LAION-Tunes

2,019,683 AI-generated music tracks from 5 platforms, annotated with captions, transcriptions, embeddings, aesthetics scores, and NSFW safety labels. Includes a full-text and vector search engine with a web UI featuring both a beginner-friendly Simple mode and a power-user Advanced mode.

Quick Stats

Metric	Value
Total tracks	2,019,683
Subsets	Suno (1,419,048), Mureka (383,549), Udio (115,140), Riffusion (99,228), Sonauto (2,718)
Has caption (Music-Whisper)	1,944,297 (96.3%)
Has transcription (Parakeet ASR)	1,463,129 (72.4%)
Instrumental	556,829 (27.6%)
NSFW flagged (very likely + likely)	33,457 (1.66%)
FAISS vector indices	6 (tag, whisper, caption, transcription, lyric, mood)
BM25 text indices	3 (tags, caption, transcription)
Total index size	~30 GB

Dataset Description

LAION-Tunes is a curated metadata and annotation dataset covering publicly available AI-generated music from Suno, Udio, Mureka, Riffusion, and Sonauto.

This dataset does NOT contain audio files. It contains metadata, annotations, embeddings, and search indices. Audio URLs pointing to the original hosting platforms are included for reference.

Note on Riffusion: Riffusion audio links went dead during the creation of this dataset. The metadata and annotations are preserved, but audio playback will not work for the 99,228 Riffusion tracks. The web UI defaults to "All except Riffusion" to avoid broken audio players.

Source Data

The Mureka, Udio, Riffusion, and Sonauto tracks come from ai-music/ai-music-deduplicated. The Suno subset combines the original ai-music-deduplicated Suno split with an extended collection from ai-music4you/ai-generated-songs, ai-music4you2/ai-generated-songs2, and ai-music4you3/ai-generated-songs3. The extended Suno collection added ~1.11 M additional tracks, bringing Suno to 1,419,048.

Platform	Tracks	Audio Format	Notable Fields	Audio Status
Suno	1,419,048	MP3	tags, prompt, model_name, explicit flag	Active
Mureka	383,549	MP3	genres, moods, model version	Active
Udio	115,140	MP3	tags (array), lyrics, prompt, likes, plays	Active
Riffusion	99,228	M4A	sound (style description), lyrics_timestamped, conditions	Dead links
Sonauto	2,718	OGG	tags (array), description, keyword	Active

What's Included

For each track:

• Metadata: title, tags, genre, mood, duration, play count, upvote count
• Music-Whisper Caption: AI-generated music description using laion/music-whisper (fine-tuned OpenAI Whisper Small)
• Parakeet ASR Transcription (public-release DB): vocal text for ~1.46 M tracks. Raw transcription text is available in the SQLite metadata.db; per-parquet raw transcription text is kept private for extended Suno tracks (the transcription embedding is public).
• Sentence Embeddings: 768-dim embeddings via google/embeddinggemma-300m for tags, captions, transcriptions, lyrics, and moods
• Whisper Audio Embeddings: 768-dim mean-pooled encoder embeddings from Music-Whisper for audio similarity search
• Aesthetics Scores: coherence, musicality, memorability, clarity, naturalness (computed from Whisper audio embeddings via a trained MLP)
• Predicted Engagement (extended Suno tracks): ML-predicted play count and upvote count (the original platform stats were not available for this subset)
• NSFW Safety Labels: three-tier classification (very_likely_nsfw / likely_nsfw / likely_sfw) across gore, sexual, and hate speech dimensions
• Language Detection: Detected language of vocal content via langdetect on the Parakeet transcription
• Pre-built Search Indices: FAISS vector indices and BM25 text indices ready to serve

Annotation Pipeline

1. Music-Whisper (laion/music-whisper): Generates music captions describing instruments, genre, mood, tempo, etc.
2. Parakeet TDT 0.6B (nvidia/parakeet-tdt-0.6b-v3): ASR transcription with word-level timestamps for vocal content
3. EmbeddingGemma 300M (google/embeddinggemma-300m): Computes 768-dim sentence embeddings for captions, transcriptions, tags, lyrics, moods
4. Whisper Encoder Embeddings: Mean-pooled encoder hidden states from Music-Whisper for audio fingerprinting/similarity
5. Aesthetics MLP: 5-head MLP trained on Whisper audio embeddings predicts coherence / musicality / memorability / clarity / naturalness
6. NSFW Classification: Cosine similarity of transcription embeddings against reference prompts for gore/violence, sexual content, and hate speech
7. Language Detection: langdetect applied to Parakeet ASR transcriptions (first 300 characters)
8. Engagement Prediction (extended Suno only): Gradient-boosted regressor over aesthetics + caption/transcription embeddings, trained on the original subset's play/upvote counts

Web UI

The search engine includes a single-page dark-mode web interface (index.html) with two modes.

