557 lines
18 KiB
Python
557 lines
18 KiB
Python
# 配置whisper语音测评功能的脚本，必须要在jar包启动前启动，要不然不会被识别到
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Whisper语音识别服务
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支持语音识别和评测功能
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多线程并发处理
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作者: AI Assistant
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版本: 2.0
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"""
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from flask import Flask, request, jsonify
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from flask_cors import CORS
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import whisper
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import os
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import tempfile
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import logging
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from logging.handlers import RotatingFileHandler
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import traceback
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from datetime import datetime
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import sys
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# ============================================
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# 日志配置
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# ============================================
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def setup_logging():
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"""配置日志系统"""
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# 创建logger
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logger = logging.getLogger('whisper_server')
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logger.setLevel(logging.INFO)
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# 控制台输出
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console_handler = logging.StreamHandler(sys.stdout)
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console_handler.setLevel(logging.INFO)
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console_formatter = logging.Formatter(
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'%(asctime)s [%(levelname)s] %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S'
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)
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console_handler.setFormatter(console_formatter)
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logger.addHandler(console_handler)
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# 文件输出（可选）
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try:
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log_dir = 'logs'
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if not os.path.exists(log_dir):
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os.makedirs(log_dir)
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file_handler = RotatingFileHandler(
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os.path.join(log_dir, 'whisper_server.log'),
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maxBytes=10*1024*1024, # 10MB
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backupCount=5,
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encoding='utf-8'
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)
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file_handler.setLevel(logging.INFO)
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file_handler.setFormatter(console_formatter)
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logger.addHandler(file_handler)
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except Exception as e:
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logger.warning(f"无法创建日志文件: {e}")
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return logger
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# 初始化日志
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logger = setup_logging()
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# ============================================
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# Flask应用配置
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# ============================================
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app = Flask(__name__)
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CORS(app) # 允许跨域请求
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app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # 最大16MB
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# ============================================
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# 全局变量
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# ============================================
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whisper_model = None
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MODEL_NAME = "tiny" # 可选: tiny, base, small, medium, large
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# ============================================
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# 模型加载
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# ============================================
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def load_whisper_model():
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"""加载Whisper模型（全局单例）"""
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global whisper_model
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if whisper_model is None:
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logger.info(f"正在加载Whisper模型 ({MODEL_NAME})...")
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try:
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whisper_model = whisper.load_model(MODEL_NAME)
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logger.info(f"✅ Whisper模型加载成功 ({MODEL_NAME})")
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except Exception as e:
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logger.error(f"❌ 模型加载失败: {e}")
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raise
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return whisper_model
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# ============================================
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# 工具函数
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# ============================================
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def convert_to_simplified(text):
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"""将繁体中文转换为简体中文"""
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if not text:
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return text
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# 方案1：使用zhconv（推荐，纯Python）
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try:
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import zhconv
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result = zhconv.convert(text, 'zh-cn')
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if result != text:
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logger.info(f"繁简转换: {text} -> {result}")
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return result
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except ImportError:
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pass
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# 方案2：使用opencc
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try:
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from opencc import OpenCC
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cc = OpenCC('t2s')
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result = cc.convert(text)
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if result != text:
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logger.info(f"繁简转换(OpenCC): {text} -> {result}")
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return result
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except ImportError:
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pass
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# 都没安装，返回原文
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logger.warning("繁简转换库未安装，请运行: pip install zhconv")
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return text
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def clean_text_strict(text):
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"""
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严格清理文本，只保留汉字、字母、数字
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用于准确度计算
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"""
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import re
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import unicodedata
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if not text:
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return ""
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# Unicode标准化
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text = unicodedata.normalize('NFKC', text)
