Translingo Call Translator

Solutions

To overcome the challenges, Hola Tech adhered to the best practices. Key components of the solution included:

– Implement streaming & chunk-based translation: Instead of waiting for a full sentence or speaker turn, segment the audio stream (WebRTC/Twilio) into 500ms-1 second chunks. Use a streaming ASR solution (or an optimized, low-latency deployment of Whisper), perform translation on these partial chunks, and utilize streaming TTS (ElevenLabs) to begin playing the translated audio immediately. This overlaps the processing steps, drastically reducing the perceived latency.

– Integrate advanced Voice Activity Detection (VAD): Supplement the ASR module with a dedicated, highly tuned VAD component at the NodeJS/WebSocket layer. This VAD microservice should analyze the incoming audio stream (WebRTC) to reliably detect speaker start and stop points. This ensures that transcription (Whisper) is only initiated when speech is active, and that the translation step (Google Translate API) receives complete, contextual speaker segments, preventing interruptions and enhancing translation accuracy.

– Leverage AWS spot instances and container orchestration for inference: Package self-hosted ASR and TTS models (or their optimized open-source variants) in Docker containers. Deploy these containers on AWS ECS/EKS utilizing Spot Instances (for significant cost reduction) and orchestrate the load dynamically based on real-time call volume. This replaces constant, expensive API calls with internal, scalable compute resources that are managed efficiently.