  We design an online end-to-end speech recognition system based on Time-Depth
Separable (TDS) convolutions and Connectionist Temporal Classification (CTC).
We improve the core TDS architecture in order to limit the future context and
hence reduce latency while maintaining accuracy. The system has almost three
times the throughput of a well tuned hybrid ASR baseline while also having
lower latency and a better word error rate. Also important to the efficiency of
the recognizer is our highly optimized beam search decoder. To show the impact
of our design choices, we analyze throughput, latency, accuracy, and discuss
how these metrics can be tuned based on the user requirements.
