语音识别之HTK入门（四）——HCompV源码解析

语音识别之HTK入门（四）——HCompV源码解析

上一篇博客中用HCompV工具通过全局数据来初始化HMM模型参数，包括proto和vFloor两个文件。同时手工制作hmmdefs模型，在这个文件中列举了每一个单因素模型的参数，它们是完全相同的。

现在一步一步看看HCompV是如何达成目标的。

如前面博客指出的，可以通过visual studio跟踪调试HCompV。

首先列出大体的流程涉及的函数：

分别是：

1）InitShell    // 对命令行参数进行处理，这个函数在多个HTKTools之间都用到，它主要把参数保存到savedCommandLine这里，后续对它进行读写处理；比如系统的参数设置，一般是“-C|S|V|D”，并把余下的参数保存到arglist二维字符数组中，计数变量为argcount；

2）然后是一堆初始化工作

InitMem();   
InitLabel();
InitMath();  
InitSigP();
InitWave();  
InitAudio();
InitVQ();    
InitModel();
if(InitParm()<SUCCESS)  HError(2000,"HCompV: InitParm failed");
if (!InfoPrinted() && NumArgs() == 0)ReportUsage();
if (NumArgs() == 0) Exit(0);

3）核心的处理函数是从CreateHMMSet开始的。

下面来详细分析下这个函数做了哪些任务，首先在代码中函数声明为

/* EXPORT->CreateHMMSet: create the basic HMMSet structure */
void CreateHMMSet(HMMSet *hset, MemHeap *heap, Boolean allowTMods)；它接受三个参数分别是HMMSet结构指针、MemHeap结构指针和allowTMods的布尔值。因此这两个结构大体长什么样子肯定要有所了解，否则后面的操作、赋值、计算都不明了。而allowTMods是个开关量。

typedef struct _HMMSet{MemHeap *hmem;          /* memory heap for this HMM Set */   Boolean *firstElem;     /* first element added to hmem during MakeHMMSet*/char *hmmSetId;         /* identifier for the hmm set */MILink mmfNames;        /* List of external file names */int numLogHMM;          /* Num of logical HMM's */int numPhyHMM;          /* Num of distinct physical HMM's */int numFiles;           /* total number of ext files */int numMacros;          /* num macros used in this set */MLink * mtab;           /* Array[0..MACHASHSIZE-1]OF MLink */PtrMap ** pmap;         /* Array[0..PTRHASHSIZE-1]OF PtrMap* */Boolean allowTMods;     /* true if HMMs can have Tee Models */Boolean optSet;         /* true if global options have been set */short vecSize;          /* dimension of observation vectors */short swidth[SMAX];     /* [0]=num streams,[i]=width of stream i */ParmKind pkind;         /* kind of obs vector components */DurKind dkind;          /* kind of duration model (model or state) */CovKind ckind;          /* cov kind - only global in V1.X */HSetKind hsKind;        /* kind of HMM set */TMixRec tmRecs[SMAX];   /* array[1..S]of tied mixture record */int numStates;          /* Number of states in HMMSet */int numSharedStates;    /* Number of shared states in HMMSet */int numMix;             /* Number of mixture components in HMMSet */int numSharedMix;       /* Number of shared mixtures in HMMSet */int numTransP;          /* Number of distinct transition matrices */int ckUsage[NUMCKIND];  /* Number of components using given ckind */InputXForm *xf;         /* Input transform of HMMSet */AdaptXForm *semiTied;   /* SemiTied transform associated with model set */short projSize;         /* dimension of vector to update *//* Adaptation information accumulates */Boolean attRegAccs;   /* have the set of accumulates been attached */Boolean attXFormInfo; /* have the set of adapt info been attached */Boolean attMInfo;     /* have the set of adapt info been attached */AdaptXForm *curXForm;AdaptXForm *parentXForm;/* Added to support LogWgts */Boolean logWt;       /* Component weights are stored as Logs *//* Added to support delayed loading of the semi-tied transform */char *semiTiedMacro;  /* macroname of semi-tied transform */} HMMSet;

看每个数据项的注释，大体意思应该比较明白了。其中第一项就是HHMSet需要涉及的内存空间，用来保存模型参数，也就是这个函数第二个形参的目的。

typedef struct {char *name;          /*            name of this memory heap            */HeapType type;       /*              type of this heap                 */float growf;         /*           succ blocks grow as 1+growf          */size_t elemSize;     /*  size of each elem              1 always       */size_t minElem;      /*  init #elems per blk      init #bytes per blk  */size_t maxElem;      /*  max #elems per block     max #bytes per blk   */size_t curElem;      /*  current #elems per blk   curr #bytes per blk  */size_t totUsed;      /*  total #elems used        total #bytes used    */size_t totAlloc;     /*  total #elems alloc'ed    total #bytes alloc'd */BlockP heap;         /*               linked list of blocks            */Boolean protectStk;  /*  MSTAK only, prevents disposal below Stack Top */
}MemHeap;

