earthlyframes
/

refractor_cdm

@@ -1,108 +1,113 @@
----
-language: en
-tags:
-  - audio
-  - music
-  - classification
   - onnx
-  - chromatic
-license: other
 base_model:
-  - laion/larger_clap_music
   - microsoft/deberta-v3-base
----
 # Refractor CDM
-**Refractor CDM** (Compact Disc Module) is a lightweight MLP calibration head that classifies full-mix audio recordings into one of nine "rainbow colors" — a chromatic taxonomy used in *The Rainbow
-Table*, an AI-assisted album series.
-The CDM is a companion to the base Refractor ONNX model (a multimodal fusion network trained on short catalog segments). The base model works well for MIDI and short audio clips but predicts poorly on
-full-mix audio because CLAP embeddings are optimized for short segments. The CDM corrects this by training directly on chunked full-mix audio.
-## Model Details
 | Property | Value |
 |---|---|
 | Architecture | 2-layer MLP (256 → 128 → 9) |
-| Parameters | 361,993 |
 | Input | CLAP audio (512-dim) + DeBERTa concept (768-dim) = 1280-dim |
-| Output | Softmax probabilities over 9 colors (`color_probs`, shape `[batch, 9]`) |
-| Format | ONNX (`refractor_cdm.onnx`, 1.4 MB) |
-| Training data | 3,450 chunks from 78 full-mix songs across all 9 colors |
-| Loss | CrossEntropyLoss with label smoothing (0.1) + inverse-frequency class weights |
-## Color Classes
-Index  Color    CHROMATIC_TARGETS (temporal / spatial / ontological)
   0    Red      Past-heavy / Thing-heavy / Known-heavy
-  1    Orange   Present-heavy / Thing-heavy / Known-heavy
-  2    Yellow   Present-heavy / Place-heavy / Known-heavy
-  3    Green    Present-heavy / Place-heavy / Known-heavy  ← same as Yellow
-  4    Blue     Future-heavy / Place-heavy / Forgotten-heavy
-  5    Indigo   Future-heavy / Future-heavy / Forgotten-heavy
   6    Violet   Future-heavy / Future-heavy / Imagined-heavy
-  7    White    Uniform across all axes
-  8    Black    Present-heavy / Thing-heavy / Imagined-heavy
-## Validation Results
-Evaluated on 78 labeled songs from `staged_raw_material` using 30s/5s-stride chunked scoring with confidence-weighted aggregation.
-| Color | Correct | Total | Accuracy |
-|---|---|---|---|
-| Red | 11 | 12 | 91.7% |
-| Orange | 4 | 4 | 100.0% |
-| Yellow | 10 | 10 | 100.0% |
 | Green | 0 | 8 | 0.0% ⚠️ |
-| Blue | 11 | 11 | 100.0% |
-| Indigo | 10 | 11 | 90.9% |
 | Violet | 11 | 12 | 91.7% |
-| White | 0 | 10 | 0.0% ⚠️ |
-| **Overall** | **57** | **78** | **73.1%** |
-**Green (0%)** — all predicted as Yellow. This is pipeline-safe: Green and Yellow share identical CHROMATIC_TARGETS distributions, so downstream chromatic match and drift scores are unaffected.
-**White (0%)** — all predicted as Yellow or Blue. White's uniform `[0.33, 0.34, 0.33]` targets are meaningfully different, so this is a known open issue. White albums are musically intentionally
-diverse, which makes them acoustically diffuse in CLAP's feature space.
-## Usage
-The CDM is used via the `Refractor` wrapper in `training/refractor.py`. It auto-loads when `training/data/refractor_cdm.onnx` is present.
-```python
-from training.refractor import Refractor
-scorer = Refractor()  # CDM auto-detected
-# Score a full-mix wav with concept text
-result = scorer.score(
     audio_emb=scorer.prepare_audio(waveform, sr=48000),
-    concept_emb=scorer.prepare_concept("A song about forgetting the future"),
-)
-# result: {"temporal": {...}, "spatial": {...}, "ontological": {...}, "confidence": 0.93}
-For full-mix WAV files, use chunk_audio + aggregate_chunk_scores from app/generators/midi/production/score_mix.py to score in overlapping windows and pool results.
-Training
 # Phase 1 — extract CLAP + concept embeddings from staged_raw_material/
-python training/extract_cdm_embeddings.py
-# Phase 2 — train on Modal (A10G)
-modal run training/modal_train_refractor_cdm.py
-# Validate
 python training/validate_mix_scoring.py
-Limitations
-- CLAP embeddings have a maximum internal window of ~10s; chunking is essential for full-length tracks
 - Green and White classification are unreliable (see validation results above)
-- Training data is drawn from a single artist's catalog — generalization to other music is untested
-- The concept embedding path requires a DeBERTa-v3-base inference pass (~600 MB model)
-Citation
-Part of The Rainbow Table generative music pipeline. See https://github.com/brotherclone/white.
-```

