> ## 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.

# Wistia Embeds → Brand Voice Source

> Extract transcripts from your entire Wistia video library and create a spoken-content brand voice profile using Mavera Brand Voice

### Scenario

Your company's video library contains hours of your team speaking — product demos, customer calls, webinars, company updates — and that spoken content carries a distinct voice that your written marketing often fails to capture. This job pulls transcripts from all your Wistia videos, feeds the combined text into Mavera's Brand Voice creation, and produces a spoken-content voice profile. The result is a brand voice guide derived not from a branding agency's aspirational document, but from how your team actually sounds when they're on camera.

### Architecture

```mermaid theme={"dark"}
flowchart LR
    A["Wistia GET /v1/medias.json"] --> B["GET /v1/medias/{id}.json (transcript)"] --> C[Combine all transcripts] --> D["POST /brand-voice"] --> E[Spoken-content brand voice profile]
```

### Code

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

  WS = os.environ["WISTIA_API_TOKEN"]
  MV = os.environ["MAVERA_API_KEY"]
  WS_BASE = "https://api.wistia.com"
  MV_BASE = "https://app.mavera.io/api/v1"
  WS_H = {"Authorization": f"Bearer {WS}", "Accept": "application/json"}
  MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

  # 1. Fetch all medias
  medias = []
  page = 1
  while True:
      resp = requests.get(f"{WS_BASE}/v1/medias.json", headers=WS_H, params={
          "per_page": 100, "page": page,
      }).json()
      if not resp:
          break
      medias.extend(resp)
      page += 1
      time.sleep(0.2)

  print(f"Found {len(medias)} videos")

  # 2. Pull transcripts for each video
  all_transcripts = []
  for media in medias:
      hashed_id = media.get("hashed_id", "")
      name = media.get("name", "Untitled")

      # Wistia auto-generates captions; fetch them
      captions_resp = requests.get(
          f"{WS_BASE}/v1/medias/{hashed_id}/captions.json", headers=WS_H
      )
      if captions_resp.status_code != 200:
          continue

      captions = captions_resp.json()
      if not captions:
          continue

      # Get English captions (or first available)
      caption_track = next(
          (c for c in captions if c.get("language") == "eng"),
          captions[0] if captions else None
      )
      if not caption_track:
          continue

      # Extract text lines from SRT data
      srt_lines = caption_track.get("text", "")
      clean_lines = []
      for line in srt_lines.split("\n"):
          line = line.strip()
          if not line or "-->" in line or line.isdigit():
              continue
          clean_lines.append(line)

      transcript = " ".join(clean_lines)
      if len(transcript) < 100:
          continue

      all_transcripts.append({
          "name": name,
          "text": transcript,
          "duration": media.get("duration", 0),
          "type": media.get("type", "Video"),
      })
      time.sleep(0.15)

  print(f"Extracted transcripts from {len(all_transcripts)} videos")
  total_words = sum(len(t["text"].split()) for t in all_transcripts)
  print(f"Total words: {total_words:,}")

  # 3. Combine transcripts (cap at context window)
  combined_text = "\n\n---\n\n".join(
      f"[{t['name']}]\n{t['text'][:2000]}"
      for t in all_transcripts[:20]
  )

  # 4. Create Brand Voice from spoken content
  brand_voice = requests.post(f"{MV_BASE}/brand-voice", headers=MV_H, json={
      "name": "Spoken Content Voice Profile",
      "source_type": "transcript",
      "content": combined_text[:30000],
      "instructions": (
          "Analyze this collection of video transcripts to extract the brand's "
          "authentic speaking voice. Focus on: vocabulary patterns, sentence rhythm, "
          "how they explain complex topics, humor style, filler words to avoid, "
          "transition phrases, opening patterns, and closing patterns. This voice "
          "should capture how the team ACTUALLY sounds — not how a branding agency "
          "WANTS them to sound."
      ),
  }).json()

