I agree that librispeech isn’t the best benchmark, but it’s the benchmark we have. If you have a significantly better benchmark that both wav2letter SOTA and Ka

I agree that librispeech isn’t the best benchmark, but it’s the benchmark we have. If you have a significantly better benchmark that both wav2letter SOTA and Kaldi SOTA have participated in, I’d love to see it. Without some kind of numbers cited, it’s easier to assume you’re speculating. Especially because, as I think we both acknowledge, wav2letter requires a lot of training power to actually use, so I doubt you’ve done the training yourself in the same way you’ve worked with Kaldi to be able to talk about it authoritatively. (One of my goals with releasing w2l models is that people will be able to do e.g. transfer learning or distillation and not repeat the expensive up front training themselves) RWTH has an impressive entry on the wer_are_we list for librispeech (2.3 / 5.0), but unfortunately I couldn’t find a way to actually use that research without a lot of work. Facebook has easy recipes posted that reproduce their results, which have been quite nice to work with. RWTH, Google, and Capio held an impressive top spot in the results for a while, which might be where you’re basing your judgement. I don’t think the biggest benefit of wav2letter was the architecture (until their SOTA paper), I think it was the speed and simplicity. I managed to port the original wav2letter inference to numpy/pytorch in two small files (same model weights, same inference output) because it was such a simple convolutional architecture. (The only fiddly part was matching the output of their featurization code) I agree the data and training overhead is a big aspect, which is why I’ve been training and releasing freely available wav2letter models on far more data than what Facebook has published using librispeech. My upcoming goal is to reproduce the SOTA results but with stronger training data. My baseline data is around 4000h currently and is far more diverse than librispeech. Unfortunately I’m independent and can’t afford the LDC datasets, though I have some neat ideas for extending FB’s semi supervised work to even more data.