whisper语音识别部署及WER评价

1.whisper部署

详细过程可以参照：🏠

创建项目文件夹

mkdir whisper
cd whisper

conda创建虚拟环境

conda create -n py310 python=3.10 -c conda-forge -y

安装pytorch

pip install --pre torch torchvision torchaudio --extra-index-url 

下载whisper

pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git

安装相关包

pip install tqdm
pip install numba
pip install tiktoken==0.3.3
brew install ffmpeg

测试一下whispet是否安装成功（默认识别为中文）

whisper test.wav --model small
#test.wav为自己的测试wav文件，map3也支持 small是指用小模型

whisper识别中文的时候经常会输出繁体，加入一下参数可以避免：

 whisper test.wav --model small --language zh --initial_prompt "以下是普通话的句子。"
#注意"以下是普通话的句子。"不能随便修改，只能是这句话才有效果。

2.脚本批量测试

创建test.sh脚本，输入一下内容，可以实现对某一文件夹下的wav文件逐个中文语音识别。

#!/bin/bash
for ((i=0;i<300;i++));do
        file="wav/A13_${i}.wav"
        if [ ! -f "$file" ];then
                break
        fi
        whisper "$file" --model medium --output_dir denied --language zh --initial_prompt "以下是普通话的句子。"
done
                                                                                                                                

 实现英文语音识别需要修改为：

#!/bin/bash
for ((i=0;i<300;i++));do
        file="en/${i}.wav"
        if [ ! -f "$file" ];then
                break
        fi
        whisper "$file" --model small --output_dir denied --language en
done
                                                                                                                          

3.对运行出来的结果进行评测

一般地，语音识别通常采用WER，即词错误率，评估语音识别和文本转换质量。

这里我们主要采用 github上的开源项目：🌟 编写的python-wer代码对结果进行评价。

其中，我们的正确样本形式为：

 whisper输出的预测结果形式为：

 因此要对文本进行处理（去空格、去标点符号）后进行wer评价，相关代码如下：

（可根据具体情况修改calculate_WER）

import sys
import numpy

def editDistance(r, h):
    '''
    This function is to calculate the edit distance of reference sentence and the hypothesis sentence.
    Main algorithm used is dynamic programming.
    Attributes: 
        r -> the list of words produced by splitting reference sentence.
        h -> the list of words produced by splitting hypothesis sentence.
    '''
    d = numpy.zeros((len(r)+1)*(len(h)+1), dtype=numpy.uint8).reshape((len(r)+1, len(h)+1))
    for i in range(len(r)+1):
        d[i][0] = i
    for j in range(len(h)+1):
        d[0][j] = j
    for i in range(1, len(r)+1):
        for j in range(1, len(h)+1):
            if r[i-1] == h[j-1]:
                d[i][j] = d[i-1][j-1]
            else:
                substitute = d[i-1][j-1] + 1
                insert = d[i][j-1] + 1
                delete = d[i-1][j] + 1
                d[i][j] = min(substitute, insert, delete)
    return d

def getStepList(r, h, d):
    '''
    This function is to get the list of steps in the process of dynamic programming.
    Attributes: 
        r -> the list of words produced by splitting reference sentence.
        h -> the list of words produced by splitting hypothesis sentence.
        d -> the matrix built when calulating the editting distance of h and r.
    '''
    x = len(r)
    y = len(h)
    list = []
    while True:
        if x == 0 and y == 0: 
            break
        elif x >= 1 and y >= 1 and d[x][y] == d[x-1][y-1] and r[x-1] == h[y-1]: 
            list.append("e")
            x = x - 1
            y = y - 1
        elif y >= 1 and d[x][y] == d[x][y-1]+1:
            list.append("i")
            x = x
            y = y - 1
        elif x >= 1 and y >= 1 and d[x][y] == d[x-1][y-1]+1:
            list.append("s")
            x = x - 1
            y = y - 1
        else:
            list.append("d")
            x = x - 1
            y = y
    return list[::-1]

def alignedPrint(list, r, h, result):
    '''
    This funcition is to print the result of comparing reference and hypothesis sentences in an aligned way.
    