  # 5. Poll for completion
  for _ in range(30):
      time.sleep(3)
      voice_status = requests.get(
          f"{MV_BASE}/brand-voice/{brand_voice['id']}", headers=MV_H
      ).json()
      if voice_status.get("status") == "completed":
          break

  profile = voice_status.get("profile", {})

  print("\nWISTIA EMBEDS → BRAND VOICE PROFILE")
  print("=" * 60)
  print(f"Videos analyzed: {len(all_transcripts)}")
  print(f"Total words: {total_words:,}")
  print(f"Voice ID: {brand_voice.get('id', 'N/A')}")
  print(f"\n{'─' * 60}")
  print("VOICE PROFILE:")
  print(f"\nTone: {profile.get('tone', 'N/A')}")
  print(f"Vocabulary Level: {profile.get('vocabulary_level', 'N/A')}")
  print(f"Sentence Style: {profile.get('sentence_style', 'N/A')}")
  print(f"\nKey Patterns:")
  for pattern in profile.get("patterns", [])[:10]:
      print(f"  • {pattern}")
  print(f"\nAvoid:")
  for avoid in profile.get("avoid", [])[:5]:
      print(f"  ✗ {avoid}")
  print(f"\nFull Profile:\n{profile.get('summary', '')[:1500]}")
  ```

  ```javascript JavaScript theme={"dark"}
  const WS = process.env.WISTIA_API_TOKEN;
  const MV = process.env.MAVERA_API_KEY;
  const WS_BASE = "https://api.wistia.com";
  const MV_BASE = "https://app.mavera.io/api/v1";
  const WS_H = { Authorization: `Bearer ${WS}`, Accept: "application/json" };
  const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

  // 1. Fetch all medias
  const medias = [];
  let page = 1;
  while (true) {
    const resp = await fetch(
      `${WS_BASE}/v1/medias.json?per_page=100&page=${page}`, { headers: WS_H }
    ).then(r => r.json());
    if (!resp.length) break;
    medias.push(...resp);
    page++;
    await new Promise(r => setTimeout(r, 200));
  }

  console.log(`Found ${medias.length} videos`);

  // 2. Pull transcripts
  const allTranscripts = [];
  for (const media of medias) {
    const hashedId = media.hashed_id || "";
    const captionsResp = await fetch(
      `${WS_BASE}/v1/medias/${hashedId}/captions.json`, { headers: WS_H }
    );
    if (!captionsResp.ok) continue;

    const captions = await captionsResp.json();
    if (!captions.length) continue;

    const track = captions.find(c => c.language === "eng") || captions[0];
    if (!track) continue;

    const transcript = (track.text || "").split("\n")
      .map(l => l.trim())
      .filter(l => l && !l.includes("-->") && !/^\d+$/.test(l))
      .join(" ");

    if (transcript.length < 100) continue;
    allTranscripts.push({
      name: media.name || "Untitled", text: transcript,
      duration: media.duration || 0, type: media.type || "Video",
    });
    await new Promise(r => setTimeout(r, 150));
  }

  console.log(`Extracted ${allTranscripts.length} transcripts`);
  const totalWords = allTranscripts.reduce((s, t) => s + t.text.split(" ").length, 0);
  console.log(`Total words: ${totalWords.toLocaleString()}`);

  // 3. Combine
  const combinedText = allTranscripts.slice(0, 20)
    .map(t => `[${t.name}]\n${t.text.slice(0, 2000)}`)
    .join("\n\n---\n\n");

  // 4. Create Brand Voice
  const brandVoice = await fetch(`${MV_BASE}/brand-voice`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      name: "Spoken Content Voice Profile",
      source_type: "transcript",
      content: combinedText.slice(0, 30000),
      instructions: "Analyze transcripts for authentic speaking voice. Focus on: vocabulary, rhythm, explanation style, humor, filler words, transitions, openers, closers. Capture how the team ACTUALLY sounds.",
    }),
  }).then(r => r.json());

