完善服务端
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1507093701
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@ -26,6 +26,12 @@ D:\ProgramData\miniconda3\envs\audio\python.exe
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# FunASR 语音识别服务
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基于阿里达摩院 [FunASR](https://github.com/alibaba-damo-academy/FunASR) 的本地语音识别解决方案。
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62
main.py
62
main.py
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@ -12,6 +12,7 @@
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import gc
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import json
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import os
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import shutil
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import subprocess
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import time
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@ -23,8 +24,7 @@ BASE_DIR = Path(__file__).parent.absolute()
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TEMP_DIR = BASE_DIR / "temp"
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OUTPUT_DIR = BASE_DIR / "output"
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# 视频文件夹路径（全局变量）
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VIDEO_FOLDER = r"D:\Userfile\Projects\AnzezxianxHazardInspectAI\Code\audio2\input\宁波北仑区鼎邦杰西雅服饰有限公司"
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# 支持的视频格式
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SUPPORTED_VIDEO_FORMATS = ["*.mp4", "*.avi", "*.mkv", "*.mov", "*.flv", "*.wmv", "*.m4v"]
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@ -271,7 +271,12 @@ def process_batch_asr(video_paths, diar_results, max_workers=1):
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print("加载 ASR 模型...")
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from asr_service import ASRService
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asr_service = ASRService(model_name="paraformer-zh", device="auto")
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asr_service = ASRService(
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model_name="paraformer-zh",
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device="auto",
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min_segment_duration=0.5, # 增加最小片段时长
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merge_gap=0.3 # 减少合并间隔
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)
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asr_service._load_model()
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print("✓ ASR 模型已加载")
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print()
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@ -489,34 +494,54 @@ def process_batch_asr(video_paths, diar_results, max_workers=1):
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return results
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def main():
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def main(path: str):
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"""主函数"""
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import torch
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# 拼接根目录
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path = os.path.join(os.path.dirname(__file__), "input", path)
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print(f"开始处理路径：{path}")
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print("\n" + "=" * 60)
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print(" 并发批量语音识别处理系统")
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print("=" * 60)
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print()
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# 0. 检查输入路径是否存在
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if not Path(path).exists():
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print(f"✗ 错误：输入路径不存在 - {path}")
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return "输入路径不存在"
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# 文件夹为空
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if Path(path).is_dir() and not list(Path(path).iterdir()):
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print("✗ 错误：文件夹为空")
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return "文件夹为空"
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# 1. 清空 temp 目录
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clear_temp_dir()
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# 2. 确保 output 目录存在
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ensure_output_dir()
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# 3. 准备视频列表（从 VIDEO_FOLDER 自动获取）
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video_folder = Path(VIDEO_FOLDER)
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if not video_folder.exists():
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print(f"✗ 错误：视频文件夹不存在 - {video_folder}")
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return
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# 3. 准备视频列表（从 path 自动获取）
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# 如果是文件夹，递归获取所有视频文件
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# 如果是文件，直接添加到列表
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video_paths = []
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if Path(path).is_dir():
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video_folder = Path(path)
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if not video_folder.exists():
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print(f"✗ 错误：视频文件夹不存在 - {video_folder}")
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return
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video_paths = get_video_list(video_folder)
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video_paths = get_video_list(video_folder)
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if not video_paths:
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print("✗ 错误：未找到任何视频文件")
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print(f"支持格式：{', '.join(SUPPORTED_VIDEO_FORMATS)}")
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print(f"请检查文件夹：{video_folder}")
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return
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else:
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video_paths.append(Path(path))
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if not video_paths:
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print("✗ 错误：未找到任何视频文件")
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print(f"支持格式：{', '.join(SUPPORTED_VIDEO_FORMATS)}")
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print(f"请检查文件夹：{video_folder}")
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return