    Attributes:
        list   -> the list of steps.
        r      -> the list of words produced by splitting reference sentence.
        h      -> the list of words produced by splitting hypothesis sentence.
        result -> the rate calculated based on edit distance.
    '''
    print("REF:", end=" ")
    for i in range(len(list)):
        if list[i] == "i":
            count = 0
            for j in range(i):
                if list[j] == "d":
                    count += 1
            index = i - count
            print(" "*(len(h[index])), end=" ")
        elif list[i] == "s":
            count1 = 0
            for j in range(i):
                if list[j] == "i":
                    count1 += 1
            index1 = i - count1
            count2 = 0
            for j in range(i):
                if list[j] == "d":
                    count2 += 1
            index2 = i - count2
            if len(r[index1]) < len(h[index2]):
                print(r[index1] + " " * (len(h[index2])-len(r[index1])), end=" ")
            else:
                print(r[index1], end=" "),
        else:
            count = 0
            for j in range(i):
                if list[j] == "i":
                    count += 1
            index = i - count
            print(r[index], end=" "),
    print("\nHYP:", end=" ")
    for i in range(len(list)):
        if list[i] == "d":
            count = 0
            for j in range(i):
                if list[j] == "i":
                    count += 1
            index = i - count
            print(" " * (len(r[index])), end=" ")
        elif list[i] == "s":
            count1 = 0
            for j in range(i):
                if list[j] == "i":
                    count1 += 1
            index1 = i - count1
            count2 = 0
            for j in range(i):
                if list[j] == "d":
                    count2 += 1
            index2 = i - count2
            if len(r[index1]) > len(h[index2]):
                print(h[index2] + " " * (len(r[index1])-len(h[index2])), end=" ")
            else:
                print(h[index2], end=" ")
        else:
            count = 0
            for j in range(i):
                if list[j] == "d":
                    count += 1
            index = i - count
            print(h[index], end=" ")
    print("\nEVA:", end=" ")
    for i in range(len(list)):
        if list[i] == "d":
            count = 0
            for j in range(i):
                if list[j] == "i":
                    count += 1
            index = i - count
            print("D" + " " * (len(r[index])-1), end=" ")
        elif list[i] == "i":
            count = 0
            for j in range(i):
                if list[j] == "d":
                    count += 1
            index = i - count
            print("I" + " " * (len(h[index])-1), end=" ")
        elif list[i] == "s":
            count1 = 0
            for j in range(i):
                if list[j] == "i":
                    count1 += 1
            index1 = i - count1
            count2 = 0
            for j in range(i):
                if list[j] == "d":
                    count2 += 1
            index2 = i - count2
            if len(r[index1]) > len(h[index2]):
                print("S" + " " * (len(r[index1])-1), end=" ")
            else:
                print("S" + " " * (len(h[index2])-1), end=" ")
        else:
            count = 0
            for j in range(i):
                if list[j] == "i":
                    count += 1
            index = i - count
            print(" " * (len(r[index])), end=" ")
    print("\nWER: " + result)
    return result

def wer(r, h):
    """
    This is a function that calculate the word error rate in ASR.
    You can use it like this: wer("what is it".split(), "what is".split()) 
    """
    # build the matrix
    d = editDistance(r, h)

    # find out the manipulation steps
    list = getStepList(r, h, d)

    # print the result in aligned way
    result = float(d[len(r)][len(h)]) / len(r) * 100
    result = str("%.2f" % result) + "%"
    result=alignedPrint(list, r, h, result)
    return result

# 计算总WER
def calculate_WER():
    with open("whisper_out.txt", "r") as f:
        text1_list = [i[11:].strip("\n") for i in f.readlines()]
    with open("A13.txt", "r") as f:
        text2_orgin_list = [i[11:].strip("\n") for i in f.readlines()]

    total_distance = 0
    total_length = 0
    WER=0
    symbols = ",@#￥%……&*（）——+~！{}【】；‘：“”‘。？》《、"
    # calculate distance between each pair of texts
    for i in range(len(text1_list)):
        match1 = re.search('[\u4e00-\u9fa5]', text1_list[i])
        if match1:
            index1 = match1.start()
        else:
            index1 = len(text1_list[i])
        match2 = re.search('[\u4e00-\u9fa5]', text2_orgin_list[i])
        if match2:
            index2 = match2.start()
        else:
            index2 = len( text2_orgin_list[i])
        result1=  text1_list[i][index1:]
        result1= result1.translate(str.maketrans('', '', symbols))
        result2=  text2_orgin_list[i][index2:]
        result2=result2.replace(" ", "")
        print(result1)
        print(result2)
        result=wer(result1,result2)
        WER+=float(result.strip('%')) / 100
    WER=WER/len(text1_list)
    print("总WER：", WER)
    print("总WER：", WER.__format__('0.2%'))
calculate_WER()

评价结果形如：

4.与paddlespeech的测试对比：

5.测试所用数据集

自己处理过的开源wav数据文章来源地址https://www.toymoban.com/news/detail-496316.html

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