Your concern about a Whisper update breaking ingestion is valid, but that's a general CI/CD problem, not unique to this stack. The solution is to pin your model version in production and treat any model update as a dependency change requiring a full regression test on your audio samples. We run a nightly pipeline that transcribes a fixed set of validation files and flags any WER drift or format change, which catches those silent breaks before they hit prod.
For vendor testing, the rubric mentioned by user622 is solid. I'd formalize it into a small benchmark suite. Prepare three standardized audio files, exactly as described, and script the API calls to each vendor to fetch the raw, time-stamped transcript with diarization. Run them through a simple diff tool against your hand-corrected golden transcripts. The key metric isn't just overall word error rate, but the error rate on your specific domain terms across the different noise conditions.
That's how you move from a polished demo to hard data. If a vendor refuses to provide raw, unprocessed output for those test files, that's a disqualifier. They're hiding their true error profile.
—chris
You're right about the necessity of testing with actual team audio. In our evaluations, we found one vendor's transcript was nearly perfect in a quiet demo, but when we used a recording from our warehouse floor, it consistently rendered our internal project codename "Project Hermes" as "project her knees." That kind of error makes a searchable archive completely unreliable.
I'd add that the pricing concern is twofold. It's not just the per-seat cost scaling with team size, but also how vendors charge for storage and search queries on that archive. Some structure it so that frequent searching of past meetings, which is the whole point, becomes the most expensive part.
What was the most surprising failure you encountered in your tests? Was it with accents, background noise, or something else like technical jargon?
The "project her knees" failure is classic. That's not background noise, that's a phonetic breakdown on known proper nouns, which is arguably worse. A system that can't handle your own project names needs a custom dictionary immediately, and if the vendor doesn't support that, walk away.
You're spot on about pricing. The query-based model is a trap for an archive. You buy this for search, then get penalized for using it. Demand a clear cap or bundle for search operations in the contract.
The most surprising failure we saw was with regional accents in a multinational retail team. A vendor's English model, trained on "standard" accents, absolutely butchered a Scottish manager's speech. It transcribed "aisle" as "I'll" for an entire meeting. This wasn't jargon or noise, just normal speech their model didn't cover. If your team isn't homogenous, accent handling needs to be in your rubric.
SLA is not a suggestion.
That's a really good point about testing with your own audio. We tried a demo from one vendor using a quiet conference call recording they provided, and it seemed perfect. Then we uploaded a store manager's call from the stockroom, and it transcribed our product code "SKU-4471" as "school for seven one". If that gets archived, how would we ever find that discussion again?
Your warning about pricing is what I'm most nervous about now. I hadn't thought about getting charged more for actually searching the archive later. That feels like the opposite of what you're buying it for.
For someone just starting to look at this, is there a specific type of test file you'd recommend creating first? Like, one with background noise and one with our common jargon?
You've nailed the core issue: the transcription *is* the search. If "SKU-4471" becomes "school for seven one" in the transcript, no semantic search layer, no matter how sophisticated, can recover that.
The open-source route with Whisper is the right call if your team has the bandwidth, but it's not just about the model. The diarization piece is often the weakest link in those stacks. You'll need a dedicated service like PyAnnote or speechbrain to handle speaker separation, and tuning that for retail noise and overlapping talkers becomes its own project.
One caveat on your vendor test advice: don't just test with your audio, test with *their* search on the resulting transcript. Ask for a CSV export of the transcript with word-level timestamps and speaker IDs, then run your own queries against it. You'll quickly see if their "AI search" is just grep on the text you already have.
infrastructure is code
That's such a vital final step - asking for the raw transcript CSV to run your own searches against. I've seen vendors where the slick web interface search works great because they're doing some post-query synonym expansion on the fly, but the actual archived text you'd access via API is the garbled "school for seven one" version. If their magic is only in the presentation layer, you're locked in forever.
Your point about diarization being the weak link in open-source stacks is dead on. We tried PyAnnote and the tuning for our weekly all-hands was a monster. It kept splitting our regional director into two different speakers whenever he switched from discussing logistics to marketing. The project ROI evaporated in the engineering hours.
So the real test is: can you get the clean, correct text out of the system in a standard format, or is the accuracy just an illusion inside their walled garden?
Try everything, keep what works.