[HN Gopher] After All Is Said and Indexed - Unlocking Informatio...
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       After All Is Said and Indexed - Unlocking Information in Recorded
       Speech
        
       Author : jeadie
       Score  : 47 points
       Date   : 2023-04-12 07:01 UTC (3 days ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | moneywoes wrote:
       | How does this compare to using Whisper and feeding that into a
       | vector DB and querying with a LLM
       | 
       | Pardon the dumb question I only have an elementary understanding
        
       | jeadie wrote:
       | A really interesting blog post I found using LLMs for audio
       | search which I think is a pretty nifty/new idea.
       | 
       | I've found it cumbersome using some of the new vector DBs
       | (chroma, faiss, etc) to make end to end systems, but with Marqo
       | it doesn't seem too hard.
        
         | thomasahle wrote:
         | > I've found it cumbersome using some of the new vector DBs
         | (chroma, faiss, etc) to make end to end systems
         | 
         | What parts are cumbersome?
        
       | rektide wrote:
       | Hadn't heard of the thing they were putting their data into,
       | Marqo, a "tensor search for humans" , https://github.com/marqo-
       | ai/marqo
        
       | notjulianjaynes wrote:
       | This is interesting but what problem does it solve better than
       | CTRL+F-ing a transcript? It seems like this would be a worse
       | solution for when the precise way someone says something could be
       | important (ex. journalists parsing an interview, students
       | studying their recorded lectures) and that it would be most
       | useful if you were working with a large volume of recorded audio,
       | such as customer service calls. This makes me somewhat
       | uncomfortable, but perhaps I am not fully understanding how it
       | works.
       | 
       | Edit: wording
        
         | UncleEntity wrote:
         | > This is interesting but what problem does it solve better
         | than CTRL+F-ing a transcript?
         | 
         | Producing the transcript?
         | 
         | Being able to classify and search data seems like a pretty big
         | deal these days too.
        
       | password4321 wrote:
       | Both speaker and speech recognition are done in the article using
       | huggingface.
       | 
       | Is there anything as good ready to use on-prem for the
       | diarization (speaker recognition)?
       | 
       | I've heard good things about whisper(.cpp) for speech recognition
       | and vosk used to be king of that hill...
        
         | boredemployee wrote:
         | vosk is really good, but also a good example of an open source
         | project with great potential, but doesn't scale up because the
         | person behind it is a douchebag.
         | 
         | documentation is poor, and what you find is sparsed outdated
         | shit on the web, so it's really hard to find help.
        
         | rolisz wrote:
         | Diarization can be done on premise using pyannote (what they
         | use in the article). Huggingface offers a library to run things
         | locally and an API to run things on their cloud. Pyannote is
         | available under an MIT licence
        
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       (page generated 2023-04-15 23:01 UTC)