Learn Artist

# 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)

# 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.

./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.

# 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.

./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.

./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.

./run.sh export brennen-leigh --to /path/to/destination

Quality Gates

Models are automatically evaluated after training:

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:

# 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:

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:

{
  "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

./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

# 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:

./run.sh train "Artist" --batch-size 2

Not Enough Training Data

Increase track count:

./run.sh train "Artist" --min-tracks 15 --min-minutes 45

Training Stuck

Check container logs:

docker logs rvc-training --tail 50

Roster Management

Check what models we have

# 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

# 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:

./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

./run.sh train "Obscure Artist" --min-tracks 3  # too few tracks for quality model

RIGHT: Ensure minimum 30 minutes of clean vocal audio

./run.sh train "Obscure Artist" --min-tracks 10 --min-minutes 30

WRONG: Ignoring quality gate metrics after training

./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

./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

./run.sh train "New Artist"  # trained but agents can't find it via /memory

RIGHT: Sync to memory after training

./run.sh train "New Artist"
./run.sh sync-memory --export-memory  # push catalog to ArangoDB

Discover new artists to train

# 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