完善服务端

README.md

@@ -26,6 +26,12 @@ D:\ProgramData\miniconda3\envs\audio\python.exe
 
 
 
+
+
+
+
+
+
 # FunASR 语音识别服务
 
 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。

main.py

@@ -12,6 +12,7 @@
 
 import gc
 import json
+import os
 import shutil
 import subprocess
 import time
@@ -23,8 +24,7 @@ BASE_DIR = Path(__file__).parent.absolute()
 TEMP_DIR = BASE_DIR / "temp"
 OUTPUT_DIR = BASE_DIR / "output"
 
-# 视频文件夹路径（全局变量）
+
-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"
 
 # 支持的视频格式
 SUPPORTED_VIDEO_FORMATS = ["*.mp4", "*.avi", "*.mkv", "*.mov", "*.flv", "*.wmv", "*.m4v"]
@@ -271,7 +271,12 @@ def process_batch_asr(video_paths, diar_results, max_workers=1):
     print("加载 ASR 模型...")
     from asr_service import ASRService
 
-    asr_service = ASRService(model_name="paraformer-zh", device="auto")
+    asr_service = ASRService(
+        model_name="paraformer-zh",
+        device="auto",
+        min_segment_duration=0.5,  # 增加最小片段时长
+        merge_gap=0.3  # 减少合并间隔
+    )
     asr_service._load_model()
     print("✓ ASR 模型已加载")
     print()
@@ -489,34 +494,54 @@ def process_batch_asr(video_paths, diar_results, max_workers=1):
     return results
 
 
-def main():
+def main(path: str):
     """主函数"""
     import torch
+    # 拼接根目录
+    path = os.path.join(os.path.dirname(__file__), "input", path)
+    print(f"开始处理路径：{path}")
 
     print("\n" + "=" * 60)
     print("     并发批量语音识别处理系统")
     print("=" * 60)
     print()
 
+    # 0. 检查输入路径是否存在
+    if not Path(path).exists():
+        print(f"✗ 错误：输入路径不存在 - {path}")
+        return "输入路径不存在"
+
+    # 文件夹为空
+    if Path(path).is_dir() and not list(Path(path).iterdir()):
+        print("✗ 错误：文件夹为空")
+        return "文件夹为空"
+
     # 1. 清空 temp 目录
     clear_temp_dir()
 
     # 2. 确保 output 目录存在
     ensure_output_dir()
 
-    # 3. 准备视频列表（从 VIDEO_FOLDER 自动获取）
+    # 3. 准备视频列表（从 path 自动获取）
-    video_folder = Path(VIDEO_FOLDER)
+    # 如果是文件夹，递归获取所有视频文件
+    # 如果是文件，直接添加到列表
+    video_paths = []
+    if Path(path).is_dir():
+        video_folder = Path(path)
         if not video_folder.exists():
             print(f"✗ 错误：视频文件夹不存在 - {video_folder}")
             return
 
         video_paths = get_video_list(video_folder)
-
         if not video_paths:
             print("✗ 错误：未找到任何视频文件")
             print(f"支持格式：{', '.join(SUPPORTED_VIDEO_FORMATS)}")
             print(f"请检查文件夹：{video_folder}")
             return
+    else:
+        video_paths.append(Path(path))
+
+    video_paths = list(set(video_paths))  # 去重
 
     print(f"找到 {len(video_paths)} 个视频文件")
     for vp in video_paths:
@@ -569,4 +594,9 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    # 视频文件夹路径（全局变量）
+    # 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"
+    # PATH = r"D:\Userfile\Projects\AnzezxianxHazardInspectAI\Code\audio2\input\temp"
+    # PATH = r"D:\Userfile\Projects\AnzezxianxHazardInspectAI\Code\audio2\input\temp\VID_20251104_085655_024.AVI"
+    PATH = r"VID_20251104_085655_024.AVI"
+    main(PATH)

