  End-to-end automatic speech recognition systems represent the state of the
art, but they rely on thousands of hours of manually annotated speech for
training, as well as heavyweight computation for inference. Of course, this
impedes commercialization since most companies lack vast human and
computational resources. In this paper, we explore training and deploying an
ASR system in the label-scarce, compute-limited setting. To reduce human labor,
we use a third-party ASR system as a weak supervision source, supplemented with
labeling functions derived from implicit user feedback. To accelerate
inference, we propose to route production-time queries across a pool of CUDA
graphs of varying input lengths, the distribution of which best matches the
traffic's. Compared to our third-party ASR, we achieve a relative improvement
in word-error rate of 8% and a speedup of 600%. Our system, called SpeechNet,
currently serves 12 million queries per day on our voice-enabled smart
television. To our knowledge, this is the first time a large-scale,
Wav2vec-based deployment has been described in the academic literature.