该HMMSet定义了hmm模型的个数，状态数，观察向量的维度转移概率矩阵的个数等等。在这个函数里只是初始化这样的结构体，而具体值需要后面根据模型文件来构建模型时填充。

 CreateHMMSet(&hset,&gstack,FALSE);pathPattern[0]='\0';while (NextArg() == SWITCHARG) {s = GetSwtArg();if (strlen(s)!=1) HError(2019,"HCompV: Bad switch %s; must be single letter",s);switch(s[0]){case 'f':if (NextArg() != FLOATARG)HError(2019,"HCompV: Variance floor scale expected");vFloorScale = GetChkedFlt(0.0,100.0,s);break;case 'l':if (NextArg() != STRINGARG)HError(2019,"HCompV: Segment label expected");segLab = GetStrArg();break;case 'm':meanUpdate = TRUE;break;case 'o':outfn = GetStrArg();break;     case 'v':if (NextArg() != FLOATARG)HError(2019,"HCompV: Minimum variance level expected");minVar = GetChkedFlt(0.0,100.0,s);break;case 'k':if (NextArg() != STRINGARG)HError(2019,"HCompV: speaker pattern expected");strcpy(spPattern,GetStrArg());if (strchr(spPattern,'%')==NULL)HError(2019,"HCompV: Speaker mask invalid");break;case 'c':if (NextArg() != STRINGARG)HError(2019,"HCompV: CMV output dir expected");strcpy(cmDir,GetStrArg());DoCMV = TRUE;break;case 'p':if (NextArg() != STRINGARG)HError(2019,"HCompV: path pattern expected");strcpy(pathPattern,GetStrArg());if (strchr(pathPattern,'%')==NULL)HError(2019,"HCompV: Path mask invalid");break;case 'q':if (NextArg() != STRINGARG)HError(2019,"HCompV: output flags (nmv)");strcpy(oflags,GetStrArg());break;case 'B':saveBinary = TRUE;break;case 'F':if (NextArg() != STRINGARG)HError(2019,"HCompV: Data File format expected");if((dff = Str2Format(GetStrArg())) == ALIEN)HError(-2089,"HCompV: Warning ALIEN Data file format set");break;case 'G':if (NextArg() != STRINGARG)HError(2019,"HCompV: Label File format expected");if((lff = Str2Format(GetStrArg())) == ALIEN)HError(-2089,"HCompV: Warning ALIEN Label file format set");break;case 'H':if (NextArg() != STRINGARG)HError(2019,"HCompV: HMM macro file name expected");AddMMF(&hset,GetStrArg());break;case 'I':if (NextArg() != STRINGARG)HError(2019,"HCompV: MLF file name expected");LoadMasterFile(GetStrArg());break;case 'L':if (NextArg()!=STRINGARG)HError(2019,"HCompV: Label file directory expected");labDir = GetStrArg();break;case 'M':if (NextArg()!=STRINGARG)HError(2019,"HCompV: Output macro file directory expected");outDir = GetStrArg();break;case 'T':if (NextArg() != INTARG)HError(2019,"HCompV: Trace value expected");trace = GetChkedInt(0,077,s); break;case 'X':if (NextArg()!=STRINGARG)HError(2019,"HCompV: Label file extension expected");labExt = GetStrArg();break;default:HError(2019,"HCompV: Unknown switch %s",s);}}/* if not doing CMV, do standard HCompV */if (DoCMV == FALSE){if (NextArg()!=STRINGARG)HError(2019,"HCompV: Source HMM file name expected");hmmfn = GetStrArg();Initialise();do {if (NextArg()!=STRINGARG)HError(2019,"HCompV: Training data file name expected");datafn = GetStrArg();LoadFile(datafn);} while (NumArgs()>0);SetCovs();FixGConsts(hmmLink);SaveModel(outfn);   if (trace&T_TOP)printf("Output written to directory %s\n",(outDir==NULL)?"./":outDir);if (vFloorScale>0.0)PutVFloor();}

while训练语句就是在处理命令行的参数，不断的读取由initshell处理后剩下的参数，在HCompV命令中，有-f 0.01 -m -M dir proto等7个参数，处理完毕后，设置了全局变量vFloorScale（协方差的最低值系数），meanUpdate（bool，同时更新期望值），outDir（模型的输出目录）和hmmfn（描述模型的文件名）。

接着，默认情况下DoCMV为false，因此进入if第一个分支，设置hmmfn值为proto，它就是我们之前为HMM模型描述的结构。然后执行Initialise()函数。

4）Initialise()

