When benchmarking, we measure the time-to-WER (word error rate) — because of subtle differences in the implementation of decoding algorithms and the complex relationships between parameters and decoding speed, some hyperparameters differed across runs. To fairly assess performance, we first sweep for parameters that achieve a baseline WER, minimizing beam size if possible.




Decoding performance on Librispeech dev-other of a pretrained wav2vec 2.0 model. TorchAudio + Flashlight decoding outperforms by an order of magnitude at low WERs.




