> Nice! Can you share a link to the streaming attention code? It uses our TensorFlow framework Returnn (https://github.com/rwth-i6/returnn https://github.com/r

> Nice! Can you share a link to the streaming attention code? It uses our TensorFlow framework Returnn (https://github.com/rwth-i6/returnn https://github.com/rwth-i6/returnn). We currently only have some configs/code online for hard attention variants, or segmental models. They can be found here: https://github.com/rwth-i6/returnn-experiments/tree/master/2019-monotonic-latent-attention https://github.com/rwth-i6/returnn-experiments/tree/master/2... Note that the configs are maybe not so cleaned up, as this is very much research. This is for a paper we submitted to ICASSP. We did not publish the paper yet elsewhere, but I can send you a copy by mail (just contact me: albzey@gmail.com). Also, as this is research focused, the encoder here is also a BLSTM, because we wanted to compare this work to other global soft attention models, and have the comparison mostly focused on the streaming attention modeling aspect. And also there is some lookahead, which is currently unlimited. So it would need a few modifications to really be used for online streaming. I'm also not sure whether this is the best model, or whether some of the many other variants (MoChA, RNN-T, etc) are maybe better. Edit: I forgot, we also have some local windowed attention variants, which can also be applied for streaming: https://github.com/rwth-i6/returnn-experiments/tree/master/2019-asr-local-attention https://github.com/rwth-i6/returnn-experiments/tree/master/2...