mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-24 16:13:54 +08:00
Fix long audio bug (#158)
* update:异步生成音频 * update:优化LLM断句 --------- Co-authored-by: hrz <1710360675@qq.com>
This commit is contained in:
欣南科技
hrz
@@ -1,7 +1,5 @@
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import os
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import sys
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import asyncio
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from typing import List, Dict, Any
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# 添加项目根目录到Python路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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@@ -10,10 +8,6 @@ sys.path.insert(0, project_root)
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from config.logger import setup_logging
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import importlib
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from datetime import datetime
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from core.utils.util import is_segment
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from core.utils.util import get_string_no_punctuation_or_emoji
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from core.utils.util import read_config, get_project_dir
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logger = setup_logging()
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@@ -27,117 +21,3 @@ def create_instance(class_name, *args, **kwargs):
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return sys.modules[lib_name].LLMProvider(*args, **kwargs)
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raise ValueError(f"不支持的LLM类型: {class_name}，请检查该配置的type是否设置正确")
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async def test_single_model(llm_name: str, llm_config: Dict[str, Any], test_prompt: str, config: Dict[str, Any]) -> Dict[str, Any]:
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"""异步测试单个模型"""
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try:
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# 获取实际的LLM类型
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llm_type = llm_config["type"] if "type" in llm_config else llm_name
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llm = create_instance(llm_type, llm_config)
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# 开始测试
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dialogue = []
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dialogue.append({"role": "system", "content": config.get("prompt")})
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dialogue.append({"role": "user", "content": test_prompt})
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start_time = datetime.now()
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llm_responses = llm.response("test", dialogue)
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response_message = []
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first_response_time = None
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total_response_time = None
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start = 0
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full_response = ""
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for content in llm_responses:
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response_message.append(content)
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full_response += content
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if is_segment(response_message):
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segment_text = "".join(response_message[start:])
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segment_text = get_string_no_punctuation_or_emoji(segment_text)
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if len(segment_text) > 0:
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if first_response_time is None:
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first_response_time = (datetime.now() - start_time).total_seconds()
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start = len(response_message)
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total_response_time = (datetime.now() - start_time).total_seconds()
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return {
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"name": llm_name,
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"type": llm_type,
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"first_response_time": first_response_time,
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"total_response_time": total_response_time,
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"response_length": len(full_response),
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"status": "成功",
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"response": full_response
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}
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except Exception as e:
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print(f"测试 {llm_name} 时发生错误: {str(e)}")
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return {
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"name": llm_name,
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"type": llm_config.get("type", llm_name),
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"first_response_time": None,
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"total_response_time": None,
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"response_length": 0,
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"status": f"失败 - {str(e)}",
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"response": ""
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}
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async def main():
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"""
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LLM模型响应速度测试和排行（异步版本）
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"""
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config = read_config(get_project_dir() + "config.yaml")
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test_prompt = "你好小智"
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print("开始并发测试所有模型...")
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# 创建所有模型的测试任务
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tasks = []
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for llm_name, llm_config in config["LLM"].items():
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task = asyncio.create_task(test_single_model(llm_name, llm_config, test_prompt, config))
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tasks.append(task)
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# 等待所有测试完成
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test_results = await asyncio.gather(*tasks)
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# 打印测试结果排行榜
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print("\n========= LLM模型性能测试排行榜 =========")
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print("测试提示词:", test_prompt)
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# 过滤出成功的结果，并确保数值有效
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successful_results = [r for r in test_results if r["status"] == "成功" and r["first_response_time"] is not None]
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if successful_results:
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print("\n1. 首次响应时间排行:")
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sorted_by_first = sorted(successful_results, key=lambda x: x["first_response_time"])
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for i, result in enumerate(sorted_by_first, 1):
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print(f"{i}. {result['name']}({result['type']}) - {result['first_response_time']:.2f}秒")
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print(f" 响应内容: {result['response'][:50]}...") # 只显示前50个字符
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print("\n2. 总响应时间排行:")
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sorted_by_total = sorted(successful_results, key=lambda x: x["total_response_time"] or float('inf'))
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for i, result in enumerate(sorted_by_total, 1):
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if result["total_response_time"] is not None:
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print(f"{i}. {result['name']}({result['type']}) - {result['total_response_time']:.2f}秒")
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print("\n3. 响应长度比较:")
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sorted_by_length = sorted(successful_results, key=lambda x: x["response_length"], reverse=True)
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for i, result in enumerate(sorted_by_length, 1):
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print(f"{i}. {result['name']}({result['type']}) - {result['response_length']}字符")
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else:
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print("\n没有成功完成测试的模型。")
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if len(test_results) != len(successful_results):
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print("\n测试失败的模型:")
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failed_results = [r for r in test_results if r["status"] != "成功" or r["first_response_time"] is None]
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for result in failed_results:
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print(f"- {result['name']}({result['type']}): {result['status']}")
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if __name__ == "__main__":
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# 运行异步主函数
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asyncio.run(main())
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+1
-25
@@ -2,8 +2,6 @@ import os
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import sys
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from config.logger import setup_logging
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import importlib
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from datetime import datetime
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from core.utils.util import read_config, get_project_dir
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logger = setup_logging()
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@@ -16,26 +14,4 @@ def create_instance(class_name, *args, **kwargs):
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sys.modules[lib_name] = importlib.import_module(f'{lib_name}')
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return sys.modules[lib_name].TTSProvider(*args, **kwargs)
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raise ValueError(f"不支持的TTS类型: {class_name}，请检查该配置的type是否设置正确")
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if __name__ == "__main__":
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"""
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响应速度测试
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"""
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config = read_config(get_project_dir() + "config.yaml")
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tts = create_instance(
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config["selected_module"]["TTS"]
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if not 'type' in config["TTS"][config["selected_module"]["TTS"]]
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else
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config["TTS"][config["selected_module"]["TTS"]]["type"],
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config["TTS"][config["selected_module"]["TTS"]],
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config["delete_audio"]
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)
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tts.output_file = get_project_dir() + tts.output_file
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start = datetime.now()
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file_path = tts.to_tts("你好，测试,我是人工智能小智")
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print("语音合成耗时：" + str(datetime.now() - start))
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start = datetime.now()
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tts.wav_to_opus_data(file_path)
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print("语音opus耗时：" + str(datetime.now() - start))
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raise ValueError(f"不支持的TTS类型: {class_name}，请检查该配置的type是否设置正确")
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@@ -34,13 +34,6 @@ def write_json_file(file_path, data):
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json.dump(data, file, ensure_ascii=False, indent=4)
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def is_segment(tokens):
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if tokens[-1] in (",", ".", "?", "，", "。", "？", "！", "!", ";", "；", ":", "："):
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return True
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else:
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return False
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def is_punctuation_or_emoji(char):
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"""检查字符是否为空格、指定标点或表情符号"""
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# 定义需要去除的中英文标点（包括全角/半角）
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