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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 | |