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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright 2016-2099 Ailemon.net
#
# This file is part of ASRT Speech Recognition Tool.
#
# ASRT is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# ASRT is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ASRT. If not, see <https://www.gnu.org/licenses/>.
# ============================================================================
"""
@author: nl8590687
ASRT语音识别内置声学特征提取模块，定义了几个常用的声学特征类
"""
import random
import numpy as np
from scipy.fftpack import fft
from .base import mfcc, delta, logfbank
class SpeechFeatureMeta:
"""
ASRT语音识别中所有声学特征提取类的基类
"""
def __init__(self, framesamplerate=16000):
self.framesamplerate = framesamplerate
def run(self, wavsignal, fs=16000):
'''
run method
'''
raise NotImplementedError('[ASRT] `run()` method is not implemented.')
class MFCC(SpeechFeatureMeta):
"""
ASRT语音识别内置的mfcc声学特征提取类
Compute MFCC features from an audio signal.
:param framesamplerate: the sample rate of the signal we are working with, in Hz.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
:param numcep: the number of cepstrum to return, default 13
:param nfilt: the number of filters in the filterbank, default 26.
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
"""
def __init__(self, framesamplerate=16000,
winlen=0.025,
winstep=0.01,
numcep=13,
nfilt=26,
preemph=0.97):
self.framesamplerate = framesamplerate
self.winlen = winlen
self.winstep = winstep
self.numcep = numcep
self.nfilt = nfilt
self.preemph = preemph
super().__init__(framesamplerate)
def run(self, wavsignal, fs=16000):
"""
计算mfcc声学特征，包含静态特征、一阶差分和二阶差分
:returns: A numpy array of size (NUMFRAMES by numcep * 3) containing features. Each row holds 1 feature vector.
"""
wavsignal = np.array(wavsignal, dtype=np.float64)
# 获取输入特征
feat_mfcc = mfcc(wavsignal[0], samplerate=self.framesamplerate, winlen=self.winlen,
winstep=self.winstep, numcep=self.numcep, nfilt=self.nfilt, preemph=self.preemph)
feat_mfcc_d = delta(feat_mfcc, 2)
feat_mfcc_dd = delta(feat_mfcc_d, 2)
# 返回值分别是mfcc特征向量的矩阵及其一阶差分和二阶差分矩阵
wav_feature = np.column_stack((feat_mfcc, feat_mfcc_d, feat_mfcc_dd))
return wav_feature
class Logfbank(SpeechFeatureMeta):
"""
ASRT语音识别内置的logfbank声学特征提取类
"""
def __init__(self, framesamplerate=16000, nfilt=26):
self.nfilt = nfilt
super().__init__(framesamplerate)
def run(self, wavsignal, fs=16000):
wavsignal = np.array(wavsignal, dtype=np.float64)
# 获取输入特征
wav_feature = logfbank(wavsignal, fs, nfilt=self.nfilt)
return wav_feature
class Spectrogram(SpeechFeatureMeta):
"""
ASRT语音识别内置的语谱图声学特征提取类
"""
def __init__(self, framesamplerate=16000, timewindow=25, timeshift=10):
self.time_window = timewindow
self.window_length = int(framesamplerate / 1000 * self.time_window) # 计算窗长度的公式，目前全部为400固定值
self.timeshift = timeshift
'''
# 保留将来用于不同采样频率
self.x=np.linspace(0, self.window_length - 1, self.window_length, dtype = np.int64)
self.w = 0.54 - 0.46 * np.cos(2 * np.pi * (self.x) / (self.window_length - 1) ) # 汉明窗
'''
self.x = np.linspace(0, 400 - 1, 400, dtype=np.int64)
self.w = 0.54 - 0.46 * np.cos(2 * np.pi * (self.x) / (400 - 1)) # 汉明窗
super().__init__(framesamplerate)
def run(self, wavsignal, fs=16000):
if fs != 16000:
raise ValueError(
f"[Error] ASRT currently only supports wav audio files with a sampling rate of 16000 Hz, but this "
f"audio is {fs} Hz.")
