___Best quality from YouTube and Soundcloud - how to squeeze out the most from the music taken from YT for separation___
Sometimes a better source just doesn’t exist, and only YouTube audio can be used for separation in some cases.
Introduction
Audio on YT in most cases is available in two formats:
1) AAC (m4a) and Opus. As I mentioned, the latter appears for older or popular uploads, or videos uploaded in QHD or 4K. Most videos will have both formats available already. Currently only browsers without Opus support plau that audio stream (iirc Safari)
AAC on YT is @128kbps with 16kHz cutoff and 44kHz (that’s not artificial cutoff - that’s how the codec normally behaves when such bitrate is set).
2) Opus on YT is 96/128/152kbps with 20kHz cutoff (spectrum up to 24kHz for videos uploaded before ~2020+, but only with some aliasing above 20kHz, probably as a result of applied resampler) always 48kHz (44kHz audio is always upsampled with built-in resampler in Opus - that’s how the Opus works - it has always 48Khz output).
1) and 2) can be downloaded, e.g. via JDownloader 2 (once you downloaded one file, you must delete the previously shown entry in link grabber and add the link once more, and now pick the Opus (m4a is default) for download).
You can also use it online too:
https://ytdlp.online/ - YouTube-dl web interface allowing downloading Opus when all others fail, then just use:
-f 251 https://www.youtube.com/watch?vXXXXXXX
Afterwards, the download link will be shown at the end of the command line - RBM on it and save the link. Registering and going to a separate download page afterwards shouldn’t be necessary.
Note - sometimes youtube-dl might fail with error code/forbidden if you don’t add "&pp=" at the end of the link format above. It’s added to the link when you use search to find some videos for tracking purposes.
Be aware that sometimes Opus/format 251 is just not available for some videos - check it in stats for nerds when you open it on YT, see all YT formats)
These sites are frequently unreliable:
https://cobalt.tools/ - (stopped supporting YT), probably it was just GUI for yt-dlp.
https://yt-dl-web.vercel.app/ - don’t always work
https://savefrom.net/ - (the one redirecting from ssyoutube.com) - works when all the
above fails, but sometimes doesn’t allow obtaining opus/webm or above 720p (then downr.org will be able to download in better quality, but audio can be still in M4A).
Opus files downloaded from JDownloader are different from Opus in webm files seen by spectrum, but I can’t compare it with Cobalt as Spek doesn’t cooperate with its webm files in at least progressive mode which is “direct vimeo stream”. yt-dlp with -x argument might be free of the issue, but I haven’t checked yet.
Don't download as Opus from JDownloader 2. The quality will be affected.
Download always as webm in any quality - all qualities will contain the same Opus audio stream in the same bitrate.
Be aware that sometimes JDownloader wrongly reports bitrate as 96kbps, while when you demux the webm file with MKVToolnix-GUI and then with MKVExtractGUI2 (compatible with MKVToolnix v 20), the result Opus file (add extension manually afterwards) will have average bitrate of not much below 128kbps (that’s how VBR works).
Don’t download in OGG from any service like previously Cobalt. It's a recompression from webm/Opus. OGG file is not on the variants list in JDownloader (and probably the same would be in CML tools like yt-dlp, so it’s simply not on YT).
However, it will have some additional information below 16kHz compared to Opus downloaded from JDownloader, probably because it was sourced from webm, and not JDownloader's Opus, but that’s it. Recompression here will add some ringing issues and compression artefacts. Details and spectrograms here.
Sometimes it happens that m4a (AAC) sounds better than Opus. It all depends on a track. It is more likely to happen if both have the same cutoff in spectrogram due to how it was uploaded on YT.
What to do to improve the audio gathered from YT?
Use IntroC/IncT AAC/Opus combiner Python script with Colab
(if you find the results muddier than OG Opus, convert from 32-bit float to 24-bit).
The output is 44kHz (so Opus is downsampled).
Matches “QAAC -TVBR 86 quantization noise cancellation with the source audio”
Alternatives
#1 Joining frequencies with EQ method
1) Download both M4A and Opus audio from YT (if Opus is available for your video)
2) Upsample M4A to 48kHz (or else you won’t align the two files perfectly) with e.g. Resampler (PPHS) in Ultra mode in Foobar 1.3.20>Convert>...
3) To have frequencies above 16kHz from Opus and better sounding frequencies up to 16kHz from AAC, we will combine the best of the both worlds by:
a) applying resonant highpass on Opus file at 15750Hz in e.g. Ozone 8/9 EQ
b) aligning the track to M4A audio file (converted to 48kHz WAV 32), so added as separate track in free DAWs like Audacity, Cakewalk, Ableton Lite, or Pro Tools Intro (or eventually Reaper with its infinite trial).
Export the mixdown as WAV24. It should be more than enough.
Using brickwall highpass instead will result in a hole in frequency in the result spectrogram (check it in Spek afterwards, and also whether there are no overlap frequencies in the crossover - consider checking also linear phase in e.g. free QRange EQ).
#2 Manual ensemble in UVR
Files ensemble with Max Spec in UVR
Instead of EQ, you can use ensemble after manual upsampling of M4A file. You can have your files aligned in UVR.
Be aware that this method is not fully transparent, and produce files a little bit brighter, and still with cutoff, but not brickwall like in M4A.
Without upsampling step, you can use Max Spec method with great results also for Soundcloud which provides 64kbit/s opus and 128kbp/s mp3 and 256kbp/s aac.
You only need to amplify the mp3 file by 3dB. Align step is also necessary here, but it can be performed in UVR.
(fixed in UVR 5.6) Be aware that a bug in manual ensemble exists which forces 16 bit output despite choosing e.g. 32-bit float. To fix it, you need to execute regular separation of a song with any AI model with a 32 bit set, and then you need to return to manual ensemble without changing any settings now, so from now on it will retain 32-bit float in manual ensemble.
