446 lines
20 KiB
Python
446 lines
20 KiB
Python
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import os
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import subprocess
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from collections import defaultdict
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from typing import Dict, List
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from utils import asr_ter
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from utils.logger import logger
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log_mid_result = int(os.getenv("log", 0)) == 1
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class AsrEvaluator:
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def __init__(self) -> None:
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self.query_count = 0 # query 数目（语音数目）
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self.voice_count = 0 # 有开始和结束时间的语音条数（用于 RTF 计算）
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self.cut_punc = [] # 切分标点符号，需要注意切分的时候根据列表中的顺序进行切分，比如 ... 应该放到 . 之前。
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# cer 属性
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self.one_minus_cer = 0 # 每个 query 的 1 - cer 和
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self.token_count = 0 # 每个 query 的字数/词数和
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# 句子切分率属性
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self.miss_count = 0 # 每个 query miss-count 和
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self.more_count = 0 # 每个 query more-count 和
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self.cut_count = 0 # 每个 query cut-count 和
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self.rate = 0 # 每个 query 的 cut-rate 和
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# detail case
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self.result = []
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def evaluate(self, eval_result):
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pass
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def post_evaluate(self):
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pass
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def gen_result(self) -> Dict:
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output_result = dict()
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output_result["query_count"] = self.query_count
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output_result["voice_count"] = self.voice_count
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output_result["token_cnt"] = self.token_count
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output_result["one_minus_cer"] = self.one_minus_cer
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output_result["one_minus_cer_metrics"] = self.one_minus_cer / self.query_count
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output_result["miss_count"] = self.miss_count
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output_result["more_count"] = self.more_count
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output_result["cut_count"] = self.cut_count
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output_result["cut_rate"] = self.rate
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output_result["cut_rate_metrics"] = self.rate / self.query_count
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output_result["rtf"] = self.rtf
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output_result["rtf_end"] = self.rtf_end
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output_result["rtf_metrics"] = self.rtf / self.voice_count
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output_result["rtf_end_metrics"] = self.rtf_end / self.voice_count
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detail_case = self.result
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return output_result, detail_case
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def _get_predict_final_sentences(self, predict_data: List[Dict]) -> List[str]:
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"""
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获取 predict data 数据，然后将其中 final 的句子拿出来，放到列表里。
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"""
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return [
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item["recoginition_results"]["text"]
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for item in predict_data
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if item["recoginition_results"]["final_result"]
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]
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def _sentence_final_index(self, sentences: List[str], tokens: List[str], tokenizer="word") -> List[int]:
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"""
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获取 sentence 结束的字对应的 token 索引值。
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"""
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token_index_list = []
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token_idx = 0
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for sentence in sentences:
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for token in Tokenizer.tokenize(sentence, tokenizer):
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if token not in tokens:
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continue
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while tokens[token_idx] != token:
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token_idx += 1
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token_index_list.append(token_idx)
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return token_index_list
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def _voice_to_cut_sentence(self, voice_sentences: List[str]) -> Dict:
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"""
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将数据集的语音片段转换为最小切分单元列表。
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使用 cut_punc 中的所有 punc 进行依次切分，最后去除掉完全空的内容
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示例：
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["你好，你好呀", "你好，我在写抽象的代码逻辑"]
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->
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cut_sentences: ["你好", "你好呀", "你好", "我在写抽象的代码逻辑"]
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cut_sentence_index_list: [1, 3] ("你好呀" 对应 1-idx, "我在写抽象的代码逻辑" 对应 3-idx)
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"""
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voice_sentences_result = defaultdict(list)
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for voice_sentence in voice_sentences:
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sentence_list = [voice_sentence]
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sentence_tmp_list = []
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for punc in self.cut_punc:
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for sentence in sentence_list:
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sentence_tmp_list.extend(sentence.split(punc))
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sentence_list, sentence_tmp_list = sentence_tmp_list, []
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sentence_list = [item for item in sentence_list if item]
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# 切分后的句子单元
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voice_sentences_result["cut_sentences"].extend(sentence_list)
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# 每个语音单元最后一个字对应的句子单元的索引
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voice_sentences_result["cut_sentence_index_list"].append(len(voice_sentences_result["cut_sentences"]) - 1)
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return voice_sentences_result
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def _voice_bytes_index(self, timestamp, sample_rate=16000, bit_depth=16, channels=1):
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"""
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timestamp: 时间, 单位秒
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"""
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bytes_per_sample = bit_depth // 8
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return timestamp * sample_rate * bytes_per_sample * channels
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class AsrZhEvaluator(AsrEvaluator):
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"""
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中文的评估方式
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"""
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def __init__(self):
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super().__init__()
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self.cut_zh_punc = ["······", "......", "。", "，", "？", "！", "；", "："]
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self.cut_en_punc = ["...", ".", ",", "?", "!", ";", ":"]
