←back to thread

685 points georgemandis | 1 comments | | HN request time: 1.418s | source
Show context
w-m ◴[] No.44378345[source]
With transcribing a talk by Andrej, you already picked the most challenging case possible, speed-wise. His natural talking speed is already >=1.5x that of a normal human. One of the people you absolutely have to set your YouTube speed back down to 1x when listening to follow what's going on.

In the idea of making more of an OpenAI minute, don't send it any silence.

E.g.

    ffmpeg -i video-audio.m4a \
      -af "silenceremove=start_periods=1:start_duration=0:start_threshold=-50dB:\
                         stop_periods=-1:stop_duration=0.02:stop_threshold=-50dB,\
                         apad=pad_dur=0.02" \
      -c:a aac -b:a 128k output_minpause.m4a -y
will cut the talk down from 39m31s to 31m34s, by replacing any silence (with a -50dB threshold) longer than 20ms by a 20ms pause. And to keep with the spirit of your post, I measured only that the input file got shorter, I didn't look at all at the quality of the transcription by feeding it the shorter version.
replies(12): >>44378492 #>>44378769 #>>44378939 #>>44378971 #>>44380884 #>>44380906 #>>44381352 #>>44382788 #>>44382864 #>>44384720 #>>44388923 #>>44388970 #
behnamoh ◴[] No.44378939[source]
> His natural talking speed is already >=1.5x that of a normal human. One of the people you absolutely have to set your YouTube speed back down to 1x when listening to follow what's going on.

I wonder if there's a way to automatically detect how "fast" a person talks in an audio file. I know it's subjective and different people talk at different paces in an audio, but it'd be cool to kinda know when OP's trick fails (they mention x4 ruined the output; maybe for karpathy that would happen at x2).

replies(7): >>44379087 #>>44379461 #>>44379539 #>>44380162 #>>44380831 #>>44383231 #>>44387266 #
1. btown ◴[] No.44379539[source]
Even a last-decade transcription model could be used to detect a rough number of syllables per unit time, and the accuracy of that model could be used to guide speed-up and dead-time detection before sending to a more expensive model. As with all things, it's a question of whether the cost savings justify the engineering work.