You can fix this by changing the 510th line of lib_v5/spec_utils.py to:
sf.write(save_path, normalize(output.T, is_normalization), samplerate, subtype='FLOAT')
then restart the program (you may not find that file if your UVR is not taken from source).
TBH, I didn’t compare directly the first EQ vs the latter Max Spec method, but the latter sounds brighter for sure than opus, and m4a.
“while it helps to make trebles more defined, it's a bit flawed, due ensembling 3 different compression methods, so 3 different compression flaws/errors and noises”.
PS. For YT I also tried downsampling Opus to 44 and to leave M4A intact, but it gave worse results (probably because of more frequencies affected by resampler in this case).
Explanation
Audio file sizes and bitrate are the same for both formats. Knowing that the cutoff in AAC is not artificial, but codec without a doubt efficiently compresses only audio up to 16kHz, leaving everything higher blank and untouched, we can come to the conclusion that frequencies up to 16kHz in AAC may sound better than in Opus, since the size and bitrate of both files is the same, and most likely bitrate in AAC is not used to frequencies above 16kHz, so full 128kbps bitrate is used only for frequencies up to 16kHz in AAC codec while in Opus for the whole spectrum up to 20 or even 24kHz in some old videos till around 2020, while keeping the same size, so that might be more harmful for frequencies up to 16kHz than in AAC.
PS. After some time, I receive explanation/reassurance on the purpose of this process here, saying it’s generally justified and Opus is actually better than AAC even above 9600Hz, so one more additional cutoff in AAC will be needed. Also, might be worthy to use phase linear EQ to get rid of some coloration of the result file.
Experimenting with it, make sure that you don’t run into overlapping frequencies in area of bypassing (e.g. you can see it here as slightly brighter area above 9.6kHz up to 12kHz) to avoid it in e.g. in RX editor, one filtered signal needs to be 10Hz away from another one. I.e. if lowpass is 12000 Hz, then highpass is 12010 Hz. “But there is a catch with iZotope RX. The 10Hz away I described is only applied to the Copy operation (when you basically select the frequency range, and just CTRL+C by copying it). But there is also Silence operation (when you select freq. range and press Delete, it eliminates the freq. in this range), and it is another way around: you need to get the other signal 10Hz inward, so they overlap. I.e.: 12000 Hz lowpass, 11990 Hz highpass. Here is the video demo: https://youtu.be/h5yE5cpqqMU”
#3 Bash script to automate the AAC/Opus quality combining from YT audio
introC eventually wrote his bash script which makes an alignment (so trimming 1600 samples from m4a), performs cutoffs and joins frequencies of both files for you - without an overlap issue (tested with white noise). The script works for multiple m4a and webm files with the same name. Probably, MSYS2 (or cygwin) is required to run this script on Windows or for W10/11 use WSL (read).
He also took a more conservative approach here and changed the cutoff frequency from 9600Hz to 1400Hz since AAC didn’t perform better in one song, but below 1400Hz it will be rather in every case. What cutoff is actually the best might be sometimes depending on a song. The script is subject to change.
#4 Method for better quality of instrumental leaks on YT by theamogusguy
“I did something really odd. (...) since you can only rip max 128kbps I did something really odd to get a higher quality instrumental:
I inverted the 128kbps AAC YouTube rip into the original to get the acapella
I took the subtracted acapella and ran it through AI (mel-roformer 2024.10) to reduce the compression artifacts
I then inverted the isolated acapella and mixed it with the lossless to get an... unusual lossless instrumental file?
also the OPUS stream goes up to 20khz but I feel like the sample rate difference is gonna cause issues, so I ended up ripping AAC (OPUS is 48khz while most music is 44.1khz)”
_____Custom UVR models__________
Mostly outdated models, see here for more submissions from 2024
0) BubbleG — 15.06.2021
Final drum model (for UVR 5 and 4band_44100.json4band_44100.json)
- Dry Paint Dealer Undr — 08.07.2021
haring wip piano model trained on almost 300 songs might continue to train might not, has an issue where it also removes bass guitar too
- BubbleG — 16.06.2021
Temp. bass model. Must use with 4band_44100.json
- viperx — 04.08.2021
My simple karaoke model that I trained in month 5 until epoch 25/28 doesn't complete the training because I've been busy with other projects, and I left this one aside, but this simple model removes the second voice, it can be useful in only some cases, it's bad but it's acceptable
- centre isolation model epoch 0 inner epoch 1 - 150 pairs for UVR 4.0.1
- K-POP FILTERS — 02.07.2021
feedback will be appreciated
Check #model-sharing for current WiP models
__Repository of old Colab notebooks__
UVR 5 (Colab by HV): https://colab.research.google.com/github/NaJeongMo/Colaboratory-Notebook-for-Ultimate-Vocal-Remover/blob/main/Vocal%20Remover%205_arch.ipynb
(On Mobile Chrome use PC mode)
Alternative UVR 5 notebook up to date (not HV’s):
MDX (Colab by CyberWaifu, 4 stem, cannot be used in Mobile Chrome even using PC mode - there's no GDrive mounting and track downloading is always 0%. Model A cleaner but with more bleeding; Audioshake is based on it, but with different model based on larger dataset iirc, UVR team consider training it on their own bigger dataset to get better results - it’s based on phase unlike UVR, but tsumeruso works on adding phase, so then it might get rewritten to UVR)
https://colab.research.google.com/drive/1R32s9M50tn_TRUGIkfnjNPYdbUvQOcfh?usp=sharing
(wait patiently, it doesn’t show the progress)
UVR 5 (old version by HV with any 2 files ensemble feature, put tracks in separated folder. As for x/z - similar results, but not the same. Put as first the one you want the result more similar to)
https://colab.research.google.com/drive/1eK4h-13SmbjwYPecW2-PdMoEbJcpqzDt?usp=sharing
https://colab.research.google.com/drive/1C6i_6pBRjdbyueVw27FuRpXmEe442n4k?usp=sharing#scrollTo=CT8TuXWLBrXF (+12 ens, no batch ens, deleted)
2021-ISMIR-MSS-Challenge-CWS-PResUNet (byteMSS) (if you run out of memory, split up the input file)
https://colab.research.google.com/drive/17m08bvihZAov_F_6Rg3luNj030t6mtyk?usp=sharing
Woosung Choi's ISMIR 2020 (Colab by CyberWaifu)
https://colab.research.google.com/drive/1jlwVgC9sRCGnZAKZTpqKgeSnzP3sIj8U
Vocal Remover 4:
https://colab.research.google.com/drive/1z0YBPfSexb4E7mhNz9LJP4Kfz3AvHf32
To fix librosa error, try adding the
!pip install librosa==0.8.0
or 0.9.? works as well
line about librosa, and if still the same, about pysound as well:
https://discord.com/channels/708579735583588363/767947630403387393/1089518963253317652
(UVR4 + models description:
https://github.com/Anjok07/ultimatevocalremovergui/tree/v4.0.1
Search for:
"Models included" at the bottom".)
