utility.py
def pitch_conversion(self, f0, source_speaker, target_speaker):
        '''Logarithm Gaussian normalization for Pitch Conversions'''

        mean_log_src = self.norm_dict[source_speaker]['log_f0s_mean']
        std_log_src = self.norm_dict[source_speaker]['log_f0s_std']

        mean_log_target = self.norm_dict[target_speaker]['log_f0s_mean']
        std_log_target = self.norm_dict[target_speaker]['log_f0s_std']

        f0_converted = np.exp((np.ma.log(f0) - mean_log_src) / std_log_src * std_log_target + mean_log_target)

        return f0_converted
Logarithm Gaussian normalization for Pitch Conversions
Logarithm Gaussian normalization for Pitch Conversions
Logarithm Gaussian normalization for Pitch Conversions
def pitch_conversion(self, f0, source_speaker, target_speaker):
        mean_log_src = self.norm_dict[source_speaker]['log_f0s_mean']
        std_log_src = self.norm_dict[source_speaker]['log_f0s_std']
        mean_log_target = self.norm_dict[target_speaker]['log_f0s_mean']
        std_log_target = self.norm_dict[target_speaker]['log_f0s_std']
        f0_converted = np.exp((np.ma.log(f0) - mean_log_src) / std_log_src * std_log_target + mean_log_target)
        return f0_converted
def pitch_conversion ( self , f0 , source_speaker , target_speaker ) : mean_log_src = self . norm_dict [ source_speaker ] [ 'log_f0s_mean' ] std_log_src = self . norm_dict [ source_speaker ] [ 'log_f0s_std' ] mean_log_target = self . norm_dict [ target_speaker ] [ 'log_f0s_mean' ] std_log_target = self . norm_dict [ target_speaker ] [ 'log_f0s_std' ] f0_converted = np . exp ( ( np . ma . log ( f0 ) - mean_log_src ) / std_log_src * std_log_target + mean_log_target ) return f0_converted
Logarithm Gaussian normalization for Pitch Conversions
Logarithm Gaussian normalization for Pitch Conversions
'''Logarithm Gaussian normalization for Pitch Conversions'''
self
f0
source_speaker
target_speaker