HAIM: Human-AI Music Dataset

Human-AI Music Datasets for AI Music Production Tracking Benchmark

Beyond binary "AI-or-human" — granular, role-level tracking of AI intervention across the music production workflow.

As generative platforms like Suno and Udio reach human-grade audio quality, AI now touches every stage of music production — not just full-song generation. HAIM moves detection beyond binary "AI-or-human" toward role-level tracking (Composer / Lyricist / Vocalist / Audio Engineer), with 153,686 tracks (67,000 with audio ≈ 229 GB + 86,686 link-based) across 13 categories that isolate each stage of AI intervention.

The viewer configs above are 100-row samples for preview/listening only — load_dataset(...) returns these samples, not the full dataset. The complete data lives in the raw folders (A_full_generation/, B_hybrid/, C_mixing/) — see Download the full dataset.

Preview

In the Data Studio above, pick a config (e.g. B_hybrid_audio_preview) → a split (e.g. B1_ai_mastered_human) → press ▶ to listen. Audio configs show per-track metadata (prompt, lyrics, mode, …); *_links configs show platform/YouTube URL tables.

What is inside

A — Full generation. Fully human music (A1: MTG-Jamendo CC audio, SONICS YouTube links) versus fully AI music (A2: locally generated ACE-Step / MusicGen audio, Lyria Pro 3 via API, plus Suno / Udio / Mureka as URL manifests). The classic real-vs-fake axis, at scale and multi-platform.

B — Hybrid production. The interesting middle: AI mastering on human tracks (B1), human mastering/mixing on AI tracks (B2–B4), AI vocal covers on human tracks (B5), AI generation conditioned on human-written lyrics (B6), and AI variation / edit / repaint of real songs (B7–B9). B3/B4 explicitly model how professional human post-production can smooth out AI artifacts and evade detectors.

C — Temporal mix-sets. Human and AI segments interleaved (concat) or blended (crossfade) inside one file — for evaluating where the AI parts are on a timeline, without boundary-specific training.

Composition

Category	Subset	Source	Tracks	Format
A1 · Full Human	MTG-Jamendo	CC-licensed music	6,000	audio
A1 · Full Human	SONICS	YouTube	48,090	links
A2 · Full AI	ACE-Step 1.5	local generation	6,000	audio
A2 · Full AI	MusicGen	local generation	5,000	audio
A2 · Full AI	Lyria Pro 3	DeepMind API	6,000	audio
A2 · Full AI	Suno / Udio / Mureka	commercial platforms	36,556	links
B · Hybrid	B1–B4 mastering & mixing	SonicMaster, FXencoder, DAW	24,000	audio
B · Hybrid	B5 AI vocal cover	YouTube	2,040	links
B · Hybrid	B6–B9 lyrics-gen, variation, edit, repaint	ACE-Step 1.5	8,000	audio
C · Temporal	C1 concat · C2 crossfade	human+AI segments	12,000	audio

Totals: 67,000 audio tracks (~240 GB) + 86,686 link-based = 153,686 tracks.

Quick start (download the full dataset)

Download the raw folders directly — not via load_dataset:

pip install -U huggingface_hub
hf download mippia/HAIM --repo-type dataset --local-dir HAIM        # everything (~250 GB)

from huggingface_hub import snapshot_download
snapshot_download("mippia/HAIM", repo_type="dataset", local_dir="HAIM")            # everything
snapshot_download("mippia/HAIM", repo_type="dataset", local_dir="HAIM",
                  allow_patterns=["B_hybrid/**"])                                  # one category only

• Per-folder metadata: each audio folder's metadata.csv (file_name, track_id, + generation params where available)
• Link manifests: A_full_generation/A2_fake/{suno,udio,mureka}.json & *_tracks.csv, B_hybrid/B5_ai_vocal_human_track/b5_tracks.csv, A_full_generation/A1_real/sonics/real_songs.csv

Preview samples via datasets (not the full dataset)

from datasets import load_dataset   # pip install "datasets[audio]"
# 100-row preview samples, NOT the full dataset
ds = load_dataset("mippia/HAIM", "B_hybrid_audio_preview", split="B1_ai_mastered_human")
suno = load_dataset("mippia/HAIM", "A2_fake_links_preview", split="suno")

Folder structure

├── A_full_generation
│   ├── A1_real
│   │   ├── MTG-Jamendo music subset/   # 6,000 mp3 + metadata.csv
│   │   ├── sonics/real_songs.csv       # 48,090 YouTube links
│   │   └── mtg_tracks.csv
│   └── A2_fake
│       ├── acestep/  lyria-pro3/                   # audio + sidecar JSON + metadata.csv
│       ├── musicgen/                               # audio + metadata.csv (no sidecar)
│       └── suno[.json|_tracks.csv]  udio…  mureka…  # link manifests
├── B_hybrid
│   ├── B1…B4, B6…B9/                   # audio + metadata.csv
│   └── B5_ai_vocal_human_track/        # b5_tracks.csv + metadata.jsonl
├── C_mixing/C1_mixset_concat/  C2_mixset_crossfade/
├── A1_real_MTG_audio/ A2_fake_audio/ B_hybrid_audio/ C_mixing_audio/   # 100-row viewer samples (parquet)
├── A2_fake_links/ B5_youtube_links/ A1_sonics_links/                   # 100-row viewer samples (parquet)
├── scripts/    figures/
├── DATASET_DESCRIPTION.txt
└── SUPPLEMENT_LEGAL_AND_ETHICAL.pdf

Large audio folders are sharded into d0/, d1/ subfolders (Hub 10k-files-per-directory limit).

Metadata reference (per-file columns)

A1_real/sonics/real_songs.csv — filename, title, artist, year, lyrics, duration, youtube_id, label

A2_fake/{suno,udio,mureka}_tracks.csv — platform, version, track_id, filename, page_url, audio_url

B_hybrid/B5_ai_vocal_human_track/b5_tracks.csv — track_id, original_title, url, channel, views, published, category

ACE-Step sidecar JSON (A2 acestep, B7–B9) — mode (variation/edit/repaint), concept.prompt, concept.lyrics, ace_step.result (seed, BPM, key, model version)

B6_metadata.csv — human K-pop lyrics used for conditioning (text only, no original audio; ref lt_dataset): LID, ARTIST, EN_TITLE, KR_TITLE, IS_OFFICIAL, URL

Pipeline

Citation

@article{go2026haim,
  title={HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark},
  author={Go, Seonghyeon and Kim, Yumin},
  journal={arXiv preprint arXiv:2606.01686},
  year={2026}
}

License

Data: CC-BY-NC-4.0, non-commercial academic research only — commercial-platform content is released as URL manifests only per platform ToS. Code: MIT (GitHub). Please credit MTG-Jamendo and SONICS per their licenses. Full analysis: SUPPLEMENT_LEGAL_AND_ETHICAL.pdf