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# 只保留汉字、字母、数字
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text = re.sub(r'[^\u4e00-\u9fffa-zA-Z0-9]', '', text)
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return text.lower()
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# ============================================
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# API路由
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# ============================================
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@app.route('/health', methods=['GET'])
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def health_check():
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"""健康检查接口"""
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return jsonify({
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"status": "ok",
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"service": "Whisper Speech Recognition",
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"model": MODEL_NAME,
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"version": "2.0",
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"timestamp": datetime.now().isoformat()
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})
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@app.route('/recognize', methods=['POST'])
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def recognize():
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"""
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语音识别接口
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只识别，不评测
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参数:
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file: 音频文件 (multipart/form-data)
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返回:
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{
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"code": 200,
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"msg": "识别成功",
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"data": {
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"text": "识别的文本"
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}
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}
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"""
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try:
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# 检查文件
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if 'file' not in request.files:
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logger.warning("请求缺少音频文件")
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return jsonify({"code": 400, "msg": "缺少音频文件", "data": None}), 400
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audio_file = request.files['file']
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if audio_file.filename == '':
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logger.warning("文件名为空")
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return jsonify({"code": 400, "msg": "文件名为空", "data": None}), 400
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logger.info(f"收到识别请求: {audio_file.filename} ({len(audio_file.read())} bytes)")
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audio_file.seek(0) # 重置文件指针
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# 保存临时文件
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with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_file:
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temp_path = temp_file.name
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audio_file.save(temp_path)
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try:
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# 加载模型
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model = load_whisper_model()
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# 识别音频
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logger.info(f"开始识别: {audio_file.filename}")
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result = model.transcribe(temp_path, language='zh', fp16=False)
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recognized_text = result['text'].strip()
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# 繁体转简体
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recognized_text = convert_to_simplified(recognized_text)
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logger.info(f"✅ 识别成功: {recognized_text}")
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return jsonify({
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"code": 200,
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"msg": "识别成功",
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"data": {
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"text": recognized_text
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}
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})
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finally:
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# 删除临时文件
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try:
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if os.path.exists(temp_path):
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os.remove(temp_path)
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except Exception as e:
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logger.warning(f"删除临时文件失败: {e}")
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except Exception as e:
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logger.error(f"识别失败: {str(e)}")
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logger.error(traceback.format_exc())
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return jsonify({
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"code": 500,
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"msg": f"识别失败: {str(e)}",
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"data": None
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}), 500
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@app.route('/evaluate', methods=['POST'])
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def evaluate():
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"""
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语音评测接口
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识别 + 评分
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参数:
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file: 音频文件 (multipart/form-data)
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text: 标准文本 (form-data)
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返回:
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{
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"code": 200,
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"msg": "评测成功",
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"data": {
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"text": "识别的文本",
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"score": 95,
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"accuracy": 98,
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"fluency": 95,
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"completeness": 100,
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"pronunciation": 96,
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"similarity": 98.5
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}
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}
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"""
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try:
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# 检查文件
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if 'file' not in request.files:
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logger.warning("请求缺少音频文件")
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return jsonify({"code": 400, "msg": "缺少音频文件", "data": None}), 400
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audio_file = request.files['file']
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standard_text = request.form.get('text', '')
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if not standard_text:
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logger.warning("请求缺少标准文本")
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return jsonify({"code": 400, "msg": "缺少标准文本", "data": None}), 400
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logger.info(f"收到评测请求: {audio_file.filename}, 标准文本: {standard_text}")
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# 保存临时文件
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with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_file:
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temp_path = temp_file.name
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audio_file.save(temp_path)
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try:
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# 1. 识别音频
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model = load_whisper_model()
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logger.info("开始识别音频...")
