Factician

• 

  Post-analysis of post-truth

Inspiration 💡

Have you ever watched a TV interview and been incredibly frustrated when none of the questions asked were actually answered?

What it does 🧐

From video/audio input, transcribes interviews and analyses the transcription for:

• Fact-checking against a trusted information bank
• Rhetorical evasiveness
• Tracking which questions have been asked and answered in the interview

How we built it 🛠️

• GCP Speech-to-text API, Cloud Storage, Cloud Function as infra for the analysis
• React/NextJS/TS for frontend display of analysis
• All analysis itself done using Claude, featuring prompt engineering and RAG for fact-checking purposes

Challenges we ran into 🥲

• Speech diarization (determining who says what in an audio clip is often inaccurate using publicly available tools). Examples demoed are cherry-picked to be particularly suited to this
• Speech-to-text APIs require audio in specific formats

Accomplishments that we're proud of 💪

Very happy with the quality of the analysis and the resulting visual representation. It proves that this is a tool that could genuinely be incorporated into news channel footage or for interviewers to help them hold interviewees to account in the moment

What we learned 🧑‍🏫

• How to use the GCP Speech-to-text APIs.
• Lots about Claude and how to get it to generate structured output using prompt engineering, XML and similar techniques

What's next for Factician 📈

• Improve accuracy of speaker diarization
• Support audio streaming from variety of sources
• Incorporate analysis into live video feed directly

Built With

• claude
• gcp
• google-web-speech-api
• python
• rag
• react
• ts