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video_paths = list(set(video_paths)) # 去重
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print(f"找到 {len(video_paths)} 个视频文件")
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for vp in video_paths:
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@ -569,4 +594,9 @@ def main():
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if __name__ == "__main__":
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main()
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# 视频文件夹路径（全局变量）
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# PATH = r"D:\Userfile\Projects\AnzezxianxHazardInspectAI\Code\audio2\input\宁波北仑区鼎邦杰西雅服饰有限公司"
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# PATH = r"D:\Userfile\Projects\AnzezxianxHazardInspectAI\Code\audio2\input\temp"
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# PATH = r"D:\Userfile\Projects\AnzezxianxHazardInspectAI\Code\audio2\input\temp\VID_20251104_085655_024.AVI"
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PATH = r"VID_20251104_085655_024.AVI"
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main(PATH)
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@ -59,5 +59,6 @@ jieba>=0.42.0
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# onnxruntime-gpu>=1.23.0
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# ---------- 可选：Web API 服务 ----------
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# Flask>=3.1.0
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Flask>=3.1.0
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waitress>=3.0.0
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# SQLAlchemy>=2.0.0
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414
server.py
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server.py
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@ -6,16 +6,71 @@ Web API Server for ASR and Speaker Diarization
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import os
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import sys
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import gc
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from pathlib import Path
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from flask import Flask, request, jsonify, send_file
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from werkzeug.utils import secure_filename
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import json
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import shutil
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import signal
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import threading
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from pathlib import Path
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from flask import Flask, request, jsonify
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from werkzeug.utils import secure_filename
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import uuid
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from datetime import datetime, timezone
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def make_response(status="success", data=None, errors=None, message=None, extra=None):
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"""
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统一 API 响应格式
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Args:
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status: 状态 ("success" 或 "error")
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data: 返回的数据
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errors: 错误列表
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message: 消息
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extra: 其他额外字段
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Returns:
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统一格式的 JSON 响应
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"""
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response = {
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"status": status,
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"data": data if data is not None else {},
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"errors": errors if errors is not None else [],
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"message": message or ("操作成功" if status == "success" else "操作失败"),
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"timestamp": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
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}
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if extra:
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response.update(extra)
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return response
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app = Flask(__name__)
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app.config['MAX_CONTENT_LENGTH'] = 500 * 1024 * 1024
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app.config['UPLOAD_FOLDER'] = 'uploads'
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app.config['RESULT_FOLDER'] = 'results'
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# 增加请求超时时间（秒），支持长时间运行的任务
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app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 300 # 5
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OUTPUT_DIR = Path('output')
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# 手动添加 CORS 响应头
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@app.after_request
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def after_request(response):
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# 获取请求的 Origin
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origin = request.headers.get('Origin', '*')
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# 设置允许的来源（不使用通配符）
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response.headers.add('Access-Control-Allow-Origin', origin)
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response.headers.add('Access-Control-Allow-Credentials', 'true')
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response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
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response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
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return response
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# 处理 OPTIONS 预检请求
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@app.route('/config', methods=['OPTIONS'])
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def config_options():
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"""处理 /config 接口的 OPTIONS 预检请求"""
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return '', 200
