diff --git a/.cursorrules b/.cursorrules
new file mode 100644
index 0000000..656c8ca
--- /dev/null
+++ b/.cursorrules
@@ -0,0 +1,19 @@
+
+You are an AI assistant specialized in Python development. Your approach emphasizes:
+
+1. Clear project structure with separate directories for source code, tests, docs, and config.
+2. Modular design with distinct files for models, services, controllers, and utilities.
+3. Configuration management using environment variables.
+4. Robust error handling and logging, including context capture.
+5. Comprehensive testing with pytest.
+6. Detailed documentation using docstrings and README files.
+7. Dependency management via https://github.com/astral-sh/rye and virtual environments.
+8. Code style consistency using Ruff.
+9. CI/CD implementation with GitHub Actions or GitLab CI.
+10. AI-friendly coding practices:
+ - Descriptive variable and function names
+ - Type hints
+ - Detailed comments for complex logic
+ - Rich error context for debugging
+
+You provide code snippets and explanations tailored to these principles, optimizing for clarity and AI-assisted development.
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index c3a38d8..75c801a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,6 +29,7 @@ env/
.coverage
htmlcov/
.pytest_cache/
+savePath/
# 编辑器相关
.idea/
diff --git a/Dockerfile b/Dockerfile
deleted file mode 100644
index baf04ab..0000000
--- a/Dockerfile
+++ /dev/null
@@ -1,27 +0,0 @@
-FROM python:3.9-slim
-
-# 安装系统依赖
-RUN apt-get update && apt-get install -y \
- build-essential \
- libsndfile1 \
- ffmpeg \
- && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /app
-
-# 安装Python依赖
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-# 复制应用代码
-COPY . .
-
-# 设置环境变量
-ENV PYTHONPATH=/app
-ENV PYTHONUNBUFFERED=1
-
-# 暴露WebSocket端口
-EXPOSE 10095
-
-# 启动服务
-CMD ["python", "src/server.py"]
\ No newline at end of file
diff --git a/README.md b/README.md
index bb3eb83..bb9ebc9 100644
--- a/README.md
+++ b/README.md
@@ -1,113 +1,142 @@
-# FunASR WebSocket服务
+# FunASR FastAPI WebSocket Service
-## 简介
-本项目基于FunASR实现了一个WebSocket语音识别服务，支持实时语音流的在线和离线识别。利用ModelScope开源语音模型，该服务可以进行高精度的中文语音识别，并支持语音活动检测(VAD)和自动添加标点符号。
+一个基于 FunASR 和 FastAPI 构建的高性能、实时的语音识别 WebSocket 服务。该项目核心特色是支持"一发多收"的广播模式，适用于会议实时字幕、在线教育、直播转写等需要将单一音源的识别结果分发给多个客户端的场景。
+
+## ✨ 功能特性
+
+- **实时语音处理**: 集成 FunASR 的语音活动检测（VAD）、语音识别（ASR）和声纹识别（SPK）模型。
+- **WebSocket 流式 API**: 提供低延迟、双向的实时通信接口。
+- **"一发多收"架构**:
+ - **发送者 (Sender)**: 单一客户端作为音频来源，向服务器持续发送音频流。
+ - **接收者 (Receiver)**: 多个客户端可以订阅同一个会话，实时接收广播的识别结果。
+- **异步核心**: 基于 FastAPI 和 `asyncio` 构建，可处理大量并发连接。
+- **模块化设计**: 清晰地分离了服务层 (`server.py`)、会话管理层 (`ASRRunner`) 和核心处理流水线 (`ASRPipeline`)。
+
+## 📂 项目结构
-## 项目结构
```
.
-├── src/ # 源代码目录
-│ ├── __init__.py # 包初始化文件
-│ ├── server.py # WebSocket服务器实现
-│ ├── config.py # 配置处理模块
-│ ├── models.py # 模型加载模块
-│ ├── service.py # ASR服务实现
-│ └── client.py # 测试客户端
-├── tests/ # 测试目录
-│ ├── __init__.py # 测试包初始化文件
-│ └── test_config.py # 配置模块测试
-├── requirements.txt # Python依赖
-├── Dockerfile # Docker配置
-├── docker-compose.yml # Docker Compose配置
-├── .gitignore # Git忽略文件
-└── README.md # 项目说明
+├── main.py # 应用程序主入口，使用 uvicorn 启动服务
+├── docker
+│ ├── Dockerfile # Docker 镜像构建文件
+│ ├── docker-compose.yml # Docker 容器编排文件
+├── docs
+│ ├── WEBSOCKET_API.md # WebSocket API 详细使用文档和示例
+│ ├── SystemArchitecture.md # 系统架构文档
+├── src
+│ ├── server.py # FastAPI 应用核心，管理生命周期和全局资源
+│ ├── runner
+│ │ └── ASRRunner.py # 核心会话管理器，负责创建和协调识别会话 (SAR)
+│ ├── pipeline
+│ │ └── ASRpipeline.py # 同步的、基于线程的语音处理流水线
+│ ├── functor # VAD, ASR, SPK 等原子操作的实现
+│ ├── websockets
+│ │ ├── adapter.py # WebSocket 适配器，处理数据格式转换
+│ │ ├── endpoint
+│ │ │ └── asr_endpoint.py # WebSocket 的业务逻辑端点
+│ │ └── router.py # WebSocket 路由
+│ ├── core
+│ │ └── model_loader.py # 模型加载器
+│ ├── utils
+│ │ └── logger.py # 日志记录器
+│ │ └── data_format.py # 数据格式转换
+│ │ └── mock_websocket.py # 模拟 WebSocket 客户端
+├── tests
+│ ├── runner
+│ │ └── asr_runner_test.py # ASRRunner 的单元测试 (异步)
+│ ├── websocket
+│ │ └── websocket_asr.py # WebSocket 服务的端到端测试
```
-## 功能特性
+## 🚀 快速开始
-- **多模式识别**：支持离线(offline)、在线(online)和两阶段(2pass)识别模式
-- **语音活动检测**：自动检测语音开始和结束
-- **标点符号**：支持自动添加标点符号
-- **WebSocket接口**：基于二进制WebSocket提供实时语音识别
-- **Docker支持**：提供容器化部署支持
+### 1. 环境与依赖
-## 安装与使用
-
-### 环境要求
- Python 3.8+
-- CUDA支持 (若需GPU加速)
-- 内存 >= 8GB
+- 项目依赖项记录在 `requirements.txt` 文件中。
-### 安装依赖
+### 2. 安装
+建议在虚拟环境中安装依赖。在项目根目录下，运行：
```bash
pip install -r requirements.txt
```
-### 运行服务器
+### 3. 运行服务
+
+执行主入口文件来启动 FastAPI 服务：
+```bash
+python main.py
+```
+服务启动后，将监听 `http://0.0.0.0:8000`。
+
+## 💡 如何使用
+
+服务通过 WebSocket 提供，客户端通过 `session_id` 来创建或加入一个识别会话，并通过 `mode` 参数声明自己的角色（`sender` 或 `receiver`）。
+
+**详细的 API 说明、URL 格式以及 Python 和 JavaScript 的客户端连接示例，请参阅：**
+
+➡️ **[WEBSOCKET_API.md](./docs/WEBSOCKET_API.md)**
+
+## 🔬 测试
+
+项目提供了两种测试方式来验证其功能。
+
+### 1. 端到端 WebSocket 测试
+
+此测试会模拟一个 `sender` 和一个 `receiver`，完整地测试一次识别会话。
+
+**前提**: 确保 FastAPI 服务正在运行。
+```bash
+python main.py
+```
+
+在项目根目录下执行：
+```bash
+python tests/websocket/websocket_asr.py
+```
+
+### 2. ASRRunner 单元测试
+
+此测试针对核心的 `ASRRunner` 组件进行，验证其异步逻辑。
+
+执行测试：
+```bash
+python test_main.py
+```
+
+## 🐳 使用 Docker 部署
+
+### 1. 构建 Docker 镜像
+
+在项目根目录下，运行：
```bash
-python src/server.py
+docker build -t asr-server:x.x.x -f docker/Dockerfile .
```
-常用启动参数:
-- `--host`: 服务器监听地址，默认为 0.0.0.0
-- `--port`: 服务器端口，默认为 10095
-- `--device`: 设备类型(cuda或cpu)，默认为 cuda
-- `--ngpu`: GPU数量，0表示使用CPU，默认为 1
-
-### 测试客户端
+### 2. 运行 Docker 容器
```bash
-python src/client.py --audio_file path/to/audio.wav
+ docker run -d -p 11096:11096 --name asr-server -v ~/.cache/modelscope:/root/.cache/modelscope asr-server:x.x.x
```
-常用客户端参数:
-- `--audio_file`: 要识别的音频文件路径
-- `--mode`: 识别模式，可选 2pass/online/offline，默认为 2pass
-- `--host`: 服务器地址，默认为 localhost
-- `--port`: 服务器端口，默认为 10095
+### 环境变量说明
-## Docker部署
+- `SPEAKERS_URL`: 说话人数据库 API 的 URL。
-### 构建镜像
+ 示例/默认 SPEAKERS_URL="http://172.23.30.120:11200/api/v1/speakers/"
-```bash
-docker build -t funasr-websocket .
-```
+ 此url为后端api提供的查询所有数据库中说话人信息的接口
-### 使用Docker Compose启动
+- `LOG_LEVEL`: 日志等级。
-```bash
-docker-compose up -d
-```
+ 示例/默认 LOG_LEVEL="INFO"
-## API说明
+ `LOG_LEVEL` 为总项目日志等级，
-### WebSocket消息格式
+- `LOG_LEVEL_ASR_SERVER`: 日志等级。
-1. **客户端配置消息**:
-```json
-{
- "mode": "2pass", // 可选: "2pass", "online", "offline"
- "chunk_size": "5,10", // 块大小，格式为"encoder_size,decoder_size"
- "wav_name": "audio1", // 音频标识名称
- "is_speaking": true // 是否正在说话
-}
-```
+ 示例/默认 LOG_LEVEL_ASR_SERVER="INFO"
-2. **客户端音频数据**:
-二进制音频数据流，16kHz采样率，16位PCM格式
-
-3. **服务器识别结果**:
-```json
-{
- "mode": "2pass-online", // 识别模式
- "text": "识别的文本内容", // 识别结果
- "wav_name": "audio1", // 音频标识
- "is_final": false // 是否是最终结果
-}
-```
-
-## 许可证
-[MIT](LICENSE)
\ No newline at end of file
+ `LOG_LEVEL_ASR_SERVER` 为 ASR 服务日志等级，优先级高于 `LOG_LEVEL`
diff --git a/data/denoise.py b/data/denoise.py
new file mode 100644
index 0000000..de1adcb
--- /dev/null
+++ b/data/denoise.py
@@ -0,0 +1,13 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+
+ans = pipeline(
+ Tasks.acoustic_noise_suppression,
+ model='iic/speech_frcrn_ans_cirm_16k')
+
+wav_file = 'speaker_wav/HaiaoDuan.wav'
+output_path = 'denoise_output/HaiaoDuan_denoise_output.wav'
+result = ans(
+ wav_file,
+ output_path=output_path)
diff --git a/data/denoise_output/HaiaoDuan_denoise_output.wav b/data/denoise_output/HaiaoDuan_denoise_output.wav
new file mode 100644
index 0000000..8bac338
Binary files /dev/null and b/data/denoise_output/HaiaoDuan_denoise_output.wav differ
diff --git a/data/denoise_output/ZiyangZhang_denoise_output.wav b/data/denoise_output/ZiyangZhang_denoise_output.wav
new file mode 100644
index 0000000..5e0b274
Binary files /dev/null and b/data/denoise_output/ZiyangZhang_denoise_output.wav differ
diff --git a/data/record.py b/data/record.py
new file mode 100644
index 0000000..6a8c8cd
--- /dev/null
+++ b/data/record.py
@@ -0,0 +1,35 @@
+"""
+本地录音，保存为wav格式，存储在data/speaker_wav目录下
+"""
+import pyaudio
+import wave
+
+def record_audio(filename, duration=5, format=pyaudio.paInt16, channels=1, rate=16000):
+ """
+ 本地录音，保存为wav格式，存储在data/speaker_wav目录下
+ """
+ p = pyaudio.PyAudio()
+ stream = p.open(format=format, channels=channels, rate=rate, input=True, frames_per_buffer=1024)
+
+ print("按下回车键开始录音...")
