import argparse import os import logging import re from whisper import Whisper def setup_logging(): """配置日志系统，同时输出到控制台和文件""" # 获取脚本所在目录 script_dir = os.path.dirname(os.path.abspath(__file__)) log_file = os.path.join(script_dir, "test_wer.log") # 配置日志格式 log_format = '%(asctime)s - %(levelname)s - %(message)s' date_format = '%Y-%m-%d %H:%M:%S' # 创建logger logger = logging.getLogger() logger.setLevel(logging.INFO) # 清除现有的handler for handler in logger.handlers[:]: logger.removeHandler(handler) # 创建文件handler file_handler = logging.FileHandler(log_file, mode='a', encoding='utf-8') file_handler.setLevel(logging.INFO) file_formatter = logging.Formatter(log_format, date_format) file_handler.setFormatter(file_formatter) # 创建控制台handler console_handler = logging.StreamHandler() console_handler.setLevel(logging.INFO) console_formatter = logging.Formatter(log_format, date_format) console_handler.setFormatter(console_formatter) # 添加handler到logger logger.addHandler(file_handler) logger.addHandler(console_handler) return logger class AIShellDataset: def __init__(self, gt_path: str): """ 初始化数据集 Args: json_path: voice.json文件的路径 """ self.gt_path = gt_path self.dataset_dir = os.path.dirname(gt_path) self.voice_dir = os.path.join(self.dataset_dir, "aishell_S0764") # 检查必要文件和文件夹是否存在 assert os.path.exists(gt_path), f"gt文件不存在: {gt_path}" assert os.path.exists(self.voice_dir), f"aishell_S0764文件夹不存在: {self.voice_dir}" # 加载数据 self.data = [] with open(gt_path, 'r', encoding='utf-8') as f: for line in f: line = line.strip() audio_path, gt = line.split(" ") audio_path = os.path.join(self.voice_dir, audio_path + ".wav") self.data.append({"audio_path": audio_path, "gt": gt}) # 使用logging而不是print logger = logging.getLogger() logger.info(f"加载了 {len(self.data)} 条数据") def __iter__(self): """返回迭代器""" self.index = 0 return self def __next__(self): """返回下一个数据项""" if self.index >= len(self.data): raise StopIteration item = self.data[self.index] audio_path = item["audio_path"] ground_truth = item["gt"] self.index += 1 return audio_path, ground_truth def __len__(self): """返回数据集大小""" return len(self.data) class CommonVoiceDataset: """Common Voice数据集解析器""" def __init__(self, tsv_path: str): """ 初始化数据集 Args: json_path: voice.json文件的路径 """ self.tsv_path = tsv_path self.dataset_dir = os.path.dirname(tsv_path) self.voice_dir = os.path.join(self.dataset_dir, "clips") # 检查必要文件和文件夹是否存在 assert os.path.exists(tsv_path), f"{tsv_path}文件不存在: {tsv_path}" assert os.path.exists(self.voice_dir), f"voice文件夹不存在: {self.voice_dir}" # 加载JSON数据 self.data = [] with open(tsv_path, 'r', encoding='utf-8') as f: f.readline() for line in f: line = line.strip() splits = line.split("\t") audio_path = splits[1] gt = splits[2] audio_path = os.path.join(self.voice_dir, audio_path) self.data.append({"audio_path": audio_path, "gt": gt}) # 使用logging而不是print logger = logging.getLogger() logger.info(f"加载了 {len(self.data)} 条数据") def __iter__(self): """返回迭代器""" self.index = 0 return self def __next__(self): """返回下一个数据项""" if self.index >= len(self.data): raise StopIteration item = self.data[self.index] audio_path = item["audio_path"] ground_truth = item["gt"] self.index += 1 return audio_path, ground_truth def __len__(self): """返回数据集大小""" return len(self.data) def get_args(): parser = argparse.ArgumentParser( prog="whisper", description="Test WER on dataset" ) parser.add_argument("--dataset", "-d", type=str, required=True, choices=["aishell", "common_voice"], help="Test dataset") parser.add_argument("--gt_path", "-g", type=str, required=True, help="Test dataset ground truth file") parser.add_argument("--max_num", type=int, default=-1, required=False, help="Maximum test data num") parser.add_argument("--model_type", "-t", type=str, choices=["tiny", "base", "small", "large", "large-v3", "turbo"], required=True, help="model type, only support tiny, base and small currently") parser.add_argument("--model_path", "-p", type=str, required=False, default="../models/models-ax650", help="model path for *.axmodel, tokens.txt, positional_embedding.bin") parser.add_argument("--language", "-l", type=str, required=False, default="zh", help="Target language, support en, zh, ja, and others. See languages.py for more options.") return parser.parse_args() def print_args(args): logger = logging.getLogger() logger.info(f"dataset: {args.dataset}") logger.info(f"gt_path: {args.gt_path}") logger.info(f"max_num: {args.max_num}") logger.info(f"model_type: {args.model_type}") logger.info(f"model_path: {args.model_path}") logger.info(f"language: {args.language}") def min_distance(word1: str, word2: str) -> int: row = len(word1) + 1 column = len(word2) + 1 cache = [ [0]*column for i in range(row) ] for i in range(row): for j in range(column): if i ==0 and j ==0: cache[i][j] = 0 elif i == 0 and j!=0: cache[i][j] = j elif j == 0 and i!=0: cache[i][j] = i else: if word1[i-1] == word2[j-1]: cache[i][j] = cache[i-1][j-1] else: replace = cache[i-1][j-1] + 1 insert = cache[i][j-1] + 1 remove = cache[i-1][j] + 1 cache[i][j] = min(replace, insert, remove) return cache[row-1][column-1] def remove_punctuation(text): # 定义正则表达式模式，匹配所有标点符号 # 这个模式包括常见的标点符号和中文标点 pattern = r'[^\w\s]|_' # 使用sub方法将所有匹配的标点符号替换为空字符串 cleaned_text = re.sub(pattern, '', text) return cleaned_text def main(): # 设置日志系统 logger = setup_logging() args = get_args() print_args(args) dataset_type = args.dataset.lower() if dataset_type == "aishell": dataset = AIShellDataset(args.gt_path) elif dataset_type == "common_voice": dataset = CommonVoiceDataset(args.gt_path) else: raise ValueError(f"Unknown dataset type {dataset_type}") max_num = args.max_num # Load model model = Whisper(args.model_type, args.model_path, args.language, "transcribe") # Iterate over dataset references = [] hyp = [] all_character_error_num = 0 all_character_num = 0 wer_file = open("wer.txt", "w") max_data_num = max_num if max_num > 0 else len(dataset) for n, (audio_path, reference) in enumerate(dataset): hypothesis = model.run(audio_path) hypothesis = remove_punctuation(hypothesis) reference = remove_punctuation(reference) character_error_num = min_distance(reference, hypothesis) character_num = len(reference) character_error_rate = character_error_num / character_num * 100 all_character_error_num += character_error_num all_character_num += character_num hyp.append(hypothesis) references.append(reference) line_content = f"({n+1}/{max_data_num}) {os.path.basename(audio_path)} gt: {reference} predict: {hypothesis} WER: {character_error_rate}%" wer_file.write(line_content + "\n") logger.info(line_content) if n + 1 >= max_data_num: break total_character_error_rate = all_character_error_num / all_character_num * 100 logger.info(f"Total WER: {total_character_error_rate}%") wer_file.write(f"Total WER: {total_character_error_rate}%") wer_file.close() if __name__ == "__main__": main()