test_wer.py

import argparse
import os
import logging
import re
from fireredasr_axmodel import FireRedASRAxModel


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, voice_dir="wav"):
        """
        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, voice_dir)

        # 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"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(
        "--language",
        "-l",
        type=str,
        required=False,
        default="zh",
        help="Target language, support en, zh, ja, and others. See languages.py for more options.",
    )
    parser.add_argument(
        "--encoder",
        type=str,
        default="axmodel/encoder.axmodel",
        help="Path to onnx encoder",
    )
    parser.add_argument(
        "--decoder_loop",
        type=str,
        default="axmodel/decoder_loop.axmodel",
        help="Path to axmodel decoder loop",
    )
    parser.add_argument(
        "--cmvn", type=str, default="axmodel/cmvn.ark", help="Path to cmvn"
    )
    parser.add_argument(
        "--dict", type=str, default="axmodel/dict.txt", help="Path to dict"
    )
    parser.add_argument(
        "--spm_model",
        type=str,
        default="axmodel/train_bpe1000.model",
        help="Path to spm model",
    )
    parser.add_argument(
        "--wavlist", type=str, default="wavlist.txt", help="File to wav path list"
    )
    parser.add_argument(
        "--hypo", type=str, default="hypo_axmodel.txt", help="File of hypos"
    )
    parser.add_argument("--beam_size", type=int, default=1, help="")
    parser.add_argument("--nbest", type=int, default=1, help="")
    parser.add_argument("--max_len", type=int, default=128, help="")
    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"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 = FireRedASRAxModel(
        args.encoder,
        args.decoder_loop,
        args.cmvn,
        args.dict,
        args.spm_model,
        decode_max_len=args.max_len,
        audio_dur=10,
    )
    # model = FireRedASROnnxModel(
    #     args.encoder,
    #     args.decoder,
    #     args.cmvn,
    #     args.dict,
    #     args.spm_model,
    #     decode_max_len=args.max_len,
    #     audio_dur=10
    # )
    # model = FireRedAsr.from_pretrained("aed", "model_convert/pretrained_models/FireRedASR-AED-L")

    # 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):
        batch_uttid = [os.path.splitext(os.path.basename(audio_path))[0]]
        batch_wav = [audio_path]
        results, _, _ = model.transcribe(batch_wav, args.beam_size, args.nbest)

        hypothesis = results["text"]

        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()