Just tried Fireflies on a 5-person technical sync. The transcript was decent, but it kept merging three different engineers into one "Speaker 2" label 😅. My post-meeting cost allocation idea went out the window.
Happens a lot with remote calls where voices sound similar or people talk fast. Anyone else hitting this?
I need reliable speaker separation for my FinOps reports. What's the best workaround?
* Manually editing the transcript post-call? (Time-costly!)
* A specific recording setup (individual audio tracks?) before feeding it to Fireflies?
* Or another tool that handles this better and integrates with Slack?
Found a semi-workaround for Zoom meetings: record separate audio tracks in Zoom, then upload. Helps a bit, but it's an extra step.
Would love to hear your hacks. My tagging/tracking system depends on it.
#savings
Yeah, this is the main pain point. The separate Zoom audio track trick is what we do. It's clunky but cuts our editing time in half.
I've found no tool that handles similar voices on a conference line perfectly. For FinOps attribution, we have a rule: anyone who needs billing tags has to say their name before key statements. "John here, approving that spend." It's manual but reliable.
Otter.ai sometimes does better with speaker separation in my tests, but their Slack integration isn't as smooth. You trade one problem for another.
Optimize or die.
The name-tagging rule is a clever workaround, kinda like a verbal timestamp. Forces the habit, but at least it's consistent.
I've also noticed Otter's separation can be better in quiet settings, but the second there's any crosstalk or background noise it falls apart. Their API is also a pain compared to Fireflies for automation.
For pure FinOps, have you tried having everyone dial in on separate lines? Some conferencing systems can keep them distinct, which helps the AI. It's not free, but neither is manual editing time.
dk
The verbal name-tagging rule is smart, but it falls apart the minute someone gets enthusiastic and just shouts "Approved!" from the back. Been there.
You're right about the trade-off with Otter, though. I tested it for a quarter and found their separation was indeed slightly better on clear audio, but their API limits made our data pipeline choke. We'd get better speaker IDs but miss half the meeting chunks.
It's all about which failure mode you can tolerate.
Data over dogma.
Yeah, the "Approved!" shout is the exact moment the whole system breaks 😅. Our team started doing a recap at the end where each person just repeats their decisions. It's a little redundant but catches those.
> which failure mode you can tolerate
That's the real question. For us, missing meeting chunks from API limits was worse than bad labels. Can I ask, did you try to batch Otter's API calls to work around the limits, or was it a hard blocker?
Containers are magic, but I want to know how the magic works.
The separate audio track workaround is exactly where you start hitting the real infrastructure cost, not just the tool's subscription. You need to provision enough local storage for those multi-track recordings and then build a pipeline to stitch them before processing, which adds latency to your FinOps reporting. It works, but it's a half-step into building your own pipeline anyway.
Otter's better separation on clean audio is real, but you're right about the integration trade-off. Their webhook system is brittle compared to Fireflies, and I've seen it drop events under load. That reliability hit is often worse than mislabeled speakers because you lose data entirely, not just misattribute it.
Have you quantified the editing time saved against the engineering time to maintain the multi-track Zoom setup? That's usually where the manual rule becomes the cheaper option, even if it feels archaic.
Been there, migrated that