modules/F0Predictor/CrepeF0Predictor.py

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