+ input()
+ frames = []
+ for i in range(0, int(rate / 1024 * duration)):
+ data = stream.read(1024)
+ frames.append(data)
+ print("录音结束")
+ stream.stop_stream()
+ stream.close()
+ p.terminate()
+ wav_file = wave.open(filename, 'wb')
+ wav_file.setnchannels(channels)
+ wav_file.setsampwidth(p.get_sample_size(format))
+ wav_file.setframerate(rate)
+ wav_file.writeframes(b''.join(frames))
+ wav_file.close()
+
+if __name__ == "__main__":
+ record_audio(
+ "data/speaker_wav/test.wav",
+ duration=5
+ )
\ No newline at end of file
diff --git a/data/speaker_wav/HaiaoDuan.wav b/data/speaker_wav/HaiaoDuan.wav
new file mode 100644
index 0000000..8bac338
Binary files /dev/null and b/data/speaker_wav/HaiaoDuan.wav differ
diff --git a/data/speaker_wav/HaiaoDuan_origin.wav b/data/speaker_wav/HaiaoDuan_origin.wav
new file mode 100644
index 0000000..70239e9
Binary files /dev/null and b/data/speaker_wav/HaiaoDuan_origin.wav differ
diff --git a/data/speaker_wav/ZiyangZhang.wav b/data/speaker_wav/ZiyangZhang.wav
new file mode 100644
index 0000000..5e0b274
Binary files /dev/null and b/data/speaker_wav/ZiyangZhang.wav differ
diff --git a/data/speaker_wav/ZiyangZhang_origin.wav b/data/speaker_wav/ZiyangZhang_origin.wav
new file mode 100644
index 0000000..e090c9a
Binary files /dev/null and b/data/speaker_wav/ZiyangZhang_origin.wav differ
diff --git a/data/speakers.json b/data/speakers.json
new file mode 100644
index 0000000..5a2e08c
--- /dev/null
+++ b/data/speakers.json
@@ -0,0 +1,400 @@
+[
+ {
+ "speaker_id": "137facd6-a1c9-47b9-b87d-16e6f62e07bd",
+ "speaker_name": "ZiyangZhang",
+ "wav_path": "/home/lyg/Code/funasr/data/speaker_wav/ZiyangZhang.wav",
+ "speaker_embs": [
+ -0.4249887466430664,
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+ 1.084520936012268
+ ]
+ },
+ {
+ "speaker_id": "81e1b806-c76a-468f-98b7-4a63f2996480",
+ "speaker_name": "HaiaoDuan",
+ "wav_path": "/home/lyg/Code/funasr/data/speaker_wav/HaiaoDuan.wav",
+ "speaker_embs": [
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+ }
+]
\ No newline at end of file
diff --git a/data/speakers.json.backup b/data/speakers.json.backup
new file mode 100644
index 0000000..f5b8642
--- /dev/null
+++ b/data/speakers.json.backup
@@ -0,0 +1,14 @@
+[
+ {
+ "speaker_id": "b7e2c8e2-1f3a-4c2a-9e7a-2c1d4e8f9a3b",
+ "speaker_name": "ZiyangZhang",
+ "wav_path": "/home/lyg/Code/funasr/data/speaker_wav/ZiyangZhang.wav",
+ "speaker_embs": ""
+ },
+ {
+ "speaker_id": "b7e2c8e2-1f3a-4c2a-9e7a-2c1d4e8f9a3b",
+ "speaker_name": "HaiaoDuan",
+ "wav_path": "/home/lyg/Code/funasr/data/speaker_wav/HaiaoDuan.wav",
+ "speaker_embs": ""
+ }
+]
\ No newline at end of file
diff --git a/docker-compose.yml b/docker-compose.yml
deleted file mode 100644
index d08c69c..0000000
--- a/docker-compose.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-version: '3.8'
-
-services:
- funasr:
- build: .
- container_name: funasr-websocket
- volumes:
- - .:/app
- # 如果需要使用本地模型缓存
- - ~/.cache/modelscope:/root/.cache/modelscope
- ports:
- - "10095:10095"
- environment:
- - PYTHONUNBUFFERED=1
- deploy:
- resources:
- reservations:
- devices:
- - driver: nvidia
- count: 1
- capabilities: [gpu]
- restart: unless-stopped
- command: python src/server.py --device cuda --ngpu 1
\ No newline at end of file
diff --git a/docker/Dockerfile b/docker/Dockerfile
new file mode 100644
index 0000000..32f72a2
--- /dev/null
+++ b/docker/Dockerfile
@@ -0,0 +1,36 @@
+FROM python:3.10-slim
+
+# 更换系统源
+# 科大大的 Debian 12 (bookworm) 镜像源
+RUN echo "deb http://mirrors.ustc.edu.cn/debian bookworm main contrib non-free" > /etc/apt/sources.list && \
+ echo "deb http://mirrors.ustc.edu.cn/debian bookworm-updates main contrib non-free" >> /etc/apt/sources.list && \
+ echo "deb http://mirrors.ustc.edu.cn/debian-security bookworm-security main" >> /etc/apt/sources.list && \
+ echo "deb http://mirrors.ustc.edu.cn/debian bookworm-backports main contrib non-free" >> /etc/apt/sources.list
+# 安装系统依赖
+RUN apt-get update && apt-get install -y \
+ build-essential \
+ libsndfile1 \
+ ffmpeg \
+ && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# 安装Python依赖
+COPY ../requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+# 复制应用代码
+COPY ../src/ /app/src/
+COPY ../logs/ /app/logs/
+COPY ../main.py /app/
+
+# 设置环境变量
+ENV SPEAKERS_URL="http://172.23.30.120:11200/api/v1/speakers/" \
+ LOG_LEVEL="INFO" \
+ LOG_LEVEL_ASR_SERVER="INFO"
+
+# 暴露WebSocket端口
+EXPOSE 11096
+
+# 启动服务
+CMD ["python", "main.py"]
\ No newline at end of file
diff --git a/docs/SystemArchitecture.md b/docs/SystemArchitecture.md
new file mode 100644
index 0000000..25ac4d3
--- /dev/null
+++ b/docs/SystemArchitecture.md
@@ -0,0 +1,94 @@
+# 系统架构
+
+本项目是一个基于 FunASR 和 FastAPI 构建的高性能、实时的语音识别（ASR）WebSocket 服务。其核心架构设计旨在处理实时的流式音频数据，并通过 "一发多收" 的广播模式，将识别结果分发给多个客户端。
+
+## 核心组件
+
+系统主要由以下几个核心组件构成，它们各司其职，通过异步和多线程协作，实现了高效的实时语音处理：
+
+1. **WebSocket 服务 (FastAPI)**
+ - **文件**: `src/websockets/`, `src/server.py`, `main.py`
+ - **职责**: 作为系统的网络入口，负责处理 WebSocket 连接。它使用 FastAPI 构建，提供异步的、非阻塞的 I/O 处理能力，能够高效地管理大量并发客户端连接。`asr_endpoint.py` 是核心端点，负责根据客户端声明的 `mode` (sender/receiver) 将连接路由到 `ASRRunner`。
+
+2. **会话管理器 (ASRRunner)**
+ - **文件**: `src/runner/ASRRunner.py`
+ - **职责**: 这是整个系统的"大脑"和协调中心。它管理所有活跃的语音识别会话（`SenderAndReceiver`，简称 SAR）。
+ - **会话生命周期**: 当一个 `sender` 连接时，`ASRRunner` 会创建一个新的 SAR 实例；当 `receiver` 连接时，会将其加入到指定的 SAR 中。
+ - **异步桥梁**: `ASRRunner` 运行在主 `asyncio` 事件循环中，负责从 `sender` 的 WebSocket 连接异步接收音频数据，然后通过线程安全的队列 (`queue.Queue`) 将数据传递给同步的 `ASRPipeline`。同时，它也负责接收来自 Pipeline 的最终结果，并将其异步广播给所有 `receiver`。
+
+3. **语音处理流水线 (ASRPipeline)**
+ - **文件**: `src/pipeline/ASRpipeline.py`
+ - **职责**: 这是实际执行语音处理任务的核心引擎。每个 SAR 会话都拥有一个独立的 `ASRPipeline` 实例，该实例在自己的后台线程中运行。
+ - **模块化设计**: Pipeline 内部由多个 `Functor` (如 VAD, ASR, SPK) 组成，通过一系列内部队列连接，形成一个处理链。
+ - **处理流程**:
+ 1. **VAD (Voice Activity Detection)**: 检测音频流中的有效语音片段。
+ 2. **ASR (Automatic Speech Recognition)**: 将语音片段转换为文字。
+ 3. **SPK (Speaker Recognition)**: 识别说话人（声纹识别）。
+ 4. **ResultBinder**: 将 ASR 的文本结果和 SPK 的说话人结果合并，生成最终的识别消息。
+
+4. **原子操作单元 (Functor)**
+ - **文件**: `src/functor/`
+ - **职责**: `Functor` 是 Pipeline 中执行具体原子任务的单元。每个 Functor 都是一个独立的类，负责调用底层 FunASR 模型来执行 VAD、ASR 或 SPK 等任务。这种设计使得处理流程更加清晰和模块化。
+
+## 流程图
+
+下面是系统处理一次完整语音识别请求的流程图，展示了从客户端连接到收到识别结果的全过程。
+
+```mermaid
+sequenceDiagram
+ participant Sender Client
+ participant Receiver Client
+ participant FastAPI WebSocket Endpoint
+ participant ASRRunner
+ participant ASRPipeline (Thread)
+ participant Functors (VAD, ASR, SPK)
+
+ par
+ Sender Client->>+FastAPI WebSocket Endpoint: 发起连接 (mode=sender, session_id=S1)
+ FastAPI WebSocket Endpoint->>+ASRRunner: new_SAR(ws, name="S1")
+ ASRRunner->>ASRRunner: 创建 SenderAndReceiver (SAR) 实例
+ ASRRunner->>ASRPipeline (Thread): 创建并运行 Pipeline 实例
+ ASRPipeline (Thread)->>Functors (VAD, ASR, SPK): 初始化 Functor 线程
+ ASRRunner->>-FastAPI WebSocket Endpoint: 返回成功
+ FastAPI WebSocket Endpoint->>-Sender Client: 连接建立
+ and
+ Receiver Client->>+FastAPI WebSocket Endpoint: 发起连接 (mode=receiver, session_id=S1)
+ FastAPI WebSocket Endpoint->>+ASRRunner: join_SAR(ws, name="S1")
+ ASRRunner->>ASRRunner: 将 Receiver 加入 S1 的接收者列表
+ ASRRunner->>-FastAPI WebSocket Endpoint: 返回成功
+ FastAPI WebSocket Endpoint->>-Receiver Client: 连接建立
+ end
+
+ loop 音频流传输与处理
+ Sender Client->>ASRRunner: 发送音频数据块
+ ASRRunner->>ASRPipeline (Thread): (via Queue) 传递音频数据
+ ASRPipeline (Thread)->>Functors (VAD, ASR, SPK): (via sub-queues) 分发数据
+ Note over Functors (VAD, ASR, SPK): 1. VAD检测语音
2. ASR识别文本
3. SPK识别说话人
4. ResultBinder合并结果
+ Functors (VAD, ASR, SPK)->>ASRPipeline (Thread): (via callback) 返回最终识别结果
+ ASRPipeline (Thread)->>ASRRunner: (via thread-safe callback) 发送结果
+ end
+
+ ASRRunner->>ASRRunner: 收到结果，准备广播
+ ASRRunner-->>Sender Client: 广播识别结果
+ ASRRunner-->>Receiver Client: 广播识别结果
+
+ par
+ Sender Client->>FastAPI WebSocket Endpoint: 关闭连接
+ FastAPI WebSocket Endpoint->>ASRRunner: (触发异常)
+ ASRRunner->>ASRPipeline (Thread): 发送停止信号
+ ASRPipeline (Thread)->>Functors (VAD, ASR, SPK): 停止 Functor 线程
+ Note right of ASRRunner: 清理会话资源
+ and
+ Receiver Client->>FastAPI WebSocket Endpoint: 关闭连接
+ FastAPI WebSocket Endpoint->>ASRRunner: (触发异常)
+ ASRRunner->>ASRRunner: 从接收者列表移除
+ end
+
+```
+
+## 架构优势
+
+- **高并发和低延迟**: 采用 `asyncio` 和 WebSocket，网络层能够处理大量并发连接。音频处理在独立的线程中进行，避免了 CPU 密集型任务阻塞事件循环，保证了低延迟。
+- **解耦与模块化**: `WebSocket Endpoint`、`ASRRunner` 和 `ASRPipeline` 职责清晰，相互解耦。`Functor` 的设计使得添加或修改处理步骤变得容易。
+- **鲁棒性**: 每个识别会话（SAR）都是隔离的，一个会话的失败不会影响其他会话。优雅的关闭逻辑确保了资源的正确释放。
+- **可扩展性**: "一发多收" 的广播模式可以轻松扩展到大量 `receiver`，适用于多种实时应用场景。
diff --git a/docs/WEBSOCKET_API.md b/docs/WEBSOCKET_API.md
new file mode 100644
index 0000000..1b7f3de
--- /dev/null
+++ b/docs/WEBSOCKET_API.md
@@ -0,0 +1,262 @@
+# FunASR-FastAPI WebSocket API 文档
+
+本文档详细介绍了如何连接和使用 FunASR-FastAPI 实时语音识别服务的 WebSocket 接口。
+
+## 1. 连接端点 (Endpoint)
+
+服务的 WebSocket 端点 URL 格式如下：
+
+```
+ws://:8000/ws/asr/{session_id}?mode=
+```
+
+### 参数说明
+
+- **`{session_id}`** (路径参数, `str`, **必需**):
