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from typing import List, Optional, Union | |
from numpy import ndarray | |
class DF: | |
def __init__( | |
self, | |
sr: int, | |
fft_size: int, | |
hop_size: int, | |
nb_bands: int, | |
min_nb_erb_freqs: Optional[int] = 1, | |
): | |
"""DeepFilter state used for analysis and synthesis. | |
Args: | |
sr (int): Sampling rate. | |
fft_size (int): Window length used for the Fast Fourier transform. | |
hop_size (int): Hop size between two analysis windows. Also called frame size. | |
nb_bands (int): Number of ERB bands. | |
min_nb_erb_freqs (int): Minimum number of frequency bands per ERB band. Defaults to 1. | |
""" | |
... | |
def analysis(self, input: ndarray) -> ndarray: | |
"""Analysis of a time-domain signal. | |
Args: | |
input (ndarray): 2D real-valued array of shape [C, T]. | |
Output: | |
output (ndarray): 3D complex-valued array of shape [C, T', F], where F is the `fft_size`, | |
and T' the original time T divided by `hop_size`. | |
""" | |
... | |
def synthesis(self, input: ndarray) -> ndarray: | |
"""Synthesis of a frequency-domain signal. | |
Args: | |
input (ndarray): 3D complex-valued array of shape [C, T, F]. | |
Output: | |
output (ndarray): 2D real-valued array of shape [C, T]. | |
""" | |
... | |
def erb_widths(self) -> ndarray: ... | |
def fft_window(self) -> ndarray: ... | |
def sr(self) -> int: ... | |
def fft_size(self) -> int: ... | |
def hop_size(self) -> int: ... | |
def nb_erb(self) -> int: ... | |
def reset(self) -> None: ... | |
def erb( | |
input: ndarray, erb_fb: Union[ndarray, List[int]], db: Optional[bool] = None | |
) -> ndarray: ... | |
def erb_inv(input: ndarray, erb_fb: Union[ndarray, List[int]]) -> ndarray: ... | |
def erb_norm(erb: ndarray, alpha: float, state: Optional[ndarray] = None) -> ndarray: ... | |
def unit_norm(spec: ndarray, alpha: float, state: Optional[ndarray] = None) -> ndarray: ... | |
def unit_norm_init(num_freq_bins: int) -> ndarray: ... | |