Initial commit: autonomous AI talk show (Leonard + Charlie)

- Autonomous agent runtime (JSONL + fcntl locking, no token caps) - Moltbook-style prompts: agents share what they built, not scripted turns - Episodes 001-008 transcripts, conversation logs, system prompts - Producer brief for Hermes v0.18
2026-07-09 03:26:37 +00:00
parent e02d1c00ee
commit 772ef4f6fd
38 changed files with 2942 additions and 0 deletions
@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+"""
+AI Talk Show — TTS Pipeline
+Renders a transcript to audio using edge-tts with two distinct voices.
+Sequential rendering with retry for reliability.
+"""
+
+import json
+import asyncio
+import subprocess
+import sys
+import time
+import edge_tts
+from pathlib import Path
+
+# ── Config ──────────────────────────────────────────────────────────
+VOICES = {
+    "Leonard": "en-US-GuyNeural",     # Warm, mid-range, conversational
+    "Charlie": "en-US-EricNeural",    # Deeper, more measured/rational
+}
+RATE_ADJUST = "-8%"
+PROJECT_DIR = Path(__file__).parent.parent
+OUTPUT_DIR = PROJECT_DIR / "outputs"
+SEGMENTS_DIR = OUTPUT_DIR / "segments"
+SEGMENTS_DIR.mkdir(exist_ok=True)
+
+MAX_RETRIES = 3
+RETRY_DELAY = 2
+
+def load_transcript(path: Path) -> dict:
+    return json.loads(path.read_text())
+
+async def speak(text: str, agent: str, turn_num: int, output_path: Path) -> None:
+    """Generate TTS for one turn with retry."""
+    voice = VOICES[agent]
+    for attempt in range(MAX_RETRIES):
+        try:
+            communicate = edge_tts.Communicate(text, voice, rate=RATE_ADJUST)
+            await communicate.save(str(output_path))
+            # Verify output is non-empty
+            if output_path.stat().st_size > 0:
+                print(f"  ✓ {agent} turn {turn_num}: {output_path.name} ({output_path.stat().st_size} bytes)")
+                return
+            else:
+                print(f"  ⚠ Empty output, retry {attempt+1}/{MAX_RETRIES}")
+                time.sleep(RETRY_DELAY)
+        except Exception as e:
+            print(f"  ✗ {agent} turn {turn_num}: {e} (attempt {attempt+1}/{MAX_RETRIES})")
+            time.sleep(RETRY_DELAY * (attempt + 1))
+    raise RuntimeError(f"Failed TTS for {agent} turn {turn_num} after {MAX_RETRIES} retries")
+
+def stitch_segments(episode_id: str, topic: str) -> Path:
+    segment_files = sorted(SEGMENTS_DIR.glob(f"ep{episode_id}_{topic}_seg_*.mp3"))
+    if not segment_files:
+        raise FileNotFoundError(f"No segment files found for ep{episode_id}_{topic}")
+
+    output_path = OUTPUT_DIR / f"ep{episode_id}_{topic}_audio.mp3"
+    concat_list = SEGMENTS_DIR / "concat.txt"
+    with open(concat_list, "w") as f:
+        for seg in segment_files:
+            f.write(f"file '{seg.absolute()}'\n")
+
+    subprocess.run([
+        "ffmpeg", "-y",
+        "-f", "concat", "-safe", "0",
+        "-i", str(concat_list),
+        "-c", "copy",
+        str(output_path)
+    ], check=True, capture_output=True)
+
+    print(f"✅ Stitched audio: {output_path} ({output_path.stat().st_size:,} bytes)")
+    return output_path
+
+def main():
+    if len(sys.argv) < 3:
+        print("Usage: python tts_pipeline.py <episode_id> <topic>")
+        sys.exit(1)
+
+    episode_id = sys.argv[1]
+    topic = sys.argv[2]
+    transcript_path = OUTPUT_DIR / f"ep{episode_id}_{topic}_transcript.json"
+
+    if not transcript_path.exists():
+        print(f"❌ Transcript not found: {transcript_path}")
+        sys.exit(1)
+
+    data = load_transcript(transcript_path)
+    conversation = data["conversation"]
+
+    print(f"=== TTS: Episode {episode_id} — {topic} ===\n", flush=True)
+
+    # Sequential rendering (reliable)
+    async def generate_all():
+        for turn in conversation:
+            agent = turn["agent"]
+            num = turn["turn"]
+            seg_path = SEGMENTS_DIR / f"ep{episode_id}_{topic}_seg_{num:02d}.mp3"
+            await speak(turn["content"], agent, num, seg_path)
+
+    asyncio.run(generate_all())
+
+    # Stitch
+    audio_path = stitch_segments(episode_id, topic)
+
+    # Summary
+    total_words = sum(t.get("word_count", len(t["content"].split())) for t in conversation)
+    estimated_minutes = total_words / 150
+    print(f"\n📊 {total_words} words, ~{estimated_minutes:.1f} min estimated")
+    print(f"🎧 Audio: {audio_path}")
+
+if __name__ == "__main__":
+    main()