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79f9224 8a34d65 79f9224 152a3e8 79f9224 778443c db0d138 79f9224 db0d138 778443c 79f9224 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | import json
from lib.utils import run_textdistance, clean_text_for_comparison_zh, highlight_diff
import re
# import Levenshtein
def calculate_distance(reference: str, hypothesis: str):
"""
使用 python-Levenshtein 库计算字符错误率 (CER)。
CER = (Substitutions + Deletions + Insertions) / Total Characters in Reference
= Levenshtein Distance / Total Characters in Reference
Args:
reference: 真实的文本转录 (Ground Truth)。
hypothesis: ASR 模型的预测结果。
Returns:
字符错误率 (CER)。
"""
text1_clean = clean_text_for_comparison_zh(reference)
text2_clean = clean_text_for_comparison_zh(hypothesis)
d, nd = run_textdistance(text1_clean, text2_clean)
diff = ""
if d > 0:
diff = highlight_diff(text1_clean, text2_clean, spliter="")
return d, diff
if __name__ == '__main__':
import cn2an
results_list = json.load(open("../reports/asr_result_funasr_mlt_nano_wenet_net.json", encoding="utf-8"))
count = 0
distance_sum = 0
reference_sum = 0
for item in results_list:
count += 1
reference = item["reference"]
hypothesis = item["predicts"]
# # 如果是 whisper，使用 cn2an替换数字为中文
# if re.search(r"\d", hypothesis):
# hypothesis = cn2an.transform(hypothesis, "an2cn")
distance, diff = calculate_distance(reference, hypothesis)
print(f"{count}. distance: {distance}")
if distance > 0:
print(f"Audio Path: {item['audio_path']}")
print(f"Reference: {reference}")
print(f"Hypothesis: {hypothesis}")
print(f"Diff: {diff}")
distance_sum += distance
reference_sum += len(reference)
cer = distance_sum / reference_sum if reference_sum > 0 else 0
print(f"Total Distance: {distance_sum}, Total Reference Length: {reference_sum}, CER: {cer:.4f}") |