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