Timmy Knight

@SuperJonotron @eternalliving Sorry for the late reply. I'm the person who made this TTS. Both the TTS and vocoder were optimized with torchscript. The TTS specifically always runs on the...

@SuperJonotron Not sure if this works, but try running this command before loading the models: `torch.backends.quantized.engine = 'qnnpack'`

@SuperJonotron Awesome! On AVX2 systems you might want to test replacing 'qnnpack' with 'xnnpack', 'fbgemm', or 'mkldnn'. I'll modify the code to autoselect a backend based on the host device....

@jhughesbiot Good to know.

@SuperJonotron A few things to note. First of all, the reason the startup is so long is that in order to load the models into RAM they made it run...

@SuperJonotron That's actually amazingly quick for a Pi. I'll try cook up a Vulkan enabled aarch64 build on my laptop later today if you'd like to try it out. I'll...

Wow I haven't checked on this repo in a looong time. So sorry I never saw this. The "vocoder-cpu-hq.pt" and lq.pt are optimized for CPU inference. Note that the low...

Another important thing to note when training your own models is that punctuation is actually extremely important. The stock GLaDOS voice dataset does not have punctuation. I had to add...

@SuperJonotron Unfortunately, the hard drive with all the original model checkpoints, logs, and datasets got wiped when I sent my laptop in for warranty repair :(. I used a model...

A much more pressing issue is the vocoder. As you've seen through testing, the vocoder causes the majority of the slowdown, especially with longer inputs. The "vocoder-cpu-lq.pt" model is fast...