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https://github.com/RVC-Boss/GPT-SoVITS.git
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78 lines
3.2 KiB
Python
78 lines
3.2 KiB
Python
import os
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import Ref_Audio_Selector.common.common as common
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import Ref_Audio_Selector.tool.audio_check as audio_check
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from Ref_Audio_Selector.config_param.log_config import logger
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def parse_text_similarity_result_txt(file_path):
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"""
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解析指定格式的txt文件，每行格式：f"{item['average_similarity_score']}|{item['count']}|{item['emotion']}"
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:param file_path: txt文件的路径
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:return: 包含解析后数据的字典列表
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"""
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data_list = []
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with open(file_path, 'r', encoding='utf-8') as file:
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for line in file:
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# 使用'|'作为分隔符分割每行数据
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parts = line.strip().split('|')
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if len(parts) == 3:
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# 将分割后的字符串转换为浮点数、整数和字符串
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try:
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item = {
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'average_similarity_score': float(parts[0]),
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'count': int(parts[1]),
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'emotion': parts[2]
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}
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data_list.append(item)
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except ValueError as e:
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# 如果转换失败，打印错误信息并跳过该行
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logger.error(f"Error parsing line: {line.strip()} - {e}")
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return data_list
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def remove_low_similarity_files(ref_audio_list, report_list, audio_text_similarity_boundary):
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"""
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根据条件删除低相似度音频文件并返回删除数量。
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:param ref_audio_list: 包含音频路径和情感属性的列表
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:param report_list: 包含相似度评分和情感属性的列表
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:param audio_text_similarity_boundary: 相似度阈值
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:return: 删除的文件数量
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"""
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deleted_count = 0
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# 筛选出平均相似度低于阈值的报告
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low_similarity_reports = [report for report in report_list if
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report['average_similarity_score'] < audio_text_similarity_boundary]
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# 遍历低相似度报告，查找并删除对应音频文件
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for report in low_similarity_reports:
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emotion = report['emotion']
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# 查找ref_audio_list中相同情感的音频文件路径
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matching_refs = [ref for ref in ref_audio_list if ref['emotion'] == emotion]
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for match in matching_refs:
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ref_path = match['ref_path']
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# 检查文件是否存在，然后尝试删除
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if os.path.exists(ref_path):
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try:
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os.remove(ref_path)
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deleted_count += 1
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logger.info(f"Deleted file: {ref_path}")
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except Exception as e:
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logger.error(f"Error deleting file {ref_path}: {e}")
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else:
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logger.error(f"File not found: {ref_path}")
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return deleted_count
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def delete_ref_audio_below_boundary(ref_audio_path, text_similarity_result_path, sync_inference_audio_dir,
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audio_text_similarity_boundary):
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ref_audio_list = common.RefAudioListManager(ref_audio_path).get_ref_audio_list()
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report_list = parse_text_similarity_result_txt(text_similarity_result_path)
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count = remove_low_similarity_files(ref_audio_list, report_list, audio_text_similarity_boundary)
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audio_check.sync_ref_audio(ref_audio_path, sync_inference_audio_dir)
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return count
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