|
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 |
|
|
