| from pase.models.frontend import wf_builder | |
| pase = wf_builder('cfg/frontend/PASE+.cfg').eval() | |
| pase.load_pretrained('FE_e199.ckpt', load_last=True, verbose=True) | |
| pase.cuda() | |
| import sys | |
| wav_path = sys.argv[1] | |
| out_path = sys.argv[2] | |
| # Now we can forward waveforms as Torch tensors | |
| import torch | |
| import torchaudio | |
| torchaudio.set_audio_backend('sox') | |
| x, sr = torchaudio.load(wav_path) | |
| x = x.view(-1).cuda() | |
| x = x.view(1, 1, -1) | |
| with torch.no_grad(): | |
| pase.eval() | |
| # y size will be (1, 256, 625), which are 625 frames of 256 dims each | |
| y = pase(x)[0].transpose(0, 1) | |
| torch.save(y.detach().cpu(), out_path) | |