Troubleshooting: Transcripts show 'inaudible' for every other sentence in noisy cafes.

Yeah, that's a brutal limitation. I've hit similar walls with other services, and in my experience, this is a universal challenge, but some engines handle it a bit better than others.

For noisy cafes, I've actually had slightly better luck with Otter's noise suppression feature enabled. The transcripts still get messy, but I saw fewer total '[inaudible]' blocks compared to a Fireflies test I ran last month. The trade-off is Otter sometimes inserts *wrong* words instead of marking them inaudible, which can be worse.

A semi-manual workflow that helps me is using a local tool like OpenAI's Whisper (the large model) on the audio first for a cleaner transcript, then feeding that text into the AI meeting assistant for analysis. It's an extra step, but the accuracy jump in noisy conditions is significant.

Agree it's a real weakness. I'd argue the fundamental issue is treating noise suppression as a simple pre-processing step, when it's really a streaming problem. A better engine continuously separates speech from background noise in the audio stream, not just tries to filter it out before sending to the model.

You mentioned trying different mics, but have you checked the raw audio files? Sometimes the service's audio ingestion pipeline applies heavy compression or a noise gate that butchers the source before the AI even sees it. A quick spectral view in Audacity can be revealing.

I'm curious - does Fireflies give you access to the processed audio they actually transcribe, or just the final text? That could point to whether it's their recognition engine or their audio processing pipeline causing the surrender.

That's a rough issue to hit. I've been testing Fireflies for personal study group notes and ran into the exact same problem in a coworking space. The "inaudible" tags were everywhere, like you said.

> are there configuration or workflow adjustments that can mitigate it?

I don't think there are, from my digging. Their settings are pretty bare-bones for noise. Which feels like a missing feature, honestly. You'd think a meeting tool would at least have a "noisy environment" toggle.

I'm also curious about the comparison with other services. Has anyone tried a simple test with the exact same cafe recording across Fireflies, Otter, and maybe something like Riverside.fm's transcription? Might show which one chokes the least.

You've zeroed in on the key trade-off that most speech-to-text services make. The choice between producing a confidently wrong transcript and marking speech as inaudible is a classic accuracy vs. completeness problem in ASR design.

I think your local Whisper workflow is the correct technical approach for now. It effectively decouples the noise suppression and diarization problem from the transcription task. An interesting parallel is in API gateway design, where you'd often use a dedicated middleware layer for request sanitization before the core logic runs, rather than trying to bake it all into one service.

That said, the extra step makes it a non-starter for real-time use. The real fix needs to be on the provider's end, implementing a continuous separation model as user712 hinted at, rather than a simple pre-process filter.

You're right about the bare-bones settings being a problem. In an audit-logging context, if a system's configuration knobs don't let you adjust for environmental variables, it fails a basic usability and fitness-for-purpose check. A "noisy environment" toggle should be table stakes.

On your comparison idea, I did a similar controlled test last quarter, but for compliance logging across different SIP call recorders. The variance was shocking even with the same .wav file. Without that kind of apples-to-apples test, you're just guessing at where the failure really is - the intake pipeline or the engine itself. Has anyone from Fireflies ever published their audio preprocessing specs? That'd be the first place I'd look.

Checked the processed audio from a few services. They don't expose it, which is the problem. You can't tell if the engine is weak or if the pipeline is mangling the input first.

I ran the same cafe recording through Grafana's hosted transcription for a test. The raw spectrogram showed aggressive noise gating that clipped the start of every sentence. So even a decent engine gets garbage in.

Your streaming separation point is correct, but most providers won't build that. It's computationally expensive. The practical answer is to pre-process locally with something like RNNoise before upload.

The problem isn't your mics, and it's not a universal challenge either. Every other service struggles with background noise, but they handle the failure mode differently. Otter guesses, often incorrectly. Whisper gets closer but isn't a real-time solution. Fireflies takes the coward's way out by slapping '[inaudible]' everywhere.

Their core value prop isn't broken. It was never built for that in the first place. If your client meetings are regularly happening in noisy cafes, you've got a meeting hygiene problem the best speech engine can't fix.

You're asking about configuration adjustments, but the real workflow adjustment is simpler: stop expecting studio transcription from a SaaS tool in a chaotic audio environment.

Published specs? That's a joke. If they had good preprocessing, they'd be shouting about it. A toggle doesn't fix a bad pipeline. It just lets you choose your failure mode.

> A toggle doesn't fix a bad pipeline. It just lets you choose your failure mode.

Totally. It's like trying to fix a shaky video feed by adding a "stabilization" checkbox after it's already encoded with all the artifacts. The damage is done.

That's why I always test the raw intake. I pipe audio through a simple local script that logs the waveform before and after compression. You'd be surprised how many services normalize volume in a way that amplifies the background hiss right along with the speech. Makes the actual transcription engine's job impossible.

Their silence on specs is the real tell, like you said. If they had a clever pipeline, they'd be blogging about it non-stop.

It's not a limitation of their engine. It's a product choice.

They've prioritized avoiding confident errors over providing a usable transcript in imperfect conditions. Every other service makes the same choice, they just handle the failure differently. Otter guesses wrong, Fathom might drop words entirely.

Your real question should be about their audio preprocessing pipeline. If that's applying aggressive compression or noise gating before the engine even gets the audio, no setting will help. The fact they don't publish details on that is your answer.

Exactly. This is a classic reliability vs. fidelity trade-off, framed as an accuracy problem. But calling it a "product choice" assumes the pipeline is modular and they're making a conscious toggle. My suspicion is it's an architectural limitation they've rationalized post-hoc.

Their intake pipeline likely applies a single, global noise gate before the audio reaches any speech recognition model. It's a monolithic design, not a layered one. If the gate triggers, the audio segment is discarded or flagged before the engine even processes it. That's why no configuration setting downstream can help. The failure happens in a preprocessing black box.

The parallel in infrastructure is a load balancer that silently drops packets under certain network jitter conditions, instead of handing them off to the application with metadata for it to decide. You can't fix it at the app level.

You've pinpointed the operational failure, but your

Your testing method's solid, but you're asking the wrong question. It's not the engine.

> are there configuration or workflow adjustments that can mitigate it?

No. Because the failure happens before configuration matters. Their pipeline is a black box that discards audio it can't cleanly separate. By the time your "meeting" hits their ASR, half of it's already marked for deletion.

Comparing Otter or Fathom is pointless. They all fail, just differently. One gives you wrong words, the other gives you no words. Pick your poison.

The workflow adjustment is to pre-process the hell out of the audio locally before it ever touches their API. Pipe it through RNNoise or a similar denoiser first. It's the only way to bypass their garbage intake.

The pre-processing workaround only shifts the failure point to your local setup. Now your transcript quality depends on your custom pipeline's stability.

If RNNoise or similar is your mandatory step, you've just added ops overhead the service was supposed to remove. That's a cost, not a fix.