Simple Mode (default)

A clean, beginner-friendly interface for quick searches:

• Large search bar with natural-language input
• Rank by: Relevance, Aesthetics Score, Most Liked, or Most Played
• Optional negative prompt (exclude certain terms/concepts)
• Language filter chips
• Sensible defaults: vector similarity on Whisper Caption, SFW only, all subsets except Riffusion, 60 s minimum duration

Advanced Mode

Full control over all search parameters:

• Search modes: Vector Similarity, BM25 Text, Combined, Music Similarity (audio upload)
• Vector fields: Whisper Caption, Tags, Lyrics, Mood, Transcription
• BM25 fields: Caption, Tags, Transcription, Lyrics (hashed)
• Ranking: Relevance, Music Similarity, Aesthetics, Play Count, Like Count
• Filters: Vocals/Instrumental, Min Duration, Min Aesthetics, Subset, Safety (SFW/NSFW), Languages
• Instrumental Subset: ~11 K curated instrumental-only tracks with dedicated FAISS/BM25 indices for faster search; selecting it auto-enables the "Instrumental" vocal filter
• Two-stage search: Refine Stage 1 results with a second query using different search modes/fields
• Negative prompts: Exclude results matching a negative query with adjustable weight
• Audio upload: Find similar tracks by uploading an audio file
• Find Similar: Click on any result to find sonically similar tracks
• Help icons (?) next to every control with detailed explanations of what each option does

Repository Structure

laion-tunes/
├── README.md                          # This file
├── server.py                          # FastAPI search server (main application)
├── index.html                         # Web UI (dark-mode, single-page app)
├── build_search_index.py              # Index builder script
├── update_indices.py                  # Incremental index updater
├── migrate_add_language.py            # Language detection migration
├── nsfw_safety_report.html            # Interactive NSFW analysis report
├── nsfw_analysis_data.json            # Raw NSFW analysis data
│
├── public/                            # Annotated metadata parquets (~12 GB)
│   ├── mureka_000000.tar.parquet      # One parquet per source TAR file
│   ├── ...                            # 49 Mureka, 14 Riffusion, 1 Sonauto,
│   ├── ...                            # 291 Suno, 30 Udio
│   └── ...                            # 385 parquet files total
│
├── search_index/                      # Pre-built search indices (~30 GB)
│   ├── metadata.db                    # SQLite database (2,019,683 tracks, 6.5 GB)
│   ├── faiss_whisper.index            # FAISS IndexFlatIP - audio embeddings (6.2 GB)
│   ├── faiss_caption.index            # FAISS IndexFlatIP - caption embeddings (5.9 GB)
│   ├── faiss_transcription.index      # FAISS IndexFlatIP - transcription embeddings (4.5 GB)
│   ├── faiss_tag.index                # FAISS IndexFlatIP - tag embeddings (2.8 GB)
│   ├── faiss_lyric.index              # FAISS IndexFlatIP - lyric embeddings (1.5 GB)
│   ├── faiss_mood.index               # FAISS IndexFlatIP - mood embeddings (1.2 GB)
│   ├── idmap_*.npy                    # Row ID mappings for each FAISS index
│   ├── bm25_caption.pkl               # BM25 text index for captions (1.5 GB)
│   ├── bm25_transcription.pkl         # BM25 text index for transcriptions (1.2 GB)
│   └── bm25_tags.pkl                  # BM25 text index for tags (119 MB)
│
└── whisper_embeddings/                # Raw Whisper encoder embeddings (~5 GB)
    ├── mureka_000000.tar.npz          # One NPZ per source TAR file
    └── ...                            # 385 NPZ files total

Data Format

Parquet Files (public/)

Each parquet file corresponds to one TAR file from the source collection. The original-subset parquets (Mureka, Udio, Riffusion, Sonauto, original Suno) contain the full original schema; the extended Suno parquets (suno_*_ai_generated_songs*.tar.parquet) contain the enriched annotation-only columns.