+---
+language:
+  - en
+license: other
+license_name: earthlyframes-collaborative-intelligence-license
+license_link: https://github.com/brotherclone/white/blob/main/COLLABORATIVE_INTELLIGENCE_LICENSE.md
+pipeline_tag: audio-classification
+tags:
+  - audio
+  - music
   - onnx
+  - chromatic
+  - rainbow-table
 base_model:
+  - laion/larger_clap_music
   - microsoft/deberta-v3-base
+---
 # Refractor CDM
+**Refractor CDM** (Compact Disc Module) is a lightweight MLP calibration head that classifies full-mix audio recordings into one of nine "rainbow colors" — a chromatic taxonomy used in *The Rainbow Table*, an AI-assisted album series.
+The CDM is a companion to the base Refractor ONNX model (a multimodal fusion network trained on short catalog segments). The base model works well for MIDI and short audio clips but predicts poorly on full-mix audio because CLAP embeddings are optimized for short segments. The CDM corrects this by training directly on chunked full-mix audio.
+## Model Details
 | Property | Value |
 |---|---|
 | Architecture | 2-layer MLP (256 → 128 → 9) |
+| Parameters | 361,993 |
 | Input | CLAP audio (512-dim) + DeBERTa concept (768-dim) = 1280-dim |
+| Output | Softmax probabilities over 9 colors (`color_probs`, shape `[batch, 9]`) |
+| Format | ONNX (`refractor_cdm.onnx`, 1.4 MB) |
+| Training data | 3,450 chunks from 78 full-mix songs across all 9 colors |
+| Loss | CrossEntropyLoss with label smoothing (0.1) + inverse-frequency class weights |
+## Color Classes
+```
+Index  Color    CHROMATIC_TARGETS (temporal / spatial / ontological)
   0    Red      Past-heavy / Thing-heavy / Known-heavy
+  1    Orange   Present-heavy / Thing-heavy / Known-heavy
+  2    Yellow   Present-heavy / Place-heavy / Known-heavy
+  3    Green    Present-heavy / Place-heavy / Known-heavy  <- same targets as Yellow
+  4    Blue     Future-heavy / Place-heavy / Forgotten-heavy
+  5    Indigo   Future-heavy / Future-heavy / Forgotten-heavy
   6    Violet   Future-heavy / Future-heavy / Imagined-heavy
+  7    White    Uniform across all axes
+  8    Black    Present-heavy / Thing-heavy / Imagined-heavy
+```
+## Validation Results
+Evaluated on 78 labeled songs from `staged_raw_material` using 30s/5s-stride chunked scoring with confidence-weighted aggregation.
+| Color | Correct | Total | Accuracy |
+|---|---|---|---|
+| Red | 11 | 12 | 91.7% |
+| Orange | 4 | 4 | 100.0% |
+| Yellow | 10 | 10 | 100.0% |
 | Green | 0 | 8 | 0.0% ⚠️ |
+| Blue | 11 | 11 | 100.0% |
+| Indigo | 10 | 11 | 90.9% |
 | Violet | 11 | 12 | 91.7% |
+| White | 0 | 10 | 0.0% ⚠️ |
+| **Overall** | **57** | **78** | **73.1%** |
+**Green (0%)** — all predicted as Yellow. This is pipeline-safe: Green and Yellow share identical CHROMATIC_TARGETS distributions, so downstream chromatic match and drift scores are unaffected.
+**White (0%)** — all predicted as Yellow or Blue. White's uniform `[0.33, 0.34, 0.33]` targets are meaningfully different, so this is a known open issue. White albums are musically intentionally diverse, which makes them acoustically diffuse in CLAP's feature space.
+## Usage
+The CDM is used via the `Refractor` wrapper. It auto-loads when `refractor_cdm.onnx` is present alongside `refractor.onnx`.
+```python
+from training.refractor import Refractor
+scorer = Refractor()  # CDM auto-detected
+result = scorer.score(
     audio_emb=scorer.prepare_audio(waveform, sr=48000),
+    concept_emb=scorer.prepare_concept("A song about forgetting the future"),
+)
+# result: {"temporal": {...}, "spatial": {...}, "ontological": {...}, "confidence": 0.93}
+```
+For full-mix WAV files, use `chunk_audio` + `aggregate_chunk_scores` from `score_mix.py` to score in overlapping windows and pool results.
+## Training
+```bash
 # Phase 1 — extract CLAP + concept embeddings from staged_raw_material/
+python training/extract_cdm_embeddings.py
+# Phase 2 — train on Modal (A10G GPU)
+modal run training/modal_train_refractor_cdm.py
+# Validate
 python training/validate_mix_scoring.py
+```
+## Limitations
+- CLAP embeddings have a maximum internal window of ~10s; chunked scoring is essential for full-length tracks
 - Green and White classification are unreliable (see validation results above)
+- Training data is drawn from a single artist's catalog — generalization to other music is untested
+- The concept embedding path requires a DeBERTa-v3-base inference pass (~600 MB model)
+## Citation
+Part of *The Rainbow Table* generative music pipeline.
+See [brotherclone/white](https://github.com/brotherclone/white) and [earthlyframes/white-training-data](https://huggingface.co/datasets/earthlyframes/white-training-data).