Case Study
Voiux FSR API
Production-ready voice transcription for developers
Built a self-hosted voice transcription API powered by Whisper, offering developers a simple REST interface for converting audio to text with speaker diarization support.
Implementation timeline
API design
Week 1Designed REST API endpoints, request/response schemas, and error handling patterns following OpenAPI best practices.
Core implementation
Week 2Built FastAPI backend with Whisper integration, audio preprocessing pipeline, and speaker diarization module.
Deployment & docs
Week 3Deployed to self-hosted server, added authentication, rate limiting, and comprehensive API documentation.
introduction
Introduction & Background
Voice transcription is a cornerstone feature for many modern applications—from meeting notes to content accessibility. But integrating speech-to-text often means choosing between expensive cloud APIs with privacy concerns or wrestling with complex ML pipelines. Voiux was built to give developers a simple, self-hosted alternative with enterprise-grade accuracy.
- Self-hosted means full data sovereignty
- Whisper delivers state-of-the-art transcription accuracy
challenge
Problem & Constraints
Developers need reliable voice transcription that works across different audio formats, handles multiple speakers, and doesn't require sending sensitive recordings to third-party cloud services. The solution had to be easy to integrate via standard REST API while supporting advanced features like speaker diarization.
- Must handle various audio formats (MP3, WAV, M4A, etc.)
- Speaker diarization required for meeting transcription use cases
solution
Solution Overview
We built a FastAPI-based transcription service that wraps OpenAI's Whisper model with production-ready features. The API accepts audio uploads, processes them through a preprocessing pipeline (normalization, noise reduction), runs Whisper transcription, and optionally performs speaker diarization using Pyannote Audio.
- Clean REST API with JSON responses
- Speaker timestamps and confidence scores included in output
implementation
Implementation Journey
The system uses FFmpeg for audio preprocessing and format conversion. Whisper handles the core transcription with support for 99+ languages. For speaker diarization, we integrated Pyannote Audio to identify and label different speakers in the recording. FastAPI provides automatic OpenAPI documentation and async request handling.
- Audio preprocessing with FFmpeg for format normalization
- Whisper large-v3 model for maximum accuracy
- Speaker diarization with Pyannote Audio segmentation
- ReSTful API with FastAPI automatic documentation
- Docker containerization for consistent deployment
results
Results & Impact
The Voiux FSR API provides developers with a straightforward endpoint for voice transcription. Running on self-hosted infrastructure gives teams full control over their audio data while still benefiting from state-of-the-art ML models.
- Simple REST integration for any programming language
- Privacy-preserving self-hosted deployment
Details we can still add
Answering these will let us enrich the manuscript with sharper narrative proof points and concrete outcomes.
- Scale: What is the maximum audio duration and concurrent request capacity?
- Languages: Which languages have been tested most extensively?
- Pricing: Is this offered as a service or only self-hosted deployment?
- Features: Are there plans for real-time streaming transcription?
Stack at a glance
- Python
- FastAPI
- OpenAI Whisper
- FFmpeg
- Pyannote Audio
- Docker
Add voice intelligence to your product
We help teams integrate speech-to-text, voice agents, and audio analytics. Bring us your use case—we'll design the pipeline.