73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
import numpy as np
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import parselmouth
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from modules.F0Predictor.F0Predictor import F0Predictor
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class PMF0Predictor(F0Predictor):
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def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
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self.hop_length = hop_length
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self.f0_min = f0_min
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self.f0_max = f0_max
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self.sampling_rate = sampling_rate
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def interpolate_f0(self,f0):
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'''
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对F0进行插值处理
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'''
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vuv_vector = np.zeros_like(f0, dtype=np.float32)
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vuv_vector[f0 > 0.0] = 1.0
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vuv_vector[f0 <= 0.0] = 0.0
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nzindex = np.nonzero(f0)[0]
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data = f0[nzindex]
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nzindex = nzindex.astype(np.float32)
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time_org = self.hop_length / self.sampling_rate * nzindex
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time_frame = np.arange(f0.shape[0]) * self.hop_length / self.sampling_rate
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if data.shape[0] <= 0:
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return np.zeros(f0.shape[0], dtype=np.float32),vuv_vector
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if data.shape[0] == 1:
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return np.ones(f0.shape[0], dtype=np.float32) * f0[0],vuv_vector
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f0 = np.interp(time_frame, time_org, data, left=data[0], right=data[-1])
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return f0,vuv_vector
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def compute_f0(self,wav,p_len=None):
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x = wav
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if p_len is None:
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p_len = x.shape[0]//self.hop_length
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else:
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assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error"
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time_step = self.hop_length / self.sampling_rate * 1000
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f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac(
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time_step=time_step / 1000, voicing_threshold=0.6,
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pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency']
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pad_size=(p_len - len(f0) + 1) // 2
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if(pad_size>0 or p_len - len(f0) - pad_size>0):
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f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
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f0,uv = self.interpolate_f0(f0)
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return f0
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def compute_f0_uv(self,wav,p_len=None):
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x = wav
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if p_len is None:
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p_len = x.shape[0]//self.hop_length
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else:
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assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error"
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time_step = self.hop_length / self.sampling_rate * 1000
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f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac(
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time_step=time_step / 1000, voicing_threshold=0.6,
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pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency']
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pad_size=(p_len - len(f0) + 1) // 2
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if(pad_size>0 or p_len - len(f0) - pad_size>0):
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f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
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f0,uv = self.interpolate_f0(f0)
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return f0,uv
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