HIT 语音信号处理 Lab3 HMM实现部分

Description

《语音信号处理》Lab3
基于HMM模型实现孤立词识别系统

• 本着不重复造轮子的原则，使用hmmlearn库搭建viterbi-BaumWelch的算法框架
• 首先使用viterbi算法迭代计算模型初始参数，然后使用BaumWelch算法进行模型参数的迭代重估
• 使用scikits.talkbox中的mfcc计算代码（python3原因装不上直接拷贝代码）

封装类

import pickle
import re
from collections import Counter
from talk.mfcc import mfcc
from scipy.io import wavfile
from hmmlearn import hmm
import numpy as np
from scipy.special import logsumexp


class Speech:
    ''' 
    Speech
    --------------
    语料的包装类
    '''

    def __init__(self, dirName, fileName):
        self.fileName = fileName    # file name
        self.dirName = dirName
        self.features = None    # feature matrix
        self.soundSamplerate, self.sound = wavfile.read(dirName + fileName)
        
        pattern = re.compile(r'(\d+)_(\d+)_(\d+).wav')
        self.personId, self.categoryId, self.idx = pattern.match(self.fileName).group(1,2,3)

    def extractFeature(self):
        ''' mfcc feature extraction '''
        self.features = mfcc(self.sound, nwin=int(
            self.soundSamplerate * 0.03), fs=self.soundSamplerate, nceps=24)[0]


class SpeechRecognizer:
    ''' 
    SpeechRecognizer
    ----------------------
    HMM模型的包装类
    '''

    def __init__(self, categoryId):
        self.categoryId = categoryId
        self.trainData = []
        self.flagDataStacked = False
        self.trainDataLengths = None
        self.hmmModel = None

        self.nComp = 5  # number of states
        self.n_iter = 10    # number of iterations

    def initModelParam(self, nComp, n_iter):
        ''' 初始化 Gaussian HMM Model的参数 '''
        self.nComp = nComp  # number of states
        self.n_iter = n_iter    # number of iterations

    def stackTrainData(self):
        if self.flagDataStacked:
            return
        self.trainDataLengths = [x.shape[0] for x in self.trainData]
        # 对数组进行堆叠[[frames * [mfccwidth*1]],...] => [[frames x mfccwidth],...]
        self.trainData = np.vstack(self.trainData)
        self.flagDataStacked = True


    def saveHmmModel(self):
        with open("models/md_{0}.pkl".format(self.categoryId), "wb") as file: 
            pickle.dump(self.hmmModel, file)

    def loadHmmModel(self):
        with open("models/md_{0}.pkl".format(self.categoryId), "rb") as file: 
            self.hmmModel = pickle.load(file)

    def initHmmModel(self,load_model=False):
        '''
        initHmmModel
        -----------------
        初始化Hmm模型，如果load_model为真则加载已有模型
        '''
        if load_model:
            self.loadHmmModel()
        else:
            self.getHmmModel()

    def getHmmModel(self):
        ''' 进行Gaussian Hmm模型的初始化 '''

        # Gaussian HMM
        # nMix  GMM 总的状态数目
        # transmat_prior 转移概率矩阵的先验
        # startprob_prior 开始转移概率的先验
        # convariance_type HMM 中使用的协方差矩阵的类型
        #   默认‘diag’代表对于每一个状态都是用一个对角协方差矩阵
        # n_iter 进行 WM 算法迭代的次数
        model = hmm.GaussianHMM(n_components=self.nComp, n_iter=self.n_iter)
        self.hmmModel = model
        self.hmmModel._init(self.trainData, self.trainDataLengths)

    def trainHmmModel(self):
        ''' 训练Gaussian Hmm 模型 '''
        self.hmmModel.init_params = ''
        self.hmmModel.fit(self.trainData, self.trainDataLengths)


    def viterbi(self):
        '''
            使用 viterbi 算法推测当前训练数据及模型情况下最可能的隐藏序列
            获得隐藏序列之后通过简单统计方法估计模型参数
        '''
        state_seq_list = []
        for (i, j) in iter_from_X_lengths(self.trainData, self.trainDataLengths):
            framelogprob = self.hmmModel._compute_log_likelihood(
                self.trainData[i:j])
            logprob, state_sequence = self.hmmModel._do_viterbi_pass(
                framelogprob)
            state_seq_list.append(state_sequence)
        # 利用 state_seq_list 计算新的模型参数

        # 计算 startprob
        accum_map = Counter([x[0] for x in state_seq_list])
        self.hmmModel.startprob_ = np.array([accum_map[x]/len(self.trainDataLengths) for x in range(self.nComp)], dtype=np.float64)
        acc_transmat = np.zeros((self.nComp, self.nComp))
        for rki, state_seq in enumerate(state_seq_list):
            for rkj, state in enumerate(state_seq[1:]):
                pre_state = state_seq[rkj-1]
                acc_transmat[pre_state][state] = acc_transmat[pre_state][state] + 1
        self.hmmModel.transmat_ = acc_transmat / np.sum(acc_transmat, axis=1)[:, None]
        
def iter_from_X_lengths(X, lengths):
    if lengths is None:
        yield 0, len(X)
    else:
        n_samples = X.shape[0]
        end = np.cumsum(lengths).astype(np.int32)
        start = end - lengths
        if end[-1] > n_samples:
            raise ValueError("more than {:d} samples in lengths array {!s}"
                             .format(n_samples, lengths))

        for i in range(len(lengths)):
            yield start[i], end[i]

Code

github