This is great! (If you don't mind me asking) How did you get around getting blocked by YouTube, and what are you using for Speech to text? I built a similar site (https://transcriberai.com), but we couldn't get around YT blocking our download requests, so we only accept file uploads.
SignalR and .Net work so well together. We also heavily leverage Hangfire (https://github.com/HangfireIO/Hangfire) in our apps for any async/background processes as well.
From my experience, you have to have a culture of code reviews to implement code reviews - if that makes sense. I have worked with a number of different teams, and have found the most success in bending the code review process to match the culture of the team. Sometimes that means all code reviews go through a single person, and other times, it means have well documented processes and bi-weekly in-person review (e.g. lots of structure).
How do you handle admins approving what gets posted? I know at a lot of events, the event organizers are worried about content being auto posted. Also, Do you have a video to a demo anywhere?
We use LLM agents to do proofreading and editing of transcripts after they are edited by people. They are good at applying our customer's specific requirements (e.g. capitalization, formatting, etc.) without us having our folks worry about any of that. We use https://transcriberai.com or https://otter.ai/ (there are a bunch) to create the first transcript for our transcriptionists.
Yes. We run run Whisper Large V3 (not Turbo) for the speech to text. It still seems to be the best open source model out there for that step. The main challenge we are trying to solve is Speaker Identification, which is a very time consuming process.