> ## 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.

# Voice of Customer → Brand Voice

> Transcribe customer interviews and testimonial recordings, extract language patterns, and feed them into Mavera Brand Voice to create customer-authentic profiles.

## Scenario

Transcribe customer interviews and testimonial recordings. Extract how customers describe pain points, features, and success stories. Aggregate language patterns across interviews and feed them into Mavera Brand Voice to create a profile that sounds like your customers, not your marketing team.

**Flow:** Deepgram `POST /v1/listen` (multiple interview files) → aggregate customer language → Mavera `POST /brand-voices` → Customer-authentic Brand Voice

## Code

<CodeGroup>
  ```python Python theme={"dark"}
  import os, requests, time

  DG = os.environ["DEEPGRAM_API_KEY"]
  MV = os.environ["MAVERA_API_KEY"]
  MV_BASE = "https://app.mavera.io/api/v1"
  MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

  INTERVIEWS = [{"file": "interviews/sarah-vp-marketing.wav", "name": "Sarah, VP Marketing"},
      {"file": "interviews/marcus-ops-lead.wav", "name": "Marcus, Ops Lead"},
      {"file": "interviews/priya-cmo.wav", "name": "Priya, CMO"},
      {"file": "interviews/james-founder.wav", "name": "James, Founder"},
      {"file": "interviews/elena-director.wav", "name": "Elena, Director"}]
  params = {"model": "nova-3", "smart_format": "true", "punctuate": "true",
      "diarize": "true", "utterances": "true", "paragraphs": "true", "language": "en"}

  all_text = []
  for iv in INTERVIEWS:
      with open(iv["file"], "rb") as f:
          resp = requests.post("https://api.deepgram.com/v1/listen", params=params,
              headers={"Authorization": f"Token {DG}", "Content-Type": "audio/wav"},
              data=f, timeout=120)
      resp.raise_for_status()
      r = resp.json()
      utts = r.get("results",{}).get("utterances",[])
      dur = r.get("metadata",{}).get("duration",0)
      spk_wc = {}
      for u in utts:
          sp = u.get("speaker",0); spk_wc[sp] = spk_wc.get(sp,0) + len(u.get("transcript","").split())
      cust_sp = max(spk_wc, key=spk_wc.get) if spk_wc else 0
      text = " ".join(u["transcript"] for u in utts if u.get("speaker") == cust_sp)
      all_text.append({"name": iv["name"], "min": round(dur/60,1), "words": len(text.split()), "text": text})
      print(f"{iv['name']} — {round(dur/60,1)}min | {len(text.split())} customer words")
      time.sleep(2)

  total = sum(c["words"] for c in all_text)
  corpus = "\n\n".join(f"### {c['name']} ({c['min']}min, {c['words']} words)\n{c['text'][:2000]}"
      for c in all_text)

  time.sleep(1)
  voice = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
      "message": f"Voice pattern analyst. {len(all_text)} customer interviews.\n\n{corpus[:10000]}\n\n"
          "Extract consistent patterns:\n"
          "1. **VOCABULARY DNA** — Words/phrases used by 3+ customers\n"
          "2. **SENTENCE PATTERNS** — How they structure descriptions\n"
          "3. **METAPHOR INVENTORY** — Analogies they reach for\n"
          "4. **EMOTIONAL REGISTER** — Where animated, where matter-of-fact\n"
          "5. **JARGON USAGE** — Industry terms used vs. avoided\n"
          "6. **STORYTELLING ARC** — How they narrate before/after\n"
          "7. **VOICE SUMMARY** — 200-word brand voice description\n"
  }).json()

  bv = requests.post(f"{MV_BASE}/brand-voices", headers=MV_H, json={
      "name": "Customer Voice — Interviews Q1 2026",
      "description": f"From {len(INTERVIEWS)} interviews ({total:,} words). "
          f"Customers: {', '.join(c['name'] for c in all_text)}.\n\n"
          f"{voice.get('content','')[:4000]}",
  }).json()
  print(f"\nBrand Voice created: {bv.get('id')}")
  print(voice.get("content","")[:3000])
  ```

  ```javascript JavaScript theme={"dark"}
  import fs from "fs";
  const DG = process.env.DEEPGRAM_API_KEY, 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" };

  const INTERVIEWS = [
    { file: "interviews/sarah-vp-marketing.wav", name: "Sarah, VP Marketing" },
    { file: "interviews/marcus-ops-lead.wav", name: "Marcus, Ops Lead" },
    { file: "interviews/priya-cmo.wav", name: "Priya, CMO" },
    { file: "interviews/james-founder.wav", name: "James, Founder" },
    { file: "interviews/elena-director.wav", name: "Elena, Director" }];
  const params = new URLSearchParams({ model: "nova-3", smart_format: "true", punctuate: "true",
    diarize: "true", utterances: "true", paragraphs: "true", language: "en" });

  const allText = [];
  for (const iv of INTERVIEWS) {
    const r = await fetch(`https://api.deepgram.com/v1/listen?${params}`, { method: "POST",
      headers: { Authorization: `Token ${DG}`, "Content-Type": "audio/wav" },
      body: fs.readFileSync(iv.file) }).then(r => r.json());
    const utts = r.results?.utterances || [];
    const spkW = {};
    for (const u of utts) spkW[u.speaker??0] = (spkW[u.speaker??0]||0) + (u.transcript||"").split(/\s+/).length;
    const custSp = Object.entries(spkW).sort((a,b) => b[1]-a[1])[0]?.[0];
    const text = utts.filter(u => String(u.speaker)===custSp).map(u => u.transcript||"").join(" ");
    allText.push({ name: iv.name, min: Math.round((r.metadata?.duration||0)/60*10)/10,
      words: text.split(/\s+/).length, text });
    await new Promise(r => setTimeout(r, 2000));
  }

  const total = allText.reduce((s,c) => s+c.words, 0);
  let corpus = allText.map(c => `### ${c.name} (${c.min}min)\n${c.text.slice(0,2000)}`).join("\n\n");
  const voice = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
    body: JSON.stringify({ message: `Voice analyst. ${allText.length} interviews.\n\n${corpus.slice(0,10000)}\n\n`
      + "Extract: VOCABULARY DNA, SENTENCE PATTERNS, METAPHOR INVENTORY, "
      + "EMOTIONAL REGISTER, JARGON USAGE, STORYTELLING ARC, VOICE SUMMARY (200 words)" }),
  }).then(r => r.json());

  const bv = await fetch(`${MV_BASE}/brand-voices`, { method: "POST", headers: MV_H,
    body: JSON.stringify({ name: "Customer Voice — Interviews Q1 2026",
      description: `From ${INTERVIEWS.length} interviews (${total.toLocaleString()} words).\n\n`
        + (voice.content||"").slice(0,4000) }),
  }).then(r => r.json());
  console.log(`Brand Voice: ${bv.id}\n\n${(voice.content||"").slice(0,3000)}`);
  ```
</CodeGroup>

