CPU infer slow #25

Thank you for sharing your code. I tried on my own dataset(Chinese) with config:
{ "train": { "log_interval": 200, "eval_interval": 1000, "seed": 1234, "epochs": 10000, "learning_rate": 2e-4, "betas": [0.8, 0.99], "eps": 1e-9, "batch_size": 32, "fp16_run": false, "lr_decay": 0.999875, "segment_size": 8192, "init_lr_ratio": 1, "warmup_epochs": 0, "c_mel": 45, "c_kl": 1.0 }, "data": { "training_files":"filelists/mt_f065_train_filelist.txt", "validation_files":"filelists/mt_f065_val_filelist.txt", "text_cleaners":["collapse_whitespace"], "max_wav_value": 32768.0, "sampling_rate": 16000, "filter_length": 1024, "hop_length": 256, "win_length": 1024, "n_mel_channels": 80, "mel_fmin": 0.0, "mel_fmax": null, "add_blank": true, "n_speakers": 0, "cleaned_text": false }, "model": { "inter_channels": 192, "hidden_channels": 192, "filter_channels": 768, "n_heads": 2, "n_layers": 6, "kernel_size": 3, "p_dropout": 0.1, "resblock": "1", "resblock_kernel_sizes": [3,7,11], "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], "upsample_rates": [8,8,2,2], "upsample_initial_channel": 512, "upsample_kernel_sizes": [16,16,4,4], "n_layers_q": 3, "use_spectral_norm": false, "gin_channels": 256 } }

And synthesis on CPU in 1 batch, but the speed is not as good as using GPU.
total audio length: 77.76s
total cost GPU: 4.84s
total cost CPU: 151.26s
average rtf GPU: 0.06
average rtf CPU: 1.95

I checked the checkpoint and found the G_*.pth file size is up to 445M.

My question is:

1. Is the CPU infer time correct?
2. Is the checkpoint I'm using correct?
3. Could you kindly give any ideas about how to make CPU infer faster?

Thank you in advance