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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import os
import cv2 as cv
import numpy as np
from python_speech_features import sigproc
from python_speech_features import mfcc
from sklearn import preprocessing
def wave2spectrum(sample_rate, wave, winlen=0.025, winstep=0.01, nfft=512):
"""计算功率谱图像"""
frames = sigproc.framesig(
sig=wave,
frame_len=winlen * sample_rate,
frame_step=winstep * sample_rate,
winfunc=np.hamming
)
spectrum = sigproc.powspec(
frames=frames,
NFFT=nfft
)
spectrum = spectrum.T
return spectrum
def wave2spectrum_image(
wave, sample_rate,
xmax=10, xmin=-50,
winlen=0.025, winstep=0.01, nfft=512,
n_low_freq=None
):
"""
:return: numpy.ndarray, shape=(time_step, n_dim)
"""
spectrum = wave2spectrum(
sample_rate, wave,
winlen=winlen,
winstep=winstep,
nfft=nfft,
)
spectrum = np.log(spectrum, out=np.zeros_like(spectrum), where=(spectrum != 0))
spectrum = spectrum.T
gray = 255 * (spectrum - xmin) / (xmax - xmin)
gray = np.array(gray, dtype=np.uint8)
if n_low_freq is not None:
gray = gray[:, :n_low_freq]
return gray
def compute_delta(specgram: np.ndarray, win_length: int = 5):
"""
:param specgram: shape=[time_steps, n_mels]
:param win_length:
:return:
"""
n = (win_length - 1) // 2
specgram = np.array(specgram, dtype=np.float32)
kernel = np.arange(-n, n + 1, 1)
kernel = np.reshape(kernel, newshape=(2 * n + 1, 1))
kernel = np.array(kernel, dtype=np.float32) / 10
delta = cv.filter2D(
src=specgram,
ddepth=cv.CV_32F,
kernel=kernel,
)
return delta
def delta_mfcc_feature(signal, sample_rate):
"""
为 GMM UBM 声纹识别模型, 编写此代码.
https://github.com/pventrella20/Speaker_identification_-GMM-UBM-
https://github.com/MChamith/Speaker_verification_gmm_ubm
:param signal: np.ndarray
:param sample_rate: frequenza del file audio
:return:
"""
# shape=[time_steps, n_mels]
mfcc_feat = mfcc(
signal=signal,
samplerate=sample_rate,
winlen=0.025,
winstep=0.01,
numcep=20,
appendEnergy=True
)
mfcc_feat = preprocessing.scale(mfcc_feat)
delta = compute_delta(mfcc_feat)
combined = np.hstack(tup=(mfcc_feat, delta))
return combined
if __name__ == '__main__':
pass
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