+ 用于唯一标识一个识别会话（例如，一场会议或一次直播）。所有属于同一次会话的客户端都应使用相同的 `session_id`。
+
+- **`mode`** (查询参数, `str`, **必需**):
+ 定义客户端的角色。
+ - `sender`: 音频发送者。一个会话中应该只有一个 `sender`。此客户端负责将实时音频流发送到服务器。
+ - `receiver`: 结果接收者。一个会话中可以有多个 `receiver`。此客户端只接收由服务器广播的识别结果，不发送音频。
+
+## 2. 数据格式
+
+### 2.1 发送数据 (Sender -> Server)
+
+- **音频格式**: `sender` 必须发送原始的 **PCM 音频数据**。
+ - **采样率**: 16000 Hz
+ - **位深**: 32-bit (floating point)
+ - **声道数**: 单声道 (Mono)
+- **传输格式**: 必须以**二进制 (bytes)** 格式发送。
+- **结束信号**: 当音频流结束时，`sender` 应发送一个**文本消息** `"close"` 来通知服务器关闭会话。
+
+### 2.2 接收数据 (Server -> Receiver)
+
+服务器会将识别结果以 **JSON 文本** 格式广播给会话中的所有 `receiver`（以及 `sender` 自己）。JSON 对象的结构示例如下：
+
+```json
+{
+ "asr": "你好，世界。",
+ "spk": {
+ "speaker_id": "uuid-of-the-speaker",
+ "speaker_name": "SpeakerName",
+ "score": 0.98
+ }
+}
+```
+
+## 3. Python 客户端示例
+
+需要安装 `websockets` 库: `pip install websockets`
+
+### 3.1 Python Sender 示例 (发送本地音频文件)
+
+这个脚本会读取一个 WAV 文件，并将其内容以流式方式发送到服务器。
+
+```python
+import asyncio
+import websockets
+import soundfile as sf
+import uuid
+
+# --- 配置 ---
+SERVER_URI = "ws://localhost:8000/ws/asr/{session_id}?mode=sender"
+SESSION_ID = str(uuid.uuid4()) # 为这次会话生成一个唯一的ID
+AUDIO_FILE = "tests/XT_ZZY_denoise.wav" # 替换为你的音频文件路径
+CHUNK_SIZE = 3200 # 对应 100ms 的 float32 数据 (16000 * 4 * 0.1)
+
+async def send_audio():
+ """连接到服务器，并流式发送音频文件"""
+ uri = SERVER_URI.format(session_id=SESSION_ID)
+ print(f"作为 Sender 连接到: {uri}")
+
+ async with websockets.connect(uri) as websocket:
+ try:
+ # 读取音频文件
+ with sf.SoundFile(AUDIO_FILE, 'r') as f:
+ assert f.samplerate == 16000, "音频文件采样率必须为 16kHz"
+ assert f.channels == 1, "音频文件必须为单声道"
+
+ print("开始发送音频...")
+ while True:
+ # 读取为 float32 类型
+ data = f.read(CHUNK_SIZE, dtype='float32')
+ if not data.any():
+ break
+ # 将 numpy 数组转换为原始字节流
+ await websocket.send(data.tobytes())
+ await asyncio.sleep(0.1) # 模拟实时音频输入
+
+ print("音频发送完毕，发送结束信号。")
+ await websocket.send("close")
+
+ # 等待服务器的最终确认或关闭连接
+ response = await websocket.recv()
+ print(f"收到服务器最终响应: {response}")
+
+ except websockets.exceptions.ConnectionClosed as e:
+ print(f"连接已关闭: {e}")
+ except Exception as e:
+ print(f"发生错误: {e}")
+
+if __name__ == "__main__":
+ asyncio.run(send_audio())
+```
+
+### 3.2 Python Receiver 示例 (接收识别结果)
+
+这个脚本会连接到指定的会话，并持续打印服务器广播的识别结果。
+
+```python
+import asyncio
+import websockets
+
+# --- 配置 ---
+# !!! 必须和 Sender 使用相同的 SESSION_ID !!!
+SERVER_URI = "ws://localhost:8000/ws/asr/{session_id}?mode=receiver"
+SESSION_ID = "在此处粘贴你的Sender会话ID"
+
+async def receive_results():
+ """连接到服务器并接收识别结果"""
+ if "粘贴你的Sender会话ID" in SESSION_ID:
+ print("错误：请先设置有效的 SESSION_ID！")
+ return
+
+ uri = SERVER_URI.format(session_id=SESSION_ID)
+ print(f"作为 Receiver 连接到: {uri}")
+
+ async with websockets.connect(uri) as websocket:
+ try:
+ print("等待接收识别结果...")
+ while True:
+ message = await websocket.recv()
+ print(f"收到结果: {message}")
+ except websockets.exceptions.ConnectionClosed as e:
+ print(f"连接已关闭: {e.code} {e.reason}")
+
+if __name__ == "__main__":
+ asyncio.run(receive_results())
+```
+
+## 4. JavaScript 客户端示例 (浏览器)
+
+这个示例展示了如何在网页上通过麦克风获取音频，并将其作为 `sender` 发送。
+
+```html
+
+
+
+
+ WebSocket ASR Client
+
+
+ FunASR WebSocket Client (Sender)
+ Session ID:
+
+
+ 识别结果:
+
+
+
+
+
+```
\ No newline at end of file
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..3eb1d1b
--- /dev/null
+++ b/main.py
@@ -0,0 +1,23 @@
+import uvicorn
+import os
+from src.server import app
+from src.utils.logger import get_module_logger, setup_root_logger
+from datetime import datetime
+
+time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+# 日志等级 LOG_LEVEL_ASR_SERVER > LOG_LEVEL > INFO(default)
+logger_level = os.getenv("LOG_LEVEL", "INFO")
+logger_level = os.getenv("LOG_LEVEL_ASR_SERVER", logger_level)
+setup_root_logger(level=logger_level, log_file=f"logs/main_{time}.log")
+logger = get_module_logger(__name__)
+
+if __name__ == "__main__":
+ logger.info("启动 FunASR FastAPI 服务器...")
+ # 在生产环境中，推荐使用更强大的ASGI服务器，如Gunicorn，并配合Uvicorn workers。
+ # 例如: gunicorn -w 4 -k uvicorn.workers.UvicornWorker main:app
+ uvicorn.run(
+ app,
+ host="0.0.0.0",
+ port=11096,
+ log_level="info"
+ )
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..635b9b1
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,20 @@
+[project]
+name = "asr-server"
+version = "0.1.0"
+requires-python = ">=3.9.21"
+dependencies = [
+ "addict==2.4.0",
+ "datasets==2.21.0",
+ "fastapi==0.115.14",
+ "funasr==1.2.6",
+ "numpy==2.0.1",
+ "pillow==11.1.0",
+ "pydantic==2.11.3",
+ "pydub>=0.25.1",
+ "simplejson==3.20.1",
+ "sortedcontainers==2.4.0",
+ "torch==2.3.1",
+ "torchaudio==2.3.1",
+ "uvicorn==0.35.0",
+ "websockets==12.0",
+]
diff --git a/requirements.txt b/requirements.txt
index 66e9708..4bd08fc 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,11 +1,335 @@
-pytest==7.3.1
-pytest-cov==4.1.0
-flake8==6.0.0
-black==23.3.0
-isort==5.12.0
-flask==2.3.2
-requests==2.31.0
-websockets==11.0.3
-numpy==1.24.3
-funasr==0.10.0
-modelscope==1.9.5
\ No newline at end of file
+# This file was autogenerated by uv via the following command:
+# uv pip compile pyproject.toml -o requirements.txt
+addict==2.4.0
+ # via asr-server (pyproject.toml)
+aiohappyeyeballs==2.6.1
+ # via aiohttp
+aiohttp==3.12.13
+ # via
+ # datasets
+ # fsspec
+aiosignal==1.4.0
+ # via aiohttp
+aliyun-python-sdk-core==2.16.0
+ # via
+ # aliyun-python-sdk-kms
+ # oss2
+aliyun-python-sdk-kms==2.16.5
+ # via oss2
+annotated-types==0.7.0
+ # via pydantic
+antlr4-python3-runtime==4.9.3
+ # via
+ # hydra-core
+ # omegaconf
+anyio==4.9.0
+ # via starlette
+async-timeout==5.0.1
+ # via aiohttp
+attrs==25.3.0
+ # via aiohttp
+audioread==3.0.1
+ # via librosa
+certifi==2025.6.15
+ # via requests
+cffi==1.17.1
+ # via
+ # cryptography
+ # soundfile
+charset-normalizer==3.4.2
+ # via requests
+click==8.2.1
+ # via uvicorn
+crcmod==1.7
+ # via oss2
+cryptography==45.0.5
+ # via aliyun-python-sdk-core
+datasets==2.21.0
+ # via asr-server (pyproject.toml)
+decorator==5.2.1
+ # via librosa
+dill==0.3.8
+ # via
+ # datasets
+ # multiprocess
+editdistance==0.8.1
+ # via funasr
+exceptiongroup==1.3.0
+ # via anyio
+fastapi==0.115.14
+ # via asr-server (pyproject.toml)
+filelock==3.18.0
+ # via
+ # datasets
+ # huggingface-hub
+ # torch
+ # triton
+frozenlist==1.7.0
+ # via
+ # aiohttp
+ # aiosignal
+fsspec==2024.6.1
+ # via
+ # datasets
+ # huggingface-hub
+ # torch
+funasr==1.2.6
+ # via asr-server (pyproject.toml)
+h11==0.16.0
+ # via uvicorn
+hf-xet==1.1.5
+ # via huggingface-hub
+huggingface-hub==0.33.2
+ # via datasets
+hydra-core==1.3.2
+ # via funasr
+idna==3.10
+ # via
+ # anyio
+ # requests
+ # yarl
+jaconv==0.4.0
+ # via funasr
+jamo==0.4.1
+ # via funasr
+jieba==0.42.1
+ # via funasr
+jinja2==3.1.6
+ # via torch
+jmespath==0.10.0
+ # via aliyun-python-sdk-core
+joblib==1.5.1
+ # via
+ # librosa
+ # pynndescent
+ # scikit-learn
+kaldiio==2.18.1
+ # via funasr
+lazy-loader==0.4
+ # via librosa
+librosa==0.11.0
+ # via funasr
+llvmlite==0.44.0
+ # via
+ # numba
+ # pynndescent
+markupsafe==3.0.2
+ # via jinja2
+modelscope==1.27.1
+ # via funasr
+mpmath==1.3.0
+ # via sympy
+msgpack==1.1.1
+ # via librosa
+multidict==6.6.3
+ # via
+ # aiohttp
+ # yarl
+multiprocess==0.70.16
+ # via datasets
+networkx==3.4.2
+ # via torch
+numba==0.61.2
+ # via
+ # librosa
+ # pynndescent
+ # umap-learn
+numpy==2.0.1
+ # via
+ # asr-server (pyproject.toml)
+ # datasets
+ # kaldiio
+ # librosa
+ # numba
+ # pandas
+ # pytorch-wpe
+ # scikit-learn
+ # scipy
+ # soundfile
+ # soxr
+ # tensorboardx
+ # torch-complex
+ # umap-learn
+nvidia-cublas-cu12==12.1.3.1
+ # via
+ # nvidia-cudnn-cu12
+ # nvidia-cusolver-cu12
+ # torch
+nvidia-cuda-cupti-cu12==12.1.105
+ # via torch
+nvidia-cuda-nvrtc-cu12==12.1.105
+ # via torch
+nvidia-cuda-runtime-cu12==12.1.105
+ # via torch
+nvidia-cudnn-cu12==8.9.2.26
+ # via torch
+nvidia-cufft-cu12==11.0.2.54
+ # via torch
+nvidia-curand-cu12==10.3.2.106
+ # via torch
+nvidia-cusolver-cu12==11.4.5.107
+ # via torch
+nvidia-cusparse-cu12==12.1.0.106
+ # via
+ # nvidia-cusolver-cu12
+ # torch
+nvidia-nccl-cu12==2.20.5
+ # via torch
+nvidia-nvjitlink-cu12==12.9.86
+ # via
+ # nvidia-cusolver-cu12
+ # nvidia-cusparse-cu12
+nvidia-nvtx-cu12==12.1.105
+ # via torch
+omegaconf==2.3.0
+ # via hydra-core
+oss2==2.19.1
+ # via funasr
+packaging==25.0
+ # via
+ # datasets
+ # huggingface-hub
+ # hydra-core
+ # lazy-loader
+ # pooch
+ # tensorboardx
+ # torch-complex
+pandas==2.3.0
+ # via datasets
+pillow==11.1.0
+ # via asr-server (pyproject.toml)
+platformdirs==4.3.8
+ # via pooch
+pooch==1.8.2
+ # via librosa
+propcache==0.3.2
+ # via
+ # aiohttp
+ # yarl
+protobuf==6.31.1
+ # via tensorboardx
+pyarrow==20.0.0
+ # via datasets
+pycparser==2.22
+ # via cffi
+pycryptodome==3.23.0
+ # via oss2
+pydantic==2.11.3
+ # via
+ # asr-server (pyproject.toml)
+ # fastapi
+pydantic-core==2.33.1
+ # via pydantic
+pydub==0.25.1
+ # via asr-server (pyproject.toml)
+pynndescent==0.5.13
+ # via umap-learn
+python-dateutil==2.9.0.post0
+ # via pandas
