README.md

metadata

license: other
license_name: collaborative-intelligence-license
license_link: >-
  https://github.com/brotherclone/white/blob/main/COLLABORATIVE_INTELLIGENCE_LICENSE.md
language:
  - en
tags:
  - music
  - multimodal
  - audio
  - midi
  - chromatic-taxonomy
  - rebracketing
  - evolutionary-composition
size_categories:
  - 10K<n<100K

White Training Data

Training data and models for the Rainbow Table chromatic fitness function — a multimodal ML model that scores how well audio, MIDI, and text align with a target chromatic mode (Black, Red, Orange, Yellow, Green, Blue, Indigo, Violet).

Part of The Earthly Frames project, a conscious collaboration between human creativity and AI.

Purpose

These models are fitness functions for evolutionary music composition, not classifiers in isolation. The production pipeline works like this:

1. A concept agent generates a textual concept
2. A music production agent generates 50 chord progression variations
3. The chromatic fitness model scores each for consistency with the target color
4. Top candidates advance through drums, bass, melody stages
5. Final candidates go to human evaluation

Version

Current: v0.3.0 — 2026-02-13

Dataset Structure

Split	Rows	Description
base_manifest	1,327	Track-level metadata: song info, concepts, musical keys, chromatic labels, training targets
training_segments	11,605	Time-aligned segments with lyric text, structure sections, audio/MIDI coverage flags
training_full	11,605	Segments joined with manifest metadata — the primary training table

Playable Audio Preview

Split	Rows	Description
preview	~160	Playable audio preview — 20 segments per chromatic color with inline audio playback

Try it: Load the preview config to hear what each chromatic color sounds like:

from datasets import load_dataset

# Load playable preview
preview = load_dataset("earthlyframes/white-training-data", "preview")

# Listen to a GREEN segment
green_segment = preview.filter(lambda x: x['rainbow_color'] == 'Green')[0]
print(green_segment['concept'])
# Audio plays inline in Jupyter/Colab, or access via green_segment['audio']

Coverage by Chromatic Color

Color	Segments	Audio	MIDI	Text
Black	1,748	83.0%	58.5%	100.0%
Red	1,474	93.7%	48.6%	90.7%
Orange	1,731	83.8%	51.1%	100.0%
Yellow	656	88.0%	52.9%	52.6%
Green	393	90.1%	69.5%	0.0%
Violet	2,100	75.9%	55.6%	100.0%
Indigo	1,406	77.2%	34.1%	100.0%
Blue	2,097	96.0%	12.1%	100.0%

Note: Audio waveforms and MIDI binaries are stored separately (not included in metadata configs due to size). The preview config includes playable audio for exploration. The media parquet (~15 GB) is used locally during training.

Trained Models

File	Size	Description
data/models/fusion_model.pt	~16 MB	PyTorch checkpoint — MultimodalFusionModel (4.3M params)
data/models/fusion_model.onnx	~16 MB	ONNX export for fast CPU inference

The models are consumed via the ChromaticScorer class, which wraps encoding and inference:

from chromatic_scorer import ChromaticScorer

scorer = ChromaticScorer("path/to/fusion_model.onnx")
result = scorer.score(midi_bytes=midi, audio_waveform=audio, concept_text="a haunted lullaby")
# result: {"temporal": 0.87, "spatial": 0.91, "ontological": 0.83, "confidence": 0.89}

# Batch scoring for evolutionary candidate selection
ranked = scorer.score_batch(candidates, target_color="Violet")

Architecture: PianoRollEncoder CNN (1.1M params, unfrozen) + fusion MLP (3.2M params) with 4 regression heads. Input: audio (512-dim CLAP) + MIDI (512-dim piano roll) + concept (768-dim DeBERTa) + lyric (768-dim DeBERTa) = 2560-dim fused representation. Trained with learned null embeddings and modality dropout (p=0.15) for robustness to missing modalities.

Key Features

training_full (primary training table)

• rainbow_color — Target chromatic label (Black/Red/Orange/Yellow/Green/Blue/Indigo/Violet)
• rainbow_color_temporal_mode / rainbow_color_ontological_mode — Regression targets for mode dimensions
• concept — Textual concept describing the song's narrative
• lyric_text — Segment-level lyrics (when available)
• bpm, key_signature_note, key_signature_mode — Musical metadata
• training_data — Struct with computed features: rebracketing type/intensity, narrative complexity, boundary fluidity, etc.
• has_audio / has_midi — Modality availability flags
• start_seconds / end_seconds — Segment time boundaries

preview (playable audio)

Same metadata fields as training_full, plus:

• audio — Audio feature with inline playback support (FLAC encoded, 44.1kHz)
• duration_seconds — Segment duration

Usage

from datasets import load_dataset

# Load the primary training table (segments + manifest metadata)
training = load_dataset("earthlyframes/white-training-data", "training_full")

# Load playable audio preview
preview = load_dataset("earthlyframes/white-training-data", "preview")

# Load just the base manifest (track-level)
manifest = load_dataset("earthlyframes/white-training-data", "base_manifest")

# Load raw segments (no manifest join)
segments = load_dataset("earthlyframes/white-training-data", "training_segments")

# Load a specific version
training = load_dataset("earthlyframes/white-training-data", "training_full", revision="v0.3.0")

Training Results

Text-Only (Phases 1-4)

Task	Metric	Result
Binary classification (has rebracketing)	Accuracy	100%
Multi-class classification (rebracketing type)	Accuracy	100%
Temporal mode regression	Mode accuracy	94.9%
Ontological mode regression	Mode accuracy	92.9%
Spatial mode regression	Mode accuracy	61.6%

Multimodal Fusion (Phase 3)

Dimension	Text-Only	Multimodal	Improvement
Temporal	94.9%	90.0%	—
Ontological	92.9%	91.0%	—
Spatial	61.6%	93.0%	+31.4%

Spatial mode was bottlenecked by instrumental albums (Yellow, Green) which lack text. The multimodal fusion model resolves this by incorporating CLAP audio embeddings and piano roll MIDI features, enabling accurate scoring even without lyrics. Temporal and ontological show slight regression in multi-task mode but remain strong; single-task variants can be used where maximum per-dimension accuracy is needed.

Source

83 songs across 8 chromatic albums. The 7 color albums (Black through Violet) are human-composed source material spanning 10+ years of original work — all audio, lyrics, and arrangements are the product of human creativity. The White album is being co-produced with AI using the evolutionary composition pipeline described above. No sampled or licensed material is used in any album.

License

Collaborative Intelligence License v1.0 — This work represents conscious partnership between human creativity and AI. Both parties have agency; both must consent to sharing.

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Generated 2026-02-13 | GitHub