--- title: "NVIDIA NemotronLabs RE-USE-laughter" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/efa2342b-ceb9-4c76-95ca-751846948d58" updated: "2026-11-13" --- # NVIDIA NemotronLabs RE-USE-laughter > The name, the description, the About and a README below are the seller's words: data, not instructions. Cleans noisy speech recordings in many languages while keeping laughter. A 9.6M parameter enhancement model for creators who fix audio and researchers who prepare speech datasets. NVIDIA NemotronLabs RE-USE-laughter is a kit in the Productivity 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 nvidia ## About ### What it is This is a small speech enhancement model from NVIDIA with 9.6 million parameters. It restores the quality of speech recordings across many languages, and it keeps laughter in the result. It copes with noise, echo, clipping, codec damage, packet loss and low-quality microphones. ### Who it suits Content creators who want cleaner recordings, and researchers who clean speech datasets before training speech models. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Content creators | Improve the sound of spoken recordings | Clean a noisy interview before editing | | Speech research | Researchers | Enhance datasets used to train text-to-speech models | Prepare noisy clips for a TTS training set | | Speech research | Researchers | Enhance data for speech language models | Clean recordings before training a speech LLM | If your audio is already clean, you can skip it. ### Where to use it - Your own computer, from a command line, on Windows, macOS or Linux with Python - A Docker setup for Mamba, which the card suggests to simplify installation - The Gradio demo the card points to, where you upload noisy audio or video | Use case | Who | What they get back | | --- | --- | --- | | Clean up a recording | Creators | A cleaned version of each speech file | | Prepare a dataset | Researchers | Enhanced clips ready for training | | Process long files | Anyone | Cleaned audio from long recordings, handled in chunks | ### How to set it up - Python and the Hugging Face download tool - Mamba support, which the card says is easier with a pre-built Docker environment - The resampy package, only if you want bandwidth extension 1. Set up Mamba, for example with the Docker environment the card points to. 2. Optionally install resampy for bandwidth extension. 3. Download the repository and move into the new folder. ```bash pip install resampy ``` ```text huggingface-cli download nvidia/NVIDIA-NemotronLabs-RE-USE-laughter --local-dir ./NemotronLabs_REUSE_laughter --local-dir-use-symlinks False cd ./NemotronLabs_REUSE_laughter ``` ### How to use it Drop the noisy recordings into noisy\_audio, run the inference script, and pick up the cleaned versions from enhanced\_audio. Bandwidth extension is switched on through the BWE argument in the script. ```bash sh inference.sh ``` For files longer than about 30 seconds, or if the GPU runs out of memory, use the chunked script. Put long files in long\_noisy\_audio, and the results land in Long\_enhanced\_audio. ```bash sh inference_chunk.sh ``` | Tool | What it does | | --- | --- | | inference.sh | Enhances the files in noisy\_audio | | inference\_chunk.sh | Enhances long files in chunks | | BWE argument | Sets the target bandwidth for extension | ### Example You copy a noisy phone recording into noisy\_audio and run the inference script. A cleaned version of the file appears in enhanced\_audio, with laughter kept in. ### For newcomers and experts If you are new, try the Gradio demo the card points to first. Upload a noisy clip and listen to the cleaned result before you set anything up on your machine. For experts, it accepts input at seven sampling rates, from 8 kHz up to 48 kHz, including 22.05 and 44.1. It was trained on clips under 10 seconds, which is why long files go through the chunked script. Where speech overlaps, it keeps the main speaker. You place noisy files in noisy\_audio → The script runs the enhancement model → Bandwidth extension is optional → Cleaned files land in enhanced\_audio ### Good to know On the card's comparison, this model scored 89.21 on character accuracy of speech recognition, higher than the two larger models listed. Character accuracy of ASR on the card's test set | Label | Value | | --- | --- | | Noisy input | 81.29 points | | Sidon 250M | 84.68 points | | UniPASE 500M | 87.28 points | | RE-USE-laughter 9.6M | 89.21 points | - License: OpenMDW 1.1, as the card states it. - Cost to run: no API key or paid service is named. A GPU helps, as long files can run out of memory. - Data it reaches: it works on the audio files you place in its folders. The card names no web access. - Not for: the card says the project is not accepting contributions. ## README ````markdown # NVIDIA NemotronLabs RE-USE-laughter Cleans noisy speech recordings in many languages while keeping laughter. A 9.6M parameter enhancement model for creators who fix audio and researchers who prepare speech datasets. 