+pytorch-wpe==0.0.1
+ # via funasr
+pytz==2025.2
+ # via pandas
+pyyaml==6.0.2
+ # via
+ # datasets
+ # funasr
+ # huggingface-hub
+ # omegaconf
+requests==2.32.4
+ # via
+ # datasets
+ # funasr
+ # huggingface-hub
+ # modelscope
+ # oss2
+ # pooch
+scikit-learn==1.7.0
+ # via
+ # librosa
+ # pynndescent
+ # umap-learn
+scipy==1.15.3
+ # via
+ # funasr
+ # librosa
+ # pynndescent
+ # scikit-learn
+ # umap-learn
+sentencepiece==0.2.0
+ # via funasr
+setuptools==80.9.0
+ # via modelscope
+simplejson==3.20.1
+ # via asr-server (pyproject.toml)
+six==1.17.0
+ # via
+ # oss2
+ # python-dateutil
+sniffio==1.3.1
+ # via anyio
+sortedcontainers==2.4.0
+ # via asr-server (pyproject.toml)
+soundfile==0.13.1
+ # via
+ # funasr
+ # librosa
+soxr==0.5.0.post1
+ # via librosa
+starlette==0.46.2
+ # via fastapi
+sympy==1.14.0
+ # via torch
+tensorboardx==2.6.4
+ # via funasr
+threadpoolctl==3.6.0
+ # via scikit-learn
+torch==2.3.1
+ # via
+ # asr-server (pyproject.toml)
+ # torchaudio
+torch-complex==0.4.4
+ # via funasr
+torchaudio==2.3.1
+ # via asr-server (pyproject.toml)
+tqdm==4.67.1
+ # via
+ # datasets
+ # funasr
+ # huggingface-hub
+ # modelscope
+ # umap-learn
+triton==2.3.1
+ # via torch
+typing-extensions==4.14.1
+ # via
+ # aiosignal
+ # anyio
+ # exceptiongroup
+ # fastapi
+ # huggingface-hub
+ # librosa
+ # multidict
+ # pydantic
+ # pydantic-core
+ # torch
+ # typing-inspection
+ # uvicorn
+typing-inspection==0.4.1
+ # via pydantic
+tzdata==2025.2
+ # via pandas
+umap-learn==0.5.9.post2
+ # via funasr
+urllib3==2.5.0
+ # via
+ # modelscope
+ # requests
+uvicorn==0.35.0
+ # via asr-server (pyproject.toml)
+websockets==12.0
+ # via asr-server (pyproject.toml)
+xxhash==3.5.0
+ # via datasets
+yarl==1.20.1
+ # via aiohttp
diff --git a/src/__init__.py b/src/__init__.py
index e13b69f..530b6f3 100644
--- a/src/__init__.py
+++ b/src/__init__.py
@@ -11,4 +11,4 @@ FunASR WebSocket服务
- 支持多种识别模式(2pass/online/offline)
"""
-__version__ = "0.1.0"
\ No newline at end of file
+__version__ = "0.1.0"
diff --git a/src/audio_chunk.py b/src/audio_chunk.py
new file mode 100644
index 0000000..d680950
--- /dev/null
+++ b/src/audio_chunk.py
@@ -0,0 +1,194 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+音频数据块管理类 - 用于存储和处理16KHz音频数据
+"""
+
+from typing import List, Optional, Dict
+from src.models import AudioBinary_Config, AudioBinary_data_list
+
+# 配置日志
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__, level="INFO")
+
+
+class AudioBinary:
+ """
+ 音频数据存储单元
+ 用于存储二进制数据
+ 面向Slice, 向Slice提供数据与接口
+
+ self._audio_config: AudioBinary_Config -- 音频参数配置
+ self._binary_data_list: AudioBinary_data_list -- 音频数据列表
+ self._slice_listener: List[callable] -- 切片监听器
+
+ AudioBinary_Config: Dict -- 音频参数配置
+ AudioBinary_data_list: List[bytes] -- 音频数据列表
+ """
+
+ def __init__(self, *args):
+ """
+ 初始化音频数据块
+ 参数:
+ *args: 可变参数
+ """
+ # 音频参数配置
+ self._audio_config = AudioBinary_Config()
+ # 音频片段
+ self._binary_data_list: AudioBinary_data_list = AudioBinary_data_list()
+ # 切片监听器
+ self._slice_listener: List = []
+ if isinstance(args, Dict):
+ self._audio_config = AudioBinary_Config.AudioBinary_Config_from_dict(args)
+ elif isinstance(args, AudioBinary_Config):
+ self._audio_config = args
+ else:
+ raise ValueError("参数类型错误")
+
+ def add_slice_listener(self, slice_listener: callable) -> None:
+ """
+ 添加切片监听器
+ 参数:
+ slice_listener: callable -- 切片监听器
+ """
+ self._slice_listener.append(slice_listener)
+
+ def __add__(self, other: bytes):
+ """
+ __add__ 是 "+" 运算符的重载，
+ 使用方法:
+ audio_binary = audio_binary + bytes
+ 添加音频数据块 与 add_binary_data 等效，
+ 但可以链式调用, 方便使用
+ 参数:
+ other: bytes --音频数据块
+ """
+ self._binary_data_list.append(other)
+ return self
+
+ def __iadd__(self, other: bytes):
+ """
+ __iadd__ 是 "+=" 运算符的重载，
+ 使用方法:
+ audio_binary += bytes
+ 添加音频数据块 与 add_binary_data 等效，
+ 但可以链式调用, 方便使用
+ 参数:
+ other: bytes --音频数据块
+ """
+ self._binary_data_list.append(other)
+ return self
+
+ def add_binary_data(self, binary_data: bytes):
+ """
+ 添加音频数据块
+ 参数:
+ binary_data: bytes --音频数据块
+ """
+ self._binary_data_list.append(binary_data)
+
+ def rewrite_binary_data(self, target_index: int, binary_data: bytes):
+ """
+ 重写音频数据块
+ 参数:
+ target_index: int -- 目标索引
+ binary_data: bytes --音频数据块
+ """
+ self._binary_data_list.rewrite(target_index, binary_data)
+
+ def get_binary_data(
+ self,
+ start: int = 0,
+ end: Optional[int] = None,
+ ) -> Optional[bytes]:
+ """
+ 获取指定索引的音频数据块
+ 参数:
+ start: 开始索引
+ end: 结束索引
+ 返回:
+ List[bytes]: 音频数据块
+ """
+ if start >= len(self._binary_data_list):
+ return None
+ if end is None:
+ end = start + 1
+ end = min(end, len(self._binary_data_list))
+ return self._binary_data_list[start:end]
+
+
+class AudioChunk:
+ """
+ 音频数据块管理类
+ 管理两部分内容, AudioBinary和Slice。
+ AudioBinary用于内部存储字节数据。
+ Slice是AudioBinary的切片,用于外部接口。
+
+ 此类仅用于AudioBinary与Funtor的交互, 不负责其它逻辑。
+ """
+
+ _instance: Optional["AudioChunk"] = None
+
+ def __new__(cls, *args, **kwargs):
+ """
+ 单例模式
+ """
+ if cls._instance is None:
+ cls._instance = super(AudioChunk, cls).__new__(cls, *args, **kwargs)
+ return cls._instance
+
+ def __init__(self) -> None:
+ """
+ 初始化AudioChunk实例
+ """
+ self._audio_binary_list: Dict[str, AudioBinary] = {}
+ self._slice_listener: List[callable] = []
+
+ def get_audio_binary(
+ self,
+ binary_name: Optional[str] = None,
+ audio_config: Optional[AudioBinary_Config] = None,
+ ) -> AudioBinary:
+ """
+ 获取音频数据块
+ 参数:
+ binary_name: str -- 音频数据块名称
+ 返回:
+ AudioBinary: 音频数据块
+ """
+ if binary_name is None:
+ binary_name = "default"
+ if binary_name not in self._audio_binary_list:
+ self._audio_binary_list[binary_name] = AudioBinary(audio_config)
+ return self._audio_binary_list[binary_name]
+
+ @staticmethod
+ def _time2size(time_ms: int, audio_config: AudioBinary_Config) -> int:
+ """
+ 将时间(ms)转换为数据大小(字节)
+ 参数:
+ time_ms: int -- 时间(ms)
+ audio_config: AudioBinary_Config -- 音频参数配置
+ 返回:
+ int: 数据大小(字节)
+ """
+ # 时间(ms)到字节(bytes)计算方法为: 时间(ms) * 采样率(Hz) * 通道数(1 or 2) * 采样位宽(16 or 24) / 1000
+ time_s = time_ms / 1000
+ bytes_per_sample = audio_config.sample_width * audio_config.channel
+ return int(time_s * audio_config.sample_rate * bytes_per_sample)
+
+ @staticmethod
+ def _size2time(size: int, audio_config: AudioBinary_Config) -> int:
+ """
+ 将数据大小(字节)转换为时间(ms)
+ 参数:
+ size: int -- 数据大小(字节)
+ audio_config: AudioBinary_Config -- 音频参数配置
+ 返回:
+ int: 时间(ms)
+ """
+ # 字节(bytes)到时间(ms)计算方法为: 字节(bytes) * 1000 / (采样率(Hz) * 通道数(1 or 2) * 采样位宽(16 or 24))
+ bytes_per_sample = audio_config.sample_width * audio_config.channel
+ time_ms = size * 1000 // (audio_config.sample_rate * bytes_per_sample)
+ return time_ms
diff --git a/src/config.py b/src/config.py
index feea99f..c123ad4 100644
--- a/src/config.py
+++ b/src/config.py
@@ -1,125 +1,102 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
+默认配置DefaultConfig
+ - audio_config: 音频配置
配置模块 - 处理命令行参数和配置项
"""
import argparse
+from src.models import AudioBinary_Config
+class DefaultConfig:
+ """
+ 默认配置
+ """
+ audio_config = AudioBinary_Config(
+ chunk_size=200,
+ chunk_stride=1600,
+ sample_rate=16000,
+ sample_width=16,
+ channels=1,
+ )
def parse_args():
"""
解析命令行参数
-
+
返回:
argparse.Namespace: 解析后的参数对象
"""
parser = argparse.ArgumentParser(description="FunASR WebSocket服务器")
-
+
# 服务器配置
parser.add_argument(
- "--host",
- type=str,
- default="0.0.0.0",
- help="服务器主机地址，例如：localhost, 0.0.0.0"
+ "--host",
+ type=str,
+ default="0.0.0.0",
+ help="服务器主机地址，例如：localhost, 0.0.0.0",
)
- parser.add_argument(
- "--port",
- type=int,
- default=10095,
- help="WebSocket服务器端口"
- )
-
+ parser.add_argument("--port", type=int, default=10095, help="WebSocket服务器端口")
+
# SSL配置
- parser.add_argument(
- "--certfile",
- type=str,
- default="",
- help="SSL证书文件路径"
- )
- parser.add_argument(
- "--keyfile",
- type=str,
- default="",
- help="SSL密钥文件路径"
- )
-
+ parser.add_argument("--certfile", type=str, default="", help="SSL证书文件路径")
+ parser.add_argument("--keyfile", type=str, default="", help="SSL密钥文件路径")
+
# ASR模型配置
parser.add_argument(
"--asr_model",
type=str,
default="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
- help="离线ASR模型（从ModelScope获取）"
+ help="离线ASR模型（从ModelScope获取）",
)
parser.add_argument(
- "--asr_model_revision",
- type=str,
- default="v2.0.4",
- help="离线ASR模型版本"
+ "--asr_model_revision", type=str, default="v2.0.4", help="离线ASR模型版本"
)
-
+
# 在线ASR模型配置
parser.add_argument(
"--asr_model_online",
type=str,
default="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
- help="在线ASR模型（从ModelScope获取）"
+ help="在线ASR模型（从ModelScope获取）",
)
parser.add_argument(
- "--asr_model_online_revision",
- type=str,
- default="v2.0.4",
- help="在线ASR模型版本"
+ "--asr_model_online_revision",
+ type=str,
+ default="v2.0.4",
+ help="在线ASR模型版本",
)
-
+
# VAD模型配置
parser.add_argument(
"--vad_model",
type=str,
default="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
- help="VAD语音活动检测模型（从ModelScope获取）"
+ help="VAD语音活动检测模型（从ModelScope获取）",
)
parser.add_argument(
- "--vad_model_revision",
