☆10Jan 26, 2021Updated 5 years ago
Alternatives and similar repositories for vae_dolphin
Users that are interested in vae_dolphin are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- ☆17Nov 25, 2019Updated 6 years ago
- A neural language modeling toolkit built on PyTorch☆19Mar 17, 2023Updated 3 years ago
- This is the code of the ICASSP 2020 paper "Joint phoneme alignment and text-informed speech separation on highly corrupted speech"☆15Apr 8, 2024Updated 2 years ago
- ☆53May 15, 2025Updated 10 months ago
- Official PyTorch implementation of MVAE for audio source separation☆43Dec 21, 2022Updated 3 years ago
- 1-Click AI Models by DigitalOcean Gradient • AdDeploy popular AI models on DigitalOcean Gradient GPU virtual machines with just a single click and start building anything your business needs.
- A fast implementation of bss_eval metrics for blind source separation☆144Mar 11, 2026Updated last month
- Julia package for Probabilistic Canonical Correlation Analysis☆12Mar 30, 2022Updated 4 years ago
- Code for the paper: Unified Gradient Reweighting for Model Biasing with Applications to Source Separation☆14Nov 16, 2020Updated 5 years ago
- Differentiable implementation of MSBG hearing loss model and MBSTOI intelligibility metric for Clarity Enhancement challenge.☆21Nov 19, 2021Updated 4 years ago
- VAE and STCN with NMF for single-channel speech enhancement☆14Mar 24, 2021Updated 5 years ago
- ☆39Oct 14, 2022Updated 3 years ago
- Speech enhancement using mimic loss☆16Oct 25, 2019Updated 6 years ago
- ☆18May 15, 2021Updated 4 years ago
- Backpropagable pytorch implementation of https://craffel.github.io/mir_eval/.☆35Jul 8, 2024Updated last year
- 1-Click AI Models by DigitalOcean Gradient • AdDeploy popular AI models on DigitalOcean Gradient GPU virtual machines with just a single click and start building anything your business needs.
- The code for multi-channel source separation and dereverberation such as FastMNMF1, FastMNMF2, and AR-FastMNMF2.