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result = model.transcribe(temp_path, language='zh', fp16=False)
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recognized_text = result['text'].strip()
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recognized_text = convert_to_simplified(recognized_text)
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# 2. 计算评分
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from difflib import SequenceMatcher
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# 清理文本
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clean_recognized = clean_text_strict(recognized_text)
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clean_standard = clean_text_strict(standard_text)
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# 相似度
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similarity = SequenceMatcher(None, clean_recognized, clean_standard).ratio()
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# 调试日志
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logger.info(f"📝 标准文本: {standard_text}")
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logger.info(f"🎤 识别文本: {recognized_text}")
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logger.info(f"🧹 清理后标准: {clean_standard}")
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logger.info(f"🧹 清理后识别: {clean_recognized}")
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logger.info(f"📊 相似度: {similarity:.4f} ({similarity*100:.2f}%)")
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# 准确度计算（带阈值优化）
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if similarity >= 0.98:
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accuracy = 100
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logger.info(f"✅ 相似度>=98%，准确度给满分: {accuracy}")
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elif similarity >= 0.95:
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accuracy = 99
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logger.info(f"✅ 相似度>=95%，准确度给99分: {accuracy}")
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else:
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accuracy = similarity * 100
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logger.info(f"📊 准确度: {accuracy:.2f}")
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# 完整度
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completeness = min(len(clean_recognized) / max(len(clean_standard), 1) * 100, 100)
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# 流畅度和发音（如果准确度满分，其他也满分）
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fluency = 100 if accuracy == 100 else accuracy * 0.95
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pronunciation = 100 if accuracy == 100 else accuracy * 0.98
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# 总分（加权）
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total_score = (accuracy * 0.3 + completeness * 0.25 + fluency * 0.3 + pronunciation * 0.15)
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logger.info(f"✅ 评测完成: 总分={total_score:.0f}, 准确度={accuracy:.0f}, 流畅度={fluency:.0f}, 完整度={completeness:.0f}, 发音={pronunciation:.0f}")
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return jsonify({
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"code": 200,
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"msg": "评测成功",
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"data": {
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"text": recognized_text,
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"score": round(total_score),
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"accuracy": round(accuracy),
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"fluency": round(fluency),
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"completeness": round(completeness),
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"pronunciation": round(pronunciation),
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"similarity": round(similarity * 100, 2)
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}
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})
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finally:
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# 删除临时文件
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try:
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if os.path.exists(temp_path):
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os.remove(temp_path)
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except Exception as e:
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logger.warning(f"删除临时文件失败: {e}")
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except Exception as e:
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logger.error(f"评测失败: {str(e)}")
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logger.error(traceback.format_exc())
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return jsonify({
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"code": 500,
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"msg": f"评测失败: {str(e)}",
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"data": None
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}), 500
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# ============================================
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# 错误处理
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# ============================================
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@app.errorhandler(413)
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def request_entity_too_large(error):
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logger.warning("请求文件过大")
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return jsonify({
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"code": 413,
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"msg": "文件太大，最大支持16MB",
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"data": None
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}), 413
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@app.errorhandler(500)
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def internal_error(error):
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logger.error(f"服务器内部错误: {error}")
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return jsonify({
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"code": 500,
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"msg": "服务器内部错误",
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"data": None
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}), 500
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@app.errorhandler(404)
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def not_found(error):
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return jsonify({
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"code": 404,
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"msg": "接口不存在",
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"data": None
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}), 404
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# ============================================
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# 环境检查
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# ============================================
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def check_environment():
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"""检查运行环境和依赖"""
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logger.info("")
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logger.info("=" * 70)
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logger.info("🔍 环境检查")
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logger.info("=" * 70)
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all_ok = True
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# 1. 检查 Python 版本
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py_version = sys.version.split()[0]
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logger.info(f" Python版本: {py_version} ✅")
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# 2. 检查 whisper
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try:
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import whisper
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logger.info(f" Whisper: 已安装 ✅")
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except ImportError:
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logger.error(f" Whisper: 未安装 ❌ (pip install openai-whisper)")
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all_ok = False
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# 3. 检查 Flask
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try:
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import flask
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logger.info(f" Flask: {flask.__version__} ✅")
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except ImportError:
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logger.error(f" Flask: 未安装 ❌ (pip install flask)")
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all_ok = False
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# 4. 检查 flask-cors
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try:
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import flask_cors
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logger.info(f" Flask-CORS: 已安装 ✅")
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except ImportError:
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logger.error(f" Flask-CORS: 未安装 ❌ (pip install flask-cors)")
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all_ok = False
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# 5. 检查 waitress
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try:
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import waitress
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logger.info(f" Waitress: 已安装 ✅")
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except ImportError:
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logger.error(f" Waitress: 未安装 ❌ (pip install waitress)")
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all_ok = False
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# 6. 检查繁简转换库
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zhconv_ok = False
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opencc_ok = False
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try:
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import zhconv
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zhconv_ok = True
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logger.info(f" zhconv: 已安装 ✅ (繁简转换)")
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except ImportError:
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pass
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try:
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from opencc import OpenCC
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opencc_ok = True
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logger.info(f" OpenCC: 已安装 ✅ (繁简转换备用)")
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except ImportError:
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pass
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if not zhconv_ok and not opencc_ok:
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logger.warning(f" 繁简转换: 未安装 ⚠️ (pip install zhconv)")
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logger.warning(f" 识别结果可能包含繁体字！")
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# 7. 检查 ffmpeg（Whisper需要）
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import shutil
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if shutil.which('ffmpeg'):
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logger.info(f" FFmpeg: 已安装 ✅")
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else:
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logger.warning(f" FFmpeg: 未找到 ⚠️ (某些音频格式可能无法处理)")
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logger.info("=" * 70)
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if all_ok:
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logger.info("✅ 环境检查通过！")
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else:
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logger.error("❌ 缺少必要依赖，请先安装后再启动！")
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sys.exit(1)
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logger.info("")
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return all_ok
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# ============================================
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# 启动服务（多线程模式）
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# ============================================
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if __name__ == '__main__':
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try:
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# 环境检查
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check_environment()
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from waitress import serve
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# 打印启动信息
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logger.info("=" * 70)
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logger.info("🚀 正在启动Whisper语音识别服务...")
