| from typing import Dict |
| from numpy import ndarray |
|
|
| import resampy |
| import torch |
| import torchcrepe |
| import numpy as np |
|
|
| from TorchJAEKWON.DataProcess.Util.UtilAudio import UtilAudio |
|
|
| class UtilAudioPlus(UtilAudio): |
| def get_pitch_crepe(self, |
| wav:ndarray, |
| sample_rate:float, |
| hop_size:int, |
| spec_time_bin_length:int, |
| f0_min:float = 50.0, |
| f0_max:float = 1100.0, |
| threshold:float=0.05, |
| device = torch.device("cuda")) -> Dict[str,ndarray]: |
| |
| wav16k = resampy.resample(wav, sample_rate, 16000) |
| wav16k_torch = torch.FloatTensor(wav16k).unsqueeze(0).to(device) |
|
|
| f0, pd = torchcrepe.predict(wav16k_torch, 16000, 80, f0_min, f0_max, pad=True, model='full', batch_size=1024, device=device, return_periodicity=True) |
|
|
| pd = torchcrepe.filter.median(pd, 3) |
| pd = torchcrepe.threshold.Silence(-60.)(pd, wav16k_torch, 16000, 80) |
| f0 = torchcrepe.threshold.At(threshold)(f0, pd) |
| f0 = torchcrepe.filter.mean(f0, 3) |
|
|
| f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0) |
| |
| nzindex = torch.nonzero(f0[0]).squeeze() |
| f0 = torch.index_select(f0[0], dim=0, index=nzindex).cpu().numpy() |
| time_org = 0.005 * nzindex.cpu().numpy() |
| time_frame = np.arange(spec_time_bin_length) * hop_size / sample_rate |
| if f0.shape[0] == 0: |
| f0 = torch.FloatTensor(time_frame.shape[0]).fill_(0) |
| print('f0 all zero!') |
| else: |
| f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1]) |
| pitch_coarse = self.f0_to_coarse(f0) |
| return {'f0':f0, 'pitch':pitch_coarse} |
|
|
| def f0_to_coarse(self, |
| f0:ndarray, |
| f0_bin:int = 256, |
| f0_min:float = 50.0, |
| f0_max:float = 1100.0) -> ndarray: |
| |
| is_torch = isinstance(f0, torch.Tensor) |
| f0_mel_min = 1127 * np.log(1 + f0_min / 700) |
| f0_mel_max = 1127 * np.log(1 + f0_max / 700) |
| f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700) |
| f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1 |
|
|
| f0_mel[f0_mel <= 1] = 1 |
| f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1 |
| f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(int) |
| assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min()) |
| return f0_coarse |
