我正在尝试计算WER来评估ASR系统，但是分数的计算却要花费很多时间（因为我想对其进行一些引导以获取置信区间以更可靠地评估系统）。
这是我到目前为止提出的代码，有没有人看到一种更有效的方法（更快，并且如果您有提高内存效率的想法，那也将受到欢迎）。
def modify_text(text):
"""
Function to modify a clean text to add some errors in it.
"""
modified_text = []
for word in true_text:
action = np.random.choice(['deletion','addition','subsitution','nothing'],
p = [0.1,0.1,0.1,0.7])
if action in ['addition','substitution']:
modified_text.append(random.choice(voca))
if action in ['addition','nothing']:
modified_text.append(word)
return modified_text
def wer(s1,s2):
d = np.zeros([len(s1)+1,len(s2)+1])
d[:,0] = np.arange(len(s1)+1)
d[0,:] = np.arange(len(s2)+1)
for j in range(1,len(s2)+1):
for i in range(1,len(s1)+1):
if s1[i-1] == s2[j-1]:
d[i,j] = d[i-1,j-1]
else:
d[i,j] = min(d[i-1,j]+1, d[i,j-1]+1, d[i-1,j-1]+1)
return d[-1,-1]/len(s1)
text = """I am happy to join with you today in what will go down in history as
the greatest demonstration for freedom in the history of our nation.
Five score years ago, a great American, in whose symbolic shadow
we stand today, signed the Emancipation Proclamation. This momentous
decree came as a great beacon light of hope to millions of Negro slaves
who had been seared in the flames of withering injustice. It came as a
joyous daybreak to end the long night of their captivity. """
true_text = list(tokenize(text))
modified_text = modify_text(true_text)
%timeit wer(true_text,modified_text)
输出：
7.04 ms ± 49.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
好的，这似乎还不错，但是我有数以万计的文本需要使用引导程序进行评估，并且文本的长度更长。因此，我想找到一种更快的方法来执行wer功能。任何的想法？
最佳答案
大多数语音识别评估首先将语音分为句子或话语。然后，通过对齐每个发音来计算WER。因为WER计算为O（n ^ 2），所以可以大大提高速度。