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logger.info("=" * 70)
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# 预加载模型
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load_whisper_model()
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# 启动信息
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logger.info("")
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logger.info("=" * 70)
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logger.info("✅ Whisper服务启动成功！")
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logger.info("=" * 70)
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logger.info(f"📍 本地地址: http://127.0.0.1:5001")
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logger.info(f"📍 局域网地址: http://0.0.0.0:5001")
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logger.info(f"📍 访问地址: http://192.168.0.106:5001")
|
||
logger.info("=" * 70)
|
||
logger.info(f"⚙️ 运行模式: 多线程并发")
|
||
logger.info(f"⚙️ Whisper模型: {MODEL_NAME}")
|
||
logger.info(f"⚙️ 工作线程: 8 个")
|
||
logger.info(f"⚙️ 并发能力: 40-60 人同时使用")
|
||
logger.info(f"⚙️ 超时时间: 300 秒")
|
||
logger.info(f"⚙️ 最大连接: 100 个")
|
||
logger.info(f"⚙️ 最大文件: 16 MB")
|
||
logger.info("=" * 70)
|
||
logger.info("")
|
||
logger.info("📌 API接口列表：")
|
||
logger.info(" [GET] /health - 健康检查")
|
||
logger.info(" [POST] /recognize - 语音识别（只识别）")
|
||
logger.info(" [POST] /evaluate - 语音评测（识别+评分）")
|
||
logger.info("=" * 70)
|
||
logger.info("")
|
||
logger.info("💡 使用示例：")
|
||
logger.info(" 健康检查: curl http://192.168.0.106:5001/health")
|
||
logger.info(" 语音识别: curl -F 'file=@audio.mp3' http://192.168.0.106:5001/recognize")
|
||
logger.info(" 语音评测: curl -F 'file=@audio.mp3' -F 'text=你好' http://192.168.0.106:5001/evaluate")
|
||
logger.info("=" * 70)
|
||
logger.info("")
|
||
logger.info("✨ 服务已就绪，等待请求...")
|
||
logger.info("✨ 按 Ctrl+C 停止服务")
|
||
logger.info("")
|
||
|
||
# 使用waitress启动（支持多线程）
|
||
serve(
|
||
app,
|
||
host='0.0.0.0',
|
||
port=5001,
|
||
threads=8, # 8个工作线程，支持40-60人并发
|
||
channel_timeout=300, # 单个请求超时5分钟
|
||
connection_limit=100, # 最多100个并发连接
|
||
backlog=64, # 连接队列长度
|
||
recv_bytes=65536, # 接收缓冲区 64KB
|
||
send_bytes=65536, # 发送缓冲区 64KB
|
||
url_scheme='http'
|
||
)
|
||
|
||
except KeyboardInterrupt:
|
||
logger.info("")
|
||
logger.info("=" * 70)
|
||
logger.info("⏹️ 收到停止信号，正在关闭服务...")
|
||
logger.info("=" * 70)
|
||
logger.info("👋 服务已停止")
|
||
|
||
except Exception as e:
|
||
logger.error("=" * 70)
|
||
logger.error(f"❌ 服务启动失败: {e}")
|
||
logger.error(traceback.format_exc())
|
||
logger.error("=" * 70)
|
||
sys.exit(1) |