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speedtest1.0
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hgy667
+8
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@@ -271,4 +271,11 @@ TTS:
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# sample_rate: 16000
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# volume: 50
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# speech_rate: 0
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# pitch_rate: 0
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# pitch_rate: 0
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# 模块测试配置
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module_test:
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test_sentences: # 自定义测试语句
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- "帮我写一首关于春天的七言绝句"
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- "Explain quantum computing in simple terms"
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- "如何快速学习Python编程？"
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@@ -0,0 +1,288 @@
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import time
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import json
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from datetime import datetime
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from tabulate import tabulate
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from typing import Dict, List
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from core.utils.llm import create_instance as create_llm_instance
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from core.utils.tts import create_instance as create_tts_instance
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from core.utils.util import read_config, get_project_dir
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import statistics
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from concurrent.futures import ThreadPoolExecutor
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import inspect
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import os
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import requests
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import logging
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# 设置全局日志级别为WARNING，抑制INFO级别日志
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logging.basicConfig(level=logging.WARNING)
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class PerformanceTester:
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def __init__(self):
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self.config = read_config(get_project_dir() + "config.yaml")
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# 从配置读取测试句子，如果不存在则使用默认
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self.test_sentences = self.config.get("module_test", {}).get(
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"test_sentences",
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["你好，请介绍一下你自己", "What's the weather like today?",
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"请用100字概括量子计算的基本原理和应用前景"]
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)
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self.results = {
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"llm": {},
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"tts": {},
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"combinations": []
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}
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def _test_llm(self, llm_name: str, config: Dict) -> Dict:
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"""测试单个LLM性能"""
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try:
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# 跳过未配置密钥的模块
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if "api_key" in config and any(x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]):
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print(f"🚫 跳过未配置的LLM: {llm_name}")
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return {"errors": 1}
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# 获取实际类型（兼容旧配置）
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module_type = config.get('type', llm_name)
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llm = create_llm_instance(module_type, config)
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# 统一使用UTF-8编码
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test_sentences = [s.encode('utf-8').decode('utf-8') for s in self.test_sentences]
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total_time = 0
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first_token_times = []
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valid_times = []
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for sentence in test_sentences:
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sentence_start = time.time() # 记录整句开始时间
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first_token_received = False
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# 遍历响应流
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for chunk in llm.response("perf_test", [{"role": "user", "content": sentence}]):
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if not first_token_received and chunk.strip() != '':
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first_token_times.append(time.time() - sentence_start)
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first_token_received = True
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# 计算整句耗时
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sentence_duration = time.time() - sentence_start
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total_time += sentence_duration
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valid_times.append(sentence_duration)
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# 新增有效性检查
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if len(first_token_times) == 0 or len(valid_times) == 0:
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print(f"⚠️ {llm_name} 无有效数据，可能配置错误")
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return {"errors": 1}
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# 过滤异常数据（超过3倍标准差）
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mean = statistics.mean(valid_times)
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stdev = statistics.stdev(valid_times) if len(valid_times) > 1 else 0
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filtered_times = [t for t in valid_times if t <= mean + 3*stdev]
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# 当有效数据不足时标记错误
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if len(filtered_times) < len(test_sentences) * 0.5:
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print(f"⚠️ {llm_name} 有效数据不足，可能网络不稳定")
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return {"errors": 1}
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return {
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"avg_response": total_time / len(test_sentences),
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"avg_first_token": sum(first_token_times)/len(first_token_times),
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"std_first_token": statistics.stdev(first_token_times) if len(first_token_times) > 1 else 0,
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"std_response": statistics.stdev(valid_times) if len(valid_times) > 1 else 0,
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"errors": 0
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}
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except Exception as e:
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print(f"LLM {llm_name} 测试失败: {str(e)}")
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return {"errors": 1}
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def _test_tts(self, tts_name: str, config: Dict) -> Dict:
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"""测试单个TTS性能"""
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try:
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# 关闭详细日志
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logging.getLogger("core.providers.tts.base").setLevel(logging.WARNING)
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# 跳过未配置密钥的模块
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token_fields = ["access_token", "api_key", "token"]
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if any(field in config and any(x in config[field] for x in ["你的", "placeholder"]) for field in token_fields):
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print(f"⏭️ TTS {tts_name} 未配置access_token/api_key，已跳过")
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return {"errors": 1}
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# 获取实际类型（兼容旧配置）
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module_type = config.get('type', tts_name)
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tts = create_tts_instance(
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module_type,
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config,
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delete_audio_file=True # 确保参数名称正确
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)
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# 简化后的输出
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print(f"\n🎵 正在测试 TTS: {tts_name}")
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print(f"🔊 测试 {tts_name}：", end="", flush=True)
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# 连接测试
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test_conn = tts.to_tts("连接测试")
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if not os.path.exists(test_conn):
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print("❌ 连接失败")
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return {"errors": 1}
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else:
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print("✅")
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total_time = 0
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test_count = len(self.test_sentences[:2])
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for i, sentence in enumerate(self.test_sentences[:2], 1):
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start = time.time()
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file_path = tts.to_tts(sentence)
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duration = time.time() - start
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total_time += duration
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# 显示简单的进度标识
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if os.path.exists(file_path):
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print(f"✓[{i}/{test_count}]", end="", flush=True)
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else:
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print(f"✗[{i}/{test_count}]", end="", flush=True)
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print() # 换行
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return {"avg_time": total_time / test_count, "errors": 0}
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except requests.exceptions.ConnectionError:
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print(f"\n⛔ {tts_name} 无法连接服务端")
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return {"errors": 1}
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except Exception as e:
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print(f"\n⚠️ {tts_name} 测试失败: {str(e)}")
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return {"errors": 1}
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def run(self):
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"""执行全量测试并自动跳过未配置的模块"""
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print("🔍 开始自动检测已配置的模块...")
