> ## 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.
# Audio Intelligence → Meeting Analysis
> Use every audio intelligence feature Deepgram offers — sentiment, summarization, topic detection, entity detection, intent detection, and speaker diarization — for structured meeting intelligence via Mavera.
## Scenario
Use every audio intelligence feature Deepgram offers — sentiment analysis, summarization, topic detection, entity detection, intent detection, and speaker diarization — in a single `/v1/listen` call with Nova-3. Feed the enriched transcript to Mavera for structured meeting analysis: decisions, action items, risk signals, and stakeholder dynamics.
**Flow:** Deepgram `POST /v1/listen?model=nova-3&smart_format=true&summarize=v2&detect_topics=true&detect_entities=true&sentiment=true&diarize=true&intents=true` → enriched transcript → Mavera `POST /mave/chat` → Structured meeting intelligence
## Code
```python Python theme={"dark"}
import os, requests, time, json
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"}
params = {
"model": "nova-3", "smart_format": "true", "summarize": "v2",
"detect_topics": "true", "detect_entities": "true", "sentiment": "true",
"diarize": "true", "intents": "true", "punctuate": "true",
"paragraphs": "true", "utterances": "true", "language": "en",
}
with open("strategy-meeting-2026-03-17.wav", "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()
summary = r["results"].get("summary", {}).get("short", "")
topics = [t["topic"] for s in r["results"].get("topics",{}).get("segments",[])
for t in s.get("topics",[])]
entities = [f"{e['label']}: {e['value']}" for s in r["results"].get("entities",{}).get("segments",[])
for e in s.get("entities",[])]
sent_dist = {}
for s in r["results"].get("sentiments",{}).get("segments",[]):
k = s.get("sentiment","neutral"); sent_dist[k] = sent_dist.get(k,0)+1
speakers = [f"Speaker {u.get('speaker','?')}: {u['transcript'][:200]}"
for u in r.get("results",{}).get("utterances",[])[:40]]
enriched = (f"SUMMARY:\n{summary}\n\nTOPICS:\n{chr(10).join(topics[:10])}\n\n"
f"ENTITIES:\n{chr(10).join(entities[:15])}\n\nSENTIMENT: {json.dumps(sent_dist)}\n\n"
f"SPEAKERS:\n{chr(10).join(speakers)}")
time.sleep(1)
analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
"message": f"Meeting intelligence analyst. Deepgram audio intelligence:\n\n"
f"{enriched[:10000]}\n\nProduce:\n"
"1. **EXECUTIVE SUMMARY** — 3 sentences\n2. **DECISIONS MADE** — Decision, owner, context\n"
"3. **ACTION ITEMS** — Task, owner, deadline\n4. **RISK SIGNALS** — Negative sentiment + mismatches\n"
"5. **STAKEHOLDER DYNAMICS** — Who drove conversation, who was silent\n"
"6. **TOPIC DEEP-DIVE** — Each topic with strategic implications\n"
}).json()
print(analysis.get("content", "")[:4000])
```
```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 ${MV}`, "Content-Type": "application/json" };
const params = new URLSearchParams({
model: "nova-3", smart_format: "true", summarize: "v2", detect_topics: "true",
detect_entities: "true", sentiment: "true", diarize: "true", intents: "true",
punctuate: "true", paragraphs: "true", utterances: "true", language: "en",
});
const r = await fetch(`https://api.deepgram.com/v1/listen?${params}`, { method: "POST",
headers: { Authorization: `Token ${DG}`, "Content-Type": "audio/wav" },
body: fs.readFileSync("strategy-meeting-2026-03-17.wav"),
}).then(r => r.json());
const topics = (r.results.topics?.segments||[]).flatMap(s => (s.topics||[]).map(t => t.topic));
const entities = (r.results.entities?.segments||[]).flatMap(s =>
(s.entities||[]).map(e => `${e.label}: ${e.value}`));
const sentCounts = {};
for (const s of r.results.sentiments?.segments||[])
sentCounts[s.sentiment||"neutral"] = (sentCounts[s.sentiment||"neutral"]||0)+1;
const speakers = (r.results.utterances||[]).slice(0,40)
.map(u => `Speaker ${u.speaker??"?"}: ${(u.transcript||"").slice(0,200)}`);
const enriched = `SUMMARY:\n${r.results.summary?.short||""}\n\nTOPICS:\n${topics.join("\n")}`
+ `\n\nENTITIES:\n${entities.join("\n")}\n\nSENTIMENT: ${JSON.stringify(sentCounts)}`
+ `\n\nSPEAKERS:\n${speakers.join("\n")}`;
await new Promise(r => setTimeout(r, 1000));
const analysis = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
body: JSON.stringify({
message: `Meeting intelligence analyst. Deepgram output:\n\n${enriched.slice(0,10000)}\n\n`
+ "Produce: EXECUTIVE SUMMARY, DECISIONS, ACTION ITEMS, RISK SIGNALS, STAKEHOLDER DYNAMICS, TOPIC DEEP-DIVE",
}),
}).then(r => r.json());
console.log(analysis.content || "");
```
## Example Output
```text theme={"dark"}
Transcript: 24,187 chars | Topics: 8 | Entities: 12 | Speakers: 4
EXECUTIVE SUMMARY
Q2 roadmap review with 4 stakeholders. Dashboard v3 ships Apr 11,
API partner program launches May 1, enterprise SSO prioritized.
DECISIONS MADE
1. Dashboard v3 ships Apr 11 — Sarah (VP Product)
2. API partner program May 1 — Marcus owns onboarding
RISK SIGNALS
- Negative sentiment spike at 14:32 re: integration timeline
- Speaker 3 silent after minute 22 — possible disengagement
```
## Error Handling
Deepgram supports files up to 2 GB. For larger files, split with `ffmpeg -i meeting.wav -f segment -segment_time 1800 -c copy chunk_%03d.wav`. Concatenate results before sending to Mavera.
Some features require minimum duration. Summarization needs 30+ seconds, topic detection needs 60+. Check nested keys before accessing.
Deepgram uses concurrency-based limits. Retry with exponential backoff: `time.sleep(2 ** attempt)`. Limit batch processing to 5 concurrent requests.