from dataclasses import dataclass
import numpy as np
import torchaudio
from lhotse.features.base import TorchaudioFeatureExtractor, register_extractor
from lhotse.utils import EPSILON, Seconds
[docs]@dataclass
class FbankConfig:
# Spectogram-related part
dither: float = 0.0
window_type: str = "povey"
# Note that frame_length and frame_shift will be converted to milliseconds before torchaudio/Kaldi sees them
frame_length: Seconds = 0.025
frame_shift: Seconds = 0.01
remove_dc_offset: bool = True
round_to_power_of_two: bool = True
energy_floor: float = EPSILON
min_duration: float = 0.0
preemphasis_coefficient: float = 0.97
raw_energy: bool = True
# Fbank-related part
low_freq: float = 20.0
high_freq: float = -400.0
num_mel_bins: int = 40
use_energy: bool = False
vtln_low: float = 100.0
vtln_high: float = -500.0
vtln_warp: float = 1.0
[docs]@register_extractor
class Fbank(TorchaudioFeatureExtractor):
"""Log Mel energy filter bank feature extractor based on ``torchaudio.compliance.kaldi.fbank`` function."""
name = 'fbank'
config_type = FbankConfig
feature_fn = staticmethod(torchaudio.compliance.kaldi.fbank)
[docs] def feature_dim(self, sampling_rate: int) -> int:
return self.config.num_mel_bins
[docs] @staticmethod
def mix(features_a: np.ndarray, features_b: np.ndarray, energy_scaling_factor_b: float) -> np.ndarray:
return np.log(
np.maximum(
# protection against log(0); max with EPSILON is adequate since these are energies (always >= 0)
EPSILON,
np.exp(features_a) + energy_scaling_factor_b * np.exp(features_b)
)
)
[docs] @staticmethod
def compute_energy(features: np.ndarray) -> float:
return float(np.sum(np.exp(features)))