Our word error rate on LibriSpeech’s test-clean set is 6.5%, which not only achieves our initial goal, but gets us close to human level performance. [...] (5.83% according to the Deep Speech 2 paper). On a MacBook Pro, using the GPU, the model can do inference at a real-time factor of around 0.3x, and around 1.4x on the CPU alone. (A real-time factor of 1x means you can transcribe 1 second of audio in 1 second.)
Bonus: Facebook AI Research Automatic Speech Recognition Toolkit (Torch+lua, BSD License) gets 4.8% WER test-clean and 14.5% WER test-other on the LibriSpeech corpus.
Kaldi provides WER of 4.28% whereas deepspeech gives 5.83% on librispeech clean data. Check this out: https://github.com/syhw/wer_are_we