Streaming Speech Translation Pipeline for Mobile Low-Latency Audio
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Solution Overview
Problem
Existing real-time speech-to-speech translation models are not optimized for the resource constraints of mobile devices, leading to inefficiencies and suboptimal performance.
Innovation Solution
A lightweight speech-to-speech translation system utilizing a streaming audio encoder, decoder neural network, and vocoder, optimized for mobile devices with parallelization and reduced model sizes, enabling efficient real-time translation while preserving speech nuances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing real-time speech-to-speech translation models are used, then translation functionality is achieved, but resource consumption (memory, compute power) exceeds mobile device constraints
Solution Approach 1:
The translation model is divided into encoder and decoder components that can process speech streams independently and concurrently. The encoder processes input speech frames while the decoder simultaneously generates output frames, allowing the system to break down the heavy computational task into manageable segments that fit within mobile device memory constraints.
Solution Approach 2:
The system uses dynamic frame processing where the encoder and decoder operate on different time steps concurrently. The encoder processes frame n+1 while the decoder generates frame n, creating a dynamic pipeline that adapts to available computational resources and maintains continuous translation flow without requiring the entire model to reside in memory simultaneously.
2Reliability
If existing real-time speech-to-speech translation models are used, then translation functionality is achieved, but processing speed and latency are suboptimal on mobile devices
Solution Approach 1:
The encoder processes and encodes speech frames in advance before the decoder needs them for translation. By the time the decoder requires encoded features for generating output frame n, the encoder has already processed frame n+1, creating a pipeline where preliminary encoding work is completed ahead of time, reducing overall latency and improving processing throughput on mobile devices.
Solution Approach 2:
The system maintains continuous translation flow by having the encoder and decoder operate simultaneously on different frames throughout the speech stream. This continuous parallel operation ensures that the mobile device processor remains consistently utilized without idle periods, maximizing productivity and minimizing end-to-end latency while maintaining translation functionality.
3Reliability
If existing real-time speech-to-speech translation models are used, then translation functionality is achieved, but the system generates excessive heat and consumes excessive energy on mobile devices
Solution Approach 1:
The system processes only the necessary portions of the speech stream at any given time through selective frame processing. The encoder processes frames ahead of what the decoder immediately needs, and the decoder generates translations based on available encoded features without waiting for complete speech input. This partial processing approach reduces overall computational load and energy consumption while maintaining functional translation output.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing speech-to-speech translation, including real-time speech-to-speech translation.