UVR 2.20 (it achieved some good results for old 70’s pop music for me where cymbals got muffled on current models, but prepare for more bleeding in some places vs VR4 and newer)
https://colab.research.google.com/drive/1gGtjAo3jK3nmHcMYTz0p8Qs8rZu8Lhb6?usp=sharing
Spleeter (11/16kHz, 2, 4, 5 stems, currently doesn’t work): https://colab.research.google.com/drive/1d-NKFQVRGCV5tvbd0GOy9spMMel6mrth?usp=sharing
According to my experience, if you don’t need piano stem, 4 stem model makes better job than 5 stem (and even vs 2 stem, and it is also reflected in SDR results). Use 11kHz models only if your input files are sampled at 22kHz (it will provide better result in this and only in this case).
If you can, use Izotope RX-8 for 22kHz 4 stem, as it provides better separation quality with aggressiveness option. It’s Spleeter, but with better model (full band).
Demucs 3.0
https://colab.research.google.com/drive/1yyEe0m8t5b3i9FQkCl_iy6c9maF2brGx?usp=sharing
To install it locally (by britneyjbitch):
I cracked the Da Vinci code on how to install Demucs V3 sweat_smile For anybody who struggled (on Windows) - I got you!
1. DL a zip folder of Demucs 3 from Github (link: https://github.com/facebookresearch/demucs) and extract it in a desired folder
2. Inside the extracted folder run cmd
3. If you want to simply separate tracks, run the following command:
python.exe -m pip install --requirement requirements_minimal.txt
4. If you want to be able to train models too, run the following command:
python.exe -m pip install --requirement requirements.txt
5. If a read error for incompatible versions of any of the modules appears (e.g. torch) run the following command:
pip install desired_module==version_of_desired_module
e.g. pip install torch==1.9.0
6. Repeat step 5 for any incompatibilities that might occur
7. Separating tracks:
python.exe -m demucs -n "desired_model_to_run_separation" "path_to_track"
8. If you want help finding all additional options (for example overlap or shifts), run:
python.exe -m demucs --help
At least that worked for me, feel free to let me know if this worked for others as well
exclamation Oh, and I forgot - between step 6 or 7, don't pay attention to a potential red error ''torchvision 0.9.1+cu111 has requirement torch==1.8.1, but you'll have torch 1.9.0 which is incompatible.''
Do NOT change back to torch 1.8.0 cuz you won't be able to run demucs
warning! If ''torchvision 0.9.1+cu111 has requirement torch==1.8.1, but you'll have torch 1.9.0 which is incompatible.'' is the only red error you're getting after executing the commands from step 3,4 and/or 5, you're good to go with separation!
Demucs (22khz, 4 stem):
https://colab.research.google.com/drive/1gRGRDhx9yA1KtafKhOaXZUpUoh2MuF8?usp=sharing
https://colab.research.google.com/github/facebookresearch/demucs/blob/master/Demucs.ipynb
https://colab.research.google.com/drive/1gRGRDhx9yA1KtafKhOaXZUpUoh2MuF_8?usp=sharing
Other one(s):
LaSAFT:
https://colab.research.google.com/drive/1XIngzXDi2mF_y6WwDrLLx4XZtI8_1FAz?usp=sharing
(original, cannot define model ATM)
If you cannot load the file, upload it manually to your Colab, or just wait patiently. Refresh Github page with CTRL+R if you can’t see the code preview.
Check out also this laSAFT download with message which says about superiority of 2020 model (said in march 2021).
Clone voice:
Matchering:
For more Colabs search for colab.research.google.com on our Discord server
__Google Colab troubleshooting (old)_
Error of authorisation during mounting:
TL:DR - you need to log into the same account in Colab you want to mount drive later, or just change your Colab account.
It was introduced to Colab at some point. Once I tried to log into another account during mounting, it displayed a new window with only one account, where the wanted account didn't appear, and when I manually signed in to it, Colab showed an error on Colab, something about unsuccessful authorisation. When I changed account in the right corner this time for the same account I wanted to choose when mounting, everything went fine as it always used to be. Full list of accounts appeared. HV Colabs already have the new mount method implemented, so the old one doesn’t cause error, but in UVR notebook you can choose between the new (default) and the old one (just in case Google changed something again).
- Try to log into another Google account(s) if you cannot connect with GPU anymore and/or you exceeded your GPU limit
- (cannot really say if it’s really helpful at this point)
Paste this code to console (Chrome: CTRL+Shift+I or …>more tools>tools for developers>console) to avoid disconnections from runtime environment or if you encounter problems while being AFK and if you run into issues of being unable to connect to GPU after reconnection after idle time or possibly after the code was executed, and you’re AFK for too long. It won’t prevent you from showing one captcha in the session.
interval = setInterval(function() {
console.log("working")
var selector = "#top-toolbar > colab-connect-button"
document.querySelector(selector).shadowRoot.querySelector("#connect").click()
setTimeout(function() {
document.querySelector(selector).shadowRoot.querySelector("#connect").click()
}, 1000)
}, 60*1000)
It will constantly reclick one window to appear in Colab to prevent idle check.