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# 测试所有LLM
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for llm_name, config in self.config.get("LLM", {}).items():
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# 特殊处理CozeLLM的配置检查
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if llm_name == "CozeLLM":
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if any(x in config.get("bot_id", "") for x in ["你的"]) \
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or any(x in config.get("user_id", "") for x in ["你的"]):
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print(f"⏭️ LLM {llm_name} 未配置bot_id/user_id，已跳过")
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continue
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# 通用的api_key配置检查
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if "api_key" in config and any(x in config["api_key"] for x in ["你的", "placeholder"]):
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print(f"⏭️ LLM {llm_name} 未配置api_key，已跳过")
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continue
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print(f"🚀 正在测试 LLM: {llm_name}")
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self.results["llm"][llm_name] = self._test_llm(llm_name, config)
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# 测试所有TTS
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for tts_name, config in self.config.get("TTS", {}).items():
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# 根据不同服务的token字段检测
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token_fields = ["access_token", "api_key", "token"]
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if any(field in config and any(x in config[field] for x in ["你的", "placeholder"]) for field in token_fields):
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print(f"⏭️ TTS {tts_name} 未配置access_token/api_key，已跳过")
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continue
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print(f"🎵 正在测试 TTS: {tts_name}")
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self.results["tts"][tts_name] = self._test_tts(tts_name, config)
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# 生成组合建议
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self._generate_combinations()
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self._print_results()
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def _generate_combinations(self):
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"""生成最佳组合建议"""
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# 调整过滤条件，例如设为 >= 0.05
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valid_llms = [
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k for k, v in self.results["llm"].items()
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if v["errors"] == 0 and v["avg_first_token"] >= 0.05
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]
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valid_tts = [k for k, v in self.results["tts"].items() if v["errors"] == 0]
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for llm in valid_llms:
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for tts in valid_tts:
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llm_weight = 0.8 if self.results["llm"][llm]["avg_first_token"] < 1.0 else 0.6
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tts_weight = 1 - llm_weight
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score = (
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self.results["llm"][llm]["avg_first_token"] * llm_weight +
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self.results["tts"][tts]["avg_time"] * tts_weight
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)
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self.results["combinations"].append({
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"llm": llm,
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"tts": tts,
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"score": score,
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"details": {
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"llm_first_token": self.results["llm"][llm]["avg_first_token"],
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"tts_time": self.results["tts"][tts]["avg_time"]
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}
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})
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# 按综合得分排序
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self.results["combinations"].sort(key=lambda x: x["score"])
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def _print_results(self):
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"""控制台输出结果"""
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# LLM结果表格
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llm_table = []
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for name, data in self.results["llm"].items():
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if data["errors"] == 0:
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llm_table.append([
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name,
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f"{data['avg_first_token']:.3f}s",
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f"{data['avg_response']:.3f}s"
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])
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if llm_table:
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print("\nLLM 性能排行:")
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print(tabulate(
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llm_table,
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headers=["模块名称", "平均首Token时间", "平均总响应时间"],
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tablefmt="github"
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))
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else:
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print("\n⚠️ 没有可用的LLM模块进行测试。")
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# TTS结果表格
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tts_table = []
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for name, data in self.results["tts"].items():
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if data["errors"] == 0:
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tts_table.append([
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name,
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f"{data['avg_time']:.3f}s"
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])
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if tts_table:
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print("\nTTS 性能排行:")
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print(tabulate(
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tts_table,
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headers=["模块名称", "平均合成时间"],
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tablefmt="github"
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))
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else:
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print("\n⚠️ 没有可用的TTS模块进行测试。")
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# 最佳组合建议
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if self.results["combinations"]:
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print("\n推荐配置组合 (综合响应速度):")
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combo_table = []
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for combo in self.results["combinations"][:5]: # 显示前5名
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combo_table.append([
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f"{combo['llm']} + {combo['tts']}",
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f"{combo['score']:.3f}",
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f"{combo['details']['llm_first_token']:.3f}s",
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f"{combo['details']['tts_time']:.3f}s"
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])
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print(tabulate(
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combo_table,
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headers=["组合方案", "综合得分", "LLM首Token", "TTS合成"],
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tablefmt="github"
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))
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else:
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print("\n⚠️ 没有可用的模块组合建议。")
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def _execute_with_timeout(self, func, args=(), kwargs={}, timeout=None):
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with ThreadPoolExecutor(max_workers=1) as executor:
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future = executor.submit(func, *args, **kwargs)
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try:
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result = future.result(timeout)
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return list(result) if inspect.isgenerator(result) else result
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except TimeoutError:
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raise Exception("操作超时")
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if __name__ == "__main__":
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tester = PerformanceTester()
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tester.run()
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