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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import math
import numpy as np
def freq2erb(freq_hz: float) -> float:
"""
https://www.cnblogs.com/LXP-Never/p/16011229.html
1 / (24.7 * 9.265) = 0.00436976
"""
return 9.265 * math.log(freq_hz / (24.7 * 9.265) + 1)
def erb2freq(n_erb: float) -> float:
return 24.7 * 9.265 * (math.exp(n_erb / 9.265) - 1)
def get_erb_widths(sample_rate: int, fft_size: int, erb_bins: int, min_freq_bins_for_erb: int) -> np.ndarray:
"""
https://github.com/Rikorose/DeepFilterNet/blob/main/libDF/src/lib.rs
:param sample_rate:
:param fft_size:
:param erb_bins: erb (Equivalent Rectangular Bandwidth) 等效矩形带宽的通道数.
:param min_freq_bins_for_erb: Minimum number of frequency bands per erb band
:return:
"""
nyq_freq = sample_rate / 2.
freq_width: float = sample_rate / fft_size
min_erb: float = freq2erb(0.)
max_erb: float = freq2erb(nyq_freq)
erb = [0] * erb_bins
step = (max_erb - min_erb) / erb_bins
prev_freq_bin = 0
freq_over = 0
for i in range(1, erb_bins + 1):
f = erb2freq(min_erb + i * step)
freq_bin = int(round(f / freq_width))
freq_bins = freq_bin - prev_freq_bin - freq_over
if freq_bins < min_freq_bins_for_erb:
freq_over = min_freq_bins_for_erb - freq_bins
freq_bins = min_freq_bins_for_erb
else:
freq_over = 0
erb[i - 1] = freq_bins
prev_freq_bin = freq_bin
erb[erb_bins - 1] += 1
too_large = sum(erb) - (fft_size / 2 + 1)
if too_large > 0:
erb[erb_bins - 1] -= too_large
return np.array(erb, dtype=np.uint64)
def get_erb_filter_bank(erb_widths: np.ndarray,
sample_rate: int,
normalized: bool = True,
inverse: bool = False,
):
num_freq_bins = int(np.sum(erb_widths))
num_erb_bins = len(erb_widths)
fb: np.ndarray = np.zeros(shape=(num_freq_bins, num_erb_bins))
points = np.cumsum([0] + erb_widths.tolist()).astype(int)[:-1]
for i, (b, w) in enumerate(zip(points.tolist(), erb_widths.tolist())):
fb[b: b + w, i] = 1
if inverse:
fb = fb.T
if not normalized:
fb /= np.sum(fb, axis=1, keepdims=True)
else:
if normalized:
fb /= np.sum(fb, axis=0)
return fb
def spec2erb(spec: np.ndarray, erb_fb: np.ndarray, db: bool = True):
"""
ERB filterbank and transform to decibel scale.
:param spec: Spectrum of shape [B, C, T, F].
:param erb_fb: ERB filterbank array of shape [B] containing the ERB widths,
where B are the number of ERB bins.
:param db: Whether to transform the output into decibel scale. Defaults to `True`.
:return:
"""
# complex spec to power spec. (real * real + image * image)
spec_ = np.abs(spec) ** 2
# spec to erb feature.
erb_feat = np.matmul(spec_, erb_fb)
if db:
erb_feat = 10 * np.log10(erb_feat + 1e-10)
erb_feat = np.array(erb_feat, dtype=np.float32)
return erb_feat
def main():
erb_widths = get_erb_widths(
sample_rate=8000,
fft_size=512,
erb_bins=32,
min_freq_bins_for_erb=2,
)
erb_fb = get_erb_filter_bank(
erb_widths=erb_widths,
sample_rate=8000,
)
print(erb_fb.shape)
return
if __name__ == "__main__":
main()
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