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Thank you very much for this comment. I just recently started dabbling with "AI/ML" by having Youtube videos transcribed and summarized. I was pretty amazed to
by cced 3y ago
Thank you very much for this comment. I just recently started dabbling with "AI/ML" by having Youtube videos transcribed and summarized. I was pretty amazed to see that I could simply 'plug' an .mp3 of audio and have openai/whisper transcribe the audio - locally - albeit at a non-trivial transcription time.
I was looking at getting those transcriptions summarized using a similarily wonderful tool but was kind of stumped when I started seeing 'maximum token length exceeded' errors.
I'm hoping to see some easy plug-n-play solutions to the summarization issue that run completely locally.
Fun times ahead.
- ca_tech 3y agoYou can work around this by chunking the data using a sliding window. Take the first X amount of sentences that fit into the token length and summarize. Now slide your window of sentences so that you still overlap just a bit with your previous selection. Summarize that information. Continue until you have a bunch of summaries for your text. You can then pass that back into the model for a more concise summary of the summaries.
- cced 3y agoI’ll be exploring a combination of pre-processing such as stemming in order to also reduce the token length while preserving as much important information. Thanks for the feedback. Edit: Won’t the sliding window solution above introduce a sort of bias? For instance, with a sliding window of 3 units, unit 1 is captured once whereas units 2 and 3 are captured twice and three times respectively.