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Fix Filtering Logic for Normalized WER Calculation #202

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Fix Filtering Logic for Normalized WER Calculation #202

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14 changes: 6 additions & 8 deletions chapters/en/chapter5/evaluation.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -348,18 +348,16 @@ normalizer = BasicTextNormalizer()
all_predictions_norm = [normalizer(pred) for pred in all_predictions]
all_references_norm = [normalizer(label) for label in common_voice_test["sentence"]]

# filtering step to only evaluate the samples that correspond to non-zero references
all_predictions_norm = [
all_predictions_norm[i]
for i in range(len(all_predictions_norm))
if len(all_references_norm[i]) > 0
]
all_references_norm = [
all_references_norm[i]
# Filtering step to only evaluate the samples that correspond to non-zero references
filtered_pairs = [
(all_predictions_norm[i], all_references_norm[i])
for i in range(len(all_references_norm))
if len(all_references_norm[i]) > 0
]

# Unpack filtered lists
all_predictions_norm, all_references_norm = zip(*filtered_pairs) if filtered_pairs else ([], [])

wer = 100 * wer_metric.compute(
references=all_references_norm, predictions=all_predictions_norm
)
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