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import os
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import sys
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import json
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import argparse
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from difflib import SequenceMatcher
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import datetime
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import re
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def check_file_exists(file_path):
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"""Check if file exists and is not empty"""
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if not os.path.exists(file_path):
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return False, f"File does not exist: {file_path}"
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if os.path.getsize(file_path) == 0:
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return False, f"File is empty: {file_path}"
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return True, ""
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def cer(ref, hyp):
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"""Character Error Rate = Edit Distance / Length of Reference"""
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import numpy as np
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ref = list(ref)
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hyp = list(hyp)
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d = np.zeros((len(ref)+1, len(hyp)+1), dtype=int)
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for i in range(len(ref)+1):
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d[i][0] = i
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for j in range(len(hyp)+1):
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d[0][j] = j
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for i in range(1, len(ref)+1):
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for j in range(1, len(hyp)+1):
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cost = 0 if ref[i-1] == hyp[j-1] else 1
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d[i][j] = min(
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d[i-1][j] + 1,
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d[i][j-1] + 1,
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d[i-1][j-1] + cost
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)
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return d[len(ref)][len(hyp)] / max(len(ref), 1)
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def is_likely_english(text):
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english_letters = re.findall(r'[a-zA-Z]', text)
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if not english_letters:
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return False
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ratio = len(english_letters) / max(len(text), 1)
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return ratio > 0.5 and len(english_letters) >= 10
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def load_transcripts(file_path):
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"""Load transcript text from file"""
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try:
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with open(file_path, 'r', encoding='utf-8') as f:
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return f.read().replace("\n", ""), ""
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except Exception as e:
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return None, str(e)
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def evaluate(system_output_file, ground_truth_file, cer_threshold=0.05):
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"""Main evaluation function: Calculate CER between system output and ground truth"""
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process_ok, process_msg = check_file_exists(system_output_file)
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if not process_ok:
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return False, False, process_msg
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process_ok, process_msg = check_file_exists(ground_truth_file)
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if not process_ok:
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return False, False, process_msg
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system_trans, msg = load_transcripts(system_output_file)
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if system_trans is None:
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return True, False, f"Failed to load system output: {msg}"
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ground_truth, msg = load_transcripts(ground_truth_file)
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if ground_truth is None:
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return True, False, f"Failed to load ground truth: {msg}"
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if not is_likely_english(system_trans):
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return True, False, "Output text does not appear to be valid English transcription"
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score = cer(ground_truth, system_trans)
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comments = [f"CER = {score:.4f}"]
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result_ok = score <= cer_threshold
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if not result_ok:
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comments.append(f"CER ({score:.4f}) exceeds threshold {cer_threshold}")
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return True, result_ok, "\n".join(comments)
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def save_results_to_jsonl(process_ok, result_ok, comments, jsonl_file):
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"""Save test results to JSONL file"""
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current_time = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
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result_data = {
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"Process": bool(process_ok),
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"Result": bool(result_ok),
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"TimePoint": current_time,
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"comments": comments
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}
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os.makedirs(os.path.dirname(jsonl_file), exist_ok=True)
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with open(jsonl_file, 'a', encoding='utf-8') as f:
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json.dump(result_data, f, ensure_ascii=False, default=str)
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f.write('\n')
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def main():
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parser = argparse.ArgumentParser(description='Evaluate speech recognition results')
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parser.add_argument('--output', required=True, help='System output file path')
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parser.add_argument('--groundtruth', required=True, help='Ground truth file path')
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parser.add_argument('--cer_threshold', type=float, default=0.10, help='CER threshold')
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parser.add_argument('--result', required=True, help='Result JSONL file path')
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args = parser.parse_args()
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process_ok, result_ok, comments = evaluate(
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args.output,
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args.groundtruth,
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args.cer_threshold
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)
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save_results_to_jsonl(process_ok, result_ok, comments, args.result)
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if not process_ok:
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print(f"Processing failed: {comments}")
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if not result_ok:
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print(f"Results do not meet requirements: {comments}")
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print("Test completed")
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if __name__ == "__main__":
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main() |
