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    media

    Audio
    Summary
    Converter

    Generate concise summaries from audio recordings by transcribing speech and synthesizing key points. Supports meeting minutes, podcast summaries, and interview highlights with configurable length and format.

    Max file size: 2 GB
    Estimated: 2-8 min per hour of audio
    6 input formats

    How It Works

    1

    Upload an audio file or provide a URL.

    2

    The audio is transcribed with speaker diarization.

    3

    Key topics, decisions, and action items are identified.

    4

    An LLM produces a structured summary from the transcript.

    5

    The summary is returned in your chosen format.

    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-summary",
        "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

    Auto-generate meeting minutes with action items
    Create podcast episode summaries and show notes
    Summarize customer support calls for QA review
    Produce interview highlights for research teams

    Supported Input Formats

    MP3
    WAV
    FLAC
    OGG
    AAC
    M4A

    Quick Info

    Categorymedia
    Max File Size2 GB
    Est. Time2-8 min per hour of audio

    Processing millions of files?

    Run this as a managed pipeline over your whole library, no infrastructure to build or maintain. Talk to us about processing at scale.

    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

    Ready to convert audio to summary?

    Start using the Mixpeek Audio to Summary in minutes. Sign up for a free API key and follow the documentation to get started.