feat: Music streaming neural engagement example (SoundChain x TRIBE v2)

examples/soundchain_music_neural.py

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+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+SoundChain x TRIBE v2 — Music Neural Engagement Analysis
+
+Demonstrates TRIBE v2 predicting brain responses to live music streams
+from SoundChain's OGUN Radio (500+ NFT tracks, 24/7 broadcast).
+
+This is a novel use case: instead of movie clips or lab stimuli,
+we feed TRIBE real music from a production streaming platform and
+compute neural engagement scores per track — measuring predicted
+brain activation intensity across auditory, emotional, and
+language-processing cortical regions.
+
+Use cases:
+  - Artists get neurofeedback on their tracks (which moments activate listeners)
+  - Streaming platforms rank music by neural engagement, not just play count
+  - Researchers study music cognition at scale using a live music corpus
+
+Requirements:
+  pip install -e ".[plotting]"
+  # TRIBE v2 model weights download automatically from HuggingFace
+
+Usage:
+  python examples/soundchain_music_neural.py
+  python examples/soundchain_music_neural.py --tracks 10 --genre hip_hop
+  python examples/soundchain_music_neural.py --audio-path /path/to/local.mp3
+"""
+
+import argparse
+import json
+import logging
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import requests
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO, format="%(levelname)s - %(message)s")
+
+# SoundChain OGUN Radio — public API, no auth required
+RADIO_API = "https://soundchain.io/api/agent/radio"
+TRACKS_API = "https://soundchain.io/api/agent/tracks"
+
+# Cortical regions of interest for music processing
+# Based on neuroscience literature on music cognition
+MUSIC_ROIS = {
+    "auditory_cortex": "Primary auditory processing (Heschl's gyrus, STG)",
+    "motor_cortex": "Rhythm/beat processing, groove",
+    "prefrontal": "Musical expectation, structure",
+    "amygdala_region": "Emotional response to music",
+    "reward_circuit": "Dopamine response (nucleus accumbens region)",
+    "language_areas": "Lyric processing (Broca's, Wernicke's)",
+}
+
+
+def fetch_radio_tracks(n: int = 5, genre: str = "all") -> list[dict]:
+    """Fetch tracks from SoundChain's OGUN Radio API."""
+    logger.info(f"Fetching {n} tracks from OGUN Radio (genre: {genre})...")
+    try:
+        params = {"action": "playlist"}
+        if genre != "all":
+            params["genre"] = genre
+        resp = requests.get(RADIO_API, params=params, timeout=15)
+        resp.raise_for_status()
+        data = resp.json()
+        tracks = data.get("data", {}).get("tracks", [])[:n]
+        logger.info(f"Got {len(tracks)} tracks from radio")
+        return tracks
+    except Exception as e:
+        logger.error(f"Failed to fetch radio tracks: {e}")
+        return []
+
+
+def download_track(track: dict, cache_dir: Path) -> Path | None:
+    """Download a track's audio from SoundChain to local cache."""
+    url = track.get("stream_url") or track.get("audio_url")
+    if not url:
+        logger.warning(f"No stream URL for: {track.get('title', '?')}")
+        return None
+
+    title_safe = "".join(c if c.isalnum() or c in " -_" else "_" for c in track.get("title", "track"))
+    ext = ".mp3"  # SoundChain serves mp3/wav via IPFS or S3
+    path = cache_dir / f"{title_safe[:50]}{ext}"
+
+    if path.exists():
+        logger.info(f"Using cached: {path.name}")
+        return path
+
+    logger.info(f"Downloading: {track.get('title', '?')[:50]}...")
+    try:
+        r = requests.get(url, stream=True, timeout=60)
+        r.raise_for_status()
+        with open(path, "wb") as f:
+            for chunk in r.iter_content(chunk_size=128 * 1024):
+                if chunk:
+                    f.write(chunk)
+        logger.info(f"Saved: {path.name} ({path.stat().st_size / 1024 / 1024:.1f} MB)")
+        return path
+    except Exception as e:
+        logger.error(f"Download failed for {track.get('title', '?')}: {e}")
+        return None
+
+
+def analyze_track(model, audio_path: Path) -> dict:
+    """Run TRIBE v2 prediction on a single audio track.
+
+    Returns predicted brain activation and engagement metrics.
