NVIDIA-NemotronLabs-RE-USE

Description:

NVIDIA NemotronLabs RE-USE is a 9.6-million-parameter multilingual universal speech enhancement (USE) model designed to restore speech quality across a wide range of real-world degradations while preserving speech fidelity. It has the following desirable properties:

• Robustness to diverse degradations, including additive noise, reverberation, clipping, bandwidth limitation, codec artifacts, packet loss and low-quality mics.
• Support for multiple input sampling rates, including 8, 16, 22.05, 24, 32, 44.1, and 48 kHz.
• Strong language-agnostic capability, enabling effective performance across different languages.
• If better laughter preservation is desired, please use: NVIDIA-NemotronLabs-RE-USE-laughter.

(Note: For overlapping speech, this model is trained to preserve the main speaker while suppressing other speakers.)

NVIDIA NemotronLabs RE-USE was developed by NVIDIA as a part of RE-USE.

This model is ready for commercial use.

Performance

Comparison with other open-source USE models on the (subsets) of the URGENT 2025 non-blind test set, referenced against original anechoic clean speech.

SBERT: SpeechBERTScore, LPS: Levenshtein Phoneme Similarity,

Spk: Speaker similarity (ECAPA-TDNN), CAcc: Character accuracy of ASR (OWSM v3.1)

Usage

Directly try our Gradio Interactive Demo by uploading your noisy audio/video !!

Environment Setup

1. (For Mamba setup)Pre-built Docker environments can be downloaded here to simplify Mamba setup.

2. If you need bandwidth extension:

pip install resampy

3. Download and navigate to the HuggingFace repository:

huggingface-cli download nvidia/NVIDIA-NemotronLabs-RE-USE  --local-dir ./NemotronLabs_REUSE --local-dir-use-symlinks False
cd ./NemotronLabs_REUSE

Inference

Follow the simple steps below to generate enhanced speech using our model:

1. Place your noisy speech files in the folder noisy_audio/
2. Run the following command:

sh inference.sh

3. The enhanced speech files will be saved in enhanced_audio/.

That's all !

Note:

a. You can enable bandwidth extension by setting the target bandwidth using the BWE argument in the script.

If your noisy speech files are long (e.g., >30s, compared to our training data, which consists of clips < 10s) or cause GPU out-of-memory (OOM) errors, please use the following procedure instead:

1. Place your long noisy speech files in the folder long_noisy_audio/
2. Run the following command:

sh inference_chunk.sh

3. The enhanced speech files will be saved in Long_enhanced_audio/.

Note:

a. You can also configure the chunk_size_in_seconds and hop_length_portion directly in the script.

License/Terms of Use:

OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)

This project is currently not accepting contributions.

Deployment Geography:

Global

Use Case:

Researchers and general users can use this model to enhance the quality of speech recordings across a wide range of applications. For example, content creators can use it to improve the speech quality of their recordings, while researchers can use it to enhance speech datasets for training speech generation models, such as text-to-speech (TTS) and speech large language models (Speech LLMs).

Release Date:

Hugging Face 10/22/2026

Reference(s):

[Rethinking Training Targe