requirements.txt

@@ -59,5 +59,6 @@ jieba>=0.42.0
 # onnxruntime-gpu>=1.23.0
 
 # ---------- 可选：Web API 服务 ----------
-# Flask>=3.1.0
+Flask>=3.1.0
+waitress>=3.0.0
 # SQLAlchemy>=2.0.0

server.py

@@ -6,16 +6,71 @@ Web API Server for ASR and Speaker Diarization
 import os
 import sys
 import gc
-from pathlib import Path
+import json
-from flask import Flask, request, jsonify, send_file
+import shutil
-from werkzeug.utils import secure_filename
+import signal
 import threading
+from pathlib import Path
+from flask import Flask, request, jsonify
+from werkzeug.utils import secure_filename
 import uuid
+from datetime import datetime, timezone
+
+
+def make_response(status="success", data=None, errors=None, message=None, extra=None):
+    """
+    统一 API 响应格式
+
+    Args:
+        status: 状态 ("success" 或 "error")
+        data: 返回的数据
+        errors: 错误列表
+        message: 消息
+        extra: 其他额外字段
+
+    Returns:
+        统一格式的 JSON 响应
+    """
+    response = {
+        "status": status,
+        "data": data if data is not None else {},
+        "errors": errors if errors is not None else [],
+        "message": message or ("操作成功" if status == "success" else "操作失败"),
+        "timestamp": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+    }
+
+    if extra:
+        response.update(extra)
+
+    return response
 
 app = Flask(__name__)
 app.config['MAX_CONTENT_LENGTH'] = 500 * 1024 * 1024
 app.config['UPLOAD_FOLDER'] = 'uploads'
 app.config['RESULT_FOLDER'] = 'results'
+# 增加请求超时时间（秒），支持长时间运行的任务
+app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 300  # 5
+OUTPUT_DIR = Path('output')
+
+# 手动添加 CORS 响应头
+@app.after_request
+def after_request(response):
+    # 获取请求的 Origin
+    origin = request.headers.get('Origin', '*')
+
+    # 设置允许的来源（不使用通配符）
+    response.headers.add('Access-Control-Allow-Origin', origin)
+    response.headers.add('Access-Control-Allow-Credentials', 'true')
+    response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
+    response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
+    return response
+
+
+# 处理 OPTIONS 预检请求
+@app.route('/config', methods=['OPTIONS'])
+def config_options():
+    """处理 /config 接口的 OPTIONS 预检请求"""
+    return '', 200
 
 os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
 os.makedirs(app.config['RESULT_FOLDER'], exist_ok=True)
@@ -27,285 +82,148 @@ DIAR_MODEL_LOADED = False
 ASR_MODEL_LOCK = threading.Lock()
 DIAR_MODEL_LOCK = threading.Lock()
 
-
+# 全局变量用于控制任务执行
-def get_asr_service():
+task_running = {}
-    global GLOBAL_ASR_SERVICE, ASR_MODEL_LOADED
+task_timeout = 600  # 10 分钟超时（秒）
-    if GLOBAL_ASR_SERVICE is None:
-        from asr_service import ASRService
-        GLOBAL_ASR_SERVICE = ASRService()
-    return GLOBAL_ASR_SERVICE
 
 
-def get_diar_service():
+@app.route('/api/recognize', methods=['POST'])
-    global GLOBAL_DIAR_SERVICE, DIAR_MODEL_LOADED
+def recognize():
-    if GLOBAL_DIAR_SERVICE is None:
+    """文件推理 - 调用 main.py 的 main 函数"""
-        from diarization_service import DiarizationService
+    task_id = str(uuid.uuid4())
-        GLOBAL_DIAR_SERVICE = DiarizationService()
+    task_running[task_id] = True
-    return GLOBAL_DIAR_SERVICE
-
-
-@app.route('/health', methods=['GET'])
-def health_check():
-    """健康检查"""
-    return jsonify({
-        'status': 'ok',
-        'asr_loaded': ASR_MODEL_LOADED,
-        'diar_loaded': DIAR_MODEL_LOADED
-    })
-
-
-@app.route('/api/asr/load', methods=['GET'])
-def load_asr_model():
-    """加载 ASR 模型"""
-    global ASR_MODEL_LOADED
-    with ASR_MODEL_LOCK:
-        if ASR_MODEL_LOADED:
-            return jsonify({'message': 'ASR 模型已加载', 'loaded': True})
 
     try:
+        # 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）
         data = request.json or {}
-            model_name = data.get('model_name', 'paraformer-zh')
+        path = data.get('path', '')
-            device = data.get('device', 'auto')
 
