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Comment by oefrha | Hacker News Reader

Probably depends on the language. I tried to transcribe some hour-long talks in Chinese with whisper the other day. Even with the large model, it manages to skip some sentences from time to time (no idea why), and repeat the same sentence over and over in other instances. I tweaked parameters for a long time to no vail. While GPT should be able to clean up the repetition (there will be false positives though, since human speakers do repeat themselves occasionally), it can’t really fill out the missing sentences.

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I have to transcribe a tonne of Chinese interviews soonish -- any further thoughts or experiments you can think of? Maybe some preprocessing steps to the audio? For example, cut it into one minute chunks with some overlap, then transcribe those, so that it can't skip those bits...? Or can we finetune it on a library of Chinese mp3s + transcripts?

If cost isn’t an issue, I’d use one of the established commercial offerings. Otherwise, you could try splitting into shorter chunks (20 minutes maybe?), do multiple runs on each chunk and pick out the best run according to some criteria, e.g. character count after removing repetitions. Whisper isn’t deterministic so some runs can be better than others; you could also tweak parameters like compression ratio or silence thresholds between runs, but in my experience there’s not going to be an obvious winner leading to a marked improvement in quality. Anyway, I’m no expert, and maybe you’ll have better luck than me. My recordings do have background music and conversations in some places that might confuse the model.

Can't use third party service -- compliance stuff. This is helpful though. Using another model to tidy it up -- maybe Alpaca -- could be an option too. Then we'll just do speaker separation etc. manually later.

Whisper cuts the audio into chunks of 30 seconds. So If you have a one-minute recording where the first half has conversation in it and the remainder nothing, then it will think that it has to find something in that second 30 seconds block without knowing how "speech" actually sounded like as it did in the first chunk.

Try to pre-process it where just "voice" is detected, not the meaning, just some speaking, and cut the audio into snippets which only contain speech, so that Whisper doesn't have to guess if the segment will contain speech or not.

Also, if you cut it up into chunks and let it transcribe each chunk and expect JSON as the output, instead of the other output methods, then you'll get a bunch of extra parameters with it which will help you identifying problematic sections. For example hallucinated repetitions usually have a higher "no_speech_prob" parameter, or segments with lower "compression_ratio" will also not be that accurate.

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