> ## 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.
# Podcast Analysis Pipeline
> Transcribe podcast episodes with topic detection and paragraph segmentation, then feed structured transcripts to Mavera for content strategy — topic heatmaps, resonance signals, and editorial calendars.
## Scenario
Transcribe podcast episodes with topic detection, paragraph segmentation, and utterance timing. Feed structured transcripts to Mavera for content strategy — which topics resonate, derivative content to create, and editorial calendar gaps.
**Flow:** Deepgram `POST /v1/listen?model=nova-3&detect_topics=true¶graphs=true&utterances=true&diarize=true` → topic-tagged transcript → Mavera `POST /mave/chat` → Content strategy
## Code
```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"}
EPISODES = [{"file": "ep-47-ai-marketing.mp3", "title": "EP47: AI in Marketing"},
{"file": "ep-48-brand-strategy.mp3", "title": "EP48: Brand Strategy 2026"},
{"file": "ep-49-content-ops.mp3", "title": "EP49: Content Ops at Scale"}]
params = {"model": "nova-3", "smart_format": "true", "punctuate": "true",
"detect_topics": "true", "paragraphs": "true", "utterances": "true",
"diarize": "true", "language": "en"}
all_eps = []
for ep in EPISODES:
with open(ep["file"], "rb") as f:
resp = requests.post("https://api.deepgram.com/v1/listen", params=params,
headers={"Authorization": f"Token {DG}", "Content-Type": "audio/mpeg"},
data=f, timeout=180)
resp.raise_for_status()
r = resp.json()
alt = r["results"]["channels"][0]["alternatives"][0]
topics = [t["topic"] for s in r["results"].get("topics",{}).get("segments",[])
for t in s.get("topics",[])]
paragraphs = alt.get("paragraphs",{}).get("paragraphs",[])
moments = [{"time": f"{int(p['start']//60)}:{int(p['start']%60):02d}",
"text": " ".join(s.get("text","") for s in p.get("sentences",[]))[:200]}
for p in paragraphs[:10]]
dur = r.get("metadata",{}).get("duration",0)
all_eps.append({"title": ep["title"], "min": round(dur/60,1),
"words": len(alt["transcript"].split()), "topics": topics[:8], "moments": moments})
print(f"{ep['title']} — {round(dur/60,1)}min | {len(alt['transcript'].split())} words | {len(topics)} topics")
time.sleep(2)
corpus = ""
for e in all_eps:
corpus += f"\n### {e['title']} ({e['min']}min, {e['words']} words)\n"
corpus += f"Topics: {', '.join(e['topics'][:5])}\n"
for m in e["moments"][:5]:
corpus += f" [{m['time']}] {m['text'][:150]}\n"
time.sleep(1)
strategy = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
"message": f"Podcast content strategist. {len(all_eps)} episodes:\n\n{corpus[:10000]}\n\n"
"Produce:\n1. **TOPIC HEATMAP** — Topics across episodes\n"
"2. **RESONANCE SIGNALS** — Longest discussion topics\n"
"3. **CONTENT DERIVATIVES** — 10 blogs, 5 social threads, 3 video clips\n"
"4. **GUEST INSIGHTS** — Key quotes to repurpose\n"
"5. **EDITORIAL CALENDAR** — Next 4 episode topics\n"
}).json()
print(strategy.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 ${MV}`, "Content-Type": "application/json" };
const EPISODES = [
{ file: "ep-47-ai-marketing.mp3", title: "EP47: AI in Marketing" },
{ file: "ep-48-brand-strategy.mp3", title: "EP48: Brand Strategy 2026" },
{ file: "ep-49-content-ops.mp3", title: "EP49: Content Ops at Scale" }];
const params = new URLSearchParams({ model: "nova-3", smart_format: "true", punctuate: "true",
detect_topics: "true", paragraphs: "true", utterances: "true", diarize: "true", language: "en" });
const allEps = [];
for (const ep of EPISODES) {
const r = await fetch(`https://api.deepgram.com/v1/listen?${params}`, { method: "POST",
headers: { Authorization: `Token ${DG}`, "Content-Type": "audio/mpeg" },
body: fs.readFileSync(ep.file) }).then(r => r.json());
const alt = r.results.channels[0].alternatives[0];
const topics = (r.results.topics?.segments||[]).flatMap(s => (s.topics||[]).map(t => t.topic));
const moments = (alt.paragraphs?.paragraphs||[]).slice(0,10).map(p => ({
time: `${Math.floor(p.start/60)}:${String(Math.floor(p.start%60)).padStart(2,"0")}`,
text: (p.sentences||[]).map(s => s.text).join(" ").slice(0,200) }));
allEps.push({ title: ep.title, min: Math.round((r.metadata?.duration||0)/60*10)/10,
words: alt.transcript.split(/\s+/).length, topics: topics.slice(0,8), moments });
await new Promise(r => setTimeout(r, 2000));
}
let corpus = allEps.map(e => `### ${e.title} (${e.min}min)\nTopics: ${e.topics.slice(0,5).join(", ")}\n`
+ e.moments.slice(0,5).map(m => ` [${m.time}] ${m.text.slice(0,150)}`).join("\n")).join("\n\n");
const strategy = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
body: JSON.stringify({ message: `Podcast strategist. ${allEps.length} episodes:\n\n${corpus.slice(0,10000)}\n\n`
+ "Produce: TOPIC HEATMAP, RESONANCE SIGNALS, CONTENT DERIVATIVES, GUEST INSIGHTS, EDITORIAL CALENDAR" }),
}).then(r => r.json());
console.log(strategy.content || "");
```
## Example Output
```text theme={"dark"}
EP47 — 42.3min | 6,847 words | 6 topics
EP48 — 38.7min | 5,921 words | 5 topics
EP49 — 51.2min | 8,334 words | 7 topics
TOPIC HEATMAP
"AI content generation" — 3/3 episodes (14 min total)
"brand consistency" — 2/3 episodes (9 min)
CONTENT DERIVATIVES
Blog: "Why AI Won't Replace Copywriters (But Will Replace Bad Ones)"
Clip: EP47 [6:12–8:30] — Hot take on AI content quality
EDITORIAL CALENDAR
EP50: Content Measurement (gap: only 4 min coverage)
EP51: AI + Brand Voice (connect EP47 + EP48 themes)
```
## Error Handling
Deepgram handles files up to 2 GB. Set `timeout=180` for episodes over 60 minutes. Process batches sequentially with 2-second delays.
Topic detection needs 5+ minutes of diverse content. Monologue-heavy episodes may return 1-2 topics. Supplement with Mavera analysis of the raw transcript.
For interviews with clear turn-taking, accuracy is 95%+. For panels with crosstalk, process each microphone track separately.