Repository of stems/multitracks from music to create your own dataset
Datasets search engine
https://datasetsearch.research.google.com/
Up-to-date list of datasets
https://github.com/Yuan-ManX/ai-audio-datasets-list#music
33 datasets compilation list:
https://sites.google.com/site/shinnosuketakamichi/publication/corpus
ZFTurbo’s list (contains duplicates from below):
https://github.com/ZFTurbo/Music-Source-Separation-Training/issues/40
Check out also:
#resources | #datasets (invite)
musdb18-hq (for described errors in the repo read)
https://drive.google.com/file/d/1ieGcVPPfgWg__BTDlIGi1TpntdOWwwdn/view?usp=sharing (14GB 7z)
https://zenodo.org/record/3338373#.Yr2x0aQ9eyU (mirror, 22GB zip, it can be slow at times)
Slakh2100 (2100 tracks), mono, guitar + piano, and a LOT of other stems, no vocals
If we were to ever train a multiple-source Demucs model, it would be greatly helpful
https://drive.google.com/file/d/1baMOSgbqogexZ5VDFsq3X6hgnIpt_bPw/view
https://github.com/ethman/slakh-utils
https://drive.google.com/file/d/1sxdNk0kekvv8FwDvzNypYe6Nf7d40Iek/view?usp=drivesdk
Jammit (torrent)
"the audio files can't be mixed directly. You need to apply a gain reduction of 0.77499997615814209 (in dB : -2.2139662170837942) on each track to get a perfect mixdown. This factor is about to set a 0dB on the original jammit mixtable."
"Total tracks: 240
How often folders exists for track: ('vocals', 239), ('drums', 238), ('bass', 236), ('guitar', 222), ('other_keys', 110), ('piano', 110), ('percussion', 99), ('bowed_strings', 45), ('other', 39), ('wind', 26), ('other_plucked', 7)"
Script to convert MoisesDB in MusDB18 format:
https://gist.github.com/kiselecheck/df62174c5d986afcc5875300fd38bf9a
Cambridge Multitrack Library
https://multitracksearch.cambridge-mt.com/ms-mtk-search.htm
A nice collection of legally available multitracks.
"I believe about 2/3rds of musdb18's tracks are taken from this."
Great for dataset for creating stem specific models like acoustic guitars, electric guitars, piano, etc. You will just get the stem file you want and combine the rest
DAMP-VSEP
https://zenodo.org/record/3553059
Smule Digital Archive of Mobile Performances - Vocal Separation
seems to be a really big dataset of instrumental-amateur vocal-mix with compression and such triplets.
Metapop
https://metapop.com/competitions?p=1&status=ended&type=all
“Most of them have a click through to download stems. You might need to automate downloads using Simple Mass Downloader browser extension or something. Some are remix competitions, some a production, but all have stems."
Guitar Hero / Rockband stems
remixpacks.ru / remixpacks.club (taken down, now it’s under https://remixpacks.net/ address [not sure if the site content is the same)
Python script by MissAllure for downloading stems from: https://docs.google.com/spreadsheets/d/1BtUSgPffbcaW4bMuGClYi8FGvaYmYyc1p4SkfpNty-U/edit?gid=0#gid=0 (only 10, but you can change it; saves you from having to open links; written by AI)
Remix packs master post (removed dead links) - still has like 2000+ stems
https://drive.google.com/file/d/11NrElQSjrXT_DbTL00r9OeMrrEBral3V/view?usp=sharing
Torrent:
or here:
magnet:?xt=urn:btih:45a805dbd78b8dec796a0a127c4b4d2466ddbb9a
(list with names:
https://docs.google.com/spreadsheets/d/1uCWmuAUfvVLonbXp9sQUb9dEODYTHmPAOyvGxulMOCA/edit?usp=sharing)
Renamer - python script
https://mega.nz/file/gEgwwaaB#BCDDMpl-VcIZDnNYQziyklOV9Vpf43wuc76hsS3JTlw
Showcase
https://www.youtube.com/watch?v=95Q31HjU04E
Archive.org copy
https://web.archive.org/web/20230105142738/https://telegra.ph/Remixpacks-Collection-Vol-01-04-12-25
Or here (but you can’t access all the sections at the bottom and after some time you get “Unable to load” error; probably using the old Manifest uBlock with blocking specific site element would work, not sure):
(separate downloads)
OG subreddit source along with the file was deleted, and back when it was online, probably it was locked from downloading and scrapping it was difficult.
Q: https://web.archive.org/web/20230105142738/https://telegra.ph/Remixpacks-Collection-Vol-01-04-12-25 contents list? I don’t want to download all of them just to find one thing (genie in a bottle stems)
A:
If there are no seeds, so the torrent is dead, “a major part of these stems are on the songstems telegram chat, including new stems that aren't in these packs”
https://t.me/+mrluHEcfixwwNzRk
“For those that aren't able to d/l the torrents anymore, or just want to d/l some of the remixpacks content,
I uploaded all 26 collections (~3TB) here: https://remixpacks.multimedia.workers.dev/
DM me to request username/password.” Bas Curtiz#5667
https://clubremixer.com/ - outrageously big database, probably reuploads from remixpacks too (but on slow Nitroflare or simply paid irc)
https://songstems.net/ - lots of remixpacks stuff reuploaded from masterposts of clubremixer.com to Yandex (free Nitroflare is 20KB/s)
Mega collection of stems/multitracks (remixpacks - Guitar Hero, Rock Band, OG)
https://docs.google.com/spreadsheets/d/1_dIFNK3LC8A40YK-qCEHhxOCFIbny7Jv4qPEoOKBrIA
Rock Band 4 stems (free Nitroflare mirror)
https://clubremixer.com/rb4-stems/
Different mixing of the RB tracks was a factor in models trained by the community. “Also, RB tracks never fade out. They are also never brickwalled.”
