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++));dofile="wav/A13_${i}.wav"if [ ! -f "$file" ];thenbreakfiwhisper "$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++));dofile="en/${i}.wav"if [ ! -f "$file" ];thenbreakfiwhisper "$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 numpydef 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] = ifor j in range(len(h)+1):d[0][j] = jfor 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] + 1insert = d[i][j-1] + 1delete = d[i-1][j] + 1d[i][j] = min(substitute, insert, delete)return ddef 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: breakelif 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 - 1y = y - 1elif y >= 1 and d[x][y] == d[x][y-1]+1:list.append("i")x = xy = y - 1elif x >= 1 and y >= 1 and d[x][y] == d[x-1][y-1]+1:list.append("s")x = x - 1y = y - 1else:list.append("d")x = x - 1y = yreturn 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 = 0for j in range(i):if list[j] == "d":count += 1index = i - countprint(" "*(len(h[index])), end=" ")elif list[i] == "s":count1 = 0for j in range(i):if list[j] == "i":count1 += 1index1 = i - count1count2 = 0for j in range(i):if list[j] == "d":count2 += 1index2 = i - count2if len(r[index1]) < len(h[index2]):print(r[index1] + " " * (len(h[index2])-len(r[index1])), end=" ")else:print(r[index1], end=" "),else:count = 0for j in range(i):if list[j] == "i":count += 1index = i - countprint(r[index], end=" "),print("\nHYP:", end=" ")for i in range(len(list)):if list[i] == "d":count = 0for j in range(i):if list[j] == "i":count += 1index = i - countprint(" " * (len(r[index])), end=" ")elif list[i] == "s":count1 = 0for j in range(i):if list[j] == "i":count1 += 1index1 = i - count1count2 = 0for j in range(i):if list[j] == "d":count2 += 1index2 = i - count2if len(r[index1]) > len(h[index2]):print(h[index2] + " " * (len(r[index1])-len(h[index2])), end=" ")else:print(h[index2], end=" ")else:count = 0for j in range(i):if list[j] == "d":count += 1index = i - countprint(h[index], end=" ")print("\nEVA:", end=" ")for i in range(len(list)):if list[i] == "d":count = 0for j in range(i):if list[j] == "i":count += 1index = i - countprint("D" + " " * (len(r[index])-1), end=" ")elif list[i] == "i":count = 0for j in range(i):if list[j] == "d":count += 1index = i - countprint("I" + " " * (len(h[index])-1), end=" ")elif list[i] == "s":count1 = 0for j in range(i):if list[j] == "i":count1 += 1index1 = i - count1count2 = 0for j in range(i):if list[j] == "d":count2 += 1index2 = i - count2if len(r[index1]) > len(h[index2]):print("S" + " " * (len(r[index1])-1), end=" ")else:print("S" + " " * (len(h[index2])-1), end=" ")else:count = 0for j in range(i):if list[j] == "i":count += 1index = i - countprint(" " * (len(r[index])), end=" ")print("\nWER: " + result)return resultdef 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 matrixd = editDistance(r, h)# find out the manipulation stepslist = getStepList(r, h, d)# print the result in aligned wayresult = float(d[len(r)][len(h)]) / len(r) * 100result = 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 = 0total_length = 0WER=0symbols = ",@#￥%……&*（）——+~！{}【】；‘：“”‘。？》《、"# calculate distance between each pair of textsfor 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('%')) / 100WER=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：

数据集	数据量	paddle (中英文分开)	paddle (同一模型)	whisper(small) （同一模型）	whisper(medium) （同一模型）
zhthchs30 (中文错字率)	250	11.61%	45.53%	24.11%	13.95%
LibriSpeech (英文错字率)	125	7.76%	50.88%	9.31%	9.31%

5.测试所用数据集

自己处理过的开源wav数据