Common columns:

Column	Type	Description
filename	str	Filename within the source TAR
tar_file	str	Source TAR filename
audio_url	str	Original audio URL (mp3/m4a/ogg)
subset	str	Source platform (suno/udio/mureka/riffusion/sonauto)
title	str	Track title
tags_text	str	Comma-separated genre/style tags
mood_text	str	Mood tags
duration_seconds	float	Track duration
play_count	int	Play count on source platform (original subset)
upvote_count	int	Like/upvote count (original subset)
predicted_play_count	float	ML-predicted play count (extended Suno)
predicted_upvote_count	float	ML-predicted upvote count (extended Suno)
music_whisper_caption	str	Music-Whisper generated caption
parakeet_transcription	str	Parakeet ASR transcription (plain text)
parakeet_transcription_with_timestamps	str	ASR with word-level timestamps (original subset)
tag_embedding	list[float]	768-dim EmbeddingGemma embedding of tags (original subset)
caption_embedding	list[float]	768-dim EmbeddingGemma embedding of caption
transcription_embedding	list[float]	768-dim EmbeddingGemma embedding of transcription
lyric_embedding	list[float]	768-dim EmbeddingGemma embedding of lyrics (original subset)
mood_embedding	list[float]	768-dim EmbeddingGemma embedding of mood (Mureka only)
score_coherence / score_musicality / score_memorability / score_clarity / score_naturalness	float	Aesthetics sub-scores (1 – 5)
score_average	float	Mean of the five aesthetics sub-scores
has_caption / has_transcription / is_instrumental	bool	Annotation flags
language	str	Detected language of vocal content
nsfw_gore_sim / nsfw_sexual_sim / nsfw_hate_sim	float	NSFW cosine similarities
nsfw_gore_label / nsfw_sexual_label / nsfw_hate_label / nsfw_overall_label	str	NSFW labels

Whisper Embeddings (whisper_embeddings/)

NPZ files containing mean-pooled Whisper encoder hidden states:

• embeddings: float32 array of shape (N, 768) – L2-normalized
• filenames: string array of filenames matching the parquet entries

SQLite Database (search_index/metadata.db)

The tracks table contains all 2,019,683 tracks with 36 columns including metadata, aesthetics scores, predicted engagement (for extended Suno tracks), annotation flags, NSFW safety labels, language codes, and instrumental flags. The row_id column is the primary key used by all FAISS indices.

FAISS Indices (search_index/faiss_*.index)

All indices are IndexFlatIP (inner product / cosine similarity for L2-normalized vectors) with 768 dimensions. Each index has a corresponding idmap_*.npy that maps FAISS internal indices to SQLite row_id values.

Index	Vectors	Coverage	Description
faiss_whisper	2,032,032	all tracks (with small deduplication)	Audio encoder embeddings (music similarity)
faiss_caption	1,922,299	all tracks with captions	Music-Whisper caption embeddings
faiss_transcription	1,470,880	all tracks with transcription	ASR transcription embeddings
faiss_tag	908,241	original subset only	Tag text embeddings
faiss_lyric	479,313	original subset only	Lyrics embeddings
faiss_mood	383,616	Mureka only	Mood text embeddings

Note: faiss_tag, faiss_lyric, and faiss_mood were not extended to the new Suno tracks because the extended source did not provide comparable free-form tag / lyric / mood text fields. Search queries on those fields therefore only hit the original subset; search on caption, transcription, and whisper covers the full dataset.

BM25 Indices (search_index/bm25_*.pkl)

Index	Documents	Coverage
bm25_caption	1,956,676	all tracks with captions (with small variance vs FAISS due to tokenization drops)
bm25_transcription	1,473,647	all tracks with transcriptions
bm25_tags	908,241	original subset only

NSFW Safety Labels

Each track has NSFW safety scores and labels across three dimensions. The classification is performed by computing cosine similarity between the track's transcription embedding and curated reference prompts for each NSFW category.