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os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
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os.makedirs(app.config['RESULT_FOLDER'], exist_ok=True)
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@ -27,285 +82,148 @@ DIAR_MODEL_LOADED = False
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ASR_MODEL_LOCK = threading.Lock()
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DIAR_MODEL_LOCK = threading.Lock()
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def get_asr_service():
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global GLOBAL_ASR_SERVICE, ASR_MODEL_LOADED
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if GLOBAL_ASR_SERVICE is None:
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from asr_service import ASRService
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GLOBAL_ASR_SERVICE = ASRService()
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return GLOBAL_ASR_SERVICE
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# 全局变量用于控制任务执行
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task_running = {}
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task_timeout = 600 # 10 分钟超时（秒）
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def get_diar_service():
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global GLOBAL_DIAR_SERVICE, DIAR_MODEL_LOADED
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if GLOBAL_DIAR_SERVICE is None:
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from diarization_service import DiarizationService
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GLOBAL_DIAR_SERVICE = DiarizationService()
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return GLOBAL_DIAR_SERVICE
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@app.route('/health', methods=['GET'])
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def health_check():
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"""健康检查"""
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return jsonify({
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'status': 'ok',
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'asr_loaded': ASR_MODEL_LOADED,
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'diar_loaded': DIAR_MODEL_LOADED
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})
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@app.route('/api/asr/load', methods=['GET'])
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def load_asr_model():
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"""加载 ASR 模型"""
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global ASR_MODEL_LOADED
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with ASR_MODEL_LOCK:
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if ASR_MODEL_LOADED:
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return jsonify({'message': 'ASR 模型已加载', 'loaded': True})
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try:
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data = request.json or {}
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model_name = data.get('model_name', 'paraformer-zh')
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device = data.get('device', 'auto')
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print(f"正在加载 ASR 模型: {model_name}, 设备: {device}")
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service = get_asr_service()
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service._load_model()
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ASR_MODEL_LOADED = True
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return jsonify({
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'message': 'ASR 模型加载成功',
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'loaded': True,
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'model': model_name,
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'device': device
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})
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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@app.route('/api/diar/load', methods=['POST'])
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def load_diar_model():
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"""加载 3D-Speaker 模型"""
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global DIAR_MODEL_LOADED
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with DIAR_MODEL_LOCK:
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if DIAR_MODEL_LOADED:
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return jsonify({'message': '3D-Speaker 模型已加载', 'loaded': True})
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try:
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data = request.json or {}
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embedding_model = data.get('embedding_model', 'eres2netv2')
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device = data.get('device', 'auto')
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cluster_threshold = data.get('cluster_threshold', 0.5)
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min_cluster_size = data.get('min_cluster_size', 10)
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print(f"正在加载 3D-Speaker 模型: {embedding_model}, 设备: {device}")
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service = get_diar_service()
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service._load_model()
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DIAR_MODEL_LOADED = True
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return jsonify({
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'message': '3D-Speaker 模型加载成功',
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'loaded': True,
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'embedding_model': embedding_model,
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'device': device
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})
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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@app.route('/api/asr/unload', methods=['POST'])
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def unload_asr_model():
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"""卸载 ASR 模型"""
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global GLOBAL_ASR_SERVICE, ASR_MODEL_LOADED
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@app.route('/api/recognize', methods=['POST'])
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def recognize():
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"""文件推理 - 调用 main.py 的 main 函数"""
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task_id = str(uuid.uuid4())
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task_running[task_id] = True
