AI Summarization of Audio Recordings (Meetings, Calls) for Mobile App

AI Summarization of Audio Recordings (Meetings, Calls) in Mobile Applications

You recorded a call — you get a transcript with tasks, decisions, and responsible parties. Without rewatching the recording or manual notes. Implementing this correctly is not three lines of code, but an architectural solution with several trade-offs.

Pipeline from Audio File to Summary

Audio file (MP3/M4A/WAV)
    ↓ Whisper API / Deepgram / AssemblyAI
Transcript with timestamps + diarization (who spoke)
    ↓ LLM (GPT-4o / Claude)
Structured summary (decisions, tasks, responsible, deadlines)

Three key choices: transcription provider, speaker diarization, summary format.

Transcription: Whisper vs Specialized Services

OpenAI Whisper API — cheap ($0.006/min), good quality on clean audio, but no diarization. Returns one text stream without speaker separation. For 5-person meeting — inconvenient.

AssemblyAI — diarization, speaker labels, auto chapters, auto action items. More expensive than Whisper ($0.012+/min), but saves development. SDKs for Python, JS, Java.

Deepgram — fastest (latency < 1s per minute for streaming), diarization, supports Russian and Ukrainian, on-prem option for private data.

Azure Speech Services — if already using Azure, integrates naturally.

For corporate recordings — AssemblyAI or Deepgram. For simple personal notes — Whisper sufficient.

Diarization and Its Limitations

Speaker diarization determines who spoke when. Result:

{
  "words": [
    {"text": "Let's", "start": 0.5, "end": 0.9, "speaker": "A"},
    {"text": "discuss", "start": 0.9, "end": 1.4, "speaker": "A"},
    {"text": "deadline", "start": 2.1, "end": 2.6, "speaker": "B"}
  ],
  "utterances": [
    {"speaker": "A", "text": "Let's discuss the project X deadline", "start": 0.5, "end": 5.2},
    {"speaker": "B", "text": "We need at least two more weeks", "start": 6.1, "end": 9.8}
  ]
}

Diarization problems: poor when multiple people talk simultaneously; doesn't know names (only "Speaker A", "Speaker B"); confused by similar voices. UI must allow manual speaker rename: "Speaker A" → "Ivan", "Speaker B" → "Maria".

Preparing Transcript for Summarization

Raw transcript with timestamps is excessive for LLM. Format as readable dialogue:

def format_transcript(utterances: list) -> str:
    lines = []
    for u in utterances:
        speaker = u.get("speaker_name") or f"Participant {u['speaker']}"
        lines.append(f"**{speaker}** [{u['start']:.0f}s]: {u['text']}")
    return "\n".join(lines)

Timestamps help model understand what's "early" vs "late" in meeting.

Prompt for Structured Summary

You're analyzing a work meeting transcript.
Extract:
1. TOPIC (one sentence)
2. KEY DECISIONS (list of decisions made)
3. TASKS (table: task | responsible | deadline)
4. OPEN QUESTIONS (what's unresolved)
5. NEXT MEETINGS (if mentioned)

Answer based only on transcript. If info missing — don't invent.
Format: Markdown.

TRANSCRIPT:
{transcript}

Structured JSON output (via response_format) is better for programmatic processing, Markdown for user display. For mobile use Markdown with renderer.

Handling Long Recordings

One-hour meeting → ~6000–8000 words transcript → ~8000–10000 tokens. Fits in GPT-4o context directly. Two-hour meeting — already 16000–20000 tokens, still fits, costs more.

For recordings > 3 hours, use same Map-Reduce: summarize 30-minute blocks, then merge. Preserve timestamps — user can click task and jump to that moment in recording.

Mobile UX for Meeting Summary

Summary card on mobile:

• Title with topic and meeting date
• Participants (if identified by diarization)
• "Decisions" block — 3–7 bullets
• Tasks table with checkboxes (user can mark done)
• "Open questions" — collapsible
• "Listen" button linking to audio file
• "Share" button — send summary as text

Tasks from summary can export to Jira, Notion, Todoist — via deep link or share sheet.

Implementation Timeline

Choose transcription provider → integrate API (upload + polling result) → format transcript → LLM for summary → mobile summary card UI → rename speakers → task export → test on real meeting recordings.

MVP with Whisper + basic summary — 2–3 weeks. Full tool with diarization, speaker rename, task export, mobile UI — 5–7 weeks.