“brickwall audio has negative influence on waveform based archs, but on spectrogram based one like all recents one, it doesn't seem to have big impact on results quality” - jarredou
GH stems from X360 instead of Wii for better quality https://www.fretsonfire.org/forums/viewtopic.php?f=5&t=57010&sid=3917a8e390f65097f07d69595dd5ba55
(free registration required, basically content of all zippyshare links of the PDF below:)
PDF with separate RB3-4 stems description and DL (lots of links are offline as zippyshare is down), page 6 shows some table of content with evaluation progress.
toaz.info-stemspdf-pr_7a1e446f01c9b1666a9bebe9fd51f419.pdf (reupload)
Huge database (probably contains some of the above)
Others:
frp.live instrumentals/acapellas
https://docs.google.com/spreadsheets/d/1NuQV8cfFPehvIwPBUGOMbiC4FSei2p923qC6af5tCV8/
22 instrumental albums and some single tracks (DL) - hard to align for inversion, even for lossless, sometimes time shifts every verse, possible artefacts/bleeding after inversion to be cleaned further with models.
127 hip-hop instrumentals with vocal chops (duplicates from the above), and 80 with scratches or harmonies (DL)
Gilliaan stems for 4 songs (EDM/Dance/House) and Mainstream Dataset with 20 songs:
https://drive.google.com/drive/folders/1JbQRMYH9DT_vUHpf4jHwD80eC6VvpDZX
Mirror (with messages below)
https://discord.com/channels/708579735583588363/1286052299931652106/1304884347492372580
50 Produce Like a Pro multitracks
https://producelikeapro.com/blog/happy-new-year-2022-3/
https://producelikeapro.lpages.co/keep-truckin-multitracks-form/
Potentially more: https://www.youtube.com/playlist?list=PLnLOmVwRMCqS1ia3o9Vv0nG5sMgcFR9Tc
Instrumentals/vocals/stems
- Metal genre dataset
Contact @33meskvlla33 (iirc 2K unique songs)
- (dead) Here is a smaller version of the metal dataset + the validation dataset (there is also not metal in there, but lots of the data is metal oriented)
302 vocals + 802 instrumentals
https://drive.google.com/drive/folders/1TlY1FXP54sVA9T0Kfq0oOXJXq03czxrv?usp=drive_link
If anybody wants to train an instrumental/vocal model on metal, this can get you started (I'm severely limited by my hardware).
A lot of the instrumentals are official instrumental versions of albums
the stuff with my username is from my stempacks except for Omega Virus, Behold the Void and Rings of Saturn (IDK why I named these with that xd)
- mesk dataset reworked (dead)
https://drive.proton.me/urls/D89RY8EEE8#x687Dk7ukX30
“This is the "stem part" of the metal dataset
this contains 255 instrumentals and 242 vocals spanning over:
- 24 albums + 6 songs released officially as stems [a.k.a. the 30 stem packs I legally bought]
- 13 Nail the Mix stem packs (labeled NTM)
- 33 stems from various games, mostly Rockband (labeled RMX)
- 2 tracks from the second part (coming later) I did prematurely, so they’re included [254 and 255, *BLOODHOST* by DARKO US and The Sea Starts Here by The Dali Thundering Concept]
There’s one track without numbering in the "vocals" folder, Crawl by Bad Omens, because it doesn’t have a corresponding instrumental, it’s only vocals.” - Mesk
- Mesk 60GB dataset
https://drive.proton.me/urls/ZFFRW5BRSC#7S8qTF07Cm4d
“This is the new, full version of my dataset. (...) I made it separate into two 30gb RARs.
This contains 1357 instrumentals and 1648 vocals for a total of 3005 files total.
Various things were used, such as :
- official stem packs
- officially released instrumentals
- playthroughs from youtube, to feed compressed files to the model (some of the instrumentals were also mp3 originally but 320 kpbs, these being the Deftones, Catsclaw, Crown Magnetar, Loathe and Cabal instrumentals)
- official stem pack vocals
- inverts of said officially released instrumentals, labeled [METAL-INVERT-VOCALS], cleaned up with 07.2025
- 3 BS-Rofo 07.2025 results, these being : DEITY by DIR EN GREY (its there 2 times) and Sonne by Rammstein
- some of my own vocals, labeled [MESK-VOCS]: these were recorded from my phone, some are reverb heavy, but most of these are just me whispering stuff
- some of Kanye West’s ‘Graduation’ vocals (I did say this dataset was kinda weird)
- Isling's vocals he graciously provided me, thank you :D
- some random vocals, labeled [VOCALS-VARIOUS-IDK] and a random sample from Vulvodynia’s ‘Psychosadistic Design’: track ‘Triple OG Slamdown’, labeled [RANDOM-ASS-SAMPLE]
- the entirety of the Extreme Metal Vocals Dataset
I spent a month on this, so please tell me if I made a mistake! I probably did some at some point haha.
Mistakes found (I’d probably update this message as I go)
1: there's only a partial output of "Frozen Tomb" by Shadow of Intent, the one with vocals.
2: vocal track numbered 34 of my stems, A Viscious Reforming of Features is in mono!
stereo version linked here: https://drive.proton.me/urls/8FMHAXVJF8#df5xtHLvVP0p
3: Food for the Maggots has a sample at the beginning, new version without here:
https://drive.proton.me/urls/Q7A0W1R2DG#pNGOCRMzFuW2
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- Index of ~7,5K songs in multitracks in the wild - 13.03.2023 (updated link above)
https://krakenfiles.com/view/XiDE82aLOR/file.html (dead)
No download links. Probably some will be available around the net if you search well.
