Hear Everything, Find Anything.
Media Archive Search (ASR + Diarization) — Turn Hours of Audio into Searchable Moments
Every archive hides voices: interviews, call-ins, broadcasts, meetings, and field recordings that rarely get revisited because they are hard to scan. Our digital products make those hours discoverable. With automatic speech recognition (ASR) and speaker diarization, you can index words with timestamps, label speakers accurately, and jump to the exact second a phrase was spoken. No hardware, no shipments—just downloadable toolkits, self-paced lessons, demo datasets, and templates that plug into the stack you already use.
From Hours to Highlights—Fast.
Search That Follows the Audio.
Words with Time, Voices with Names.
About Us
We are a small team of engineers, archivists, and educators who care about access. We’ve digitized tapes, cleaned noisy interviews, and watched researchers give up when search failed them. These products distill what we wished we had: clear pipelines, practical UX, honest metrics, and respectful policies. We iterate constantly—adding assets, refining labs, and updating patterns as the field evolves—so your library keeps improving without starting over.
Video lessons: clear demonstrations of VAD, chunking, diarization tuning, and UX patterns.
Jupyter notebooks: end-to-end pipelines from ingest to index to UI.
Schema templates: JSON/JSONL definitions for transcripts, segments, and snippets.
Sample audio sets: diverse sources with ground truth for experimentation.
Precision You Can Explain.
Implementation Blueprint
Week 1 — Prototype
Import sample media, run the baseline notebook, index transcripts, and deploy the demo player. Validate jumps, snippets, and speaker labels on a small set.
Week 2 — Integrate
Point pipelines at your storage, enable hybrid search, and connect dashboards. Create a reviewer queue for low-confidence segments and define redaction policy.
Week 3 — Scale
Process a larger batch, test multilingual routing, calibrate diarization with short hand-labels, and set up versioning so reprocessing won’t break links.
ASR & Diarization Fundamentals Bootcamp for Media Archives
Description Learn the building blocks of media archive search with automatic speech recognition (ASR) and speaker diarization. You will understand how audio is segmented, how voice activity detection finds spoken regions, and why chunk size matters for retrieval.
Compliance & Redaction Toolkit for Media Search
Description Operate a responsible archive with practical tools for compliance, privacy, and content risk. You will learn to detect and tag personally identifiable information in transcripts and redact audio segments when required.
Multilingual ASR for Archives: Accents, Code-Switching & Search
Description Design a multilingual archive that respects accents, dialects, and code-switching without losing search precision. You will evaluate language detection strategies and routing logic for mixed-language segments within a single file.
Clips Built from Confidence.
Why This Approach Works
Most projects fail not at transcription, but at structure. We focus on granularity that helps retrieval: sentence-level segments, consistent speaker IDs, and contextual windows that decouple snippet quality from raw WER. The system makes conservative choices where they matter—e.g., preserving ambiguous diarization as anonymous speakers rather than guessing—so results remain credible. Hybrid search unifies keyword precision with semantic recall, and the UI teaches users to trust the index by highlighting precisely where a query matches and letting the player prove it.
Do I need a data scientist to use this?
No. If you can run notebooks or CLI scripts, you can ship a prototype. Advanced tuning is optional and explained clearly.
What about poor audio or heavy accents?
We provide settings and examples for noisy rooms, phone lines, and overlapping speech. You will learn when to favor robustness over speed.
Can I run everything offline?
Yes. Assets are designed for on-prem or private cloud. Sample pipelines run on modest hardware, and all schemas are open formats.
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