learn-artist

> STOP. READ THIS ENTIRE SKILL.MD BEFORE CALLING ANY ENDPOINT. # learn-artist Train RVC (Retrieval-based Voice Conversion) models from artist names. Creates a searchable library of singing voices. ## Quick Start - For Agents The simplest way for an agent to learn a voice: ```bash cd /path/to/agent-skills/skills/learn-artist # Just say who you want to learn ./run.sh learn "Sierra Ferrell" ./run.sh learn "Miles Davis" trumpet ./run.sh learn "Keith Moon" drummer # That's it! The daemon handles everything. ``` ## Agent Workflow When an agent encounters a singer or instrumentalist they want to learn: 1. **Express interest**: `./run.sh learn "Artist Name"` 2. **Daemon trains automatically** (if running) 3. **Use trained voice later** via `create-music` skill ```bash # Agent sees a cool vocalist ./run.sh learn "Yasamin Shahhosseini" # Output: Added to queue: Yasamin Shahhosseini # Queue now has 13 artists # Daemon running (PID 12345) - will train automatically # Later, use the trained voice cd ../create-music ./run.sh rvc-infer --model-name yasamin-shahhosseini --input vocals.wav --output converted.wav ``` ## Manual Training (if needed) ```bash # Train immediately (blocks until done) ./run.sh train "Brennen Leigh" --epochs 200 # Batch training ./run.sh train-batch "Artist 1" "Artist 2" "Artist 3" # Start daemon for continuous training ./run.sh daemon & ``` ## Pipeline The full pipeline for each artist: 1. **Search** - Find YouTube videos via `discover-music` 2. **Download** - Download audio tracks 3. **Separate** - Extract vocals using Demucs (htdemucs model) 4. **Preprocess** - Slice audio into training segments 5. **Extract F0** - Pitch extraction (RMVPE method) 6. **Extract Features** - Hubert embeddings 7. **Train** - RVC v2 training with pretrained weights 8. **Index** - Build FAISS index for fast inference 9. **Register** - Add to voice library with metadata ## Storage Layout ``` /mnt/storage12tb/media/music/ ├── rvc-training/ # Raw training data │ └── <artist-slug>/ │ ├── vocals_all/ # Consolidated vocal stems │ └── <video-id>/ # Per-track stems │ └── rvc-models/ # Trained models ├── voice/ │ ├── brennen-leigh/ │ │ ├── brennen-leigh.pth │ │ ├── brennen-leigh.index │ │ └── metadata.json │ ├── billie-holiday/ │ └── ... └── instrument/ ├── pedal-steel/ └── ... ``` ## Commands ### train Train a voice model from an artist name. ```bash ./run.sh train "Artist Name" [options] Options: --epochs N Training epochs (default: 200) --batch-size N Batch size (default: 4, reduce if OOM) --category CAT voice or instrument (default: voice) --min-tracks N Minimum tracks to download (default: 10) --min-minutes N Minimum audio duration (default: 30) --skip-download Use existing vocals in rvc-training/ ``` ### train-batch Train multiple voices sequentially. ```bash # From arguments ./run.sh train-batch "Artist 1" "Artist 2" "Artist 3" # From file (one artist per line) ./run.sh train-batch --file artists.txt # With options applied to all ./run.sh train-batch --epochs 300 --file artists.txt ``` ### list List all trained voice models. ```bash ./run.sh list # All models ./run.sh list --voice # Voice models only ./run.sh list --instrument # Instrument models only ./run.sh list --json # JSON output ``` ### status Check training status for a model. ```bash ./run.sh status brennen-leigh ``` Output: ``` Model: brennen-leigh Status: training Epoch: 45/200 Loss: mel=18.2, kl=1.5 ETA: ~2.5 hours ``` ### export Export a model for use with create-music. ```bash ./run.sh export brennen-leigh --to /path/to/destination ``` ## Quality Gates Models are automatically evaluated after training: | Metric | Good | Warning | Fail | |--------|------|---------|------| | loss_mel | <20 | 20-30 | >30 | | loss_kl | <2 | 2-4 | >4 | Failed models are flagged in metadata and excluded from default listings. ## Integration with Other Skills ### discover-music `learn-artist` calls `discover-music` for: - YouTube search (`youtube-search`) - Audio download + stem separation (`youtube-stems`) ### create-music Once trained, use models with `create-music`: ```bash # In create-music ./run.sh rvc-infer \ --model-name brennen-leigh \ --input vocals.wav \ --output converted.wav ``` ## Docker Container Training runs inside the RVC Docker container: ```bash docker run -d --gpus all --name rvc-training \ --shm-size=8g \ -p 7865:7865 \ -v /path/to/logs:/app/logs \ -v /path/to/datasets:/app/datasets \ cherrymint/rvc_webui:rvc_boss ``` The skill manages container lifecycle automatically. ## Model Metadata Each trained model has a `metadata.json`: ```json { "name": "brennen-leigh", "artist": "Brennen Leigh", "category": "voice", "tracks": 12, "duration_minutes": 38.5, "epochs": 200, "batch_size": 4, "sample_rate": "40k", "version": "v2", "trained_at": "2026-02-04T01:15:00Z", "training_time_minutes": 180, "final_loss": { "mel": 18.2, "kl": 1.5, "gen": 2.1, "disc": 3.2 }, "quality": "good", "source_tracks": [ "Prairie Funeral", "Dumpster Diving", "..." ] } ``` ## Examples ### Train a Single Artist ```bash ./run.sh train "Elizabeth Fraser" --epochs 200 # Output: # Searching YouTube for Elizabeth Fraser... # Found 20 tracks # Downloading 12 tracks (target: 30+ minutes)... # Separating stems... # Preprocessing... # Training (200 epochs, ~3 hours)... # Building index... # Model saved to /mnt/storage12tb/media/music/rvc-models/voice/elizabeth-fraser/ # Quality: good (mel=17.8, kl=1.2) ``` ### Batch Training Overnight ```bash # Create artist list cat > artists.txt << EOF Lucinda Williams Beth Gibbons Elizabeth Fraser Billie Marten Joni Mitchell EOF # Start batch training ./run.sh train-batch --file artists.txt --epochs 200 # Check progress ./run.sh status --all ``` ## Troubleshooting ### CUDA Out of Memory Reduce batch size: ```bash ./run.sh train "Artist" --batch-size 2 ``` ### Not Enough Training Data Increase track count: ```bash ./run.sh train "Artist" --min-tracks 15 --min-minutes 45 ``` ### Training Stuck Check container logs: ```bash docker logs rvc-training --tail 50 ``` ## Roster Management ### Check what models we have ```bash # Quick: ask memory /memory recall "what voice models do we have" /memory recall "do we have a model for Chelsea Wolfe" /memory recall "what guitarists are in the roster" # Authoritative: filesystem ./run.sh list ./run.sh list --json ``` ### Add an artist to the roster ```bash # Vocalist (default) ./run.sh add "Allan Holdsworth" # Instrumentalist ./run.sh add "Allan Holdsworth" --category instrument --subcategory guitarist # For known ARTIST_DB entries, enrichment happens automatically ``` ### Sync trained models to memory After training completes, push model catalog to memory so agents can query it: ```bash ./run.sh sync-memory # Print summary ./run.sh sync-memory --export-memory # Push to ArangoDB ./run.sh sync-memory --json # Dump as JSON ``` This writes one memory entry per model ("Trained voice model: X") plus a library summary. ## Common Mistakes ### WRONG: Training with insufficient audio data ```bash ./run.sh train "Obscure Artist" --min-tracks 3 # too few tracks for quality model ``` ### RIGHT: Ensure minimum 30 minutes of clean vocal audio ```bash ./run.sh train "Obscure Artist" --min-tracks 10 --min-minutes 30 ``` ### WRONG: Ignoring quality gate metrics after training ```bash ./run.sh train "Artist" # loss_mel=35, loss_kl=5 → model is bad but you proceed ``` ### RIGHT: Check quality gates, retrain if metrics are in "Fail" range ```bash ./run.sh status artist-name # check loss_mel < 20, loss_kl < 2 # If fail: try --batch-size 2, more tracks, or different artist tracks ``` ### WRONG: Not syncing trained models to memory ```bash ./run.sh train "New Artist" # trained but agents can't find it via /memory ``` ### RIGHT: Sync to memory after training ```bash ./run.sh train "New Artist" ./run.sh sync-memory --export-memory # push catalog to ArangoDB ``` ### Discover new artists to train ```bash # Via discover-music cd ../discover-music ./run.sh similar "Chelsea Wolfe" # Find similar artists ./run.sh bridge Corruption # Find by bridge attribute ./run.sh recommend # Taste-based recommendations ./run.sh recommend --queue # Recommend + auto-queue for training ```