- Here’s a magnet link with some stems:
https://web.archive.org/web/20200606113408/https://pastebin.com/6bZtpvur
- “From the 90s hit maker Moby himself, 500 multitracks (unreleased songs, copyright free):
- Official accapellas, instrumentals and stems
https://infinity101.wolf.usbx.me/filebrowser/share/Q9HHlUB6
- “beatmania the rhythm game makes charts very interestingly because they are all keysounded, but what's interesting is that someone made a chart to reaper project converter
and essentially it just gives you stems. I think people could probably export a shit ton of electronic stems and improve models because there are a LOT of bms charts” src
- Songstems.net Telegram group where you might find some music stems
https://t.me/+mrluHEcfixwwNzRk
- Lots of instrumentals (sometimes with backing vocals) - click
- The Spheres Dataset
(orchestral)
https://zenodo.org/records/17347681
- Metal dataset
https://zenodo.org/records/8406322
760 audio excerpts from 1 to 30 seconds in mono.
“iirc, the audio samples can be very short, it may need pre-processing (merging multiple samples in 1 file) to be used for training”- jarredou
I think mesk did the job already for his dataset (it’s uploaded later below).
Around a hundred of Eminem’s acapellas leaked:
https://drive.google.com/drive/folders/141t33Qa2h3rEi2T0lYokvBPiiDBbC6dQ
Official and unofficial Eminem instrumentals (single links):
Justice (electronic duo) stems
https://archive.org/download/justice-stems
Mesk’s metal dataset (old)
“resharing my metal dataset for people to claw their hands on
https://drive.google.com/drive/folders/1ajlzmyAuX-fsiKiaypN8y2GT8EYBAws5?usp=drive_link
This consists of:
official instrumentals, straight from my stems, what's not labelled with my name are official as well [remixpacks stuff were curated]
vocal folder has official vocals (from the stems again), remixpacks (curated), inverted vocals and some weird whispery sh!t from yours truly”
Mesk’s metal dataset, full:
https://drive.google.com/drive/folders/1ajlzmyAuX-fsiKiaypN8y2GT8EYBAws5?usp=drive_link
“this has 1982 instrumentals and 1807 vocals
Credit to both me and bascurtiz (...) if you decide to use it for various projects. Most of the dataset contents is from his part.
all things titled with my name were 100% legally bought stems.”
https://multitracks.pages.dev/ (only a list, no DL links)
English and Spanish multitracks
- Around 30GB of T.Swft stems (1 (not necessarily a mirror) | 2)
Orchestra:
https://www.upf.edu/web/mtg/phenicx-anechoic
https://web.archive.org/web/20241209233028/https://www.lam.jussieu.fr/Projets/index.php?page=AVAD-VR
https://www.openair.hosted.york.ac.uk/?page_id=310
https://zenodo.org/records/4955282
- Expressive Anechoic Recordings of Speech (EARS) dataset.
- 100 h of speech data from 107 speakers
- high-quality recordings at 48 kHz in an anechoic chamber
- high speaker diversity with speakers from different ethnicities and age range from 18 to 75 years
- full dynamic range of human speech, ranging from whispering to yelling
- 18 minutes of freeform monologues per speaker
- sentence reading in 7 different reading styles (regular, loud, whisper, high pitch, low pitch, fast, slow)
- emotional reading and freeform tasks covering 22 different emotions for each speaker
https://github.com/facebookresearch/ears_dataset
DL:
1 | 2 | 3 | 4 | 5 | 6 | 7 “The dataset is made of 107 zip files that you can download one by one manually”
“What is great with this dataset is that it was recorded in anechoic chamber, so no reverb, no echo, with high-end hardware. You can use it as baseline for reverb removal, speech enhancing, etc...” jarredou
Sites (for general search):
Multitracks’ section of rutracker (requires free account):
https://rutracker.org/forum/tracker.php?f=2492
“You can just go here https://rutracker.org/forum/tracker.php?f=1674 (sample libraries category) and type the instrument you want, it will pop all the sample packs.
Maybe add "loop" to the search too, will filter out some weird packs”
Seems 'acapella tools' or 'instrumental tools' are good key-words to search for.
Multitracks/multitrack queries on The Pirate Bay
https://thepiratebay.org/search.php?q=multitrack&all=on&search=Pirate+Search&page=0&orderby=
https://thepiratebay.org/search.php?q=multitracks&all=on&search=Pirate+Search&page=0&orderby=
You’ll probably find something useful there too (at least 160 lossy/261 lossless, big packs of stems from labels and whole albums with stems)
Some are covers of original tracks, but that shouldn't matter, since they represent the same.
This is on Deezer, but u might find others on Tidal.
There’s also some stuff available on Soulseek (P2P service)
There is a lot of filtered trash, but you can also find official acapellas.
https://www.acapellas4u.co.uk/
Collection of 40K instrumentals and accapellas (lossy, rather avoid using such files for training, and search for lossless if possible)
Multitracks. Looks like paid, but it has also few pages with some free ones (e.g. Fleetwood Mac, not sure if free) “They’re 16kHz mp3s re-encoded to 48kHz”, the same for:
This is also paid, but it has less known music
“those are covers from famous songs, but all in multitracks.
And from what I've listened to so far, is that they are pretty conservative.
The vocals all seem to be dry and none seem to contain bleed so far.
Also, the instrument stems are proper / not mixed up with other instrumentals.
The stems are the exact same duration.
All in all, a solid dataset right off-the-bat imo.
I should've calculated it prior, what the better subscription was, the 10GB or 20GB a day one vs. price vs. content approx. in total.
52mb (wav) * 12 (multitracks) = 624mb per song
4.766 songs * 624 = 2973984 mb = 2.97tb
weekly limit = 70gb * 4 (weeks) = 280gb = 280000mb
2973984 / 280000 = 10,6 weeks in total.