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self.cut_punc = self.cut_zh_punc + self.cut_en_punc
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def evaluate(self, eval_result) -> Dict:
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self.query_count += 1
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self.voice_count += len(eval_result["voice"])
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# 获取，标注结果 & 语音单元（非句子单元）
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label_voice_sentences = [item["answer"] for item in eval_result["voice"]]
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# print("label_voice_sentences", label_voice_sentences)
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# 获取，标注结果 & 语音单元 -> 句子单元的转换情况
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voice_to_cut_info = self._voice_to_cut_sentence(label_voice_sentences)
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# print("voice_to_cut_info", voice_to_cut_info)
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# 获取，标注结果 & 句子单元
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label_sentences = voice_to_cut_info["cut_sentences"]
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# 获取，标注结果 & 语音单元 -> 句子单元的映射关系，每个语音单元最后一个字对应的句子单元的索引
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cut_sentence_index_list = voice_to_cut_info["cut_sentence_index_list"]
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# 标注结果 & 句子单元 & norm 操作
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label_sentences = [self._sentence_norm(sentence) for sentence in label_sentences]
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if log_mid_result:
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logger.info(f"label_sentences {label_sentences}")
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# print("label_sentences", label_sentences)
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# 预测结果 & 句子单元
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predict_sentences_raw = self._get_predict_final_sentences(eval_result["predict_data"])
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# print("predict_sentences_raw", predict_sentences_raw)
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# 预测结果 & 句子单元 & norm 操作
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predict_sentences = [self._sentence_norm(sentence) for sentence in predict_sentences_raw]
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if log_mid_result:
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logger.info(f"predict_sentences {predict_sentences}")
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# print("predict_sentences", predict_sentences)
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# 基于最小编辑距离进行 token 匹配，获得匹配后的 token 列表
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label_tokens, predict_tokens = self._sentence_transfer("".join(label_sentences), "".join(predict_sentences))
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# cer 计算
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cer_info = self.cer(label_sentences, predict_sentences)
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if log_mid_result:
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logger.info(f"cer_info {cer_info}")
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# print("cer_info", cer_info)
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self.one_minus_cer += cer_info["one_minus_cer"]
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self.token_count += cer_info["token_count"]
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# 句子切分准召率
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cut_info = self.cut_rate(label_sentences, predict_sentences, label_tokens, predict_tokens)
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if log_mid_result:
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logger.info(f"{cut_info['miss_count']}, {cut_info['more_count']}, {cut_info['rate']}")
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# print("cut_info", cut_info)
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# print(cut_info["miss_count"], cut_info["more_count"], cut_info["rate"])
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self.miss_count += cut_info["miss_count"]
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self.more_count += cut_info["more_count"]
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self.cut_count += cut_info["cut_count"]
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self.rate += cut_info["rate"]
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self.result.append(
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{
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"label_tokens": label_tokens,
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"predict_tokens": predict_tokens,
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"one_minus_cer": cer_info["one_minus_cer"],
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"token_count": cer_info["one_minus_cer"],
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"miss_count": cut_info["miss_count"],
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"more_count": cut_info["more_count"],
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"cut_count": cut_info["cut_count"],
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"rate": cut_info["rate"],
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}
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)
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def cer(self, label_sentences, predict_sentences):
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pred_str = ''.join(predict_sentences) if predict_sentences is not None else ''
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label_str = ''.join(label_sentences)
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r = asr_ter.calc_ter_speechio(pred_str, label_str)
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one_minus_cer = max(1.0 - r['ter'], 0)
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token_count = r['ref_all_token_cnt']
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return {"one_minus_cer": one_minus_cer, "token_count": token_count}
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def cut_rate(self, label_sentences, predict_sentences, label_tokens, predict_tokens):
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label_final_index_list = set(self._sentence_final_index(label_sentences, label_tokens))
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pred_final_index_list = set(self._sentence_final_index(predict_sentences, predict_tokens))
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label_sentence_count = len(label_final_index_list)
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miss_count = len(label_final_index_list - pred_final_index_list)
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more_count = len(pred_final_index_list - label_final_index_list)
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rate = max(1 - (miss_count + more_count * 2) / label_sentence_count, 0)
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return {
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"miss_count": miss_count,
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"more_count": more_count,
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"cut_count": label_sentence_count,
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"rate": rate,
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"label_final_index_list": label_final_index_list,
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"pred_final_index_list": pred_final_index_list,
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}
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def _sentence_norm(self, sentence, tokenizer="word"):
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"""
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对句子进行 norm 操作
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"""
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from utils.speechio import textnorm_zh as textnorm
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if tokenizer == "word":
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normalizer = textnorm.TextNorm(
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to_banjiao=True,
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to_upper=True,
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to_lower=False,
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remove_fillers=True,
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remove_erhua=False, # 这里同批量识别不同，改成了 False
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check_chars=False,
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remove_space=False,
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cc_mode="",
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)
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return normalizer(sentence)
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else:
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logger.error("tokenizer error, not support.")