Classification Method

1. For each track with a transcription, the 768-dim EmbeddingGemma embedding is compared against reference prompt embeddings for three categories: gore/violence, sexual content, and hate speech
2. Cosine similarity scores are computed for each category
3. Two thresholds per category define the three-tier labeling:
   • very_likely_nsfw: cosine similarity above the strict threshold
   • likely_nsfw: cosine similarity between strict and moderate thresholds
   • likely_sfw: cosine similarity below the moderate threshold
4. The nsfw_overall_label is conservative: the worst (most NSFW) label across all three dimensions is used

Thresholds and Distribution (over the full 2,019,683 tracks)

Dimension	Strict Threshold	Moderate Threshold	Very Likely NSFW	Likely NSFW
Gore/Violence	>= 0.3779	>= 0.3540	7,908 (0.39%)	7,726 (0.38%)
Sexual Content	>= 0.3584	>= 0.3234	5,528 (0.27%)	6,182 (0.31%)
Hate Speech	>= 0.3633	>= 0.3382	5,752 (0.28%)	6,396 (0.32%)
Overall (conservative)	–	–	17,053 (0.84%)	16,404 (0.81%)

Tracks without a transcription are labeled likely_sfw for all dimensions by default (there is no vocal content to flag).

NSFW Fields in the Dataset

Field	Type	Description
nsfw_gore_sim	float	Raw cosine similarity for gore/violence
nsfw_sexual_sim	float	Raw cosine similarity for sexual content
nsfw_hate_sim	float	Raw cosine similarity for hate speech
nsfw_gore_label / nsfw_sexual_label / nsfw_hate_label	str	very_likely_nsfw / likely_nsfw / likely_sfw
nsfw_overall_label	str	Conservative overall label (worst of the three)

The raw cosine similarity scores are stored so you can apply your own thresholds. The nsfw_safety_report.html file in this repository provides an interactive visual analysis of the NSFW distribution.

Filtering Behavior in the UI

• SFW Only (default in UI): Keeps only tracks with nsfw_overall_label = likely_sfw (excludes ~1.66% of tracks)
• NSFW Only: Keeps only tracks with nsfw_overall_label != likely_sfw
• All: No filtering

Running the Search Server

Prerequisites

pip install fastapi uvicorn faiss-cpu numpy pandas sentence-transformers torch scipy tqdm python-multipart transformers

Important — embedder compatibility. The shipped FAISS caption / transcription / tag / lyric / mood indices were built with the PyTorch google/embeddinggemma-300m model via sentence-transformers (which applies the model's specific LastToken pooling and task prompts). You must query the indices with the same model in the same configuration, otherwise retrieval lands in a different embedding subspace and returns semantically random results. google/embeddinggemma-300m is a gated model, so accept the terms on its HF page and export an HF_TOKEN before running the server.

Option 1 (default, recommended): PyTorch google/embeddinggemma-300m

# Requires HF_TOKEN for the gated google/embeddinggemma-300m model
HF_TOKEN=your_token python server.py --port 7860 --gpu 0

Loads google/embeddinggemma-300m via sentence-transformers in bfloat16 on the first available CUDA device (or CPU if CUDA is absent). This is the configuration that matches the shipped FAISS indices. On GPU, query embedding takes ~15 ms; on CPU, ~430 ms.

Option 2 (same model, fastest on CPU): HF Text Embeddings Inference

TEI runs google/embeddinggemma-300m in an optimized server container and provides ~25 ms CPU embeddings. Same model, same pooling — fully compatible with the indices.

docker run -d --name tei-embeddings \
  -p 8090:80 \
  -e HF_TOKEN=your_token \
  ghcr.io/huggingface/text-embeddings-inference:cpu-latest \
  --model-id google/embeddinggemma-300m \
  --max-batch-requests 4

python server.py --port 7860 --gpu 0 --tei-url http://localhost:8090

Option 3 (opt-in, NOT compatible with shipped indices): ONNX quantized

The non-gated onnx-community/embeddinggemma-300m-ONNX repo does not ship a sentence-transformers config, so SentenceTransformer falls back to naive mean-pooling — yielding a different subspace than the one the FAISS indices were built in. Using ONNX against the shipped indices produces ~0.04 cosine on a query that should score ~0.9, and nonsense search results.

Only enable this path if you rebuild the FAISS indices locally with the same ONNX embedder + mean pooling:

LAIONTUNES_USE_ONNX_EMBEDDER=1 python server.py --port 7860 --no-whisper
# The server will print a WARNING at startup reminding you retrieval will be degraded on the shipped indices.