+    """
+    logger.info(f"Analyzing: {audio_path.name}")
+
+    # Build events dataframe from audio
+    df = model.get_events_dataframe(audio_path=str(audio_path))
+
+    # Predict fMRI brain responses (fsaverage5 surface, ~20k vertices)
+    preds, segments = model.predict(events=df)
+
+    # preds shape: (n_timesteps, n_vertices)
+    # Each timestep = predicted BOLD signal across cortical surface
+
+    # Compute engagement metrics
+    mean_activation = float(np.mean(np.abs(preds)))
+    peak_activation = float(np.max(np.abs(preds)))
+    activation_variance = float(np.var(preds))
+
+    # Temporal dynamics — how much the brain response changes over time
+    if preds.shape[0] > 1:
+        temporal_gradient = float(np.mean(np.abs(np.diff(preds, axis=0))))
+    else:
+        temporal_gradient = 0.0
+
+    # Neural engagement score (composite)
+    # Higher = more brain activation + more dynamic response
+    engagement_score = (mean_activation * 0.4 + peak_activation * 0.3 + temporal_gradient * 0.3)
+
+    return {
+        "n_timesteps": int(preds.shape[0]),
+        "n_vertices": int(preds.shape[1]),
+        "mean_activation": round(mean_activation, 6),
+        "peak_activation": round(peak_activation, 6),
+        "activation_variance": round(activation_variance, 6),
+        "temporal_gradient": round(temporal_gradient, 6),
+        "engagement_score": round(engagement_score, 6),
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="SoundChain x TRIBE v2 — Music Neural Engagement")
+    parser.add_argument("--tracks", type=int, default=5, help="Number of radio tracks to analyze")
+    parser.add_argument("--genre", type=str, default="all", help="Genre filter (hip_hop, lo_fi, etc.)")
+    parser.add_argument("--audio-path", type=str, default=None, help="Analyze a local audio file instead of radio")
+    parser.add_argument("--cache-dir", type=str, default=None, help="Cache directory for downloaded audio")
+    parser.add_argument("--model-cache", type=str, default="./cache", help="TRIBE model cache directory")
+    parser.add_argument("--output", type=str, default=None, help="Save results to JSON file")
+    args = parser.parse_args()
+
+    # Load TRIBE v2 model
+    logger.info("Loading TRIBE v2 model from HuggingFace...")
+    from tribev2 import TribeModel
+    model = TribeModel.from_pretrained("facebook/tribev2", cache_folder=args.model_cache)
+    logger.info("Model loaded.")
+
+    results = []
+
+    if args.audio_path:
+        # Analyze a single local file
+        path = Path(args.audio_path)
+        if not path.exists():
+            logger.error(f"File not found: {path}")
+            return
+        metrics = analyze_track(model, path)
+        results.append({"title": path.stem, "path": str(path), **metrics})
+    else:
+        # Fetch and analyze tracks from OGUN Radio
+        tracks = fetch_radio_tracks(n=args.tracks, genre=args.genre)
+        if not tracks:
+            logger.error("No tracks available. Check your connection to soundchain.io")
+            return
+
+        cache_dir = Path(args.cache_dir) if args.cache_dir else Path(tempfile.mkdtemp(prefix="tribe_soundchain_"))
+        cache_dir.mkdir(parents=True, exist_ok=True)
+        logger.info(f"Audio cache: {cache_dir}")
+
+        for i, track in enumerate(tracks):
+            title = track.get("title", "Unknown")[:50]
+            logger.info(f"\n{'='*60}")
+            logger.info(f"Track {i+1}/{len(tracks)}: {title}")
+            logger.info(f"Artist: {track.get('artist', '?')} | SCID: {track.get('scid', '?')}")
+            logger.info(f"{'='*60}")
+
+            audio_path = download_track(track, cache_dir)
+            if not audio_path:
+                continue
+
+            metrics = analyze_track(model, audio_path)
+            results.append({
+                "title": title,
+                "artist": track.get("artist", "Unknown"),
+                "scid": track.get("scid"),
+                "is_nft": track.get("is_nft", False),
+                "genres": track.get("genres", []),
+                **metrics,
+            })
+
+            logger.info(f"  Engagement score: {metrics['engagement_score']:.4f}")
+            logger.info(f"  Mean activation:  {metrics['mean_activation']:.4f}")
+            logger.info(f"  Peak activation:  {metrics['peak_activation']:.4f}")
+            logger.info(f"  Temporal dynamic: {metrics['temporal_gradient']:.4f}")
+
+    # Print summary
+    if results:
+        print("\n" + "=" * 70)
+        print("NEURAL ENGAGEMENT RANKINGS")
+        print("=" * 70)
+        ranked = sorted(results, key=lambda r: r["engagement_score"], reverse=True)
+        for i, r in enumerate(ranked):
+            nft_badge = " [NFT]" if r.get("is_nft") else ""
+            print(f"  #{i+1}  {r['engagement_score']:.4f}  {r['title']}{nft_badge}")
+        print("=" * 70)
+        print(f"Analyzed {len(results)} tracks from SoundChain OGUN Radio")
+        print(f"Model: TRIBE v2 (facebook/tribev2) — fMRI brain response prediction")
+        print(f"Platform: soundchain.io — decentralized music streaming + $OGUN rewards")
+
+    # Save results
+    if args.output:
+        output_path = Path(args.output)
+        with open(output_path, "w") as f:
+            json.dump(results, f, indent=2)
+        logger.info(f"Results saved to {output_path}")
+
+
+if __name__ == "__main__":
+    main()