> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mavera.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Ad Script → Production-Ready Audio

## Scenario

Full pipeline from creative brief to broadcast-ready audio. Generate ad script variations with Mavera, produce audio with ElevenLabs, then assess quality with Mave Agent — iterating voice settings until production standards are met.

**Flow:** Mavera `POST /generations` (ad scripts) → ElevenLabs TTS (voice settings) → Mavera `POST /mave/chat` (quality feedback) → Adjust → Iterate

## Code

<CodeGroup>
  ```python Python theme={"dark"}
  import os, requests, time, re
  EL_KEY = os.environ["ELEVENLABS_API_KEY"]
  EL_BASE = "https://api.elevenlabs.io/v1"
  MV = os.environ["MAVERA_API_KEY"]
  MV_BASE = "https://app.mavera.io/api/v1"
  MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
  os.makedirs("ad_production", exist_ok=True)
  VOICE_ID, QUALITY_THRESHOLD = "21m00Tcm4TlvDq8ikWAM", 8

  # 1. Generate ad script variations
  gen = requests.post(f"{MV_BASE}/generations", headers=MV_H, json={
      "prompt": "Write 3 variations of a 30-second radio ad for 'Beacon' AI marketing analytics. "
          "Target: marketing directors. Confident tone. Each: hook, proof point, CTA.\n"
          "VARIATION A: problem-focused. VARIATION B: outcome-focused. VARIATION C: story-focused.",
  }).json()
  script_text = gen.get("output") or gen.get("content") or ""
  variations = [v.strip() for v in re.split(r"VARIATION\s+[A-C]", script_text) if v.strip()]
  print(f"Generated {len(variations)} variations")

  results = []
  for vi, script in enumerate(variations[:3]):
      label = chr(65 + vi)
      settings = {"stability": 0.5 + vi * 0.05, "similarity_boost": 0.75 + vi * 0.05,
                  "style": 0.3 + vi * 0.1, "use_speaker_boost": True}
      best_score, best_path = 0, None

      for take in range(1, 4):
          # 2. Generate audio
          tts = requests.post(f"{EL_BASE}/text-to-speech/{VOICE_ID}",
              headers={"xi-api-key": EL_KEY, "Content-Type": "application/json"},
              json={"text": script, "model_id": "eleven_multilingual_v2", "voice_settings": settings})
          if tts.status_code != 200:
              print(f"  TTS error {tts.status_code}"); time.sleep(3); continue

          path = f"ad_production/var_{label}_take_{take}.mp3"
          with open(path, "wb") as f: f.write(tts.content)
          print(f"Var {label} Take {take}: {path} ({len(tts.content) // 1024} KB)")
          time.sleep(2)

          # 3. Quality assessment
          quality = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
              "message": f"Audio ad quality assessor. Variation {label}, Take {take}.\n"
                  f"SCRIPT:\n{script}\nSETTINGS: {settings}\n\n"
                  "Score 1-10: PACING, HOOK STRENGTH, CLARITY, EMOTIONAL IMPACT, CTA EFFECTIVENESS.\n"
                  "OVERALL SCORE (1-10). If below 8, suggest adjusted stability/similarity_boost/style values.",
          }).json()
          feedback = quality.get("content", "")
          score = int(m[1]) if (m := re.search(r"OVERALL[^0-9]*(\d+)", feedback, re.I)) else 0
          print(f"  Score: {score}/10")
          if score > best_score: best_score, best_path = score, path
          if score >= QUALITY_THRESHOLD: print("  Production-ready!"); break

          # 4. Adjust voice settings from feedback
          for key, pat in [("stability", r"stability[:\s]*([\d.]+)"),
                           ("similarity_boost", r"similarity[_ ]?boost[:\s]*([\d.]+)"),
                           ("style", r"style[:\s]*([\d.]+)")]:
              if m := re.search(pat, feedback, re.I):
                  settings[key] = min(1.0, max(0.0, float(m[1])))
          time.sleep(2)

      results.append({"var": label, "score": best_score, "path": best_path})

  print(f"\n{'='*60}\nPRODUCTION SUMMARY\n{'='*60}")
  for r in results:
      status = "APPROVED" if r["score"] >= QUALITY_THRESHOLD else "NEEDS REVIEW"
      print(f"  Variation {r['var']}: {r['score']}/10 — {status} — {r['path']}")
  winner = max(results, key=lambda r: r["score"])
  print(f"\nRecommended: Variation {winner['var']} ({winner['score']}/10)")
  ```

  ```javascript JavaScript theme={"dark"}
  import fs from "fs";

