| from modules.F0Predictor.F0Predictor import F0Predictor | |
| from modules.F0Predictor.crepe import CrepePitchExtractor | |
| import torch | |
| class CrepeF0Predictor(F0Predictor): | |
| def __init__(self,hop_length=512,f0_min=50,f0_max=1100,device=None,sampling_rate=44100,threshold=0.05,model="full"): | |
| self.F0Creper = CrepePitchExtractor(hop_length=hop_length,f0_min=f0_min,f0_max=f0_max,device=device,threshold=threshold,model=model) | |
| self.hop_length = hop_length | |
| self.f0_min = f0_min | |
| self.f0_max = f0_max | |
| self.device = device | |
| self.threshold = threshold | |
| self.sampling_rate = sampling_rate | |
| def compute_f0(self,wav,p_len=None): | |
| x = torch.FloatTensor(wav).to(self.device) | |
| if p_len is None: | |
| p_len = x.shape[0]//self.hop_length | |
| else: | |
| assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error" | |
| f0,uv = self.F0Creper(x[None,:].float(),self.sampling_rate,pad_to=p_len) | |
| return f0 | |
| def compute_f0_uv(self,wav,p_len=None): | |
| x = torch.FloatTensor(wav).to(self.device) | |
| if p_len is None: | |
| p_len = x.shape[0]//self.hop_length | |
| else: | |
| assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error" | |
| f0,uv = self.F0Creper(x[None,:].float(),self.sampling_rate,pad_to=p_len) | |
| return f0,uv |