import os, sys import argparse from SenseVoiceAx import SenseVoiceAx from tokenizer import SentencepiecesTokenizer from print_utils import rich_transcription_postprocess, rich_print_asr_res from download_utils import download_model import logging import re import emoji 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="w", 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[3] 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() 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( "--language", "-l", required=False, type=str, default="auto", choices=["auto", "zh", "en", "yue", "ja", "ko"], ) parser.add_argument( "--max_num", type=int, default=-1, required=False, help="Maximum test data num" ) return parser.parse_args() 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(): logger = setup_logging() args = get_args() language = args.language use_itn = False # 标点符号预测 max_num = args.max_num 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}") # model_path_root = download_model("SenseVoice") model_path = os.path.join("sensevoice_ax650", "sensevoice.axmodel") bpemodel = "chn_jpn_yue_eng_ko_spectok.bpe.model" assert os.path.exists(model_path), f"model {model_path} not exist" logger.info(f"dataset: {args.dataset}") logger.info(f"language: {language}") logger.info(f"use_itn: {use_itn}") logger.info(f"model_path: {model_path}") pipeline = SenseVoiceAx( model_path, language=language ) # Iterate over dataset hyp = [] references = [] all_character_error_num = 0 all_character_num = 0 max_data_num = max_num if max_num > 0 else len(dataset) for n, (audio_path, reference) in enumerate(dataset): reference = remove_punctuation(reference).lower() asr_res = pipeline.infer(audio_path, print_rtf=False) hypothesis = rich_print_asr_res( asr_res, will_print=False, remove_punc=True ).lower() hypothesis = emoji.replace_emoji(hypothesis, replace="") 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}%" 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}%") if __name__ == "__main__": main()