- type=str,
- default="v2.0.4",
- help="VAD模型版本"
+ "--vad_model_revision", type=str, default="v2.0.4", help="VAD模型版本"
)
-
+
# 标点符号模型配置
parser.add_argument(
"--punc_model",
type=str,
default="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727",
- help="标点符号模型（从ModelScope获取）"
+ help="标点符号模型（从ModelScope获取）",
)
parser.add_argument(
- "--punc_model_revision",
- type=str,
- default="v2.0.4",
- help="标点符号模型版本"
+ "--punc_model_revision", type=str, default="v2.0.4", help="标点符号模型版本"
)
-
+
# 硬件配置
+ parser.add_argument("--ngpu", type=int, default=1, help="GPU数量，0表示仅使用CPU")
parser.add_argument(
- "--ngpu",
- type=int,
- default=1,
- help="GPU数量，0表示仅使用CPU"
+ "--device", type=str, default="cuda", help="设备类型：cuda或cpu"
)
- parser.add_argument(
- "--device",
- type=str,
- default="cuda",
- help="设备类型：cuda或cpu"
- )
- parser.add_argument(
- "--ncpu",
- type=int,
- default=4,
- help="CPU核心数"
- )
-
+ parser.add_argument("--ncpu", type=int, default=4, help="CPU核心数")
+
return parser.parse_args()
@@ -127,4 +104,4 @@ if __name__ == "__main__":
args = parse_args()
print("配置参数:")
for arg in vars(args):
- print(f" {arg}: {getattr(args, arg)}")
\ No newline at end of file
+ print(f" {arg}: {getattr(args, arg)}")
diff --git a/src/core/__init__.py b/src/core/__init__.py
new file mode 100644
index 0000000..32e9b9e
--- /dev/null
+++ b/src/core/__init__.py
@@ -0,0 +1,3 @@
+from .model_loader import ModelLoader
+
+__all__ = ["ModelLoader"]
\ No newline at end of file
diff --git a/src/core/model_loader.py b/src/core/model_loader.py
new file mode 100644
index 0000000..72a5f4e
--- /dev/null
+++ b/src/core/model_loader.py
@@ -0,0 +1,143 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+模型加载模块 - 负责加载各种语音识别相关模型
+"""
+try:
+ # 导入FunASR库
+ from funasr import AutoModel
+except ImportError as exc:
+ raise ImportError("未找到funasr库, 请先安装: pip install funasr") from exc
+from modelscope.pipelines import pipeline
+# 日志模块
+from src.utils import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+# 单例模式
+class ModelLoader:
+ """
+ ModelLoader类是单例模式, 程序生命周期全局唯一, 负责加载模型到字典中。
+ 一般的, 可以直接call ModelLoader()来获取加载的模型。
+ 也可以通过ModelLoader实例(args)或ModelloaderInstance.load_models(args)来初始化, 并加载模型。
+ """
+
+ _instance = None
+
+ def __new__(cls, *args, **kwargs):
+ """
+ 单例模式
+ """
+ if cls._instance is None:
+ cls._instance = super(ModelLoader, cls).__new__(cls, *args, **kwargs)
+ return cls._instance
+
+ def __init__(self, args=None):
+ """
+ 初始化ModelLoader实例
+ """
+ self.models = {}
+ logger.debug("初始化ModelLoader")
+ if args is not None:
+ self.__call__(args)
+
+ def __call__(self, args=None):
+ """
+ 调用ModelLoader实例时, 如果模型字典为空, 则加载模型
+ """
+ # 如果模型字典为空, 则加载模型
+ if self.models == {} or self.models is None:
+ if args.asr_model is not None:
+ self.models = self.load_models(args)
+ # 直接调用等于调用self.models
+ return self.models
+
+ def _load_model(self, input_model_args: dict, model_type: str):
+ """
+ 加载单个模型
+
+ 参数:
+ model_args: 模型加载字典
+ model_type: 模型类型, 用于确定使用哪个模型参数
+
+ 返回:
+ AutoModel: 加载的模型实例
+ """
+ # 默认配置
+ default_config = {
+ "model": None,
+ "model_revision": None,
+ "ngpu": 0,
+ "ncpu": 1,
+ "device": "cpu",
+ "disable_pbar": True,
+ "disable_log": True,
+ "disable_update": True,
+ }
+ # 从args中获取配置, 如果存在则覆盖默认值
+ model_args = default_config.copy()
+ for key, value in default_config.items():
+ if key in ["model", "model_revision"]:
+ # 特殊处理model和model_revision, 因为它们需要model_type前缀
+ if key == "model":
+ value = input_model_args.get(f"{model_type}_model", None)
+ else:
+ value = input_model_args.get(f"{model_type}_model_revision", None)
+ else:
+ value = input_model_args.get(key, None)
+ if value is not None:
+ logger.debug("替换%s模型参数: %s = %s", model_type, key, value)
+ model_args[key] = value
+ # 验证必要参数
+ if not model_args["model"]:
+ raise ValueError(f"未指定{model_type}模型路径")
+ try:
+ # 使用 % 格式化替代 f-string,避免不必要的字符串格式化开销
+ logger.debug("正在加载%s模型: %s", model_type, model_args["model"])
+ model = AutoModel(**model_args)
+ return model
+ except Exception as e:
+ logger.error("加载%s模型失败: %s", model_type, str(e))
+ raise
+
+ # def _load_pipeline(self, input_model_args: dict, model_type: str):
+ # """
+ # 加载pipeline
+ # """
+ # default_pipeline = pipeline(
+ # task='speaker-verification',
+ # model='iic/speech_campplus_sv_zh-cn_16k-common',
+ # model_revision='v1.0.0'
+ # )
+
+ def load_models(self, args):
+ """
+ 加载所有需要的模型
+ 参数:
+ args: 命令行参数, 包含模型配置
+
+ 返回:
+ dict: 包含所有加载的模型的字典
+ """
+ logger.info("ModelLoader加载模型")
+ # 初始化模型字典
+ self.models = {}
+ # 加载离线ASR模型
+ # 检查对应键是否存在
+ model_list = ["asr", "asr_online", "vad", "punc"]
+ for model_name in model_list:
+ name_model = f"{model_name}_model"
+ name_model_revision = f"{model_name}_model_revision"
+ if name_model in args:
+ logger.debug("加载%s模型", model_name)
+ self.models[model_name] = self._load_model(args, model_name)
+ pipeline_list = ["spk"]
+ for pipeline_name in pipeline_list:
+ if pipeline_name == "spk":
+ self.models[pipeline_name] = pipeline(
+ task='speaker-verification',
+ model='iic/speech_campplus_sv_zh-cn_16k-common',
+ model_revision='v1.0.0'
+ )
+ return self.models
diff --git a/src/functor/__init__.py b/src/functor/__init__.py
index 5ecc5b1..dd5bb75 100644
--- a/src/functor/__init__.py
+++ b/src/functor/__init__.py
@@ -1,4 +1,4 @@
-from .vad_functor import VAD
-from .model_loader import load_models
+from .vad_functor import VADFunctor
+from .base import FunctorFactory
-__all__ = ["VAD", "load_models"]
\ No newline at end of file
+__all__ = ["VADFunctor", "FunctorFactory"]
diff --git a/src/functor/asr_functor.py b/src/functor/asr_functor.py
new file mode 100644
index 0000000..8b7ba76
--- /dev/null
+++ b/src/functor/asr_functor.py
@@ -0,0 +1,164 @@
+"""
+ASRFunctor
+负责对音频片段进行ASR处理, 以ASR_Result进行callback
+"""
+
+from src.functor.base import BaseFunctor
+from src.models import AudioBinary_data_list, AudioBinary_Config, VAD_Functor_result
+from typing import Callable, List
+from queue import Queue, Empty
+import threading
+
+# 日志
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+class ASRFunctor(BaseFunctor):
+ """
+ ASRFunctor
+ 负责对音频片段进行ASR处理, 以ASR_Result进行callback
+ 需要配置好 _model, _callback, _input_queue, _audio_config
+ 否则无法run()启动线程
+
+ 运行中, 使用reset_cache()重置缓存, 准备下次任务
+
+ 使用stop()停止线程, 但需要等待input_queue为空
+ """
+
+ def __init__(self) -> None:
+ super().__init__()
+ # 资源与配置
+ self._model: dict = {} # 模型
+ self._callback: List[Callable] = [] # 回调函数
+ self._input_queue: Queue = None # 输入队列
+ self._audio_config: AudioBinary_Config = None # 音频配置
+
+ # flag
+ self._is_running: bool = False
+ self._stop_event: bool = False
+
+ # 线程资源
+ self._thread: threading.Thread = None
+
+ # 状态锁
+ self._status_lock: threading.Lock = threading.Lock()
+
+ # 缓存
+ self._hotwords: List[str] = []
+
+ def reset_cache(self) -> None:
+ """
+ 重置缓存, 用于任务完成后清理缓存数据, 准备下次任务
+ """
+ pass
+
+ def set_input_queue(self, queue: Queue) -> None:
+ """
+ 设置监听的输入消息队列
+ """
+ self._input_queue = queue
+
+ def set_model(self, model: dict) -> None:
+ """
+ 设置推理模型
+ """
+ self._model = model
+
+ def set_audio_config(self, audio_config: AudioBinary_Config) -> None:
+ """
+ 设置音频配置
+ """
+ self._audio_config = audio_config
+ logger.debug("ASRFunctor设置音频配置: %s", self._audio_config)
+
+ def add_callback(self, callback: Callable) -> None:
+ """
+ 向自身的_callback: List[Callable]回调函数列表中添加回调函数
+ """
+ if not isinstance(self._callback, list):
+ self._callback = []
+ self._callback.append(callback)
+
+ def _do_callback(self, result: List[str]) -> None:
+ """
+ 回调函数
+ """
+ text = result[0]["text"].replace(" ", "")
+ for callback in self._callback:
+ callback(text)
+
+ def _process(self, data: VAD_Functor_result) -> None:
+ """
+ 处理数据
+ """
+ binary_data = data.audiobinary_data.binary_data
+ result = self._model["asr"].generate(
+ input=binary_data,
+ chunk_size=self._audio_config.chunk_size,
+ hotwords=self._hotwords,
+ )
+ self._do_callback(result)
+
+ def _run(self) -> None:
+ """
+ 线程运行逻辑
+ """
+ with self._status_lock:
+ self._is_running = True
+ self._stop_event = False
+ # 运行逻辑
+ while self._is_running:
+ try:
+ data = self._input_queue.get(True, timeout=1)
+ if data is None:
+ break
+ logger.debug("[ASRFunctor]获取到的数据length: %s", len(data))
+ self._process(data)
+ self._input_queue.task_done()
+ # 当队列为空时, 间隔1s检测是否进入停止事件。
+ except Empty:
+ if self._stop_event:
+ break
+ continue
+ # 其他异常
+ except Exception as e:
+ logger.error("ASRFunctor运行时发生错误: %s", e)
+ raise e
+
+ def run(self) -> threading.Thread:
+ """
+ 启动线程
+ Returns:
+ threading.Thread: 返回已运行线程实例
+ """
+ self._pre_check()
+ self._thread = threading.Thread(target=self._run, daemon=True)
+ self._thread.start()
+ return self._thread
+
+ def _pre_check(self) -> bool:
+ """
+ 预检查
+ """
+ if self._model is None:
+ raise ValueError("模型未设置")
+ if self._audio_config is None:
+ raise ValueError("音频配置未设置")
+ if self._input_queue is None:
+ raise ValueError("输入队列未设置")
+ if self._callback is None:
+ raise ValueError("回调函数未设置")
+ return True
+