-            print(f"正在加载 ASR 模型: {model_name}, 设备: {device}")
+        if not path:
-            service = get_asr_service()
+            return jsonify(make_response(
-            service._load_model()
+                status="error",
-            ASR_MODEL_LOADED = True
+                message="请提供文件路径",
+                errors=["缺少必要参数：path"]
+            )), 400
+
+        print(f"\n{'='*60}")
+        print(f"API 收到请求：path={path}, task_id={task_id}")
+        print(f"{'='*60}")
+        print(f"开始调用 main 函数...")
+
+        # 切换到 server.py 所在目录
+        server_dir = Path(__file__).parent.absolute()
+        os.chdir(server_dir)
+
+        print(f"当前工作目录：{os.getcwd()}")
+
+        # 设置超时处理
+        def timeout_handler(signum, frame):
+            task_running[task_id] = False
+            raise TimeoutError(f"任务执行超时 ({task_timeout}秒)")
+
+        # 注册信号处理器（仅在 Unix/Linux/Mac 有效，Windows 下会被忽略）
+        try:
+            signal.signal(signal.SIGALRM, timeout_handler)
+            signal.alarm(task_timeout)
+            use_alarm = True
+        except (AttributeError, ValueError):
+            # Windows 不支持 SIGALRM
+            use_alarm = False
+            print("注意：Windows 系统，使用超时检测线程")
+
+        try:
+            # 调用 main.py 的 main 函数
+            from main import main
+            main(path)
+
+            # 取消超时
+            if use_alarm:
+                signal.alarm(0)
+
+            print(f"main 函数执行完成")
+
+            return jsonify(make_response(
+                status="success",
+                message="文件推理完成",
+                data={"path": path, "task_id": task_id}
+            )), 200
+
+        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:
+            # 清理信号处理器
+            if use_alarm:
+                signal.alarm(0)
+            task_running[task_id] = False
 
-            return jsonify({
-                'message': 'ASR 模型加载成功',
-                'loaded': True,
-                'model': model_name,
-                'device': device
-            })
     except Exception as e:
-            return jsonify({'error': str(e)}), 500
+        import traceback
+        traceback.print_exc()
+        task_running[task_id] = False
+        return jsonify(make_response(
+            status="error",
+            message=str(e),
+            errors=[str(e)]
+        )), 500
 
 
-@app.route('/api/diar/load', methods=['POST'])
+@app.route('/api/result', methods=['GET'])
-def load_diar_model():
+def result():
-    """加载 3D-Speaker 模型"""
+    """获取文件推理结果"""
-    global DIAR_MODEL_LOADED
-    with DIAR_MODEL_LOCK:
-        if DIAR_MODEL_LOADED:
-            return jsonify({'message': '3D-Speaker 模型已加载', 'loaded': True})
-
     try:
-            data = request.json or {}
+        # 从请求参数获取路径
-            embedding_model = data.get('embedding_model', 'eres2netv2')
+        path = request.args.get('path', '')
-            device = data.get('device', 'auto')
-            cluster_threshold = data.get('cluster_threshold', 0.5)
-            min_cluster_size = data.get('min_cluster_size', 10)
 
-            print(f"正在加载 3D-Speaker 模型: {embedding_model}, 设备: {device}")
+        if not path:
-            service = get_diar_service()
+            return jsonify(make_response(
-            service._load_model()
+                status="error",
-            DIAR_MODEL_LOADED = True
+                message="请提供文件路径",
+                errors=["缺少必要参数：path"]
+            )), 400
 
