{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spleeter-a-fast-and-state-of-the-art-music","title":"Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models","arxiv_id":null,"date":"2019-11-04","proceeding":"ISMIR 2019  Late-Breaking/Demo 2019 11","authors":["Romain Hennequin","Anis Khlif","Felix Voituret","Manuel Moussallam"],"abstract":"We  present  and  release  a  new  tool  for  music  source  separation  with  pre-trained  models  called  Spleeter.Spleeter was designed with ease of use, separation performance and speed in mind.  Spleeter is based onTensorflow [1] and makes it possible to:•separate audio files into2,4or5stems with a single command line using pre-trained models.•train source separation models or fine-tune pre-trained ones with Tensorflow (provided you have a dataset of isolated sources).The performance of the pre-trained models are very close to the published state of the art and is,  to the authors knowledge, the best performing4stems separation model on the common musdb18 benchmark [6]to be publicly released.  Spleeter is also very fast as it can separate a mix audio file into4stems100timesfaster  than  real-time1on  a  single  Graphics  Processing  Unit  (GPU)  using  the  pre-trained4-stems  model. Spleeter is packaged within Docker which makes it usable as is on various platforms.","url_abs":"https://archives.ismir.net/ismir2019/latebreaking/","url_pdf":"https://archives.ismir.net/ismir2019/latebreaking/000036.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spleeter-a-fast-and-state-of-the-art-music","repo_url":"https://github.com/deezer/spleeter","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"spleeter-a-fast-and-state-of-the-art-music","repo_url":"https://github.com/FaceOnLive/Spleeter-Android-iOS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"spleeter-a-fast-and-state-of-the-art-music","repo_url":"https://github.com/james34602/SpleeterRT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"music-source-separation","task_name":"Music Source Separation"},{"task_slug":"speech-enhancement","task_name":"Speech Enhancement"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"elu","method_name":"ELU"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/music-source-separation-on-musdb18","task":"Music Source Separation","dataset":"MUSDB18","model":"Spleeter (MWF)","rank_in_archive_order":19,"of":27,"metrics":{"SDR (avg)":"5.91","SDR (bass)":"5.51","SDR (drums)":"6.71","SDR (other)":"4.02","SDR (vocals)":"6.86"},"uses_additional_data":true}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}