  const EL_KEY = process.env.ELEVENLABS_API_KEY;
  const EL_BASE = "https://api.elevenlabs.io/v1";
  const MV = process.env.MAVERA_API_KEY;
  const MV_BASE = "https://app.mavera.io/api/v1";
  const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
  fs.mkdirSync("ad_production", { recursive: true });

  const VOICE_ID = "21m00Tcm4TlvDq8ikWAM";

  const gen = await fetch(`${MV_BASE}/generations`, { method: "POST", headers: MV_H,
    body: JSON.stringify({ prompt: "Write 3 variations of a 30-second radio ad for 'Beacon' AI analytics. "
      + "Target: marketing directors. Label: VARIATION A (problem), B (outcome), C (story)." }),
  }).then(r => r.json());
  const variations = (gen.output || gen.content || "").split(/VARIATION\s+[A-C]/).filter(v => v.trim());

  const results = [];
  for (let vi = 0; vi < Math.min(3, variations.length); vi++) {
    const label = String.fromCharCode(65 + vi), script = variations[vi].trim();
    let settings = { stability: 0.5 + vi * 0.05, similarity_boost: 0.75 + vi * 0.05,
      style: 0.3 + vi * 0.1, use_speaker_boost: true };
    let bestScore = 0, bestPath = null;

    for (let take = 1; take <= 3; take++) {
      const tts = await fetch(`${EL_BASE}/text-to-speech/${VOICE_ID}`, { method: "POST",
        headers: { "xi-api-key": EL_KEY, "Content-Type": "application/json" },
        body: JSON.stringify({ text: script, model_id: "eleven_multilingual_v2", voice_settings: settings }),
      });
      if (!tts.ok) { await new Promise(r => setTimeout(r, 3000)); continue; }
      const buf = Buffer.from(await tts.arrayBuffer());
      const outPath = `ad_production/var_${label}_take_${take}.mp3`;
      fs.writeFileSync(outPath, buf);
      console.log(`Var ${label} Take ${take}: ${Math.round(buf.length / 1024)} KB`);
      await new Promise(r => setTimeout(r, 2000));

      const quality = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
        body: JSON.stringify({ message: `Ad assessor. Var ${label}, Take ${take}.\nSCRIPT:\n${script}\n`
          + `SETTINGS: ${JSON.stringify(settings)}\nScore 1-10: PACING, HOOK, CLARITY, IMPACT, CTA. `
          + `OVERALL SCORE. If <8, suggest stability/similarity_boost/style values.` }),
      }).then(r => r.json());

      const feedback = quality.content || "";
      const score = parseInt((feedback.match(/OVERALL[^0-9]*(\d+)/i) || [])[1] || "0");
      console.log(`  Score: ${score}/10`);
      if (score > bestScore) { bestScore = score; bestPath = outPath; }
      if (score >= 8) { console.log("  Production-ready!"); break; }

      const sm = feedback.match(/stability[:\s]*([\d.]+)/i);
      const sbm = feedback.match(/similarity[_ ]?boost[:\s]*([\d.]+)/i);
      const stm = feedback.match(/style[:\s]*([\d.]+)/i);
      if (sm) settings.stability = Math.min(1, Math.max(0, parseFloat(sm[1])));
      if (sbm) settings.similarity_boost = Math.min(1, Math.max(0, parseFloat(sbm[1])));
      if (stm) settings.style = Math.min(1, Math.max(0, parseFloat(stm[1])));
      await new Promise(r => setTimeout(r, 2000));
    }
    results.push({ var: label, score: bestScore, path: bestPath });
  }

  results.forEach(r => console.log(`Var ${r.var}: ${r.score}/10 — ${r.score >= 8 ? "APPROVED" : "REVIEW"}`));
  ```
</CodeGroup>

## Example Output

```text theme={"dark"}
Var A Take 1: 112 KB — 7/10 (pacing fast) → Take 2: 118 KB — 8/10 ✓
Var B Take 1: 124 KB — 6/10 (CTA rushed) → Take 2: 121 KB — 8/10 ✓
Var C Take 1: 134 KB — 9/10 ✓

PRODUCTION SUMMARY
  Variation A: 8/10 — APPROVED — var_A_take_2.mp3
  Variation B: 8/10 — APPROVED — var_B_take_2.mp3
  Variation C: 9/10 — APPROVED — var_C_take_1.mp3
Recommended: Variation C (9/10)
```

## Error Handling

<AccordionGroup>
  <Accordion title="Voice settings range">All values accept 0.0–1.0; code clamps parsed feedback. Higher stability = consistent delivery; higher style = more expressive but less predictable.</Accordion>
  <Accordion title="Iteration cost management">3 variations × 3 takes = up to 9 TTS calls at \~400-500 characters each. Threshold 7 for drafts, 8+ for production.</Accordion>
  <Accordion title="Audio format options">Default is 128kbps MP3. For broadcast quality use `output_format: "mp3_44100_192"` or `"pcm_44100"` for uncompressed PCM.</Accordion>
</AccordionGroup>