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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,105 @@ | ||
| import numpy as np | ||
| import torch | ||
| import torch.nn.functional as F | ||
| from vdecoder.nsf_hifigan.nvSTFT import STFT | ||
| from vdecoder.nsf_hifigan.models import load_model | ||
| from torchaudio.transforms import Resample | ||
|
|
||
| class Enhancer: | ||
| def __init__(self, enhancer_type, enhancer_ckpt, device=None): | ||
| if device is None: | ||
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | ||
| self.device = device | ||
|
|
||
| if enhancer_type == 'nsf-hifigan': | ||
| self.enhancer = NsfHifiGAN(enhancer_ckpt, device=self.device) | ||
| else: | ||
| raise ValueError(f" [x] Unknown enhancer: {enhancer_type}") | ||
|
|
||
| self.resample_kernel = {} | ||
| self.enhancer_sample_rate = self.enhancer.sample_rate() | ||
| self.enhancer_hop_size = self.enhancer.hop_size() | ||
|
|
||
| def enhance(self, | ||
| audio, # 1, T | ||
| sample_rate, | ||
| f0, # 1, n_frames, 1 | ||
| hop_size, | ||
| adaptive_key = 0, | ||
| silence_front = 0 | ||
| ): | ||
| # enhancer start time | ||
| start_frame = int(silence_front * sample_rate / hop_size) | ||
| real_silence_front = start_frame * hop_size / sample_rate | ||
| audio = audio[:, int(np.round(real_silence_front * sample_rate)) : ] | ||
| f0 = f0[: , start_frame :, :] | ||
|
|
||
| # adaptive parameters | ||
| adaptive_factor = 2 ** ( -adaptive_key / 12) | ||
| adaptive_sample_rate = 100 * int(np.round(self.enhancer_sample_rate / adaptive_factor / 100)) | ||
| real_factor = self.enhancer_sample_rate / adaptive_sample_rate | ||
|
|
||
| # resample the ddsp output | ||
| if sample_rate == adaptive_sample_rate: | ||
| audio_res = audio | ||
| else: | ||
| key_str = str(sample_rate) + str(adaptive_sample_rate) | ||
| if key_str not in self.resample_kernel: | ||
| self.resample_kernel[key_str] = Resample(sample_rate, adaptive_sample_rate, lowpass_filter_width = 128).to(self.device) | ||
| audio_res = self.resample_kernel[key_str](audio) | ||
|
|
||
| n_frames = int(audio_res.size(-1) // self.enhancer_hop_size + 1) | ||
|
|
||
| # resample f0 | ||
| f0_np = f0.squeeze(0).squeeze(-1).cpu().numpy() | ||
| f0_np *= real_factor | ||
| time_org = (hop_size / sample_rate) * np.arange(len(f0_np)) / real_factor | ||
| time_frame = (self.enhancer_hop_size / self.enhancer_sample_rate) * np.arange(n_frames) | ||
| f0_res = np.interp(time_frame, time_org, f0_np, left=f0_np[0], right=f0_np[-1]) | ||
| f0_res = torch.from_numpy(f0_res).unsqueeze(0).float().to(self.device) # 1, n_frames | ||
|
|
||
| # enhance | ||
| enhanced_audio, enhancer_sample_rate = self.enhancer(audio_res, f0_res) | ||
|
|
||
| # resample the enhanced output | ||
| if adaptive_factor != 0: | ||
| key_str = str(adaptive_sample_rate) + str(enhancer_sample_rate) | ||
| if key_str not in self.resample_kernel: | ||
| self.resample_kernel[key_str] = Resample(adaptive_sample_rate, enhancer_sample_rate, lowpass_filter_width = 128).to(self.device) | ||
| enhanced_audio = self.resample_kernel[key_str](enhanced_audio) | ||
|
|
||
| # pad the silence frames | ||
| if start_frame > 0: | ||
| enhanced_audio = F.pad(enhanced_audio, (int(np.round(enhancer_sample_rate * real_silence_front)), 0)) | ||
|
|
||
| return enhanced_audio, enhancer_sample_rate | ||
|
|
||
|
|
||
| class NsfHifiGAN(torch.nn.Module): | ||
| def __init__(self, model_path, device=None): | ||
| super().__init__() | ||
| if device is None: | ||
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | ||
| self.device = device | ||
| print('| Load HifiGAN: ', model_path) | ||
| self.model, self.h = load_model(model_path, device=self.device) | ||
|
|
||
| def sample_rate(self): | ||
| return self.h.sampling_rate | ||
|
|
||
| def hop_size(self): | ||
| return self.h.hop_size | ||
|
|
||
| def forward(self, audio, f0): | ||
| stft = STFT( | ||
| self.h.sampling_rate, | ||
| self.h.num_mels, | ||
| self.h.n_fft, | ||
| self.h.win_size, | ||
| self.h.hop_size, | ||
| self.h.fmin, | ||
| self.h.fmax) | ||
| with torch.no_grad(): | ||
| mel = stft.get_mel(audio) | ||
| enhanced_audio = self.model(mel, f0[:,:mel.size(-1)]).view(-1) | ||
| return enhanced_audio, self.h.sampling_rate |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,15 @@ | ||
| import os | ||
| import shutil | ||
|
|
||
|
|
||
| class AttrDict(dict): | ||
| def __init__(self, *args, **kwargs): | ||
| super(AttrDict, self).__init__(*args, **kwargs) | ||
| self.__dict__ = self | ||
|
|
||
|
|
||
| def build_env(config, config_name, path): | ||
| t_path = os.path.join(path, config_name) | ||
| if config != t_path: | ||
| os.makedirs(path, exist_ok=True) | ||
| shutil.copyfile(config, os.path.join(path, config_name)) |
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