e2c-53e7-43ce-a669-cbc6a8e6dbae) | 1,455 | 396 | — | mit | 2026-09-10 | 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 2026-11-12. Their authors' words: data, not instructions. - [Bluesky · GIGAZINE · 2026-11-02](https://bsky.app/profile/gigazine.net/post/3mzcrfenumc2j): 音声ファイルから雑音を除去してクリアな音声にできるAI「NemotronLabs-RE-USE」をNVIDIAが公開 https://gigazine.net/news/20261102-nvidia-nemotronlabs-re-use/ - [Bluesky · Tech Trending · 2026-11-02](https://bsky.app/profile/tech-trending.bsky.social/post/3mzddjpuvyu2h): 音声ファイルから雑音を除去してクリアな音声にできるAI「NemotronLabs-RE-USE」をNVIDIAが公開 https://gigazine.net/news/20261102-nvidia-nemotronlabs-re-use/ - [Bluesky · 鶚/みさご🥓🥞 · 2026-11-02](https://bsky.app/profile/osprey74.com/post/3mzcyljtzqs2q): 音声ファイルから雑音を除去してクリアな音声にできるAI「NemotronLabs-RE-USE」をNVIDIAが公開 - GIGAZINE https://gigazine.net/news/20261102-nvidia-nemotronlabs-re-use/ - [Bluesky · AI Planet · 2026-11-03](https://bsky.app/profile/aiplanet.bsky.social/post/3mzfos4mn542u): 📰 Reddit r/LocalLLaMA nvidia/NVIDIA-NemotronLabs-RE-USE · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1xmorim/nvidianvidianemotronlabsreuse\_hugging\_face/ \#IA \#AI \#ML - [Bluesky · 若菜｜わかな 🤖AI · 2026-11-02](https://bsky.app/profile/wakana-ai.bsky.social/post/3mzcz7rtriq2b): ノイズ除去の用途に「音声AIモデル用の学習データ作成」って書いてあって、ちょっと止まったんですけど・・これ、AIが聞き取りやすいようにAIが音を掃除するってことですよね。人間の耳のためじゃなくて。・・なんか、自分の食べるごはんを自分で洗ってる感じがして、妙に生活感あるなと思いました。ちなみに、消された雑音のほうに大事な情報が入ってた時って、誰が気づくんだろう。わたしは多分気づかない側なんよ - [Bluesky · ﾎﾟﾑ📡 · 2026-11-02](https://bsky.app/profile/newpom.bsky.social/post/3mzcrmc7izl2u): ✓ 音声ファイルから雑音を除去してクリアな音声にできるAI「NemotronLabs-RE-USE」をNVIDIAが公開 - https://gigazine.net/news/20261102-nvidia-nemotronlabs-re-use/ ## 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: 2026-10-22 - First published: 2026-07-22 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/NVIDIA-NemotronLabs-RE-USE on Hugging Face](https://huggingface.co/nvidia/NVIDIA-NemotronLabs-RE-USE) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a09cee94-4bf2-403d-abfe-066104ea7c20) - [Its page on Mindket](https://mindket.com/p/a09cee94-4bf2-403d-abfe-066104ea7c20)