GMM-UBM speaker model HTK MSR-Identity-Toolkit Matlab operations Matlab Functions Used Programs.md
- Transform WAV To Readable file
- Extract MFCC Features From Readable File
- Generate 3 Important File
- Use MSR ToolKit To Analyze MFCC Features
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Download TIMIT corpus.
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Create a file (copy TIMIT's two files in it：(1)TEST (2)TRAIN)which has the same directory form as TIMIT corpus： main.py
import os, sys # path1 = 'D:/Project/Speaker-Recognition/timit_wav8000' # path1 = 'D:/Project/Speaker-Recognition/timit_mfcc' path1 = 'D:/Project/Speaker-Recognition/timit_wav16000' print(f'the path to be deleted is {path1}') files1 = os.listdir(path1) count = 0 for file1 in files1: path2 = path1 + '/' + file1 files2 = os.listdir(path2) for file2 in files2: path3 = path2 + '/' + file2 files3 = os.listdir(path3) for file3 in files3: path4 = path3 + '/' + file3 files4 = os.listdir(path4) for file4 in files4: path5 = path4 + '/' + file4 # print(path5) count += 1 os.unlink(path5) print(count) print('complete delete')
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Transform WAV to readable files and save in the above file：
conver_wav.m(入口函数)
path1 = 'D:\Project\Speaker-Recognition\TIMIT'; path2 = 'D:\Project\Speaker-Recognition\timit_wav16000'; len1 = length(path1); % 36 wav_filesName = find_wav(path1); % wav_filesName(1,:) is 'D:\Project\Speaker-Recognition\TIMIT\TEST\DR1\FAKS0\SA1.WAV' % p1 = strfind(wav_filesName(1,:),'.W') % 56 % wav_filesName(1,37:56+3) % is '\TEST\DR1\FAKS0\SA1.WAV' [m,n] = size(wav_filesName); % 保存wav文件名，添加人名前缀 tic hwt = waitbar(0,'please wait....'); for i = 1:m [x,fs] = audioread(wav_filesName(i,:)); p = strfind(wav_filesName(i,:),'.W'); writefileName = [path2,wav_filesName(i,len1+1:p+3)]; audiowrite(writefileName,x,fs); % y = resample(x,1,2); % p = strfind(wav_filesName(i,:),'.W'); % writefileName = [path2,wav_filesName(i,len1+1:p+3)]; % audiowrite(writefileName,y,8000); waitbar(i/m); end close(hwt); toc
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find_wav.m
function [ wav_files ] = find_wav( path ) %FIND_WAV, find all wav file recursively wav_files = []; if(isdir(path) == 0) % is path is not a folder, is a file, then return return; end path_files = dir(path); % path is the folder content of path fileNum = length(path_files); for k= 3:fileNum file = [path,'\', path_files(k).name]; % disp(sprintf('file is %s',file)); if (path_files(k).isdir == 1) ret = find_wav(file); % if path_files is a folder, do the find_wav function to the input(file) % disp(sprintf('ret is %s',ret)); if(isempty(ret) ~= 1) % disp(sprintf('ret is %s',ret)); if(isempty(wav_files)) % disp(sprintf('ret is %s',ret)); wav_files = char(ret); % disp(sprintf('wav_files is %s',wav_files)); else wav_files = char(wav_files, ret); end end elseif strfind(path_files(k).name, '.WAV') if(isempty(wav_files)) wav_files = char(file); else wav_files = char(wav_files, file); end end end end
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check_wav.m
%clear all; %files = find_wav('D:\D\Speaker-Recognition\timit_wav8000'); path2 = 'D:\Project\Speaker-Recognition\timit_wav8000'; files = find_wav(path2); tic hwt = waitbar(0,'please wait....'); for fileIdx = 1:length(files) file = files(fileIdx,:); [y, fs] = audioread(file); if(fs~=8000) fprintf('%s: fs~=8000\n', file); end waitbar(fileIdx/length(files)); end close(hwt); toc
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Download HTK ToolKit and set up its environment.