# wav波形 加时间窗以及时移10ms
time_window = 25 # 单位ms
window_length = int(fs / 1000 * time_window) # 计算窗长度的公式，目前全部为400固定值
wav_arr = np.array(wavsignal)
# wav_length = len(wavsignal[0])
# wav_length = wav_arr.shape[1]
range0_end = int(len(wavsignal[0]) / fs * 1000 - time_window) // 10 + 1 # 计算循环终止的位置，也就是最终生成的窗数
data_input = np.zeros((range0_end, window_length // 2), dtype=np.float64) # 用于存放最终的频率特征数据
data_line = np.zeros((1, window_length), dtype=np.float64)
for i in range(0, range0_end):
p_start = i * 160
p_end = p_start + 400
data_line = wav_arr[0, p_start:p_end]
data_line = data_line * self.w # 加窗
data_line = np.abs(fft(data_line))
data_input[i] = data_line[0: window_length // 2] # 设置为400除以2的值（即200）是取一半数据，因为是对称的
data_input = np.log(data_input + 1)
return data_input
class SpecAugment(SpeechFeatureMeta):
"""
复现谷歌SpecAugment数据增强特征算法，基于Spectrogram语谱图基础特征
"""
def __init__(self, framesamplerate=16000, timewindow=25, timeshift=10):
self.time_window = timewindow
self.window_length = int(framesamplerate / 1000 * self.time_window) # 计算窗长度的公式，目前全部为400固定值
self.timeshift = timeshift
'''
# 保留将来用于不同采样频率
self.x=np.linspace(0, self.window_length - 1, self.window_length, dtype = np.int64)
self.w = 0.54 - 0.46 * np.cos(2 * np.pi * (self.x) / (self.window_length - 1) ) # 汉明窗
'''
self.x = np.linspace(0, 400 - 1, 400, dtype=np.int64)
self.w = 0.54 - 0.46 * np.cos(2 * np.pi * (self.x) / (400 - 1)) # 汉明窗
super().__init__(framesamplerate)
def run(self, wavsignal, fs=16000):
if fs != 16000:
raise ValueError(
f"[Error] ASRT currently only supports wav audio files with a sampling rate of 16000 Hz, but this "
f"audio is {fs} Hz.")
# wav波形 加时间窗以及时移10ms
time_window = 25 # 单位ms
window_length = int(fs / 1000 * time_window) # 计算窗长度的公式，目前全部为400固定值
wav_arr = np.array(wavsignal)
# wav_length = len(wavsignal[0])
# wav_length = wav_arr.shape[1]
range0_end = int(len(wavsignal[0]) / fs * 1000 - time_window) // 10 + 1 # 计算循环终止的位置，也就是最终生成的窗数
data_input = np.zeros((range0_end, window_length // 2), dtype=np.float64) # 用于存放最终的频率特征数据
data_line = np.zeros((1, window_length), dtype=np.float64)
for i in range(0, range0_end):
p_start = i * 160
p_end = p_start + 400
data_line = wav_arr[0, p_start:p_end]
data_line = data_line * self.w # 加窗
data_line = np.abs(fft(data_line))
data_input[i] = data_line[0: window_length // 2] # 设置为400除以2的值（即200）是取一半数据，因为是对称的
# print(data_input.shape)
data_input = np.log(data_input + 1)
# 开始对得到的特征应用SpecAugment
mode = random.randint(1, 100)
h_start = random.randint(1, data_input.shape[0])
h_width = random.randint(1, 100)
v_start = random.randint(1, data_input.shape[1])
v_width = random.randint(1, 100)
if mode <= 60: # 正常特征 60%
pass
elif 60 < mode <= 75: # 横向遮盖 15%
data_input[h_start:h_start + h_width, :] = 0
elif 75 < mode <= 90: # 纵向遮盖 15%
data_input[:, v_start:v_start + v_width] = 0
else: # 两种遮盖叠加 10%
data_input[h_start:h_start + h_width, :v_start:v_start + v_width] = 0
return data_input
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