Compare with other parallel TTS about glow-tts HOT 4 CLOSED

For average length sentences, the inference time of Glow-TTS on a V100 GPU is about 40ms, whereas the inference time of FastSpeech was reported as ~25ms. You can find out in the papers (FastSpeech: Table 2, Glow-TTS: Section 5.2. Sampling Speed).

Glow-TTS is slower than FastSpeech, but I don't think the difference is significant in end-to-end setting, because vocoders are usually way slower than parallel TTS models.

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@jaywalnut310 Do you try to inference on CPU, and compare with GPU?

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I tried to measure inference on CPU, but I don't remember accurately. So, I cannot tell in certain, but I thought it was about 500ms to generate average length sentences on CPU.

You can try it with the pretrained model and inference notebook.
Closing the issue.

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How would one configure the inference notebook to be CPU compatible?

I modified the script by adding the following after the import statements:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

Then I replaced every call to cuda() with to(device). The script is able to nearly finish, but fails on the second-to-last line of code.

When it tries to execute:
audio = waveglow.infer(y_gen_tst, sigma=.666)
it raises
RuntimeError: Found no NVIDIA driver on your system.

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