Welcome to the Grit.AI Voice Transcription System — a scalable, modular voice transcription application that captures audio inputs, processes them in real-time via API, and outputs structured, word-level transcripts.
This project is under active development, with plans for biometric voice identification, multi-speaker handling, confidence calibration, and much more.
- Real-Time Voice Capture: Seamless recording of conversations.
- Structured Transcripts: Word-by-word breakdowns with timestamps and confidence scores.
- API-Based Processing: External API integration for fast and debounced data retrieval.
- Speaker Identification (Basic): Maps utterances to users (limited in environments with overlapping audio).
- Modular Architecture: Designed to support future expansion — voice biometrics, speaker diarization, UI dashboards, etc.
git clone https://github.com/your-username/grit-ai-voice-transcription.git
cd grit-ai-voice-transcription
npm install- Wrap up a meeting where the MeetStream.ai Bot has transcribed for
- Run the Application:
node transcript-processor/index.js -b <MeetStream.io Bot ID> -s <File name of the working CSV file>- Receive New Customer Data:
- The data extracted from the transcript would be inserted into the working CSV
- 🔒 Voice Fingerprinting: Unique identification of speakers even on shared mics.
- 🧠 Advanced Speaker Diarization: Better multi-speaker separation.
- 📈 Analytics Dashboard: Visualize speaking patterns, word clouds, and sentiment.
- 🧹 Noise Handling: Smarter background noise elimination.
- 🛡️ Data Privacy Options: GDPR compliance and secure transcript storage.
- 📦 Plugin System: Support for external extensions (e.g., Slack integration, meeting summarizers).
- 🖥️ Web UI: Front-end interface for managing recordings and transcripts.
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
- Open an issue to discuss feature ideas or bugs
- Fork the repo and submit a pull request
- Follow the coding style guidelines described in the project's wiki (TBD)
This project is licensed under the MIT License.
Special thanks to early contributors, testers, and users who helped shape the initial direction of the project.