Spaces:
Sleeping
Sleeping
| from __future__ import absolute_import, division, print_function, unicode_literals | |
| import glob | |
| import os | |
| import numpy as np | |
| import argparse | |
| import json | |
| import torch | |
| from scipy.io.wavfile import write | |
| from .env import AttrDict | |
| from .meldataset import MAX_WAV_VALUE | |
| from .models import Generator | |
| def load_checkpoint(filepath, device): | |
| assert os.path.isfile(filepath) | |
| print("Loading '{}'".format(filepath)) | |
| checkpoint_dict = torch.load(filepath, map_location=device) | |
| print("Complete.") | |
| return checkpoint_dict | |
| def scan_checkpoint(cp_dir, prefix): | |
| pattern = os.path.join(cp_dir, prefix + '*') | |
| cp_list = glob.glob(pattern) | |
| if len(cp_list) == 0: | |
| return '' | |
| return sorted(cp_list)[-1] | |
| class HiFiGAN: | |
| def __init__(self,checkpoint:os.PathLike, h=None, device='cuda'): | |
| config_file = os.path.join(os.path.split(checkpoint)[0], 'config.json') | |
| with open(config_file) as f: | |
| data = f.read() | |
| json_config = json.loads(data) | |
| h = AttrDict(json_config) | |
| self.generator = Generator(h).to(device) | |
| state_dict_g = load_checkpoint(checkpoint, device) | |
| self.generator.load_state_dict(state_dict_g['generator']) | |
| self.generator.eval() | |
| self.generator.remove_weight_norm() | |
| self.device = device | |
| def inference(self,x): | |
| with torch.no_grad(): | |
| if isinstance(x,np.ndarray): | |
| x = torch.FloatTensor(x).to(self.device) | |
| else: | |
| x = x.to(self.device) | |
| y_g_hat = self.generator(x.unsqueeze(0)) | |
| audio = y_g_hat.squeeze() | |
| audio = audio.cpu().numpy() | |
| return audio | |
| def __call__(self,x): | |
| return self.inference(x) | |