# Code for Word Error Rate in Python Tutorial

`wer_basic.py`

``````def calculate_wer(reference, hypothesis):
ref_words = reference.split()
hyp_words = hypothesis.split()

# Counting the number of substitutions, deletions, and insertions
substitutions = sum(1 for ref, hyp in zip(ref_words, hyp_words) if ref != hyp)
deletions = len(ref_words) - len(hyp_words)
insertions = len(hyp_words) - len(ref_words)

# Total number of words in the reference text
total_words = len(ref_words)

# Calculating the Word Error Rate (WER)
wer = (substitutions + deletions + insertions) / total_words
return wer

if __name__ == "__main__":
reference = "the cat sat on the mat"
hypothesis = "the cat mat"
print(calculate_wer(reference, hypothesis))``````

`wer_accurate.py`

``````import numpy as np

def calculate_wer(reference, hypothesis):
# Split the reference and hypothesis sentences into words
ref_words = reference.split()
hyp_words = hypothesis.split()
# Initialize a matrix with size |ref_words|+1 x |hyp_words|+1
# The extra row and column are for the case when one of the strings is empty
d = np.zeros((len(ref_words) + 1, len(hyp_words) + 1))
# The number of operations for an empty hypothesis to become the reference
# is just the number of words in the reference (i.e., deleting all words)
for i in range(len(ref_words) + 1):
d[i, 0] = i
# The number of operations for an empty reference to become the hypothesis
# is just the number of words in the hypothesis (i.e., inserting all words)
for j in range(len(hyp_words) + 1):
d[0, j] = j
# Iterate over the words in the reference and hypothesis
for i in range(1, len(ref_words) + 1):
for j in range(1, len(hyp_words) + 1):
# If the current words are the same, no operation is needed
# So we just take the previous minimum number of operations
if ref_words[i - 1] == hyp_words[j - 1]:
d[i, j] = d[i - 1, j - 1]
else:
# If the words are different, we consider three operations:
# substitution, insertion, and deletion
# And we take the minimum of these three possibilities
substitution = d[i - 1, j - 1] + 1
insertion = d[i, j - 1] + 1
deletion = d[i - 1, j] + 1
d[i, j] = min(substitution, insertion, deletion)
# The minimum number of operations to transform the hypothesis into the reference
# is in the bottom-right cell of the matrix
# We divide this by the number of words in the reference to get the WER
wer = d[len(ref_words), len(hyp_words)] / len(ref_words)
return wer

if __name__ == "__main__":
reference = "The cat is sleeping on the mat."
hypothesis = "The cat is playing on mat."
print(calculate_wer(reference, hypothesis))``````

`wer_jiwer.py`

``````from jiwer import wer

if __name__ == "__main__":
# reference = "the cat sat on the mat"
# hypothesis = "the cat mat"
reference = "The cat is sleeping on the mat."
hypothesis = "The cat is playing on mat."
print(wer(reference, hypothesis))``````

`wer_evaluate.py`

``````import evaluate

wer = evaluate.load("wer")

# reference = "the cat sat on the mat"
# hypothesis = "the cat mat"
reference = "The cat is sleeping on the mat."
hypothesis = "The cat is playing on mat."
print(wer.compute(references=[reference], predictions=[hypothesis]))``````