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import numpy as np
from fairseq.data.audio.feature_transforms import (
AudioFeatureTransform,
register_audio_feature_transform,
)
@register_audio_feature_transform("utterance_cmvn")
class UtteranceCMVN(AudioFeatureTransform):
"""Utterance-level CMVN (cepstral mean and variance normalization)"""
@classmethod
def from_config_dict(cls, config=None):
_config = {} if config is None else config
return UtteranceCMVN(
_config.get("norm_means", True),
_config.get("norm_vars", True),
)
def __init__(self, norm_means=True, norm_vars=True):
self.norm_means, self.norm_vars = norm_means, norm_vars
def __repr__(self):
return (
self.__class__.__name__
+ f"(norm_means={self.norm_means}, norm_vars={self.norm_vars})"
)
def __call__(self, x):
mean = x.mean(axis=0)
square_sums = (x ** 2).sum(axis=0)
if self.norm_means:
x = np.subtract(x, mean)
if self.norm_vars:
var = square_sums / x.shape[0] - mean ** 2
std = np.sqrt(np.maximum(var, 1e-10))
x = np.divide(x, std)
return x
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