For streaming we continuously stream audio bytes of fixed size to the server and send the completed segments back to the client while incrementing the timestamp_offset.
But I'm happy to be proven wrong. That's why I would like to see some actual numbers. Maybe it's still okish enough, maybe it's actually really bad. I'm curious. But I don't just want to see a demo or a sloppy statement like "it's working ok".
Note that this is a highly non-trivial problem, to make a streamable speech recognition system with low latency and still good performance. There is a big research community working on just this problem.
I actually have worked on this problem myself. E.g. see our work "Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition" (https://arxiv.org/abs/2309.08436), which will be presented at ICASSP 2024. E.g. for a median latency of 1.11s ec, we get a WER of 7.5% on TEDLIUM-v2 dev, which is almost as good as the offline model with 7.4% WER. This is a very good result (only very minor WER degradation). Or with a latency of 0.78 sec, we get 7.7% WER. Our model currently does not work too well when we go to even lower latencies (or the computational overhead becomes impractical).
Or see Emformer (https://arxiv.org/abs/2010.10759) as another popular model.
I was impressed by Kaldi's models for streaming ASR: https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index... ; I suspect that the Nvidia/Suno Parakeet models will also be pretty good for streaming https://huggingface.co/nvidia/parakeet-ctc-0.6b
This is TensorFlow-based. But I also have another PyTorch-based implementation already, also public (inside our other repo, i6_experiments). It's not so easy currently to set this up, but I'm working on a simpler pipeline in PyTorch.
We don't have the models online yet, but we can upload them later. But I'm not sure how useful they are outside of research, as they are specifically for those research tasks (Librispeech, Tedlium), and probably don't perform too well on other data.