Inference time is so slow.

[chazo1994]

I used GPU RTX 2080TI to train and infer model. Training time is so quick, but inference time so slow (I run gen_wavernn.py from wav file ). I saw that after upsample, the number of frames increase hundred times. I know that WaveRNN is very quick in compared with others Neural Vocoder.

[mindmapper15]

Without batched generation, the inference speed of WaveRNN Vocoder is slow just like the any other WaveNet-based Neural Vocoders.

What makes WaveRNN generates audio fast is batched mode generation, which splits a single utterance into multiple segments to generate audio parallely.

To use batched mode generation for increasing audio generation speed, set voc_gen_batched=True in hparams.py

NOTE: batched mode generation is trade-off feature, if you set voc_target in hparams.py to smaller value, the generation speed will increase but the quality of generated audio goes worse.

[chazo1994]

Without batched generation, the inference speed of WaveRNN Vocoder is slow just like the any other WaveNet-based Neural Vocoders.

What makes WaveRNN generates audio fast is batched mode generation, which splits a single utterance into multiple segments to generate audio parallely.

To use batched mode generation for increasing audio generation speed, set voc_gen_batched=True in hparams.py

NOTE: batched mode generation is trade-off feature, if you set voc_target in hparams.py to smaller value, the generation speed will increase but the quality of generated audio goes worse.

I already use batch processing, but it still so slow.

[mindmapper15]

I already use batch processing, but it still so slow.

I don't know how much speed you expect but decreasing voc_target in hparams.py would help to speed up inference. But the quality of synthesized audio will become worse.