Server Flags

Flag	Default	Description
--port	7860	HTTP port
--host	0.0.0.0	Bind address
--gpu	0	GPU ID for the text embedder and the Whisper encoder
--tei-url	None	TEI server URL for text embeddings (skips loading the local embedder)
--no-whisper	False	Skip loading the Music-Whisper encoder (disables audio-upload similarity search)

Environment Variables

Variable	Default	Description
HF_TOKEN	–	Required for google/embeddinggemma-300m (and for laion/music-whisper if audio-upload search is enabled).
LAIONTUNES_USE_ONNX_EMBEDDER	0	Set to 1 to opt into the ONNX embedder. Retrieval quality on the shipped FAISS indices will be degraded — only set if you have rebuilt the indices with the same ONNX embedder.
LAIONTUNES_SKIP_FAISS	–	Comma-separated list of FAISS fields to not load at startup (e.g. tag,lyric,mood). Useful for memory-constrained deployments. Skipped fields still accept query requests but return 404-style empty results.
LAIONTUNES_SKIP_BM25	–	Comma-separated list of BM25 fields to skip (e.g. tags,transcription).

What Loads at Startup

1. 6 FAISS indices (tag, whisper, caption, transcription, lyric, mood) — unless listed in LAIONTUNES_SKIP_FAISS
2. 3 BM25 indices (tags, caption, transcription) — unless listed in LAIONTUNES_SKIP_BM25
3. SQLite database (2,019,683 tracks)
4. Text embedder — in order of preference: TEI if --tei-url is set; otherwise google/embeddinggemma-300m via SentenceTransformer (default); otherwise onnx-community/embeddinggemma-300m-ONNX only if LAIONTUNES_USE_ONNX_EMBEDDER=1.
5. Music-Whisper encoder (optional, on GPU): for audio-upload similarity search
6. Instrumental subset indices (optional): separate FAISS/BM25 indices for the ~11K curated instrumental subset

Total memory: ~35 GB RAM + ~1 GB GPU VRAM when Whisper + EmbeddingGemma are both loaded on GPU.

Search API Reference

The FastAPI server exposes the following endpoints.

GET /

Serves the HTML search frontend.

GET /nsfw-report

Serves the interactive NSFW safety analysis report.

---

POST /api/search

Main search endpoint supporting vector similarity, BM25 text search, and combined mode with optional two-stage refinement.

Request Body (JSON)

Field	Type	Default	Description
query	str	required	Search query text
negative_query	str | null	null	Negative prompt (subtracted from query embedding in vector mode)
search_type	str	"bm25"	"vector" | "bm25" | "combined"
vector_field	str	"caption"	FAISS index: "tag" | "caption" | "lyric" | "mood" | "transcription"
bm25_field	str	"caption"	BM25 index: "tags" | "caption" | "transcription" | "lyrics_hashed"
rank_by	str	"similarity"	"similarity" | "aesthetics" | "plays" | "likes"
min_aesthetics	float | null	null	Minimum aesthetics score (0 – 5 scale)
min_similarity	float | null	null	Minimum cosine similarity score
subset_filter	str | null	null	"suno" | "udio" | "mureka" | "riffusion" | "sonauto" | "no_riffusion" | "instrumental_subset"
vocal_filter	str | null	null	"instrumental" | "vocals"
min_duration	float | null	60.0	Minimum duration in seconds
languages	list[str] | null	null	Language codes to include (e.g. ["en", "es"]), null = all
negative_weight	float	0.7	Weight for negative query subtraction (0.0 – 1.0)
nsfw_filter	str | null	null	"sfw_only" | "nsfw_only" | null (all)
top_k	int	50	Number of results to return
stage2_enabled	bool	false	Enable two-stage refinement
stage2_query	str | null	null	Query text for Stage 2
stage2_search_type	str	"vector"	"vector" | "bm25"
stage2_vector_field	str	"caption"	Vector field for Stage 2
stage2_bm25_field	str	"caption"	BM25 field for Stage 2
stage2_top_k	int	50	Number of results after Stage 2 re-ranking

Example Request

curl -X POST http://localhost:7860/api/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "dreamy ambient synth pad",
    "search_type": "vector",
    "vector_field": "caption",
    "rank_by": "similarity",
    "subset_filter": "no_riffusion",
    "nsfw_filter": "sfw_only",
    "top_k": 20
  }'

POST /api/search_similar

Find tracks similar to an existing track by row_id, using the Whisper audio embeddings.