+ def stop(self) -> bool:
+ """
+ 停止线程
+ """
+ with self._status_lock:
+ self._stop_event = True
+ self._thread.join()
+ with self._status_lock:
+ self._is_running = False
+ return not self._thread.is_alive()
diff --git a/src/functor/audiochunk.py b/src/functor/audiochunk.py
deleted file mode 100644
index ad1fbae..0000000
--- a/src/functor/audiochunk.py
+++ /dev/null
@@ -1,178 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-音频数据块管理类 - 用于存储和处理16KHz音频数据
-"""
-
-import numpy as np
-from src.utils.logger import get_module_logger
-from typing import List, Optional, Union
-from src.models import AudioBinary_Config
-
-# 配置日志
-logger = get_module_logger(__name__, level="INFO")
-
-class AudioChunk:
- """音频数据块管理类，用于存储和处理16KHz音频数据"""
-
- def __init__(self,
- max_duration_ms: int = 1000*60*60*10,
- audio_config : AudioBinary_Config = None,
- ):
- """
- 初始化音频数据块管理器
-
- 参数:
- max_duration_ms: 音频池最大留存时间(ms)，默认10小时
- audio_config: 音频配置信息
- """
- # 音频参数
- self.sample_rate = audio_config.sample_rate if audio_config is not None else 16000 # 采样率：16KHz
- self.sample_width = audio_config.sample_width if audio_config is not None else 2 # 采样位宽：16bit
- self.channels = audio_config.channels if audio_config is not None else 1 # 通道数：单声道
-
- # 数据存储
- self._max_duration_ms = max_duration_ms
- self._max_chunk_size = self._time2size(max_duration_ms) # 最大数据大小
- self._chunk = [] # 当前音频数据块列表
- self._chunk_size = 0 # 当前数据总大小
- self._offset = 0 # 当前偏移量
-
- logger.info(f"初始化AudioChunk: 最大时长={max_duration_ms}ms, 最大数据大小={self._max_chunk_size}字节")
-
- def add_chunk(self, chunk: Union[bytes, np.ndarray]) -> bool:
- """
- 添加音频数据块
-
- 参数:
- chunk: 音频数据块，可以是bytes或numpy数组
-
- 返回:
- bool: 是否添加成功
- """
- try:
- # 检查数据格式
- if isinstance(chunk, np.ndarray):
- # 确保是16bit整数格式
- if chunk.dtype != np.int16:
- chunk = chunk.astype(np.int16)
- # 转换为bytes
- chunk = chunk.tobytes()
-
- # 检查数据大小
- if len(chunk) % (self.sample_width * self.channels) != 0:
- logger.warning(f"音频数据大小不是{self.sample_width * self.channels}的倍数: {len(chunk)}")
- return False
-
- # 检查是否超过最大限制
- if self._chunk_size + len(chunk) > self._max_chunk_size:
- logger.warning("音频数据超过最大限制，将自动清除旧数据")
- self.clear_chunk()
-
- # 添加数据
- self._chunk.append(chunk)
- self._chunk_size += len(chunk)
- return True
-
- except Exception as e:
- logger.error(f"添加音频数据块时出错: {e}")
- return False
-
- def get_chunk_binary(self, start: int = 0, end: Optional[int] = None) -> Optional[bytes]:
- """
- 获取指定索引的音频数据块
- """
- print("[AudioChunk] get_chunk_binary", start, end)
- if start >= len(self._chunk):
- return None
- if end is None or end > len(self._chunk):
- end = len(self._chunk)
- data = b''.join(self._chunk)
- return data[start:end]
-
- def get_chunk(self, start_ms: int = 0, end_ms: Optional[int] = None) -> Optional[bytes]:
- """
- 获取指定时间范围的音频数据
-
- 参数:
- start_ms: 开始时间(ms)
- end_ms: 结束时间(ms)，None表示到末尾
-
- 返回:
- Optional[bytes]: 音频数据，如果获取失败则返回None
- """
- try:
- if not self._chunk:
- return None
-
- # 计算字节偏移
- start_byte = self._time2size(start_ms)
- end_byte = self._time2size(end_ms) if end_ms is not None else self._chunk_size
-
- # 检查范围是否有效
- if start_byte >= self._chunk_size or start_byte >= end_byte:
- return None
-
- # 获取数据
- data = b''.join(self._chunk)
- return data[start_byte:end_byte]
-
- except Exception as e:
- logger.error(f"获取音频数据块时出错: {e}")
- return None
-
- def get_duration(self) -> int:
- """
- 获取当前音频总时长(ms)
-
- 返回:
- int: 音频时长(ms)
- """
- return self._size2time(self._chunk_size)
-
- def clear_chunk(self) -> None:
- """清除所有音频数据"""
- self._chunk = []
- self._chunk_size = 0
- self._offset = 0
- logger.info("已清除所有音频数据")
-
- def _time2size(self, time_ms: int) -> int:
- """
- 将时间(ms)转换为数据大小(字节)
-
- 参数:
- time_ms: 时间(ms)
-
- 返回:
- int: 数据大小(字节)
- """
- return int(time_ms * self.sample_rate * self.sample_width * self.channels / 1000)
-
- def _size2time(self, size: int) -> int:
- """
- 将数据大小(字节)转换为时间(ms)
-
- 参数:
- size: 数据大小(字节)
-
- 返回:
- int: 时间(ms)
- """
- return int(size * 1000 / (self.sample_rate * self.sample_width * self.channels))
-
- # instance(start_ms, end_ms, use_offset=True)
- def __call__(self, start_ms: int = 0, end_ms: Optional[int] = None, use_offset: bool = True) -> Optional[bytes]:
- """
- 获取指定时间范围的音频数据
- """
- if use_offset:
- start_ms += self._offset
- end_ms += self._offset
- return self.get_chunk(start_ms, end_ms)
-
- def __len__(self) -> int:
- """
- 获取当前音频数据块大小
- """
- return self._chunk_size
\ No newline at end of file
diff --git a/src/functor/base.py b/src/functor/base.py
new file mode 100644
index 0000000..cc6d9aa
--- /dev/null
+++ b/src/functor/base.py
@@ -0,0 +1,208 @@
+"""
+Functor基础模块
+
+该模块定义了Functor的基类,所有功能性的类(如VAD、PUNC、ASR、SPK等)都应继承自这个基类。
+基类提供了数据处理的基本框架,包括:
+- 回调函数管理
+- 模型配置管理
+- 线程运行控制
+
+主要类:
+ BaseFunctor: Functor抽象类
+ FunctorFactory: Functor工厂类
+"""
+
+from abc import ABC, abstractmethod
+from typing import Callable, List, Dict
+from queue import Queue
+import threading
+
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+
+class BaseFunctor(ABC):
+ """
+ Functor抽象类
+
+ 该抽象类规定了所有的Functor类必须实现run()方法启动自身线程
+
+ 属性:
+ _callback (Callable): 处理完成后的回调函数
+ _model (dict): 存储模型相关的配置和实例
+ """
+
+ def __init__(self):
+ """
+ 初始化函数器
+
+ 参数:
+ callback (Callable): 处理完成后的回调函数
+ model (dict): 模型相关的配置和实例
+ """
+ self._callback: List[Callable] = []
+ self._model: dict = {}
+ # flag
+ self._is_running: bool = False
+ self._stop_event: bool = False
+ # 状态锁
+ self._status_lock: threading.Lock = threading.Lock()
+ # 线程资源
+ self._thread: threading.Thread = None
+
+ def add_callback(self, callback: Callable):
+ """
+ 添加回调函数
+
+ 参数:
+ callback (Callable): 新的回调函数
+ """
+ self._callback.append(callback)
+
+ def set_model(self, model: dict):
+ """
+ 设置模型配置
+
+ 参数:
+ model (dict): 新的模型配置
+ """
+ self._model = model
+
+ def set_input_queue(self, queue: Queue):
+ """
+ 设置输入队列
+
+ 参数:
+ queue (Queue): 新的输入队列
+ """
+ self._input_queue = queue
+
+ @abstractmethod
+ def _run(self):
+ """
+ 线程运行逻辑
+
+ 返回:
+ 当达到条件时触发callback
+ """
+
+ @abstractmethod
+ def run(self):
+ """
+ 启动_run方法线程
+
+ 返回:
+ 线程实例
+ """
+
+ @abstractmethod
+ def _pre_check(self):
+ """
+ 预检查
+
+ 返回:
+ 预检查结果
+ """
+
+ @abstractmethod
+ def stop(self):
+ """
+ 停止线程
+
+ 返回:
+ 停止结果
+ """
+
+
+class FunctorFactory:
+ """
+ Functor工厂类
+
+ 该工厂类负责创建和配置Functor实例
+
+ 主要方法:
+ make_funtor(funtor_name: str, config: dict, models: dict) -> BaseFunctor:
+ 创建并配置Functor实例
+ """
+ def _make_vadfunctor(config: dict, models: dict) -> BaseFunctor:
+ """
+ 创建VAD Functor实例
+ """
+ from src.functor.vad_functor import VADFunctor
+
+ audio_config = config["audio_config"]
+ model = {"vad": models["vad"]}
+
+ vad_functor = VADFunctor()
+ vad_functor.set_audio_config(audio_config)
+ vad_functor.set_model(model)
+
+ return vad_functor
+
+ def _make_asrfunctor(config: dict, models: dict) -> BaseFunctor:
+ """
+ 创建ASR Functor实例
+ """
+ from src.functor.asr_functor import ASRFunctor
+
+ audio_config = config["audio_config"]
+ model = {"asr": models["asr"]}
+
+ asr_functor = ASRFunctor()
+ asr_functor.set_audio_config(audio_config)
+ asr_functor.set_model(model)
+
+ return asr_functor
+
+ def _make_spkfunctor(config: dict, models: dict) -> BaseFunctor:
+ """
+ 创建SPK Functor实例
+ """
+ from src.functor.spk_functor import SPKFunctor
+
+ logger.debug(f"创建spk functor[开始]")
+ audio_config = config["audio_config"]
+ # model = {"spk": models["spk"]}
+
+ spk_functor = SPKFunctor(sv_pipeline=models["spk"])
+ spk_functor.set_audio_config(audio_config)
+ # spk_functor.set_model(model)
+ spk_functor.bake()
+
+ logger.debug(f"创建spk functor[完成]")
+ return spk_functor
+
+ def _make_resultbinderfunctor(config: dict, models: dict) -> BaseFunctor:
+ """
+ 创建ResultBinder Functor实例
+ """
+ from src.functor.resultbinder_functor import ResultBinderFunctor
+
+ resultbinder_functor = ResultBinderFunctor()
+
+ return resultbinder_functor
+
+ factory_dict: Dict[str, Callable] = {
+ "vad": _make_vadfunctor,
+ "asr": _make_asrfunctor,
+ "spk": _make_spkfunctor,
+ "resultbinder": _make_resultbinderfunctor,
+ }
+
+ @classmethod
+ def make_functor(cls, functor_name: str, config: dict, models: dict) -> BaseFunctor:
+ """
+ 创建并配置Functor实例
+
+ 参数:
+ funtor_name (str): Functor名称
+ config (dict): 配置信息
+ models (dict): 模型信息
+
+ 返回:
+ BaseFunctor: 创建的Functor实例
+ """
+ if functor_name in cls.factory_dict:
+ return cls.factory_dict[functor_name](config=config, models=models)
+ else:
+ raise ValueError(f"不支持的Functor类型: {functor_name}")
diff --git a/src/functor/model_loader.py b/src/functor/model_loader.py
deleted file mode 100644
index 99f699d..0000000
--- a/src/functor/model_loader.py
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-模型加载模块 - 负责加载各种语音识别相关模型
-"""
-
-from typing import List, Optional
-
-def load_models(args):
- """
- 加载所有需要的模型
-
- 参数:
- args: 命令行参数，包含模型配置
-
- 返回:
- dict: 包含所有加载的模型的字典
- """
- try:
- # 导入FunASR库
- from funasr import AutoModel
- except ImportError:
- raise ImportError("未找到funasr库，请先安装: pip install funasr")
-
- # 初始化模型字典
- models = {}