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def _sentence_transfer(self, label_sentence: str, predict_sentence: str, tokenizer="char"):
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"""
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基于最小编辑距离，将 label 和 predict 进行字的位置匹配，并生成转换后的结果
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args:
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label: "今天的通话质量不错呀昨天的呢"
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predict: "今天的通话质量不错昨天呢星期"
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tokenizer: 分词方式
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return:
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label: ["今", "天", "的", "通", "话", "质", "量", "不", "错", "呀", "昨", "天", "的", "呢", None, None]
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predict: ["今", "天", "的", "通", "话", "质", "量", "不", "错", None, "昨", "天", None, "呢", "星", "期"]
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"""
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from utils.speechio import error_rate_zh as error_rate
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if tokenizer == "char":
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alignment, score = error_rate.EditDistance(
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error_rate.tokenize_text(label_sentence, tokenizer),
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error_rate.tokenize_text(predict_sentence, tokenizer),
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)
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label_tokens, pred_tokens = [], []
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for align in alignment:
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# print(align.__dict__)
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label_tokens.append(align.ref)
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pred_tokens.append(align.hyp)
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return (label_tokens, pred_tokens)
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else:
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logger.error("tokenizer 出错了，暂时不支持其它的")
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def _pred_data_transfer(self, predict_data, recv_time):
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"""
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predict_data = [
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{"recoginition_results": {"text": "1", "final_result": False, "para_seq": 0}},
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{"recoginition_results": {"text": "12", "final_result": False, "para_seq": 0}},
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{"recoginition_results": {"text": "123", "final_result": True, "para_seq": 0}},
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{"recoginition_results": {"text": "4", "final_result": False, "para_seq": 0}},
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{"recoginition_results": {"text": "45", "final_result": False, "para_seq": 0}},
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{"recoginition_results": {"text": "456", "final_result": True, "para_seq": 0}},
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]
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recv_time = [1, 3, 5, 6, 7, 8]
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->
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[
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[{'text': '1', 'time': 1}, {'text': '12', 'time': 3}, {'text': '123', 'time': 5}],
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[{'text': '4', 'time': 6}, {'text': '45', 'time': 7}, {'text': '456', 'time': 8}],
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]
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"""
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pred_sentence_info = []
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pred_sentence_index = 0
