High Performance
Spleeter utilizes deep learning technologies to achieve high-quality separation of vocals and other musical elements, making it a powerful tool for audio processing tasks.
Open Source
Being an open-source project, Spleeter is freely accessible and can be modified and improved by the community, fostering innovation and collaboration.
Ease of Use
With pre-trained models and straightforward API, Spleeter is user-friendly, allowing users to quickly start separating audio without needing extensive background in machine learning.
Speed
Spleeter is optimized for fast processing, enabling quick separation of tracks even on standard hardware, which is beneficial for users needing rapid results.
Community and Documentation
The project has an active community and comprehensive documentation, offering support and resources to help users resolve issues and maximise the toolโs potential.
Spleeter is generally considered a good tool for those needing to separate audio tracks into stems. Its ease of use, effectiveness, and free availability make it popular among musicians, producers, and audio engineers.
We have collected here some useful links to help you find out if Spleeter is good.
Check the traffic stats of Spleeter on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Spleeter on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Spleeter's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Spleeter on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Spleeter on Reddit. This can help you find out how popualr the product is and what people think about it.
The category of tools leveraging AI for stem separation works best when you treat them like a utility, not a creative oracle. They are sophisticated pattern recognition systems, not mind-readers. I learned this the hard way. On one test, I tried splitting a heavily distorted guitar track layered with synths. The result sounded watery and thin. That wasnโt the tool failingโit was me expecting too much from a... - Source: dev.to / 7 months ago
Either creating stems from karaoke multitracks (e.g. [0]) or using Spleeter [1] 5-stem mode, probably [0] https://www.karaoke-version.com/ [1] https://github.com/deezer/spleeter. - Source: Hacker News / over 1 year ago
Absolutely wonderful! > "We obtain these times from MIDI files, though in the future Iโd like to explore more automated ways of extracting them from audio." Same here. In case it helps: I suspect a suitable option is (python libs) Spleeter (https://github.com/deezer/spleeter) for beat times. I haven't ventured into this yet though so I may be off. My ultimate goal is to be able to do it 'on the fly', i.e. In a... - Source: Hacker News / almost 2 years ago
Virtual dj and others stem separator is shrinked model of this https://github.com/deezer/spleeter you will get better results downloading original + their large model. Source: over 2 years ago
I have used multiple tools at this point. It depends on the scene. I use https://ultimatevocalremover.com/, https://github.com/deezer/spleeter/, iZotope RX. There are also multiple options online, I would personally recommend https://vocalremover.org/. Source: over 2 years ago
I tried to use it but I had some issues as others in the thread. I have tried many sources and method over the years and settled on spleeter [0]. Works well even for 10+ minute songs, varying styles from flamenco to heavy metal. [0] https://github.com/deezer/spleeter. - Source: Hacker News / about 3 years ago
Spleeter is pretty good https://github.com/deezer/spleeter. Apparently it is used in some dj applications. Source: about 3 years ago
I was going to post basic pitch from Spotify but it looks like billconan beat me to it. That said I can give you a bit more advice. The Spotify basic pitch model isn't too good at multi-track input. It's capable of it, but you may actually get better results if you separate out the tracks first and then run them individually through the basic pitch model. In order to do this you can use a source/stem separation... - Source: Hacker News / about 3 years ago
I'm chasing my white whale, which is to create a real-time version of the audio stem separation tool 'Spleeter' that I've been using for a few years now to remove instruments like drums/bass guitar from existing music so that I can play along at home. Source: about 3 years ago
I'm a Machine Learning Engineer and have been using Rekordbox for DJing. Recently, I discovered that Rekordbox utilizes the Spleeter model from Deezer (found here: https://github.com/deezer/spleeter). This model is based on TensorFlow, and I found it within one of the Windows directories. Source: about 3 years ago
I don't think they exist but I use this sometimes to split tracks into instrumentals https://github.com/deezer/spleeter. Source: about 3 years ago
Many of the apps that use AI to separate vocals are based on an open source project called Spleeter. Source: about 3 years ago
Spleeter worked well for me in 4 stems mode. There might be some examples on this forum as well: https://github.com/deezer/spleeter. Source: over 3 years ago
Some of you have probably heard of spleeter, a machine learning program developed by Deezer that isolates instruments. It was pretty good, but it had some obvious weaknesses. But what if I told you that there's something even better? Ultimate Vocal Remover is so good I audibly said "holy sh*t" when I listened to what it produced. It recently released a full-band model (UVR-MDX-NET Inst HQ 1), unlike spleeter which... Source: over 3 years ago
I'm actually planning on using something like spleeter to create drumless tracks from youtube videos on a website where people can share the pre-synced music sheets and audio. Source: over 3 years ago
I had some "success" with Deezer Spleeter when I was remastering an old video made using commercial music that I wanted to replace with royalty free, but the original footage was long gone, but the results weren't great, the voiceover didn't sound great when I was done with it. Source: over 3 years ago
2) It's possible to separate vocals (and other stems) using software like Demucs, LALAL.AI, etc... While it isn't a perfect solution, the technology for this is getting better and better. I originally used Spleeter but now exclusively use Demucs (v4) since the results are highly impressive. If you're not comfortable setting up a python environment, there are some websites like AudioStrip that allow you to try it... Source: over 3 years ago
If you want better results, you can use Spleter , source separation library including pretrained models . Source: over 3 years ago
I think Koala is using Spleeter. There's also an open source alternative called Demucs, which yields far better results, especially on vocals and drums. Source: over 3 years ago
Also if anyone is wondering, I'm using spleeter to separate tracks into stems: https://github.com/deezer/spleeter It's not perfect but it gets the job done. And the outro is done using Valhalla Supermassive and twiddling with the knobs (I like that trick for cool "whoosh-y" outros). Source: over 3 years ago
The foundation that is used in a lot of the comercial software is a library called spleeter. It does stems separation and can be used by anyone who is able to write python scripts. Source: over 3 years ago
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