-            return jsonify({
+        # 读取结果
-                'message': '3D-Speaker 模型加载成功',
+        print(Path(path).stem)
-                'loaded': True,
+        result_file = OUTPUT_DIR / f"{Path(path).stem}_result.json"
-                'embedding_model': embedding_model,
+        if result_file.exists():
-                'device': device
+            with open(result_file, 'r', encoding='utf-8') as f:
-            })
+                result_data = json.load(f)
-        except Exception as e:
+            return jsonify(make_response(
-            return jsonify({'error': str(e)}), 500
+                status="success",
-
+                message="获取成功",
-
+                data=result_data
-@app.route('/api/asr/unload', methods=['POST'])
+            )), 200
-def unload_asr_model():
-    """卸载 ASR 模型"""
-    global GLOBAL_ASR_SERVICE, ASR_MODEL_LOADED
-
-    try:
-        GLOBAL_ASR_SERVICE = None
-        ASR_MODEL_LOADED = False
-        gc.collect()
-
-        return jsonify({'message': 'ASR 模型已卸载'})
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-
-@app.route('/api/diar/unload', methods=['POST'])
-def unload_diar_model():
-    """卸载 3D-Speaker 模型"""
-    global GLOBAL_DIAR_SERVICE, DIAR_MODEL_LOADED
-
-    try:
-        GLOBAL_DIAR_SERVICE = None
-        DIAR_MODEL_LOADED = False
-        gc.collect()
-
-        return jsonify({'message': '3D-Speaker 模型已卸载'})
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-
-@app.route('/api/recognize/single', methods=['POST'])
-def recognize_single():
-    """单文件推理"""
-    try:
-        if 'file' not in request.files:
-            return jsonify({'error': '请上传音频文件'}), 400
-
-        file = request.files['file']
-        if file.filename == '':
-            return jsonify({'error': '文件名不能为空'}), 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))
-        output_format = data.get('format', 'json')
-
-        filename = secure_filename(file.filename) if file.filename is not None else secure_filename("unnamed_file")
-        task_id = str(uuid.uuid4())[:8]
-        audio_path = os.path.join(app.config['UPLOAD_FOLDER'], f"{task_id}_{filename}")
-        file.save(audio_path)
-
-        try:
-            global ASR_MODEL_LOADED
-            service = get_asr_service()
-            if not ASR_MODEL_LOADED:
-                service._load_model()
-                ASR_MODEL_LOADED = True
-
-            sentences = service.recognize(
-                audio_path
-            )
-
-            result = {
-                'file': filename,
-                'total_sentences': len(sentences),
-                'sentences': [s.to_dict() for s in sentences]
-            }
-
-            if output_format == 'json':
-                result_path = os.path.join(app.config['RESULT_FOLDER'], f"{task_id}_result.json")
-                with open(result_path, 'w', encoding='utf-8') as f:
-                    import json
-                    json.dump(result, f, ensure_ascii=False, indent=2)
-                return jsonify(result)
         else:
-                srt_path = os.path.join(app.config['RESULT_FOLDER'], f"{task_id}_result.srt")
+            return jsonify(make_response(
-                service.export_to_srt([s for s in sentences], srt_path)
+                status="error",
-                return send_file(srt_path, as_attachment=True, download_name=f"{task_id}_result.srt")
+                message="处理完成但未找到结果文件",
-
+                errors=["结果文件不存在"]
-        finally:
+            )), 404
-            if os.path.exists(audio_path):
-                os.remove(audio_path)
-
     except Exception as e:
         import traceback
         traceback.print_exc()
-        return jsonify({'error': str(e)}), 500
+        return jsonify(make_response(
-
+            status="error",
-
+            message=str(e),
-@app.route('/api/recognize/batch', methods=['POST'])
+            errors=[str(e)]
-def recognize_batch():
+        )), 500
-    """批量推理"""
-    try:
-        if 'files' not in request.files:
-            return jsonify({'error': '请上传音频文件'}), 400
-
-        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)
-
-    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'
-    })
-
 
 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("  POST /api/recognize - 文件推理")
-    print("  GET  /api/status          - 获取模型状态")
+    print("  POST /api/result - 获取文件推理结果")
-    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("\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)