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for predict_item, recv_time_item in zip(predict_data, recv_time):
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if len(pred_sentence_info) == pred_sentence_index:
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pred_sentence_info.append([])
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pred_sentence_info[pred_sentence_index].append(
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{
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"text": predict_item["recoginition_results"]["text"],
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"time": recv_time_item,
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}
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)
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if predict_item["recoginition_results"]["final_result"]:
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pred_sentence_index += 1
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return pred_sentence_info
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class AsrEnEvaluator(AsrEvaluator):
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"""
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英文的评估方式
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"""
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def evaluate(self, eval_result) -> Dict:
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self.query_count += 1
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|
self.voice_count += len(eval_result["voice"])
|
|||
|
|
|
|||
|
|
# 获取，标注结果 & 语音单元（非句子单元）
|
|||
|
|
label_voice_sentences = [item["answer"] for item in eval_result["voice"]]
|
|||
|
|
# print("label_voice_sentences", label_voice_sentences)
|
|||
|
|
# 获取，标注结果 & 语音单元 -> 句子单元的转换情况
|
|||
|
|
voice_to_cut_info = self._voice_to_cut_sentence(label_voice_sentences)
|
|||
|
|
# print("voice_to_cut_info", voice_to_cut_info)
|
|||
|
|
# 获取，标注结果 & 句子单元
|
|||
|
|
label_sentences = voice_to_cut_info["cut_sentences"]
|
|||
|
|
# 获取，标注结果 & 语音单元 -> 句子单元的映射关系，每个语音单元最后一个字对应的句子单元的索引
|
|||
|
|
cut_sentence_index_list = voice_to_cut_info["cut_sentence_index_list"]
|
|||
|
|
# 标注结果 & 句子单元 & norm 操作
|
|||
|
|
label_sentences = self._sentence_list_norm(label_sentences)
|
|||
|
|
# [self._sentence_norm(sentence) for sentence in label_sentences]
|
|||
|
|
# print("label_sentences", label_sentences)
|
|||
|
|
if log_mid_result:
|
|||
|
|
logger.info(f"label_sentences {label_sentences}")
|
|||
|
|
|
|||
|
|
# 预测结果 & 句子单元
|
|||
|
|
predict_sentences_raw = self._get_predict_final_sentences(eval_result["predict_data"])
|
|||
|
|
# print("predict_sentences_raw", predict_sentences_raw)
|
|||
|
|
# 预测结果 & 句子单元 & norm 操作
|
|||
|
|
predict_sentences = self._sentence_list_norm(predict_sentences_raw)
|
|||
|
|
# [self._sentence_norm(sentence) for sentence in predict_sentences_raw]
|
|||
|
|
# print("predict_sentences", predict_sentences)
|
|||
|
|
if log_mid_result:
|
|||
|
|
logger.info(f"predict_sentences {predict_sentences}")
|
|||
|
|
|
|||
|
|
label_tokens, predict_tokens = self._sentence_transfer(" ".join(label_sentences), " ".join(predict_sentences))
|
|||
|
|
# print(label_tokens)
|
|||
|
|
# print(predict_tokens)
|
|||
|
|
|
|||
|
|
# cer 计算
|
|||
|
|
cer_info = self.cer(label_tokens, predict_tokens)
|
|||
|
|
# print("cer_info", cer_info)
|
|||
|
|
if log_mid_result:
|
|||
|
|
logger.info(f"cer_info {cer_info}")
|
|||
|
|
self.one_minus_cer += cer_info["one_minus_cer"]
|
|||
|
|
self.token_count += cer_info["token_count"]
|
|||
|
|
|
|||
|
|
# 句子切分准召率
|
|||
|
|
cut_info = self.cut_rate(label_sentences, predict_sentences, label_tokens, predict_tokens)
|
|||
|
|
# print(cut_info["miss_count"], cut_info["more_count"], cut_info["rate"])
|
|||
|
|
# print("cut_info", cut_info)
|
|||
|
|
if log_mid_result:
|
|||
|
|
logger.info(f"{cut_info['miss_count']}, {cut_info['more_count']}, {cut_info['rate']}")
|
|||
|
|
self.miss_count += cut_info["miss_count"]
|
|||
|
|
self.more_count += cut_info["more_count"]
|
|||
|
|
self.cut_count += cut_info["cut_count"]
|
