Hey HN, I built Ellis, an AI notetaker for individuals, not orgs. And since my personal schedule includes meetings outside of Zoom, it was important that it works well for in-person meetings.
In fact to me, in-person conversations have always been a greater source of learning and creative ideas than virtual. On Zoom, the fixed screen and talking points tend to bring out a more transactional nature in me.
Yet building a notetaker for in-person isn't trivial. Online, each speaker has their own audio stream. In a room (or during a walk outside) everyone shares the same mic.
Models like AssemblyAI give you transcriptions with Speaker A and Speaker B, but you still have to mark yourself as the one who said X and not Y.
This is especially true if I want to get coached from my conversations. A useful use case for me as an introvert.
Here's how Ellis does this:
1) You create a voice profile of yourself (Deepgram diarization + pyannote's wespeaker model)
2) It splits the recording into speaker segments using AssemblyAI
3) It surfaces the most distinctive line from each unnamed speaker — "Moana is the best movie ever!"
4) You tap to confirm.
Remember to ask for consent! Ellis prompts you with ways to ask at the start of a recording.
Still not solved: two near-identical voices in the same room (e.g. me and my dad). Curious if anyone found a diarization approach that separates near-identical voices without per-speaker enrollment?
If you have an opinion about the tool, I'm all ears! You can try it for free.
I have a solution to scrape job portals like LinkedIn, Indeed, and a couple more. But the jobs data's value plummet very sharply over time and extracting this data at a large scale is quite extensive. Not sure what is your though.
Is the problem that this kind of tool doesn't exist because hiring data is so fragmented? Some data on different ATS platforms, some on LinkedIn, but all with restricted APIs?
In fact to me, in-person conversations have always been a greater source of learning and creative ideas than virtual. On Zoom, the fixed screen and talking points tend to bring out a more transactional nature in me.
Yet building a notetaker for in-person isn't trivial. Online, each speaker has their own audio stream. In a room (or during a walk outside) everyone shares the same mic.
Models like AssemblyAI give you transcriptions with Speaker A and Speaker B, but you still have to mark yourself as the one who said X and not Y.
This is especially true if I want to get coached from my conversations. A useful use case for me as an introvert.
Here's how Ellis does this: 1) You create a voice profile of yourself (Deepgram diarization + pyannote's wespeaker model) 2) It splits the recording into speaker segments using AssemblyAI 3) It surfaces the most distinctive line from each unnamed speaker — "Moana is the best movie ever!" 4) You tap to confirm.
Remember to ask for consent! Ellis prompts you with ways to ask at the start of a recording.
Still not solved: two near-identical voices in the same room (e.g. me and my dad). Curious if anyone found a diarization approach that separates near-identical voices without per-speaker enrollment?
If you have an opinion about the tool, I'm all ears! You can try it for free.