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try:
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GLOBAL_ASR_SERVICE = None
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ASR_MODEL_LOADED = False
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gc.collect()
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# 从请求参数获取路径（只接受文件名或 input 目录下的相对路径）
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data = request.json or {}
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path = data.get('path', '')
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return jsonify({'message': 'ASR 模型已卸载'})
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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if not path:
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return jsonify(make_response(
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status="error",
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message="请提供文件路径",
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errors=["缺少必要参数：path"]
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)), 400
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print(f"\n{'='*60}")
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print(f"API 收到请求：path={path}, task_id={task_id}")
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print(f"{'='*60}")
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print(f"开始调用 main 函数...")
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@app.route('/api/diar/unload', methods=['POST'])
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def unload_diar_model():
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"""卸载 3D-Speaker 模型"""
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global GLOBAL_DIAR_SERVICE, DIAR_MODEL_LOADED
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# 切换到 server.py 所在目录
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server_dir = Path(__file__).parent.absolute()
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os.chdir(server_dir)
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try:
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GLOBAL_DIAR_SERVICE = None
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DIAR_MODEL_LOADED = False
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gc.collect()
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print(f"当前工作目录：{os.getcwd()}")
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return jsonify({'message': '3D-Speaker 模型已卸载'})
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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# 设置超时处理
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def timeout_handler(signum, frame):
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task_running[task_id] = False
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raise TimeoutError(f"任务执行超时 ({task_timeout}秒)")
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@app.route('/api/recognize/single', methods=['POST'])
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def recognize_single():
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"""单文件推理"""
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try:
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if 'file' not in request.files:
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return jsonify({'error': '请上传音频文件'}), 400
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file = request.files['file']
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if file.filename == '':
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return jsonify({'error': '文件名不能为空'}), 400
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data = request.form or {}
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use_3d_speaker = data.get('use_3d_speaker', 'false').lower() == 'true'
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embedding_model = data.get('embedding_model', 'eres2netv2')
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cluster_threshold = float(data.get('cluster_threshold', 0.5))
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min_cluster_size = int(data.get('min_cluster_size', 10))
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output_format = data.get('format', 'json')
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filename = secure_filename(file.filename) if file.filename is not None else secure_filename("unnamed_file")
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task_id = str(uuid.uuid4())[:8]
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audio_path = os.path.join(app.config['UPLOAD_FOLDER'], f"{task_id}_{filename}")
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file.save(audio_path)
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# 注册信号处理器（仅在 Unix/Linux/Mac 有效，Windows 下会被忽略）
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try:
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signal.signal(signal.SIGALRM, timeout_handler)
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signal.alarm(task_timeout)
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use_alarm = True
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except (AttributeError, ValueError):
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# Windows 不支持 SIGALRM
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use_alarm = False
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print("注意：Windows 系统，使用超时检测线程")
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try:
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global ASR_MODEL_LOADED
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service = get_asr_service()
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if not ASR_MODEL_LOADED:
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service._load_model()
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ASR_MODEL_LOADED = True
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# 调用 main.py 的 main 函数
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from main import main
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main(path)
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sentences = service.recognize(
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audio_path
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)
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# 取消超时
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if use_alarm:
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signal.alarm(0)
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result = {
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'file': filename,
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'total_sentences': len(sentences),
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'sentences': [s.to_dict() for s in sentences]