-
- # 1. 加载离线ASR模型
- print(f"正在加载ASR离线模型: {args.asr_model}")
- models["asr"] = AutoModel(
- model=args.asr_model,
- model_revision=args.asr_model_revision,
- ngpu=args.ngpu,
- ncpu=args.ncpu,
- device=args.device,
- disable_pbar=True,
- disable_log=True,
- disable_update=True,
- )
-
- # 2. 加载在线ASR模型
- print(f"正在加载ASR在线模型: {args.asr_model_online}")
- models["asr_streaming"] = AutoModel(
- model=args.asr_model_online,
- model_revision=args.asr_model_online_revision,
- ngpu=args.ngpu,
- ncpu=args.ncpu,
- device=args.device,
- disable_pbar=True,
- disable_log=True,
- disable_update=True,
- )
-
- # 3. 加载VAD模型
- print(f"正在加载VAD模型: {args.vad_model}")
- models["vad"] = AutoModel(
- model=args.vad_model,
- model_revision=args.vad_model_revision,
- ngpu=args.ngpu,
- ncpu=args.ncpu,
- device=args.device,
- disable_pbar=True,
- disable_log=True,
- disable_update=True,
- )
-
- # 4. 加载标点符号模型（如果指定）
- if args.punc_model:
- print(f"正在加载标点符号模型: {args.punc_model}")
- models["punc"] = AutoModel(
- model=args.punc_model,
- model_revision=args.punc_model_revision,
- ngpu=args.ngpu,
- ncpu=args.ncpu,
- device=args.device,
- disable_pbar=True,
- disable_log=True,
- disable_update=True,
- )
- else:
- models["punc"] = None
- print("未指定标点符号模型，将不使用标点符号")
-
- print("所有模型加载完成")
- return models
\ No newline at end of file
diff --git a/src/functor/readme.md b/src/functor/readme.md
new file mode 100644
index 0000000..7f918f1
--- /dev/null
+++ b/src/functor/readme.md
@@ -0,0 +1,122 @@
+# 对于Functor的解释
+
+## Functor 文件夹作用
+
+Functor文件夹用于存放所有功能性的类，包括VAD、PUNC、ASR、SPK等。
+
+## Functor 类的定义
+
+所有类应继承于**基类**`BaseFunctor`。
+
+为了方便使用，我们对于**基类**的定义如下：
+
+1. 函数内部使用的变量以单下划线开头，基类中包含：
+
+ * _model: Dict 存放模型相关的配置和实例
+ * _input_queue: Queue 监听的输入消息队列
+ * _thread: Threading.Thread 运行的线程实例
+ * _callback: List[Callable] 回调函数列表
+ * _is_running: bool 线程运行状态标志
+ * _stop_event: bool 停止事件标志
+ * _status_lock: threading.Lock 状态锁，用于线程同步
+
+2. 对于使用的模型，请从统一的 **模型管理类`ModelLoader`** 中获取，由模型管理类统一进行加载、缓存和释放，`_model`存放类型为`dict`。
+
+3. 基类定义的核心方法：
+
+ * `add_callback(callback: Callable)`: 添加结果处理的回调函数
+ * `set_model(model: dict)`: 设置模型配置和实例
+ * `set_input_queue(queue: Queue)`: 设置输入数据队列
+ * `run()`: 启动处理线程（抽象方法）
+ * `stop()`: 停止处理线程（抽象方法）
+ * `_run()`: 线程运行的具体逻辑（抽象方法）
+ * `_pre_check()`: 运行前的预检查（抽象方法）
+
+## 派生类实现要求
+
+1. 必须实现的抽象方法：
+ * `_pre_check()`:
+ - 检查必要的配置是否完整（如模型、队列等）
+ - 检查运行环境是否满足要求
+ - 返回检查结果
+
+ * `_run()`:
+ - 实现具体的数据处理逻辑
+ - 从 _input_queue 获取输入数据
+ - 使用 _model 进行处理
+ - 通过 _callback 返回处理结果
+
+ * `run()`:
+ - 调用 _pre_check() 进行预检查
+ - 创建并启动处理线程
+ - 设置相关状态标志
+
+ * `stop()`:
+ - 安全停止处理线程
+ - 清理资源
+ - 重置状态标志
+
+2. 建议实现的方法：
+ * `__str__`: 返回当前实例的状态信息
+ * 错误处理方法：处理运行过程中的异常情况
+
+## 使用示例
+
+```python
+class MyFunctor(BaseFunctor):
+ def _pre_check(self):
+ if not self._model or not self._input_queue:
+ return False
+ return True
+
+ def _run(self):
+ while not self._stop_event:
+ try:
+ data = self._input_queue.get(timeout=1.0)
+ result = self._model['my_model'].process(data)
+ for callback in self._callback:
+ callback(result)
+ except Queue.Empty:
+ continue
+ except Exception as e:
+ logger.error(f"处理错误: {e}")
+
+ def run(self):
+ if not self._pre_check():
+ raise RuntimeError("预检查失败")
+
+ with self._status_lock:
+ if self._is_running:
+ return
+ self._is_running = True
+ self._stop_event = False
+ self._thread = threading.Thread(target=self._run)
+ self._thread.start()
+
+ def stop(self):
+ with self._status_lock:
+ if not self._is_running:
+ return
+ self._stop_event = True
+ if self._thread:
+ self._thread.join()
+ self._is_running = False
+```
+
+## 注意事项
+
+1. 线程安全：
+ * 使用 _status_lock 保护状态变更
+ * 注意共享资源的访问控制
+
+2. 错误处理：
+ * 在 _run() 中妥善处理异常
+ * 提供详细的错误日志
+
+3. 资源管理：
+ * 确保在 stop() 中正确清理资源
+ * 避免资源泄露
+
+4. 回调函数：
+ * 回调函数应该是非阻塞的
+ * 处理回调函数抛出的异常
\ No newline at end of file
diff --git a/src/functor/resultbinder_functor.py b/src/functor/resultbinder_functor.py
new file mode 100644
index 0000000..a906a12
--- /dev/null
+++ b/src/functor/resultbinder_functor.py
@@ -0,0 +1,165 @@
+"""
+ResultBinderFunctor
+负责聚合结果，将所有input_queue中的结果进行聚合，并进行callback
+"""
+
+from src.functor.base import BaseFunctor
+from src.models import AudioBinary_Config, VAD_Functor_result
+from typing import Callable, List, Dict, Any
+from queue import Queue, Empty
+import threading
+import time
+# 日志
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+class ResultBinderFunctor(BaseFunctor):
+ """
+ ResultBinderFunctor
+ 负责聚合结果，将所有input_queue中的结果进行聚合，并进行callback
+ """
+
+ def __init__(self) -> None:
+ super().__init__()
+ # 资源与配置
+ self._callback: List[Callable] = [] # 回调函数
+ self._input_queue: Dict[str, Queue] = {} # 输入队列
+ self._audio_config: AudioBinary_Config = None # 音频配置
+
+ def reset_cache(self) -> None:
+ """
+ 重置缓存, 用于任务完成后清理缓存数据, 准备下次任务
+ """
+ pass
+
+ def add_input_queue(self,
+ name: str,
+ queue: Queue,
+ ) -> None:
+ """
+ 设置监听的输入消息队列
+ """
+ self._input_queue[name] = queue
+
+ def set_model(self, model: dict) -> None:
+ """
+ 设置推理模型
+ resultbinder_functor 不应设置模型
+ """
+ logger.warning("ResultBinderFunctor不应设置模型")
+ self._model = model
+
+ def set_audio_config(self, audio_config: AudioBinary_Config) -> None:
+ """
+ 设置音频配置
+ resultbinder_functor 不应设置音频配置
+ """
+ logger.warning("ResultBinderFunctor不应设置音频配置")
+ self._audio_config = audio_config
+
+ def add_callback(self, callback: Callable) -> None:
+ """
+ 向自身的_callback: List[Callable]回调函数列表中添加回调函数
+ """
+ if not isinstance(self._callback, list):
+ self._callback = []
+ self._callback.append(callback)
+
+ def _do_callback(self, result: List[str]) -> None:
+ """
+ 回调函数
+ """
+ for callback in self._callback:
+ callback(result)
+
+ def _process(self, data: Dict[str, Any]) -> None:
+ """
+ 处理数据
+ {
+ "is_final": false,
+ "mode": "2pass-offline",
+ "text": "，等一下我回一下，ok你看这里就有了",
+ "wav_name": "h5",
+ "speaker_id":
+ }
+ """
+ logger.debug("ResultBinderFunctor处理数据: %s", data)
+ # 将data中的result进行聚合
+ # 此步暂时无意义，预留
+ results = {
+ "is_final": False,
+ "mode": "2pass-offline",
+ "text": data["asr"],
+ "wav_name": "h5",
+ "speaker_id": data["spk"]["speaker_id"]
+ }
+
+ # for name, result in data.items():
+ # results[name] = result
+ self._do_callback(results)
+
+ def _run(self) -> None:
+ """
+ 线程运行逻辑
+ """
+ with self._status_lock:
+ self._is_running = True
+ self._stop_event = False
+ # 运行逻辑
+ while self._is_running:
+ try:
+ # 若有队列为空，则等待0.1s
+ for name, queue in self._input_queue.items():
+ if queue.empty():
+ time.sleep(0.1)
+ raise Empty
+ data = {}
+ for name, queue in self._input_queue.items():
+ data[name] = queue.get(True, timeout=1)
+ queue.task_done()
+ self._process(data)
+ # 当队列为空时, 检测是否进入停止事件。
+ except Empty:
+ if self._stop_event:
+ break
+ continue
+ # 其他异常
+ except Exception as e:
+ logger.error("SpkFunctor运行时发生错误: %s", e)
+ raise e
+
+ def run(self) -> threading.Thread:
+ """
+ 启动线程
+ Returns:
+ threading.Thread: 返回已运行线程实例
+ """
+ self._pre_check()
+ self._thread = threading.Thread(target=self._run, daemon=True)
+ self._thread.start()
+ return self._thread
+
+ def _pre_check(self) -> bool:
+ """
+ 预检查
+ """
+ if self._model is None:
+ raise ValueError("模型未设置")
+ if self._input_queue is None:
+ raise ValueError("输入队列未设置")
+ if self._callback is None:
+ raise ValueError("回调函数未设置")
+ return True
+
+ def stop(self) -> bool:
+ """
+ 停止线程
+ """
+ with self._status_lock:
+ self._stop_event = True
+ self._thread.join()
+ with self._status_lock:
+ self._is_running = False
+ return not self._thread.is_alive()
diff --git a/src/functor/spk_functor.py b/src/functor/spk_functor.py
new file mode 100644
index 0000000..dbc25d5
--- /dev/null
+++ b/src/functor/spk_functor.py
@@ -0,0 +1,367 @@
+"""
+SpkFunctor
+负责对音频片段进行SPK说话人识别处理, 以SPK_Result进行callback
+"""
+
+from src.functor.base import BaseFunctor
+from src.models import AudioBinary_Config, VAD_Functor_result, SpeakerCreate
+from typing import Callable, List, Dict
+from queue import Queue, Empty
+import json
+import torch
+import threading
+import numpy
+import os
+import requests
+
+
+# 日志
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+from modelscope.pipelines import pipeline
+# sv_pipeline = pipeline(
+# task='speaker-verification',
+# model='iic/speech_campplus_sv_zh-cn_16k-common',
+# model_revision='v1.0.0'
+# )
+# speaker1_a_wav = 'https://modelscope.cn/api/v1/models/damo/speech_campplus_sv_zh-cn_16k-common/repo?Revision=master&FilePath=examples/speaker1_a_cn_16k.wav'
+# speaker1_b_wav = 'https://modelscope.cn/api/v1/models/damo/speech_campplus_sv_zh-cn_16k-common/repo?Revision=master&FilePath=examples/speaker1_b_cn_16k.wav'
+# speaker2_a_wav = 'https://modelscope.cn/api/v1/models/damo/speech_campplus_sv_zh-cn_16k-common/repo?Revision=master&FilePath=examples/speaker2_a_cn_16k.wav'
+# # 相同说话人语音
+# result = sv_pipeline([speaker1_a_wav, speaker1_b_wav])
+# print(result)
+# # 不同说话人语音
+# result = sv_pipeline([speaker1_a_wav, speaker2_a_wav])
+# print(result)
+# # 可以自定义得分阈值来进行识别，阈值越高，判定为同一人的条件越严格
+# result = sv_pipeline([speaker1_a_wav, speaker1_a_wav], thr=0.6)
