> ## 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.

# Whisper Transcription → Meetings Pipeline

> Transcribe meeting audio with Whisper and analyze transcripts with Mavera's Mave Agent to extract action items, decisions, themes, and follow-ups

## Scenario

You have recorded meetings — sales calls, standups, strategy sessions — sitting as audio files. This job transcribes them with Whisper, then sends the transcript to Mavera's Mave Agent for structured analysis: action items, decisions made, themes discussed, follow-up owners, and deadlines.

**Flow:** OpenAI Whisper `POST /audio/transcriptions` → transcript text → Mavera `POST /mave/chat` → Structured meeting analysis

## Code

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

  client = OpenAI(api_key=os.environ["OPENAI_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"}

  AUDIO_FILE = "meeting-2026-03-17.mp3"

  # 1. Transcribe with Whisper
  print(f"Transcribing {AUDIO_FILE}...")
  with open(AUDIO_FILE, "rb") as f:
      transcript = client.audio.transcriptions.create(
          model="whisper-1", file=f,
          response_format="verbose_json",
          timestamp_granularities=["segment"],
      )

  text = transcript.text
  duration = getattr(transcript, "duration", 0)
  print(f"Transcribed: {len(text)} chars, {duration:.0f}s duration")

  time.sleep(1)

  # 2. Send to Mavera for structured analysis
  analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
      "message": f"Meeting analyst. Analyze this {duration:.0f}-second meeting transcript.\n\n"
          f"TRANSCRIPT:\n{text[:12000]}\n\n"
          "Extract:\n"
          "1. **MEETING SUMMARY** — 3-sentence overview\n"
          "2. **DECISIONS MADE** — Each decision, who made it, context\n"
          "3. **ACTION ITEMS** — Task, owner, deadline\n"
          "4. **KEY THEMES** — Top 5 themes with time ranges\n"
          "5. **FOLLOW-UPS** — What needs to happen before the next meeting\n"
          "6. **SENTIMENT** — Overall tone, any tension noted\n"
  }).json()

  print(f"\n{'='*60}\nMEETING ANALYSIS\n{'='*60}")
  print(analysis.get("content", "")[:4000])
  ```

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

  const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
  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" };

  const transcript = await client.audio.transcriptions.create({
    model: "whisper-1", file: fs.createReadStream("meeting-2026-03-17.mp3"),
    response_format: "verbose_json", timestamp_granularities: ["segment"],
  });
  console.log(`Transcribed: ${transcript.text.length} chars, ${Math.round(transcript.duration || 0)}s`);
  await new Promise(r => setTimeout(r, 1000));

  const analysis = await fetch(`${MV_BASE}/mave/chat`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      message: `Meeting analyst. Analyze this transcript.\n\nTRANSCRIPT:\n`
        + `${transcript.text.slice(0, 12000)}\n\nExtract:\n1. MEETING SUMMARY\n`
        + "2. DECISIONS MADE\n3. ACTION ITEMS\n4. KEY THEMES\n5. FOLLOW-UPS\n6. SENTIMENT",
    }),
  }).then(r => r.json());
  console.log(analysis.content || "");
  ```
</CodeGroup>

## Example Output

```text theme={"dark"}
Transcribed: 18,432 chars, 2,713s duration

MEETING SUMMARY
Weekly product sync covering Q2 roadmap prioritization and the
enterprise launch. Dashboard redesign ships Apr 4, mobile SDK deferred to Q3.

DECISIONS MADE
1. Dashboard redesign ships Apr 4 — Sarah (VP Product)
2. Mobile SDK deferred to Q3 — full team
3. Hire 2 data engineers before May 1 — Dan owns

ACTION ITEMS
- [ ] Sarah: Share wireframes by Mar 21
- [ ] Dan: Post job listings by Mar 19
- [ ] Priya: Load tests on staging by Mar 24

SENTIMENT: Collaborative, focused. Minor tension on SDK timeline — resolved.
```

## Error Handling

<AccordionGroup>
  <Accordion title="File too large for Whisper">Whisper accepts files up to 25 MB. For longer recordings, split with `ffmpeg -i meeting.mp3 -f segment -segment_time 600 -c copy chunk_%03d.mp3` and transcribe each chunk. Concatenate transcripts before sending to Mavera.</Accordion>
  <Accordion title="Unsupported audio format">Whisper supports mp3, mp4, mpeg, mpga, m4a, wav, and webm. Convert other formats with `ffmpeg -i input.ogg output.mp3` before uploading.</Accordion>
  <Accordion title="Rate limit on transcriptions">Whisper has per-minute request limits. If you get a 429, implement exponential backoff: `time.sleep(2 ** attempt)`. Batch multiple files with 2-second gaps between requests.</Accordion>
</AccordionGroup>