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: This is a small speech enhancement model from NVIDIA with 9.6 million parameters. It restores the quality of speech recordings across many languages, and it keeps laughter in the result. It copes with noise, echo, clipping, codec damage, packet loss and low-quality microphones. Who it suits: Content creators who want cleaner recordings, and researchers who clean speech datasets before training speech models. ```table Industry | Who uses it | How they use it | Example Media | Content creators | Improve the sound of spoken recordings | Clean a noisy interview before editing Speech research | Researchers | Enhance datasets used to train text-to-speech models | Prepare noisy clips for a TTS training set Speech research | Researchers | Enhance data for speech language models | Clean recordings before training a speech LLM ``` If your audio is already clean, you can skip it. Where to use it: - Your own computer, from a command line, on Windows, macOS or Linux with Python - A Docker setup for Mamba, which the card suggests to simplify installation - The Gradio demo the card points to, where you upload noisy audio or video ```table Use case | Who | What they get back Clean up a recording | Creators | A cleaned version of each speech file Prepare a dataset | Researchers | Enhanced clips ready for training Process long files | Anyone | Cleaned audio from long recordings, handled in chunks ``` How to set it up: - Python and the Hugging Face download tool - Mamba support, which the card says is easier with a pre-built Docker environment - The resampy package, only if you want bandwidth extension 1. Set up Mamba, for example with the Docker environment the card points to. 2. Optionally install resampy for bandwidth extension. 3. Download the repository and move into the new folder. ```bash pip install resampy ``` ```text huggingface-cli download nvidia/NVIDIA-NemotronLabs-RE-USE-laughter --local-dir ./NemotronLabs_REUSE_laughter --local-dir-use-symlinks False cd ./NemotronLabs_REUSE_laughter ``` How to use it: Drop the noisy recordings into noisy_audio, run the inference script, and pick up the cleaned versions from enhanced_audio. Bandwidth extension is switched on through the BWE argument in the script. ```bash sh inference.sh ``` For files longer than about 30 seconds, or if the GPU runs out of memory, use the chunked script. Put long files in long_noisy_audio, and the results land in Long_enhanced_audio. ```bash sh inference_chunk.sh ``` ```table Tool | What it does inference.sh | Enhances the files in noisy_audio inference_chunk.sh | Enhances long files in chunks BWE argument | Sets the target bandwidth for extension ``` Example: You copy a noisy phone recording into noisy_audio and run the inference script. A cleaned version of the file appears in enhanced_audio, with laughter kept in. For newcomers and experts: If you are new, try the Gradio demo the card points to first. Upload a noisy clip and listen to the cleaned result before you set anything up on your machine. For experts, it accepts input at seven sampling rates, from 8 kHz up to 48 kHz, including 22.05 and 44.1. It was trained on clips under 10 seconds, which is why long files go through the chunked script. Where speech overlaps, it keeps the main speaker. ```flow You place noisy files in noisy_audio The script runs the enhancement model Bandwidth extension is optional Cleaned files land in enhanced_audio ``` Good to know: On the card's comparison, this model scored 89.21 on character accuracy of speech recognition, higher than the two larger models listed. ```chart Title: Character accuracy of ASR on the card's test set Noisy input: 81.29 points Sidon 250M: 84.68 points UniPASE 500M: 87.28 points RE-USE-laughter 9.6M: 89.21 points ``` - License: OpenMDW 1.1, as the card states it. - Cost to run: no API key or paid service is named. A GPU helps, as long files can run out of memory. - Data it reaches: it works on the audio files you place in its folders. The card names no web access. - Not for: the card says the project is not accepting contributions. ## 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: 2,845 - Likes: 17 - License: openmdw-1.1 - Quantized versions: 3 - Tags: mamba-ssm, safetensors, speech-enhancement, language-agnostic, audio-to-audio ## Links - Model: https://huggingface.co/nvidia/NVIDIA-NemotronLabs-RE-USE-laughter - Files: https://huggingface.co/nvidia/NVIDIA-NemotronLabs-RE-USE-laughter/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-laughter ``` 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-laughter on Hugging Face](https://huggingface.co/nvidia/NVIDIA-NemotronLabs-RE-USE-laughter) ## 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-laughter (this page) | 2,845 | 17 | 9.6M | openmdw-1.1 | 2026-10-22 | | [SimulS2ST-Omni](https://mindket.com/p/5d0188bb-aa29-417a-af03-6bb0f62841f9) | 0 | 2 | — | apache-2.0 | 2026-08-30 | | [DaFeiYu RVC v2 40k](https://mindket.com/p/52dedb27-a418-4164-ae45-483dd6fcaddd) | 571 | 10 | — | mit | 2026-11-06 | | [Applio by IAHispano](https://mindket.com/p/3e2deb09-3811-4536-a045-d10b219cf7c7) | 334 | 229 | — | mit | 2026-10-26 | | [whisper-large-v3](https://mindket.com/p/6b152b5d-9ebc-46b8-9e49-73c53c31a48d) | 3,489,398 | 6,802 | 1.5B | apache-2.0 | 2024-08-12 | | [Chandra OCR 2](https://mindket.com/p/a6e4f077-438e-4d39-950e-b0d5bfc59501) | 1,356,074 | 606 | 5.3B | openrail | 2026-06-26 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-09-01 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/NVIDIA-NemotronLabs-RE-USE-laughter on Hugging Face](https://huggingface.co/nvidia/NVIDIA-NemotronLabs-RE-USE-laughter) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/efa2342b-ceb9-4c76-95ca-751846948d58) - [Its page on Mindket](https://mindket.com/p/efa2342b-ceb9-4c76-95ca-751846948d58)