10,6 / 4 = 2,65 so 3 months x $30 = 90 bucks”
https://www.epidemicsound.com/music/search/
Can be ripped. Some tracks there will be a subject to rule out due to bleeding. Plenty of genres. Might be good for diverse dataset.
https://bleep.com/stream/stems
Looks like official stems for sale. ~45 songs in total.
FullSOL (only for premium users; min. 200EU for year)
https://forum.ircam.fr/projects/detail/fullsol/
19,91 GB of audio samples. No percussion.
Instruments: Bass Tuba, Horn, Trombone, Trumpet in C, Accordion, Harp, Guitar, Violin, Viola, Violoncello, Contrabass, Bassoon, Bb Clarinet, Flute, Oboe, Alto Saxophone
(jarredou has it)
Vocals/speech
MedleyVox dataset (for separating different singers) of which they refrain from releasing the model for (and Cyrus eventually did it single-handedly):
https://github.com/CBeast25/MedleyVox (13 different singing datasets of 400 hours and 460 hours of LibriSpeech data for training)
https://zenodo.org/record/7984549
k_multisinger (data folder struct reconstructed to male/female subfolder only. and not includes labels)
for train RoFormer Chrous male/female separator:
https://drive.google.com/file/d/18evyY82ec4IdNT2z8q76zm30EWhfc-9j/view?usp=sharing
k_multitimbre a.k.a. K_multitembre (Original Folder Struct):
https://drive.google.com/file/d/1Ic4P8gCGwbLshR118N8V3tbAU-D9Us-i/view?usp=sharing
Potentially more here:
https://sites.google.com/site/shinnosuketakamichi/home
Be aware that the only one MedleyVox dataset which remains unobtainable to this day is TONAS, but it’s small, esp. compare to the Korean datasets. Besides this one, queer and Cyrus have them all on our Discord, but they’re huge. Ksinger and Ktimbre takes ~300GB unzipped for both.
ChoralSynth dataset
https://zenodo.org/records/10137883
- screaming, cheering, applause, whistling, mumble, etc... dataset by jarredou’s (@rigo2) “collected from all the sources I've found, to help model creation:
+5000 stereo wav files, 44100hz
~37 hours of audio data”
Hit him on Discord for the link
- ”Ultimate laugh tracks for sitcoms, game shows, talk shows, and comedy projects (available on Amazon Music and Apple Music (ripped, YT upload has similarly looking spectrograms)
- Laughter-Crowd Dataset #2.zip https://terabox.com/s/1xLuZWvpGX0LTQypO1p7u_g
- There is 768.44 GB of K-pop stems somewhere in the wild (maybe ask .mikeyyyyy)
- Gabox karaoke dataset (2GB)
https://gofile.io/d/TyzaH8 (dead; “i won't upload it again since becruily told me that dataset type 4 (iirc) was the best for karaoke”)
“(may need a check, iirc there were songs without bv, also it doesn't have the vocals part)”
Gabox type 4 karaoke dataset V2 (21GB)
(thanks dca100fb8)
“Some vocals have inst bleed, if someone is willing to help it'd be much appreciated (...)
my dataset "other" folder vocals are private”
- dca100fb8’s WIP Karaoke dataset (30.2GB, 674 pairs)
https://gofile.io/d/OgOxfG (“it might expire soon so remember to download it if it's useful for you”
Reupload: https://gofile.io/d/pSbWEL)
New re-up: https://gofile.io/d/khmSN6
“I apologize for the mistakes in this dataset, there ARE errors like duplicate songs, BVinsts without vocals at all, or LV stems with noise/bleed, or even low quality files, if someone is able to clean this dataset, it would be appreciated. Also I'm aware it has double leads (double stack - 2 lead vocals sang together in mono center channel, layered) which prob shouldn't be kept in the data as it could confuse the AI, while other songs from the list clearly separate lead vocals (thanks gilliaan for noticing), thank you for your comprehension”
- RawStems
https://huggingface.co/datasets/yongyizang/RawStems (DL)
https://github.com/yongyizang/music-source-restoration/blob/main/preprint.pdf (paper)
“A dataset annotation of 578 songs with unprocessed source signals organized into 8 primary and 17 secondary instrument groups, totaling 354.13 hours. To the best of our knowledge, RawStems is the first dataset that contains unprocessed music stems with hierarchical categories”.
- https://sonovox.ai/products/demo-vocal-dataset
“A free demo pack — 50 full vocal stem packs, dry WAV, AI-ready. Completely free to download”.
DnR (speech, music, effects)
Divide and Remaster v3: Multilingual Validation & Test Set
https://zenodo.org/records/12658755
“but there are a bunch of other versions of v3 for specific languages, I'm not sure what is the difference.
There are separate versions for English, Spanish, French, etc.
