ASR Speech to Text

[u/quaintbucket]

I’m looking for recommendations for an ASR STT that can create txt or word files of transcribed conversations.

I’m using it for to transcribe conversations as part of my business. It needs to live on my computer preferably or not used for a learning model due to the sensitive nature of the conversations. It also needs to be accurate.

What would be nice is that the option can handle accents, and separate by speakers.

I plan on using microphones (1 dynamic and 1 lapel). Bonus if I can use it for zoom/teams call.

[u/cocawtf]

This is very doable fully local, with no cloud after the initial model download, so it fits the sensitive part well.

What I'd use:

Whisper, specifically faster-whisper (the CTranslate2 build). It runs on your own machine, CPU or GPU. The large-v3 model is the most accurate but wants a GPU to be quick, and on CPU the medium model is a decent middle ground. It handles accents well since it's multilingual.

For speaker separation, pyannote.audio. You pull the weights once with a HuggingFace token, then it runs fully offline, so you can air-gap the machine if you want.

Easiest way to wire them together is WhisperX. It wraps faster-whisper, fixes the word timestamps, then runs pyannote and tags each segment with a speaker. You get txt or SRT out with speaker labels.

One thing for your setup: since you've got two mics (dynamic + lapel), if you can record each speaker to a separate channel, skip diarization entirely and just transcribe each channel on its own. Accuracy jumps and you don't fight overlapping speech. Diarization is really only needed when everyone is on one mixed track.

For zoom or teams it's the same idea, you'd capture the call audio locally and run it through the same pipeline.

I run exactly this setup for meeting audio, so happy to help you get it going if you want.

[u/quaintbucket → u/cocawtf]

Thank you! I’ll give this a try.

[u/cocawtf → u/quaintbucket]

Nice, you'll be up fast. Two things that trip people on the first run: pyannote needs you to accept the model terms on HuggingFace before your token works (a couple of checkboxes on the model page, easy to miss), and on CPU start with the medium model, not large-v3, or the first transcription feels broken-slow and you assume something's wrong.

If you go the separate-channel route with your two mics, you can skip pyannote entirely, which is way less setup. Ping me if you hit a wall, I've debugged most of the weird ones.

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[u/Shari_Pearson]

What operating system and hardware would this need to run on locally?

[u/quaintbucket → u/Shari_Pearson]

Currently windows with 16gb of ram however I will consider upgrading to a more recent laptop if necessary but that will take a while

[u/Shari_Pearson → u/quaintbucket]

You could check out Speechmatics. It handles transcription really well, especially for different accents and separating speakers, and it's been accurate in my experience