  // 5. Poll
  let voiceStatus;
  for (let i = 0; i < 30; i++) {
    await new Promise(r => setTimeout(r, 3000));
    voiceStatus = await fetch(`${MV_BASE}/brand-voice/${brandVoice.id}`, { headers: MV_H }).then(r => r.json());
    if (voiceStatus.status === "completed") break;
  }

  const profile = voiceStatus.profile || {};

  console.log("\nWISTIA EMBEDS → BRAND VOICE PROFILE");
  console.log("=".repeat(60));
  console.log(`Videos: ${allTranscripts.length} | Words: ${totalWords.toLocaleString()} | Voice ID: ${brandVoice.id}`);
  console.log(`\nTone: ${profile.tone || "N/A"}`);
  console.log(`Vocabulary: ${profile.vocabulary_level || "N/A"}`);
  console.log(`Sentence Style: ${profile.sentence_style || "N/A"}`);
  console.log("\nKey Patterns:");
  (profile.patterns || []).slice(0, 10).forEach(p => console.log(`  • ${p}`));
  console.log("\nAvoid:");
  (profile.avoid || []).slice(0, 5).forEach(a => console.log(`  ✗ ${a}`));
  console.log(`\n${(profile.summary || "").slice(0, 1500)}`);
  ```
</CodeGroup>

### Example Output

```text theme={"dark"}
Found 87 videos
Extracted transcripts from 62 videos
Total words: 184,200

WISTIA EMBEDS → BRAND VOICE PROFILE
============================================================
Videos: 62 | Words: 184,200 | Voice ID: bv_sp0k3nv01c3

Tone: Conversational expert — confident without being authoritative,
  approachable without being casual.
Vocabulary: Mid-level technical. Uses industry terms but always defines
  them in the same sentence ("anomaly detection — basically, the system
  flags things that look weird before you even notice").
Sentence Style: Medium-length, parenthetical-heavy. Frequent use of
  dashes and asides for clarification.

Key Patterns:
  • Opens with a question: "So, have you ever..." (found in 71% of videos)
  • Uses "right?" as a rhetorical confirmation after statements
  • Explains features through metaphor: "Think of it like a..."
  • Transitions with "Now here's where it gets interesting"
  • Closes with a challenge: "Try it and tell me if..."
  • Uses specific numbers: "saves 4 hours" not "saves time"
  • Self-deprecating humor: "We tried 6 approaches before this one worked"
  • Acknowledges the viewer's skepticism before addressing it

Avoid:
  ✗ Corporate jargon: "leverage," "synergize," "best-in-class" (never used)
  ✗ Passive voice (only 4% of sentences)
  ✗ Long preambles before the point (team gets to the point in <10 seconds)
  ✗ Hyperbolic claims without numbers ("the best" without proof)
  ✗ Acronyms without explanation on first use
```

### Error Handling

<AccordionGroup>
  <Accordion title="Captions not available">Wistia auto-generates captions for videos on paid plans. Free accounts may not have captions. Use `GET /v1/medias/{id}/captions.json` to check availability. If empty, upload SRT files manually or use Mavera's Video Analysis for transcript extraction.</Accordion>
  <Accordion title="Transcript quality">Auto-generated captions have 85-95% accuracy. Speaker names, technical terms, and brand names are often misrecognized. For brand voice extraction, the volume of text (60+ videos) compensates for per-transcript errors.</Accordion>
  <Accordion title="Context window limits">The combined transcript (30,000 chars) represents roughly 20 videos. For larger libraries, prioritize videos by type: customer-facing content first, internal recordings last. The voice profile reflects the input sample.</Accordion>
</AccordionGroup>

***

## What's Next

<CardGroup cols={2}>
  <Card title="Wistia Integration" icon="circle-play" href="/integrations/wistia">
    Back to Wistia integration overview
  </Card>

  <Card title="CTA Performance × Focus Group" icon="bullseye" href="/integrations/wistia/cta-focus-group">
    Optimize CTA placement and messaging
  </Card>

  <Card title="Lead-Qualified Viewer Follow-up" icon="envelope" href="/integrations/wistia/lead-qualified-followup">
    Generate personalized follow-up emails
  </Card>

  <Card title="Brand Voice API" icon="microphone" href="/api-reference/brand-voice">
    Full reference for POST /api/v1/brand-voice
  </Card>
</CardGroup>