POST /api/search_by_audio

Upload an audio file (multipart/form-data, first 30 s used) to find similar tracks by audio fingerprint using the Music-Whisper encoder.

GET /api/stats

Returns dataset statistics and index information.

Response Body (example)

{
  "total_tracks": 2019683,
  "subsets": {
    "suno": 1419048,
    "mureka": 383549,
    "udio": 115140,
    "riffusion": 99228,
    "sonauto": 2718
  },
  "score_average": { "mean": 3.27, "min": 1.4, "max": 4.77 },
  "with_caption": 1944297,
  "with_transcription": 1463129,
  "faiss_indices": {
    "whisper": 2032032,
    "caption": 1922299,
    "transcription": 1470880,
    "tag": 908241,
    "lyric": 479313,
    "mood": 383616
  },
  "bm25_indices": {
    "caption": 1956676,
    "transcription": 1473647,
    "tags": 908241,
    "lyrics_hashed": 0
  },
  "instrumental_count": 556829,
  "whisper_embeddings": 2032032,
  "instrumental_subset_tracks": 11391
}

Result Track Object

Every search endpoint returns results as a list of track objects with these fields:

Field	Type	Description
row_id	int	Unique track identifier (primary key in SQLite)
title	str	Track title
audio_url	str	URL to the audio file on the source platform
subset	str	Source platform: suno, udio, mureka, riffusion, sonauto
tags_text	str	Comma-separated genre/style tags
mood_text	str	Mood descriptors
genre_tags / scene_tags / emotion_tags	list[str]	Parsed taxonomy tags
score_average, score_coherence, score_musicality, score_memorability, score_clarity, score_naturalness	float | null	Aesthetics scores (1 – 5)
play_count / upvote_count	int	Source-platform stats (0 for extended Suno tracks; use predicted_* instead)
duration_seconds	float | null	Track duration in seconds
music_whisper_caption	str	AI-generated music description
has_caption / has_transcription / is_instrumental	bool	Annotation flags
language	str	Detected language code (e.g. "en") or "unknown"
score	float | null	Search relevance score
score_type	str	cosine_similarity, bm25, aesthetics, play_count, upvote_count
has_whisper_emb	bool	Whether the track has a Whisper audio embedding
nsfw_overall_label, nsfw_gore_label, nsfw_sexual_label, nsfw_hate_label	str	NSFW labels
nsfw_gore_sim, nsfw_sexual_sim, nsfw_hate_sim	float | null	NSFW cosine similarity scores
stage1_score, stage2_score	float	Only present if Stage 2 was enabled

Building the Index from Scratch

python build_search_index.py --force

This reads all parquets from public/, builds the SQLite database, FAISS indices, and BM25 indices. Expect ~1 – 2 h on the full 2 M-track dataset.

Related Datasets

• laion/laion-tunes-rpg-music — 2,580 instrumental tracks from LAION-Tunes (Suno + Udio) annotated with Gemini 3 Flash Preview across 18 RPG genres (high fantasy, cosmic horror, cyberpunk, …) and evoked-emotion tags. Each genre has its own FAISS index over per-track "situation" lists, so you can search by natural-language scenario (e.g. "sneaking through a dark dungeon"). Includes a dedicated FastAPI server (rpg_server.py) and purple-themed web UI (rpg_index.html).

Models Used

Model	Purpose	Output
laion/music-whisper	Music captioning + audio embeddings	Text caption + 768-dim encoder embedding
nvidia/parakeet-tdt-0.6b-v3	ASR transcription	Text + word-level timestamps
google/embeddinggemma-300m	Text sentence embeddings	768-dim L2-normalized vectors
onnx-community/embeddinggemma-300m-ONNX	Text embeddings (ONNX, non-gated)	768-dim L2-normalized vectors (int8 quantized)

License

Apache 2.0

Citation

@misc{laion-tunes-2026,
  title={LAION-Tunes: Annotated AI Music Search Dataset},
  author={LAION},
  year={2026},
  url={https://huggingface.co/datasets/laion/laion-tunes}
}