|||
|
|
self.rate += cut_info["rate"]
|
|||
|
|
|
|||
|
|
self.result.append(
|
|||
|
|
{
|
|||
|
|
"label_tokens": label_tokens,
|
|||
|
|
"predict_tokens": predict_tokens,
|
|||
|
|
"one_minus_cer": cer_info["one_minus_cer"],
|
|||
|
|
"token_count": cer_info["one_minus_cer"],
|
|||
|
|
"miss_count": cut_info["miss_count"],
|
|||
|
|
"more_count": cut_info["more_count"],
|
|||
|
|
"cut_count": cut_info["cut_count"],
|
|||
|
|
"rate": cut_info["rate"],
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def cer(self, label_tokens, predict_tokens):
|
|||
|
|
s, d, i, c = 0, 0, 0, 0
|
|||
|
|
for label_token, predict_token in zip(label_tokens, predict_tokens):
|
|||
|
|
if label_token == predict_token:
|
|||
|
|
c += 1
|
|||
|
|
elif predict_token is None:
|
|||
|
|
d += 1
|
|||
|
|
elif label_token is None:
|
|||
|
|
i += 1
|
|||
|
|
else:
|
|||
|
|
s += 1
|
|||
|
|
cer = (s + d + i) / (s + d + c)
|
|||
|
|
one_minus_cer = max(1.0 - cer, 0)
|
|||
|
|
token_count = s + d + c
|
|||
|
|
return {"one_minus_cer": one_minus_cer, "token_count": token_count}
|
|||
|
|
|
|||
|
|
def cut_rate(self, label_sentences, predict_sentences, label_tokens, predict_tokens):
|
|||
|
|
label_final_index_list = set(self._sentence_final_index(label_sentences, label_tokens, "whitespace"))
|
|||
|
|
pred_final_index_list = set(self._sentence_final_index(predict_sentences, predict_tokens, "whitespace"))
|
|||
|
|
label_sentence_count = len(label_final_index_list)
|
|||
|
|
miss_count = len(label_final_index_list - pred_final_index_list)
|
|||
|
|
more_count = len(pred_final_index_list - label_final_index_list)
|
|||
|
|
rate = max(1 - (miss_count + more_count * 2) / label_sentence_count, 0)
|
|||
|
|
return {
|
|||
|
|
"miss_count": miss_count,
|
|||
|
|
"more_count": more_count,
|
|||
|
|
"cut_count": label_sentence_count,
|
|||
|
|
"rate": rate,
|
|||
|
|
"label_final_index_list": label_final_index_list,
|
|||
|
|
"pred_final_index_list": pred_final_index_list,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def _sentence_list_norm(self, sentence_list, tokenizer="whitespace"):
|
|||
|
|
pwd = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
|||
|
|
with open('./predict.txt', 'w', encoding='utf-8') as fp:
|
|||
|
|
for idx, sentence in enumerate(sentence_list):
|
|||
|
|
fp.write('%s\t%s\n' % (idx, sentence))
|
|||
|
|
subprocess.run(
|
|||
|
|
f'PYTHONPATH={pwd}/utils/speechio python {pwd}/utils/speechio/textnorm_en.py --has_key --to_upper ./predict.txt ./predict_norm.txt',
|
|||
|
|
shell=True,
|
|||
|
|
check=True,
|
|||
|
|
)
|
|||
|
|
sentence_norm = []
|
|||
|
|
with open('./predict_norm.txt', 'r', encoding='utf-8') as fp:
|
|||
|
|
for line in fp.readlines():
|
|||
|
|
line_split_result = line.strip().split('\t', 1)
|
|||
|
|
if len(line_split_result) >= 2:
|
|||
|
|
sentence_norm.append(line_split_result[1])
|
|||
|
|
# 有可能没有 norm 后就没了
|
|||
|
|
return sentence_norm
|
|||
|
|
|
|||
|
|
def _sentence_transfer(self, label_sentence: str, predict_sentence: str, tokenizer="whitespace"):
|
|||
|
|
"""
|
|||
|
|
基于最小编辑距离，将 label 和 predict 进行字的位置匹配，并生成转换后的结果
|
|||
|
|
args:
|
|||
|
|
label: "HELLO WORLD ARE U OK YEP"
|
|||
|
|
predict: "HELLO WORLD U ARE U OK YEP"
|
|||
|
|
tokenizer: 分词方式
|
|||
|
|
return:
|
|||
|
|
label: ["HELLO", "WORLD", None, "ARE", "U", "OK", "YEP"]
|
|||
|
|
predict: ["HELLO", "WORLD", "U", "ARE", "U", "OK", "YEP"]
|
|||
|
|
"""
|
|||
|
|
from utils.speechio import error_rate_zh as error_rate
|
|||
|
|
|
|||
|
|
if tokenizer == "whitespace":
|
|||
|
|
alignment, score = error_rate.EditDistance(
|
|||
|
|
error_rate.tokenize_text(label_sentence, tokenizer),
|
|||
|
|
error_rate.tokenize_text(predict_sentence, tokenizer),
|
|||
|
|
)
|
|||
|
|
label_tokens, pred_tokens = [], []
|
|||
|
|
for align in alignment:
|
|||
|
|
label_tokens.append(align.ref)
|
|||
|
|
pred_tokens.append(align.hyp)
|
|||
|
|
return (label_tokens, pred_tokens)
|
|||
|
|
else:
|
|||
|
|
logger.error("tokenizer 出错了，暂时不支持其它的")
|
|||
|
|
|
|||
|
|
def post_evaluate(self) -> Dict:
|
|||
|
|
pass
|