facebookresearch/denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. observed · 2026-08-28
Health v2 · maintenance only
10/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases archived no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2193
- days_rel: n/a
- days_push: 1268
- n_releases_24m: 0
Adoption not part of the score
1899 stars · 320 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A PyTorch library implementing a causal, real-time speech enhancement model that operates on raw waveforms to remove background noise and reverb. It is based on the Demucs encoder-decoder architecture and ships with pretrained models usable offline or in streaming mode.
Use cases
- remove background noise from speech recordings
- real-time noise suppression for voice calls
- denoise audio in python
- speech enhancement pretrained model
- remove room reverb from voice
- streaming audio denoising
When to choose
- you need real-time or streaming speech denoising on CPU
- you want a pretrained model out of the box via pip
- you need to remove stationary, non-stationary noise, or reverb from speech
When to avoid
- you need commercial use - the model weights are CC-BY-NC licensed
- you need music separation or general audio enhancement beyond speech
- you need actively maintained software - releases have been infrequent since 2023
Facets
library · maturity maintenance
audio-processing machine-learning deep-learning speech-processing machine-learning python cross-platform windows speech-enhancement noise-reduction pytorch real-time demucs pretrained-model research audio macos linux
2 sources
- readme: https://github.com/facebookresearch/denoiser · fetched 2026-08-28 · ed83aeb09f51
- registry_pypi: https://pypi.org/pypi/denoiser/json · fetched 2026-08-29 · 2b2ae6a749f6
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| facebookresearch/denoiser | main | 10 |
For agents
markdown · JSON · MCP: product_card(name="facebookresearch/denoiser")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem