| import os
|
| import json
|
| import argparse
|
| import datetime
|
| import numpy as np
|
|
|
|
|
| def check_file_exists(file_path):
|
| """Check if file exists and is not empty"""
|
| if not os.path.exists(file_path):
|
| return False, f"File does not exist: {file_path}"
|
| if os.path.getsize(file_path) == 0:
|
| return False, f"File is empty: {file_path}"
|
| return True, ""
|
|
|
|
|
| def cer(ref, hyp):
|
| """Character Error Rate using Levenshtein distance"""
|
| r = ref
|
| h = hyp
|
| d = np.zeros((len(r)+1)*(len(h)+1), dtype=np.uint8).reshape((len(r)+1, len(h)+1))
|
|
|
| for i in range(len(r)+1):
|
| d[i][0] = i
|
| for j in range(len(h)+1):
|
| d[0][j] = j
|
|
|
| for i in range(1, len(r)+1):
|
| for j in range(1, len(h)+1):
|
| cost = 0 if r[i-1] == h[j-1] else 1
|
| d[i][j] = min(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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|
|
| return d[len(r)][len(h)] / max(len(r), 1)
|
|
|
|
|
| def load_text(file_path):
|
| """Load full text content from file"""
|
| try:
|
| with open(file_path, 'r', encoding='utf-8') as f:
|
| return f.read().replace('\n', '').strip(), ""
|
| except Exception as e:
|
| return None, str(e)
|
|
|
|
|
| def evaluate(system_output_file, ground_truth_file, cer_threshold=0.05):
|
| """Evaluate CER between system output and ground truth"""
|
|
|
| process_ok, process_msg = check_file_exists(system_output_file)
|
| if not process_ok:
|
| return False, False, process_msg
|
|
|
| process_ok, process_msg = check_file_exists(ground_truth_file)
|
| if not process_ok:
|
| return False, False, process_msg
|
|
|
|
|
| sys_text, msg1 = load_text(system_output_file)
|
| gt_text, msg2 = load_text(ground_truth_file)
|
|
|
| if sys_text is None:
|
| return True, False, f"Failed to load system output: {msg1}"
|
| if gt_text is None:
|
| return True, False, f"Failed to load ground truth: {msg2}"
|
|
|
| score = cer(gt_text, sys_text)
|
| comment = f"CER = {score:.4f}"
|
| if score > cer_threshold:
|
| comment += f" ❌ Exceeds threshold {cer_threshold}"
|
| return True, False, comment
|
| else:
|
| comment += f" ✅ Within threshold {cer_threshold}"
|
| return True, True, comment
|
|
|
|
|
| def save_results_to_jsonl(process_ok, result_ok, comments, jsonl_file):
|
| """Save test results to JSONL file"""
|
| current_time = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
|
|
|
| result_data = {
|
| "Process": bool(process_ok),
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| "Result": bool(result_ok),
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| "TimePoint": current_time,
|
| "comments": comments
|
| }
|
|
|
| os.makedirs(os.path.dirname(jsonl_file), exist_ok=True)
|
|
|
| with open(jsonl_file, 'a', encoding='utf-8') as f:
|
| json.dump(result_data, f, ensure_ascii=False, default=str)
|
| f.write('\n')
|
|
|
|
|
| def main():
|
| parser = argparse.ArgumentParser(description='Evaluate speech recognition results (no speaker separation)')
|
| parser.add_argument('--output', required=True, help='System output file path')
|
| parser.add_argument('--groundtruth', required=True, help='Ground truth file path')
|
| parser.add_argument('--cer_threshold', type=float, default=0.10, help='CER threshold')
|
| parser.add_argument('--result', required=True, help='Result JSONL file path')
|
|
|
| args = parser.parse_args()
|
|
|
| process_ok, result_ok, comments = evaluate(
|
| args.output,
|
| args.groundtruth,
|
| args.cer_threshold
|
| )
|
|
|
| save_results_to_jsonl(process_ok, result_ok, comments, args.result)
|
|
|
| if not process_ok:
|
| print(f"Processing failed: {comments}")
|
| elif not result_ok:
|
| print(f"Results do not meet requirements: {comments}")
|
| else:
|
| print("✅ Test passed")
|
| print(comments)
|
|
|
|
|
| if __name__ == "__main__":
|
| main()
|
|
|
