Seis to Seven

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💬 What It Does

Seis to Seven enables natural, two-way speech translation in real time. From doctor–patient consultations to travelers asking for directions, it empowers seamless conversations across languages while preserving tone and emotion.

Our system auto-detects the spoken language, translates it using Gemini, and reproduces it with ElevenLabs voice cloning — so each speaker hears the other in their own language, in the same voice, creating true connection rather than machine output.

🧩 How We Built It

We engineered a cross-platform, low-latency translation pipeline combining:

Spring Boot backend for stable networking and stream orchestration.

Python bridge service for ASR → Gemini Translation → ElevenLabs TTS.

WebSockets for real-time streaming and bi-directional data flow.

Audio chunking for phrase-by-phrase inference, reducing delay.

Voice caching to store cloned voices once and reuse them for faster response.

Every decision was designed to reduce latency and humanize machine speech, making real-time translation not only possible but natural and inclusive.

⚙️ Challenges We Ran Into

Synchronizing audio chunks without cutting words mid-sentence.

Managing latency bottlenecks between multiple APIs and frameworks.

Persisting cloned voices securely to save time and storage.

Achieving consistent stream quality across different browsers and networks.

🏆 Accomplishments That We’re Proud Of

Reached sub-second translation feedback for real-time dialogue.

Created a scalable, modular architecture ready for web and mobile.

Unified Gemini + ElevenLabs in a single low-latency pipeline.

Built something that could genuinely improve accessibility for multilingual communication worldwide.

📚 What We Learned

The art of balancing speed, accuracy, and humanity in machine translation.

Designing for stream synchronization and concurrency between Java and Python.

How emotional realism in TTS can change how users feel when using translation tools.

🚀 What’s Next for Seis to Seven

Expand to mobile and wearable devices for real-world translation use.

Integrate emotion transfer and contextual understanding for empathy in speech.

Enable multi-party translation for classrooms, meetings, and global teams.

Partner with NGOs and accessibility groups to bring real-time translation to underserved communities.

Creating ultra low latency through localized models (web sockets and grcp to address models that can't be localized) as well as developing custom phrase chunking to have faster dynamic processing, killing the latency issue