import numpy as np import parselmouth from modules.F0Predictor.F0Predictor import F0Predictor class PMF0Predictor(F0Predictor): def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100): self.hop_length = hop_length self.f0_min = f0_min self.f0_max = f0_max self.sampling_rate = sampling_rate self.name = "pm" def interpolate_f0(self,f0): ''' 对F0进行插值处理 ''' vuv_vector = np.zeros_like(f0, dtype=np.float32) vuv_vector[f0 > 0.0] = 1.0 vuv_vector[f0 <= 0.0] = 0.0 nzindex = np.nonzero(f0)[0] data = f0[nzindex] nzindex = nzindex.astype(np.float32) time_org = self.hop_length / self.sampling_rate * nzindex time_frame = np.arange(f0.shape[0]) * self.hop_length / self.sampling_rate if data.shape[0] <= 0: return np.zeros(f0.shape[0], dtype=np.float32),vuv_vector if data.shape[0] == 1: return np.ones(f0.shape[0], dtype=np.float32) * f0[0],vuv_vector f0 = np.interp(time_frame, time_org, data, left=data[0], right=data[-1]) return f0,vuv_vector def compute_f0(self,wav,p_len=None): x = wav 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" time_step = self.hop_length / self.sampling_rate * 1000 f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac( time_step=time_step / 1000, voicing_threshold=0.6, pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency'] pad_size=(p_len - len(f0) + 1) // 2 if(pad_size>0 or p_len - len(f0) - pad_size>0): f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant') f0,uv = self.interpolate_f0(f0) return f0 def compute_f0_uv(self,wav,p_len=None): x = wav 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" time_step = self.hop_length / self.sampling_rate * 1000 f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac( time_step=time_step / 1000, voicing_threshold=0.6, pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency'] pad_size=(p_len - len(f0) + 1) // 2 if(pad_size>0 or p_len - len(f0) - pad_size>0): f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant') f0,uv = self.interpolate_f0(f0) return f0,uv