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}
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print(f"main 函数执行完成")
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if output_format == 'json':
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result_path = os.path.join(app.config['RESULT_FOLDER'], f"{task_id}_result.json")
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with open(result_path, 'w', encoding='utf-8') as f:
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import json
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json.dump(result, f, ensure_ascii=False, indent=2)
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return jsonify(result)
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else:
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srt_path = os.path.join(app.config['RESULT_FOLDER'], f"{task_id}_result.srt")
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service.export_to_srt([s for s in sentences], srt_path)
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return send_file(srt_path, as_attachment=True, download_name=f"{task_id}_result.srt")
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return jsonify(make_response(
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status="success",
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message="文件推理完成",
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data={"path": path, "task_id": task_id}
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)), 200
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except TimeoutError as e:
|
||||
print(f"任务超时：{e}")
|
||||
return jsonify(make_response(
|
||||
status="error",
|
||||
message=str(e),
|
||||
errors=[str(e)]
|
||||
)), 504 # Gateway Timeout
|
||||
|
||||
finally:
|
||||
if os.path.exists(audio_path):
|
||||
os.remove(audio_path)
|
||||
# 清理信号处理器
|
||||
if use_alarm:
|
||||
signal.alarm(0)
|
||||
task_running[task_id] = False
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return jsonify({'error': str(e)}), 500
|
||||
task_running[task_id] = False
|
||||
return jsonify(make_response(
|
||||
status="error",
|
||||
message=str(e),
|
||||
errors=[str(e)]
|
||||
)), 500
|
||||
|
||||
|
||||
@app.route('/api/recognize/batch', methods=['POST'])
|
||||
def recognize_batch():
|
||||
"""批量推理"""
|
||||
@app.route('/api/result', methods=['GET'])
|
||||
def result():
|
||||
"""获取文件推理结果"""
|
||||
try:
|
||||
if 'files' not in request.files:
|
||||
return jsonify({'error': '请上传音频文件'}), 400
|
||||
# 从请求参数获取路径
|
||||
path = request.args.get('path', '')
|
||||
|
||||
files = request.files.getlist('files')
|
||||
if not files or len(files) == 0:
|
||||
return jsonify({'error': '文件列表为空'}), 400
|
||||
|
||||
data = request.form or {}
|
||||
use_3d_speaker = data.get('use_3d_speaker', 'false').lower() == 'true'
|
||||
embedding_model = data.get('embedding_model', 'eres2netv2')
|
||||
cluster_threshold = float(data.get('cluster_threshold', 0.5))
|
||||
min_cluster_size = int(data.get('min_cluster_size', 10))
|
||||
|
||||
task_id = str(uuid.uuid4())[:8]
|
||||
audio_paths = []
|
||||
|
||||
for f in files:
|
||||
if f.filename:
|
||||
filename = secure_filename(f.filename)
|
||||
audio_path = os.path.join(app.config['UPLOAD_FOLDER'], f"{task_id}_{filename}")
|
||||
f.save(audio_path)
|
||||
audio_paths.append(audio_path)
|
||||
|
||||
try:
|
||||
global ASR_MODEL_LOADED
|
||||
service = get_asr_service()
|
||||
if not ASR_MODEL_LOADED:
|
||||
service._load_model()
|
||||
ASR_MODEL_LOADED = True
|
||||
|
||||
results = []
|
||||
for audio_path in audio_paths:
|
||||
try:
|
||||
sentences = service.recognize(
|
||||
audio_path,
|
||||
)
|
||||
results.append({
|
||||
'file': os.path.basename(audio_path),
|
||||
'total_sentences': len(sentences),
|
||||
'sentences': [s.to_dict() for s in sentences]
|
||||
})
|
||||
except Exception as e:
|
||||
results.append({
|
||||
'file': os.path.basename(audio_path),
|
||||
'error': str(e)
|
||||
})
|
||||
|
||||
result_path = os.path.join(app.config['RESULT_FOLDER'], f"{task_id}_batch_result.json")
|
||||
import json
|
||||
with open(result_path, 'w', encoding='utf-8') as f:
|
||||
json.dump({
|
||||
'task_id': task_id,
|
||||
'total_files': len(results),
|
||||
'results': results
|
||||
}, f, ensure_ascii=False, indent=2)
|
||||
|
||||
return jsonify({
|
||||
'task_id': task_id,
|
||||
'total_files': len(results),
|
||||
'results': results,
|
||||
'result_file': result_path
|
||||
})
|
||||
|
||||
finally:
|
||||
for audio_path in audio_paths:
|
||||
if os.path.exists(audio_path):
|
||||
os.remove(audio_path)
|
||||
if not path:
|
||||
return jsonify(make_response(
|
||||
status="error",
|
||||
message="请提供文件路径",
|
||||
errors=["缺少必要参数：path"]
|
||||
)), 400
|
||||
|
||||
# 读取结果
|
||||
print(Path(path).stem)
|
||||
result_file = OUTPUT_DIR / f"{Path(path).stem}_result.json"
|
||||
if result_file.exists():
|
||||
with open(result_file, 'r', encoding='utf-8') as f:
|
||||
result_data = json.load(f)
|
||||
return jsonify(make_response(
|
||||
status="success",
|
||||
message="获取成功",
|
||||
data=result_data
|
||||
)), 200
|
||||
else:
|
||||
return jsonify(make_response(
|
||||
status="error",
|
||||
message="处理完成但未找到结果文件",
|
||||
errors=["结果文件不存在"]
|
||||
)), 404
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return jsonify({'error': str(e)}), 500
|
||||
|
||||
|
||||
@app.route('/api/status', methods=['GET'])
|
||||
def get_status():
|
||||
"""获取模型状态"""
|
||||
return jsonify({
|
||||
'asr_loaded': ASR_MODEL_LOADED,
|
||||
'diar_loaded': DIAR_MODEL_LOADED,
|
||||
'asr_model': 'paraformer-zh',
|
||||
'diar_model': '3D-Speaker'
|
||||
})
|
||||
|
||||
return jsonify(make_response(
|
||||
status="error",
|
||||
message=str(e),
|
||||
errors=[str(e)]
|
||||
)), 500
|
||||
|
||||
if __name__ == '__main__':
|
||||
print("=" * 60)
|
||||
print(" ASR & Speaker Diarization API Server")
|
||||
print("=" * 60)
|
||||
print("\nAPI 接口:")
|
||||
print(" GET /health - 健康检查")
|
||||
print(" GET /api/status - 获取模型状态")
|
||||
print(" POST /api/asr/load - 加载 ASR 模型")
|
||||
print(" POST /api/diar/load - 加载 3D-Speaker 模型")
|
||||
print(" POST /api/asr/unload - 卸载 ASR 模型")
|
||||
print(" POST /api/diar/unload - 卸载 3D-Speaker 模型")
|
||||
print(" POST /api/recognize/single - 单文件推理")
|
||||
print(" POST /api/recognize/batch - 批量推理")
|
||||
print(" POST /api/recognize - 文件推理")
|
||||
print(" POST /api/result - 获取文件推理结果")
|
||||
print("\n" + "=" * 60)
|
||||
print("启动服务: http://localhost:5000")
|
||||
print("启动服务：http://localhost:5000")
|
||||
print("使用 Waitress WSGI 服务器（无超时限制）")
|
||||
print("=" * 60)
|
||||
app.run(host='0.0.0.0', port=5000, debug=False)
|
||||
|
||||
from waitress import serve
|
||||
serve(app, host='0.0.0.0', port=5000, threads=4, connection_limit=100)
|
||||
|
|
|
|||
Loading…
Reference in New Issue