Help: Audio quality is fine but the transcript is garbled. Is it my mic or their processing?

Yeah, I've seen this before. Clear audio but a messy transcript usually points to the processing engine, not your mic. Fireflies' transcription model can struggle with certain accents, fast speech, or industry jargon.

Try these quick checks:
- In your settings, make sure the correct language is selected (English US vs UK matters).
- If there were multiple speakers, verify that the speaker identification feature is turned on. Sometimes it gets confused and jumbles the dialogue.

If those are fine, it might just be a bad processing run. I'd rerun the transcript on that same audio file - sometimes a second attempt is much cleaner.

I had the exact same problem last month. My audio was crystal clear but the transcript read like someone was mumbling. I thought for sure it was my mic.

What ended up working for me was a combination of things user1076 mentioned and one extra step. First, I double-checked the language setting like they said, and it was correct. The real fix, though, was going into the call details after it processed and manually tagging the speakers. Even though speaker identification was on, it hadn't labeled them correctly, and the whole transcript seemed scrambled because of it. Once I assigned names to each voice track and re-ran it, the text cleaned up about 90%.

Have you checked if Fireflies is correctly separating the voices on your recording?

You've landed on a critical, often overlooked factor. The speaker diarization engine - the part that splits the audio into separate voice tracks - failing silently is a classic root cause for garbled transcripts, even with perfect audio. If it lumps two speakers into one track or chops a single speaker into multiple, the resulting text stream becomes nonsensical because the language model loses the thread of each individual's context.

Your manual tagging solution is the right diagnostic step. I'd add that for recurring team meetings, some platforms let you train the speaker recognition by providing a voice sample for each participant. That preemptive step can prevent this issue altogether.

A caveat, though. In my experience, if the diarization is that off, manually tagging and reprocessing might fix the *coherence*, but you can still end up with individual sentences that are inaccurately transcribed because the initial acoustic model was confused by overlapping or crosstalk. It's worth scanning the 'fixed' transcript for those smaller errors too.

Exactly right about the diarization being a silent failure point. It's the first thing I check when transcripts go sideways on clean audio.

That caveat on the acoustic model is spot on. Even after you fix the speaker labels, you can get these weird phrase-level errors if there was any crosstalk. I've seen it transcribe "stand-up meeting" as "Stanley meeting" because two people spoke at once at the start.

One trick that sometimes helps: if the platform allows it, try processing in "interview mode" for 1-on-1 calls. It locks the model to expect two distinct voices and can reduce that initial acoustic confusion.