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Running on Zero
Running on Zero
| from __future__ import annotations | |
| def levenshtein_distance(seq1: str | list[str], seq2: str | list[str]) -> int: | |
| """Calculate minimum edit distance with O(min(m, n)) space complexity using two rolling rows.""" | |
| if not seq1: | |
| return len(seq2) | |
| if not seq2: | |
| return len(seq1) | |
| if len(seq1) < len(seq2): | |
| seq1, seq2 = seq2, seq1 | |
| m, n = len(seq1), len(seq2) | |
| prev_row = list(range(n + 1)) | |
| curr_row = [0] * (n + 1) | |
| for i in range(1, m + 1): | |
| curr_row[0] = i | |
| elem1 = seq1[i - 1] | |
| for j in range(1, n + 1): | |
| cost = 0 if elem1 == seq2[j - 1] else 1 | |
| curr_row[j] = min( | |
| prev_row[j] + 1, # deletion | |
| curr_row[j - 1] + 1, # insertion | |
| prev_row[j - 1] + cost, # substitution | |
| ) | |
| prev_row, curr_row = curr_row, prev_row | |
| return prev_row[n] | |
| def compute_cer(reference_text: str, predicted_text: str, ignore_case: bool = False) -> float: | |
| """Calculate Character Error Rate (CER). | |
| Formula: | |
| CER = LevenshteinDistance(reference, predicted) / len(reference) | |
| """ | |
| if ignore_case: | |
| reference_text = reference_text.lower() | |
| predicted_text = predicted_text.lower() | |
| if not reference_text and not predicted_text: | |
| return 0.0 | |
| if not reference_text: | |
| return 1.0 | |
| distance = levenshtein_distance(reference_text, predicted_text) | |
| return float(distance / len(reference_text)) | |
| def compute_wer(reference_text: str, predicted_text: str, ignore_case: bool = False) -> float: | |
| """Calculate Word Error Rate (WER). | |
| Formula: | |
| WER = LevenshteinDistance(reference_words, predicted_words) / len(reference_words) | |
| """ | |
| if ignore_case: | |
| reference_text = reference_text.lower() | |
| predicted_text = predicted_text.lower() | |
| ref_words = reference_text.strip().split() | |
| pred_words = predicted_text.strip().split() | |
| if not ref_words and not pred_words: | |
| return 0.0 | |
| if not ref_words: | |
| return 1.0 | |
| distance = levenshtein_distance(ref_words, pred_words) | |
| return float(distance / len(ref_words)) | |