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find_wav.m
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wav_mfcc_path.m(入口函数)生成targetlist.txt文件
path1 = 'D:\Project\Speaker-Recognition\timit_wav8000'; path2 = 'D:\Project\Speaker-Recognition\timit_mfcc'; filename = 'D:\Project\Speaker-Recognition\Program-Code\03-extract-mfcc-from-wav\targetlist.txt'; wav_filesName = find_wav(path1); [m,n] = size(wav_filesName); len1 = length(path1); fid = fopen(filename, 'w+t'); if fid < 0 fprintf('error opening file\n'); return; end tic hwt = waitbar(0,'please wait....'); count = 0; for i = 1:m p = strfind(wav_filesName(i,:),'.W'); wav_filename = wav_filesName(i,:); mfcc_filename = [path2, wav_filesName(i,len1+1:p), 'mfcc']; one_line_txt = [wav_filename,' ',mfcc_filename]; fprintf(fid,'%s\n',one_line_txt); count = count + 1; waitbar(i/m); end fprintf('count is %g\n', count); close(hwt); toc
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将targetlist.txt和analysis.conf这两个文件都放在XXX\htk\bin.win32目录下
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执行如下cmd命令，会在指定目录生成mfcc文件：
Hcopy -A -D -C analysis.conf -S targetlist.txt
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ubm.lst用于训练UBM
里面包含5300个speech的mfcc的文件路径
D:\Project\Speaker-Recognition\timit_mfcc\TEST\DR1\FAKS0\SA1.mfcc D:\Project\Speaker-Recognition\timit_mfcc\TEST\DR1\FAKS0\SA2.mfcc D:\Project\Speaker-Recognition\timit_mfcc\TEST\DR1\FAKS0\SI1573.mfcc D:\Project\Speaker-Recognition\timit_mfcc\TEST\DR1\FAKS0\SI2203.mfcc D:\Project\Speaker-Recognition\timit_mfcc\TEST\DR1\FAKS0\SI943.mfcc ... 共5300行 -
speaker_model_maps.lst用于适应训练人
一共630人，已经挑走530人作UBM，还有100人，每个人都有10句话。这里就是这个100人的前9句话的mfcc文件的人名 文件路径
MTXS0 D:\Project\Speaker-Recognition\timit_mfcc\TRAIN\DR6\MTXS0\SA1.mfcc MTXS0 D:\Project\Speaker-Recognition\timit_mfcc\TRAIN\DR6\MTXS0\SA2.mfcc MTXS0 D:\Project\Speaker-Recognition\timit_mfcc\TRAIN\DR6\MTXS0\SI1060.mfcc MTXS0 D:\Project\Speaker-Recognition\timit_mfcc\TRAIN\DR6\MTXS0\SI1690.mfcc ... 共900行 -
trials.lst用于测试
剩余的100人，每个人都跟其他99人形成target和impostor的关系，所以从1人到第100个人做100论。一共10000行，对应所有的测试语句。
格式为人名 特征路径名 标签
MTXS0 D:\Project\Speaker-Recognition\timit_mfcc\TRAIN\DR6\MTXS0\SX70.mfcc target MTXS0 D:\Project\Speaker-Recognition\timit_mfcc\TRAIN\DR7\FBLV0\SX68.mfcc impostor MTXS0 D:\Project\Speaker-Recognition\timit_mfcc\TRAIN\DR7\FCJS0\SX77.mfcc impostor MTXS0 D:\Project\Speaker-Recognition\timit_mfcc\TRAIN\DR7\FCRZ0\SX73.mfcc impostor ... 第100 行 ... impostor 第101 行 ... target 第201 行 ... target ... 第10000行 ... impostor
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Notice 4 important m file：
- demo_gmm_ubm.m
- demo_gmm_ubm_artificial.m
- demo_ivector_plda.m
- demo_ivector_plda_artificial.m
