AudioChaptersConverter
Automatically segment audio recordings into topic-based chapters with titles, timestamps, and summaries. Uses speech transcription combined with topic modeling to detect natural topic boundaries in podcasts, lectures, meetings, and audiobooks.
How It Works
Upload an audio file or provide a URL to the Mixpeek API.
The audio is transcribed with speaker diarization and sentence-level timestamps.
Topic modeling identifies semantic shift points where the subject matter changes.
An LLM generates a descriptive title and brief summary for each detected chapter.
Chapters are returned with start/end timestamps, titles, and summaries.
Code Examples
import os, requests
API = "https://api.mixpeek.com"
H = {"Authorization": f"Bearer {os.environ['MIXPEEK_API_KEY']}",
"X-Namespace": os.environ["NAMESPACE_ID"]}
# 1. a bucket, with a schema that declares the field you will send
bucket = requests.post(f"{API}/v1/buckets", headers=H, json={
"bucket_name": "audio-inputs",
"bucket_schema": {"properties": {"audio": {"type": "audio"}}},
}).json()
# 2. land the file as an object. the URL goes in data, on the blob
requests.post(f"{API}/v1/buckets/{bucket['bucket_id']}/objects", headers=H, json={
"key_prefix": "run-1",
"blobs": [{"property": "audio", "type": "audio",
"data": "https://example.com/call.mp3"}],
})
# 3. a collection over that bucket, running the extractor
collection = requests.post(f"{API}/v1/collections", headers=H, json={
"collection_name": "audio-to-chapters",
"source": {"type": "bucket", "bucket_ids": [bucket["bucket_id"]]},
"feature_extractor": {"feature_extractor_name": "multimodal_extractor", "version": "v1"},
}).json()
# 4. run extraction over the bucket
requests.post(f"{API}/v1/buckets/{bucket['bucket_id']}/batches", headers=H, json={
"collection_ids": [collection["collection_id"]],
"auto_submit": True,
})
# 5. read the output
docs = requests.get(
f"{API}/v1/collections/{collection['collection_id']}/documents", headers=H
).json()
print(docs)Use Cases
Supported Input Formats
Quick Info
Run it over a library
Mixpeek runs this conversion as a pipeline over a whole library in your object storage, with the output landing as queryable documents. It is not a single-file converter.
Frequently Asked Questions
Related Converters
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Transcribe audio files into text with high accuracy. Supports speaker diarization, punctuation restoration, timestamps, and over 50 languages. Handles podcasts, calls, meetings, and broadcast audio.
Audio to Summary
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Audio to Keywords
Extract semantically relevant keywords and key phrases from audio recordings. Transcribes speech, identifies salient terms using NLP, and ranks them by relevance and frequency. Ideal for content tagging, topic detection, and search optimization.
Video to Chapters
Automatically segment videos into topic-based chapters with titles, timestamps, and summaries by analyzing both the visual content and spoken dialogue. Produces chapter markers compatible with YouTube, Vimeo, and custom video players.
Ready to convert audio to chapters?
Start using the Mixpeek Audio to Chapters in minutes. Sign up for a free API key and follow the documentation to get started.