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import glob |
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import json |
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import os |
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import re |
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import librosa |
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import torch |
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import utils |
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from modules.hifigan.hifigan import HifiGanGenerator |
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from utils.hparams import hparams, set_hparams |
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from network.vocoders.base_vocoder import register_vocoder |
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from network.vocoders.pwg import PWG |
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from network.vocoders.vocoder_utils import denoise |
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def load_model(config_path, file_path): |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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ext = os.path.splitext(file_path)[-1] |
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if ext == '.pth': |
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if '.yaml' in config_path: |
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config = set_hparams(config_path, global_hparams=False) |
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elif '.json' in config_path: |
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config = json.load(open(config_path, 'r', encoding='utf-8')) |
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model = torch.load(file_path, map_location="cpu") |
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elif ext == '.ckpt': |
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ckpt_dict = torch.load(file_path, map_location="cpu") |
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if '.yaml' in config_path: |
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config = set_hparams(config_path, global_hparams=False) |
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state = ckpt_dict["state_dict"]["model_gen"] |
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elif '.json' in config_path: |
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config = json.load(open(config_path, 'r', encoding='utf-8')) |
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state = ckpt_dict["generator"] |
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model = HifiGanGenerator(config) |
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model.load_state_dict(state, strict=True) |
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model.remove_weight_norm() |
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model = model.eval().to(device) |
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print(f"| Loaded model parameters from {file_path}.") |
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print(f"| HifiGAN device: {device}.") |
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return model, config, device |
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total_time = 0 |
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@register_vocoder |
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class HifiGAN(PWG): |
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def __init__(self): |
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base_dir = hparams['vocoder_ckpt'] |
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config_path = f'{base_dir}/config.yaml' |
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if os.path.exists(config_path): |
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file_path = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.*'), key= |
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lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).*', x.replace('\\','/'))[0]))[-1] |
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print('| load HifiGAN: ', file_path) |
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self.model, self.config, self.device = load_model(config_path=config_path, file_path=file_path) |
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else: |
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config_path = f'{base_dir}/config.json' |
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ckpt = f'{base_dir}/generator_v1' |
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if os.path.exists(config_path): |
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self.model, self.config, self.device = load_model(config_path=config_path, file_path=file_path) |
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def spec2wav(self, mel, **kwargs): |
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device = self.device |
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with torch.no_grad(): |
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c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(device) |
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with utils.Timer('hifigan', print_time=hparams['profile_infer']): |
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f0 = kwargs.get('f0') |
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if f0 is not None and hparams.get('use_nsf'): |
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f0 = torch.FloatTensor(f0[None, :]).to(device) |
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y = self.model(c, f0).view(-1) |
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else: |
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y = self.model(c).view(-1) |
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wav_out = y.cpu().numpy() |
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if hparams.get('vocoder_denoise_c', 0.0) > 0: |
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wav_out = denoise(wav_out, v=hparams['vocoder_denoise_c']) |
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return wav_out |
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