https://zenodo.org/communities/opencass/records?q=&l=list&p=1&s=10&sort=newest=”
In fact, you can train on validation too, but it’s not necessary anymore as the dataset was published already:
https://github.com/kwatcharasupat/divide-and-remaster-v3/wiki/Getting-the-Dataset
SFX
Datasets for potential SFX separation model
- https://cocktail-fork.github.io/ (SPEECH-VOICE-SFX (3 stems), 174GB)
- https://sounds.spriters-resource.com
a.k.a. https://www.sounds-resource.com/
(collection/database of SFX from lots of games)
- https://mixkit.co/free-sound-effects/game/
- https://opengameart.org/content/library-of-game-sounds
- https://pixabay.com/sound-effects/search/game/
- https://www.boomlibrary.com/shop/?swoof=1&pa_producttype=free-sound-effects
- https://www.adobe.com/products/audition/offers/adobeauditiondlcsfx.html
- Spongebob stems (500MB)
- Nickelodeon leak (2024 Nick Giga Leak7.zip/nick.7z) (10.7GB)
https://myrient.erista.me/files/Miscellaneous/Nickelodeon%20Leaks/
(not a full leak, as it has 500GB and only some people have it)
- Sound effects HQ by soniss 2024 (27.5GB+)
- “Free sound FX samples packs from Adobe”:
https://www.adobe.com/products/audition/offers/adobeauditiondlcsfx.html
- GTA San Andreas lossless SFX
https://gtaforums.com/topic/957917-sa-uncompressed-sfx-pack/?tab=comments#comment-1071266243
- “I ripped all audio files from a game called Dead as Disco. It has FX, SFX. dialogue and some song stems. I think this could possibly help with datasets i think (also MX_Moonlight_152_VOX_Stem.ogg is a filtered thing in the game files for some reason)”
https://drive.google.com/drive/folders/1CDU0eqiOMFi4mmfX8vqx8VfVtHsWdjJQ?usp=sharing
Note: “It's very possible to have a dataset of sfx from tv shows and movies on Netflix - the 5.1 surround sound for movies/tv shows is different to that of Dolby Surround sound of music. Music has a lot of bleed and muddiness (artifacts) so that no one stem is soloed in a channel, and when it is, it's too muddy to be usable for anything, however in movies and TV shows, the surround sound is completely clean and has the sound effects pretty much soloed in a channel” - isling
Piano
“MAESTRO” is a dataset composed of about 200 hours of virtuosic piano performances captured with fine alignment (~3 ms) between note labels and audio waveforms.
https://magenta.tensorflow.org/datasets/maestro
GiantMIDI-Piano is a classical piano MIDI dataset contains 10,855 MIDI files of 2,786 composers. The curated subset by constraining composer surnames contains 7,236 MIDI files of 1,787 composers. GiantMIDI-Piano are transcribed from live recordings with a high-resolution piano transcription system
https://github.com/bytedance/GiantMIDI-Piano
Drums
StemGMD: A Large-Scale Audio Dataset of Isolated Drum Stems for Deep Drums Demixing
(although drumsep used bigger dataset consisting of MIDI sounds to avoid bleeding, with XLN only)
Virtual drumkits
The “advantage is that you can have zero bleed between elements, which is not possible with real live drums.
You can create “more than 300 drumkits as virtual instruments (toontrack, kontakt, xln, slate, bfd, XLN ones are nice too (from their trigger and drums VST) + a Reaper framework to multiply that by 10 (using heavily different mixing processes for each drum elements), so potentially 3000 different sounding drumkits “
“one could use producer sample packs/kits for more modern samples” there are tons of packs around the net.
jarredou (rigo2):
“For those interested, I'm sharing on demand my drums separation dataset.
It's not a final version. I've realised after generating 130h of audio data that I've made a mistake in routing, leading to some occasional cowbell in snare stems. So it's [Kick/Snare-cowbell/Toms/HiHat/Ride/Crash] stems.
I've stopped it's rendering and will not make the final "mastering" stage that was planned.
I will make a clean no-cowbell version, but as I'm lacking free time, I don't know when, and as this one is here and already great sounding why not using it in the meantime.
Just don't mind the cowbell!”
Looks like it’s the thing:
http://rigaudio.fr/datasets/DrumsDataset.zip
Newer version in a better formatted version, with train/valid separated parts, generated mixtures and a fixed filename that was containing an extra space:
https://rigaudio.fr/datasets/DrumsDatasetv2.zip (25GB)
(still the same issues with some occasional cowbell in snare stems. “There are also few other percussions here and there on some little parts for some tracks (like tambourine in ride stem).”)
“I realise now that I totally forgot to lowercase all filenames before reuploading the dataset.
To avoid issues where some awaited filenames are hardcoded in ZFTurbo's script, the best way is so to lowercase all filenames in train/valid parts, convert the valid part to .wav files (no need for the train part that can handle flac correctly).
And lowercase the stem names in training part of the config file accordingly.”
“Can probably be useful to create electro drums separation dataset, free 50,000 drums MIDI files:” https://abasynthphony.gumroad.com/l/50000MIDIFilesforDanceMusicDrum?layout=profile
mesk’s metal drums dataset (drums in one stem):
??? (maybe ask him, looks I forgot pasting the link and can’t find it anymore)
Rhythm and lead guitar
“He has isolated tracks for his videos that he makes, and it's free (or he does not know how to properly Patreon lock certain content on his website).
You can navigate to any tab page and look for the header: "Isolated TRACKS (mp3)" and find the textbox below where it says:
"Please sign up on Patreon, or if you are already a member, please login."
The last word links to a Patreon signup page, and if you sign in, it does not check if you are subscribed to his Patreon or not, it will give you access regardless.
Boom! Now you have access to 250+ Lead and rhythm guitar pairs. There is a goldmine worth of metal stuff in there too.
This is probably the closes we could ever get to having a contemporary rhythm/lead guitar dataset that is both relatively large, the rhythm and lead has their own tracks, its diverse, and actually includes songs that we like/listen to.
Only problem is all of them are exported in mp3 with a cutoff of 16khz, so it is equivalent 128 kbps, and the denoising that was done in post is pretty lazy.
However, I think if these parts are upscaled with FlashSR it would be great.
Or maybe Re-Amp a low pass filter version of the stems with the Ampltibe 5 presets that he also attaches to all tabs, and ensemble the remaining frequencies that way.
I personally would not recommend using the drum and bass stems, only the guitar parts, since the drum and bass are both programmed and are uniform.
The tone for every video is unique and tone matched to their respective albums and tracks. Even if it's not dead on, it's better than trying to use some yayhoos guitar doodles that uses the same amp/cabinet/simulator for every track.” Vinctekan
Ernhu
[China traditional music instrument dataset]
https://zenodo.org/records/8012071
EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb
https://zenodo.org/records/7044411
Noise
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https://www.monotostereo.info/
“Helped me find not only tools but also other resources like research papers, etc on audio source separation in general. A fantastic resource for anyone into audio source separation”
For more links, check #resources and #datasets and Post dataset (you may encounter duplicates)
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