video in AudioSet
A list of about a million ten-second YouTube clips, each labelled with the sounds in it, that Google published in 2017. Labs use it to train and test general audio and music models.
Including Mullavalli kudilil (Kuyiline Thedi)
Report shyama built from the datasets alone
Yes. shyama has 3 songs and 1 video listed in datasets used to train and test AI music models.
This page only knows about the videos the datasets list. To check every upload on the channel:
songs in LAION-DISCO-12M under the artist name
A list of 12 million songs on YouTube Music, gathered by walking artist pages, that LAION published in 2024 for training general music models.
Including Kondu Vaa Thangamayile, Ragava Rama, Hilere Hile Hile
Listed under the artist name 3 songs in LAION-DISCO-12M
Shyama 2 songs
Probable match by artist name. If this is not you, ignore this section. Artist page on YouTube Music.
Kondu Vaa Thangamayile
Listed as Ram Surendar, Shyama – Kondu Vaa Thangamayile (Sree Murukan) · Watch on YouTube
youtube_video aTTDXuBlIV8 · row aTTDXuBlIV8
LAION-DISCO-12M
Ragava Rama
Listed as Ram Surendar, Siju Thuravoor, Shyama – Ragava Rama (Ragava Rama) · Watch on YouTube
youtube_video oUfLzI0mR5w · row oUfLzI0mR5w
LAION-DISCO-12M
Shyama 1 songs
Probable match by artist name. If this is not you, ignore this section. Artist page on YouTube Music.
Hilere Hile Hile
Listed as Abhijit Majumdar, Shyama – Hilere Hile Hile (Hilere Hile Hile) · Watch on YouTube
youtube_video 5idAJnQinU4 · row 5idAJnQinU4
LAION-DISCO-12M
AudioSet 1 videos, 1 rows
Mullavalli kudilil (Kuyiline Thedi)
Clip 60s to 70s · Open clip on YouTube · Labels: Singing, Music, Classical music
youtube_video EX95TjRiulY · row unbalanced_train:EX95TjRiulY:60:70
AudioSetunbalanced_train
What this means
Last reviewed 2026-09-24.
Presence in a public research dataset shows that a clip of your recording was listed by academic or industry researchers as material for building or testing audio models. It does not by itself show which commercial products, if any, used the clip. The legal picture is moving. On 31 July 2026 the Regional Court of Munich I ruled in GEMA's case against Suno (case 42 O 763/25) that reproducing works for training, holding them in a model in retrievable form, and generating similar outputs can each infringe copyright under German and EU law; the judgment is not final and can be appealed. In the United States, Warner Music Group settled its claims against Suno on 25 November 2025 with a licensing deal, while the parallel claims by Universal and Sony were still pending at that point. Since then BMG (12 August 2026) and Believe/TuneCore (8 September 2026) have signed opt-in licensing deals with Suno.
- DLA Piper: Creation of music with generative AI may be copyright infringement (GEMA v Suno, LG München I, 42 O 763/25), 2026-08-28
- Music Business Worldwide: Warner Music Group settles with Suno, strikes first-of-its-kind deal, 2025-11-25
- Music Ally: Suno signs another music licensing deal, with BMG, 2026-08-12
- Music Business Worldwide: Suno inks global deal with Believe, 2026-09-08
What you can do
3 routes. None is a guarantee.
- UK, PRS members. Send PRS for Music your report and ask what collective or licensing route covers your works.
- Germany, GEMA members. GEMA won a first-instance ruling against Suno on 31 July 2026 and has a licensing model. Ask how they apply to your repertoire.
- Signed to Warner, BMG or Believe/TuneCore. Each has an opt-in licensing deal with Suno. Ask your label what opting in or out means for you and whether any settlement money reaches artists.
Keep a copy
Provenance
JSON export SHA-256 c44954d8562d78142a2519316ec85d77fd2fd54ca7a9f4166dce60022b97fc95
Dataset versions indexed
| Dataset | File | Rows | Loaded | SHA-256 |
|---|---|---|---|---|
| audioset | balanced_train_segments.csv | 8,836 | 2026-09-24T10:30:36+00:00 | 3b83e5ff9ed72cfd… |
| audioset | eval_segments.csv | 7,848 | 2026-09-24T10:30:36+00:00 | f7055f53935f70e7… |
| audioset | unbalanced_train_segments.csv | 1,063,333 | 2026-09-24T10:30:43+00:00 | bb2dfd5e4f1ced39… |
| laion_disco_12m | train-00000-of-00005.parquet | 2,464,184 | 2026-09-24T12:18:19+00:00 | f8b1f0034f8f1e94… |
| laion_disco_12m | train-00001-of-00005.parquet | 2,464,183 | 2026-09-24T12:32:29+00:00 | 71d78ad14ee3f85d… |
| laion_disco_12m | train-00002-of-00005.parquet | 2,464,183 | 2026-09-24T12:49:06+00:00 | 0feba570a0e8e73c… |
| laion_disco_12m | train-00003-of-00005.parquet | 2,464,183 | 2026-09-24T13:06:37+00:00 | 21a77d99fd85ad2f… |
| laion_disco_12m | train-00004-of-00005.parquet | 2,464,183 | 2026-09-24T13:24:36+00:00 | 6ac5f59958b6b813… |
| musiccaps | musiccaps-public.csv | 5,521 | 2026-09-24T10:29:42+00:00 | 8cc109ac507b5e5a… |
These pages report where identifiers from public research datasets point. Presence in a dataset is a fact about the dataset, not a claim about the channel or artist, and says nothing about what any company did with the material. This report is informational and is not legal advice. It shows that identifiers matching your work are present in the named public research datasets; it does not establish how any particular company used them. If you are the subject of this page and want it removed, see removals.