## Example Output

```text theme={"dark"}
Sarah (18.4min, 2847w) | Marcus (12.7min, 1923w) | Priya (22.1min, 3412w)
James (9.8min, 1541w) | Elena (15.3min, 2398w) → 12,121 words total
Brand Voice created: bv_3f8a2c9d

VOCABULARY DNA (3+ customers)
  "game changer" — 4/5 | "before/after" framing — 5/5
  "my team" — 4/5 center on team, not themselves
SENTENCE PATTERNS
  Short declarative + example: "It just works. Last week we shipped
  40 assets in a day — that used to take a full sprint."
METAPHOR INVENTORY
  "flying blind" → "having a map" (3/5)

VOICE SUMMARY
  Practical, results-oriented. Leads with what changed, not what
  the product does. Tone: "competent peer sharing a win."
```

## Error Handling

<AccordionGroup>
  <Accordion title="Interview audio quality">Zoom/phone recordings have artifacts. Nova-3 handles this well, but aim for 16kHz mono minimum. If diarization struggles, use separate tracks per speaker.</Accordion>
  <Accordion title="Not enough interviews">Brand voice needs 5+ interviews (10,000+ words) for reliable patterns. Fewer over-fits to individual styles. Supplement with written reviews or support tickets.</Accordion>
  <Accordion title="Brand Voice description length">Mavera accepts up to 5,000 characters. For richer profiles, create two brand voices: tone/vocabulary rules + example rewrites and do/don't lists.</Accordion>
</AccordionGroup>