4 ms·
Spleeter [1] does a really good job of it. It can separate out the vocals, accompaniment, drums, bass, and piano. It does a really phenomenal job at it, thoug
by MereInterest 5y ago
Spleeter [1] does a really good job of it. It can separate out the vocals, accompaniment, drums, bass, and piano. It does a really phenomenal job at it, though there can be a bit of reverb in the vocals if there's a lot of reverb in the base. Splitting out individual vocalists is a lot harder, and I haven't found any pre-trained models that manage it. There's a lot of speaker separation [2] models, but they are trained on separate sentences overlapping.
[1] https://github.com/deezer/spleeter/releases https://github.com/deezer/spleeter/releases
[2] https://paperswithcode.com/task/speaker-separation https://paperswithcode.com/task/speaker-separation
- nmstoker 5y agoI immediately thought of spleeter too as I've had great success in experiments to strip out unwanted music overlays (eg podcasts that insist on adding their end of show theme tune whilst guests are still speaking!) I haven't tried speaker separation, but it seems there are some models from SpeechBrain on HuggingFace that seem to handle this: https://huggingface.co/models?filter=Speech%20Separation https://huggingface.co/models?filter=Speech%20Separation