函数前面的注释 /* Initialise: load HMMs and create accumulators */

下面把这个函数的主要调用关系罗列出来，然后分析它们各自都干了什么。

void Initialise(void)
{int s,V;Boolean eSep;char base[MAXSTRLEN];char path[MAXSTRLEN];char ext[MAXSTRLEN];/* Load HMM defs */     if(MakeOneHMM(&hset,BaseOf(hmmfn,base))<SUCCESS)HError(2028,"Initialise: MakeOneHMM failed");if(LoadHMMSet(&hset,PathOf(hmmfn,path),ExtnOf(hmmfn,ext))<SUCCESS)HError(2028,"Initialise: LoadHMMSet failed");SetParmHMMSet(&hset);/* Create a heap to store the input data */CreateHeap(&iStack,"InBuf", MSTAK, 1, 0.5, 100000, LONG_MAX);/* Get a pointer to the physical HMM */hmmId = GetLabId(base,FALSE);macroLink = FindMacroName(&hset,'h',hmmId);hmmLink = (HLink)macroLink->structure;/* Find out for which streams full covariance is needed */CheckVarianceKind( );/* Create accumulators for the mean and variance */for (s=1;s<=hset.swidth[0]; s++){V = hset.swidth[s];accs[s].meanSum=CreateVector(&gstack,V);ZeroVector(accs[s].meanSum);if (fullcNeeded[s]) {accs[s].squareSum.inv=CreateSTriMat(&gstack,V);accs[s].fixed.inv=CreateSTriMat(&gstack,V);ZeroTriMat(accs[s].squareSum.inv);}else {accs[s].squareSum.var=CreateSVector(&gstack,V);accs[s].fixed.var=CreateSVector(&gstack,V);ZeroVector(accs[s].squareSum.var);}}/* Create an object to hold the input parameters */SetStreamWidths(hset.pkind,hset.vecSize,hset.swidth,&eSep);obs=MakeObservation(&gstack,hset.swidth,hset.pkind,FALSE,eSep);if(segLab != NULL) {segId = GetLabId(segLab,TRUE);}}

可以看出首先调用MakeOneHMM，看看它完成了哪些操作。

/* EXPORT->MakeOneHMM: Create a singleton for the HMM hname */
ReturnStatus MakeOneHMM(HMMSet *hset, char *hname)
{if(InitHMMSet(hset, hname, TRUE)<SUCCESS){ResetHMMSet(hset);return(FAIL);}return(SUCCESS);
}

它实际上调用了初始化InitHMMSet函数，通过读取proto这个hmm模型文件。proto文件的内容如下：

~o <VecSize> 39 <MFCC_0_D_A>
~h "proto"
<BeginHMM><NumStates> 5<State> 2<Mean> 390.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 <Variance> 391.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0<State> 3<Mean> 390.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 <Variance> 391.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0<State> 4<Mean> 390.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 <Variance> 391.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0<TransP> 50.0 1.0 0.0 0.0 0.00.0 0.6 0.4 0.0 0.00.0 0.0 0.6 0.4 0.00.0 0.0 0.0 0.7 0.30.0 0.0 0.0 0.0 0.0
<EndHMM>

其实这个hmm原型文件描述了HMM主要的参数，包括5个状态，观察向量的维度是39，它是语音的MFCC以及一阶、二阶参数（MFCC_0_D_A）。这个原型文件只描述了一个hmm模型，名字就是"proto"。

在InitHMMSet函数中调用了CreateHMM函数完成模型的单个模型的构建。模型名称为“proto”，并保存到HMMSet中，但是实际上这时这个模型的参数没有设定，只是有了一个名字的空模型。

在接下来的循环语句中，LoadFile函数读取每个MFCC文件，并计算特征向量的均值和方差，通过累加的方式。

SetCovs()函数完成方差的计算，并复制给每个状态，来初始化状态的概率密度函数。

/* SetCovs: set covariance values in hmm */
void SetCovs(void)
{int i,s,m;StateElem *se;StreamElem *ste;MixtureElem *me;MixPDF *mp;CalcCovs();  // 计算均值和方差 保存在全局的CovAcc对象中。if (trace&T_TOP) {printf("Updating HMM ");if (meanUpdate) printf("Means and ");printf("Covariances\n");}for (i=2,se=hmmLink->svec+2; i < hmmLink->numStates; i++,se++)for (s=1,ste=se->info->pdf+1; s <= hset.swidth[0]; s++,ste++)for (m=1,me = ste->spdf.cpdf+1; m<=ste->nMix; m++, me++) {mp = me->mpdf;if (meanUpdate && !IsSeenV(mp->mean)){     /* meanSum now holds mean */CopyVector(accs[s].meanSum,mp->mean);   // 把计算的均方差复制给模型的状态TouchV(mp->mean);}if (!IsSeenV(mp->cov.var)){if (mp->ckind==FULLC)CopyMatrix(accs[s].fixed.inv,mp->cov.inv);else if (fullcNeeded[s])  /* dont need full cov, but its all we have */                TriDiag2Vector(accs[s].fixed.inv,mp->cov.var);elseCopyVector(accs[s].fixed.var,mp->cov.var);TouchV(mp->cov.var);}}ClearSeenFlags(&hset,CLR_ALL);
}