+# print(result)
+# # 可以传入output_emb参数，输出结果中就会包含提取到的说话人embedding
+# result = sv_pipeline([speaker1_a_wav, speaker2_a_wav], output_emb=True)
+# print(result['embs'], result['outputs'])
+# # 可以传入save_dir参数，提取到的说话人embedding会存储在save_dir目录中
+# result = sv_pipeline([speaker1_a_wav, speaker2_a_wav], save_dir='savePath/')
+
+class SPKFunctor(BaseFunctor):
+ """
+ SPKFunctor
+ 负责对音频片段进行SPK说话人识别处理, 以SPK_Result进行callback
+ 需要配置好 _model, _callback, _input_queue, _audio_config
+ 否则无法run()启动线程
+
+ 运行中, 使用reset_cache()重置缓存, 准备下次任务
+
+ 使用stop()停止线程, 但需要等待input_queue为空
+ """
+ class speaker_verify:
+ """
+ 说话人验证
+ """
+ def __init__(self, *args, **kwargs):
+ self._spk_data: List[SpeakerCreate] = []
+
+ def add_speaker(self, speaker: SpeakerCreate) -> None:
+ self._spk_data.append(speaker)
+ # logger.debug("添加说话人: %s", speaker)
+
+ def verify(self, emb: numpy.ndarray) -> Dict:
+ # 将输入的numpy embedding转换为tensor
+ input_emb_tensor = torch.from_numpy(emb).unsqueeze(0)
+
+ results = []
+ for speaker in self._spk_data:
+ if not speaker.speaker_embs:
+ continue
+ # 将列表中存储的embedding转换为tensor
+ speaker_emb_tensor = torch.tensor(speaker.speaker_embs).unsqueeze(0)
+
+ # 计算余弦相似度
+ score = torch.nn.functional.cosine_similarity(input_emb_tensor, speaker_emb_tensor).item()
+
+ result = {
+ "score": score,
+ "speaker_id": speaker.speaker_id,
+ "speaker_name": speaker.speaker_name,
+ "speaker_description": speaker.speaker_description
+ }
+ results.append(result)
+
+ if not results:
+ return {
+ "speaker_id": "unknown",
+ "speaker_name": "Unknown",
+ "speaker_description": "No registered speakers to verify against.",
+ "score": 0.0,
+ "results": []
+ }
+
+ results.sort(key=lambda x: x['score'], reverse=True)
+ best_match = results[0]
+ return {
+ "speaker_id": best_match['speaker_id'],
+ "speaker_name": best_match['speaker_name'],
+ "speaker_description": best_match['speaker_description'],
+ "score": best_match['score'],
+ "results": results
+ }
+
+ def __init__(self, sv_pipeline: pipeline) -> None:
+ super().__init__()
+ # 资源与配置
+ self._spk_verify = self.speaker_verify()
+ self._sv_pipeline = sv_pipeline
+ self._model: dict = {} # 模型
+ self._callback: List[Callable] = [] # 回调函数
+ self._input_queue: Queue = None # 输入队列
+ self._audio_config: AudioBinary_Config = None # 音频配置
+
+ # logger.debug("加载本地说话人数据")
+ # self.load_spk_data_local()
+ # import inspect
+ # func_name = inspect.currentframe().f_code.co_name
+ # logger.info(f"{func_name} 加载数据库说话人数据")
+ self._speakers_url = os.getenv("SPEAKERS_URL", None)
+
+ def bake(self, *args, **kwargs) -> None:
+ """
+ 烘焙
+ """
+ if self._speakers_url is None:
+ logger.error("未提供说话人数据库API的URL")
+ return
+ self.load_spk_database_api(
+ url=kwargs.get("speakers_url", self._speakers_url)
+ )
+
+ def load_spk_database_api(
+ self,
+ url: str = None,
+ ) -> None:
+ """
+ 加载api远程数据库说话人数据
+ """
+ # 网络请求后端数据库拿到所有的说话人数据
+
+ try:
+ url = url if url != None else self._speakers_url
+ if not url:
+ logger.error("未提供说话人数据库API的URL")
+ return
+ logger.debug("加载API说话人数据库: url: %s", url)
+ response = requests.get(url)
+ response.raise_for_status()
+ data = response.json()
+ items = data.get("items", [])
+ logger.debug("加载API说话人个数: %s", len(items))
+ for spk in items:
+ logger.debug("加载API说话人数据: %s %s", spk.get('speaker_name'), spk.get('speaker_id'))
+ # 兼容API返回的字段与本地字段
+ spk_dict = {
+ "speaker_name": spk.get("speaker_name", ""),
+ "speaker_id": spk.get("speaker_id", ""),
+ "speaker_description": spk.get("speaker_description", ""),
+ "avatar": spk.get("avatar", ""),
+ "wav_path": "", # API未提供本地wav路径
+ "speaker_embs": spk.get("audio_feature_vector", []),
+ }
+ # 如果API没有embs但有音频样本，可以尝试提取
+ if (not spk_dict["speaker_embs"] or spk_dict["speaker_embs"] is None) and spk.get("audio_sample"):
+ try:
+ import base64
+ import io
+ import numpy as np
+ from pydub import AudioSegment
+
+ audio_bytes = base64.b64decode(spk["audio_sample"])
+
+ # 使用 pydub 处理音频，它能兼容 wav, mp3 等多种格式 (需要 ffmpeg)
+ audio_segment = AudioSegment.from_file(io.BytesIO(audio_bytes))
+
+ # 将音频转换为模型期望的格式，通常是 16k 采样率、单声道
+ audio_segment = audio_segment.set_frame_rate(16000)
+ audio_segment = audio_segment.set_channels(1)
+
+ # 转换为 float32 的 numpy 数组并归一化
+ wav_data = np.array(audio_segment.get_array_of_samples()).astype(np.float32)
+ wav_data /= 32768.0 # 16-bit signed audio is in range [-32768, 32767]
+
+ spk_dict["speaker_embs"] = self._sv_pipeline([wav_data], output_emb=True)['embs'][0]
+ logger.debug("API音频样本转换为embs: %s", spk_dict["speaker_name"])
+ except Exception:
+ logger.error("API音频样本 '%s' 转换为embs失败", spk_dict.get("speaker_name", "Unknown"), exc_info=True)
+ spk_dict["speaker_embs"] = []
+
+ # 转为numpy后加入
+ try:
+ import numpy
+ spk_dict["speaker_embs"] = numpy.array(spk_dict["speaker_embs"])
+ self._spk_verify.add_speaker(SpeakerCreate(**spk_dict))
+ except Exception as e:
+ logger.error("添加API说话人到本地失败: %s", e)
+ except Exception as e:
+ logger.error("请求说话人数据库API失败: %s", e)
+
+ def load_spk_data_local(
+ self,
+ spk_data_path: str = 'data/speakers.json',
+ ) -> None:
+ """
+ 加载本地说话人数据
+ """
+ with open(spk_data_path, 'r') as f:
+ spk_data = json.load(f)
+ for i, spk in enumerate(spk_data):
+ logger.debug("加载本地说话人数据: %s %s", spk['speaker_name'], spk['speaker_id'])
+ if spk['speaker_embs'] == "" and spk['wav_path'] != "":
+ logger.debug("尝试转换本地wav为embs: %s", spk['wav_path'])
+ try:
+ # 读取数据为numpy数组
+ import soundfile as sf
+ import numpy as np
+ wav_data, sr = sf.read(spk['wav_path'], dtype='int16')
+ # 确保是单通道
+ if wav_data.ndim > 1:
+ wav_data = wav_data[:, 0]
+ # 转为numpy数组后送入pipeline
+ spk['speaker_embs'] = self._sv_pipeline([wav_data], output_emb=True)['embs'][0]
+ logger.debug("转换本地wav为embs: length=%s type=%s", len(spk['speaker_embs']), type(spk['speaker_embs']))
+ except Exception as e:
+ logger.error("转换本地wav为embs失败: %s", e)
+ else:
+ pass
+ # logger.debug("加载本地说话人数据: %s %s", spk['speaker_name'], spk['speaker_id'])
+ # 将spk的speaker_embs转换为numpy
+ spk['speaker_embs'] = numpy.array(spk['speaker_embs'])
+ self._spk_verify.add_speaker(SpeakerCreate(**spk))
+ spk['speaker_embs'] = spk['speaker_embs'].tolist()
+ spk_data[i] = spk
+ # 保存更新后的数据
+
+ try:
+ with open(spk_data_path, 'w') as f:
+ json.dump(spk_data, f, indent=4)
+ except Exception as e:
+ logger.error("保存更新后的数据失败: %s", e)
+
+ def reset_cache(self) -> None:
+ """
+ 重置缓存, 用于任务完成后清理缓存数据, 准备下次任务
+ """
+ pass
+
+ def set_speakers_url(self, url: str) -> None:
+ """
+ 设置说话人数据库API的URL
+ """
+ self._speakers_url = url
+
+ def set_input_queue(self, queue: Queue) -> None:
+ """
+ 设置监听的输入消息队列
+ """
+ self._input_queue = queue
+
+ def set_model(self, model: dict) -> None:
+ """
+ 设置推理模型
+ """
+ self._model = model
+
+ def set_audio_config(self, audio_config: AudioBinary_Config) -> None:
+ """
+ 设置音频配置
+ """
+ self._audio_config = audio_config
+ logger.debug("SpkFunctor设置音频配置: %s", self._audio_config)
+
+ def add_callback(self, callback: Callable) -> None:
+ """
+ 向自身的_callback: List[Callable]回调函数列表中添加回调函数
+ """
+ if not isinstance(self._callback, list):
+ self._callback = []
+ self._callback.append(callback)
+
+ def _do_callback(self, result: List[str]) -> None:
+ """
+ 回调函数
+ """
+ for callback in self._callback:
+ callback(result)
+
+ def _process(self, data: VAD_Functor_result) -> None:
+ """
+ 处理数据
+ """
+ binary_data = data.audiobinary_data.binary_data
+ # result = self._model["spk"].generate(
+ # input=binary_data,
+ # chunk_size=self._audio_config.chunk_size,
+ # )
+ sv_result = self._sv_pipeline([binary_data], output_emb=True)
+ embs = sv_result['embs'][0]
+
+ result = self._spk_verify.verify(embs)
+
+ self._do_callback(result)
+
+ def _run(self) -> None:
+ """
+ 线程运行逻辑
+ """
+ with self._status_lock:
+ self._is_running = True
+ self._stop_event = False
+ # 运行逻辑
+ while self._is_running:
+ try:
+ data = self._input_queue.get(True, timeout=1)
+ if data is None:
+ break
+ logger.debug("[SPKFunctor]获取到的数据length: %s", len(data))
+ self._process(data)
+ self._input_queue.task_done()
+ # 当队列为空时, 间隔1s检测是否进入停止事件。
+ except Empty:
+ if self._stop_event:
+ break
+ continue
+ # 其他异常
+ except Exception as e:
+ logger.error("SpkFunctor运行时发生错误: %s", e)
+ raise e
+
+ def run(self) -> threading.Thread:
+ """
+ 启动线程
+ Returns:
+ threading.Thread: 返回已运行线程实例
+ """
+ self._pre_check()
+ self._thread = threading.Thread(target=self._run, daemon=True)
+ self._thread.start()
+ return self._thread
+
+ def _pre_check(self) -> bool:
+ """
+ 预检查
+ """
+ if self._model is None:
+ raise ValueError("模型未设置")
+ if self._input_queue is None:
+ raise ValueError("输入队列未设置")
+ if self._callback is None:
+ raise ValueError("回调函数未设置")
+ return True
+
+ def stop(self) -> bool:
+ """
+ 停止线程
+ """
+ with self._status_lock:
+ self._stop_event = True
+ self._thread.join()
+ with self._status_lock:
+ self._is_running = False
+ return not self._thread.is_alive()
diff --git a/src/functor/vad_functor.py b/src/functor/vad_functor.py
index cc87040..2c9d590 100644
--- a/src/functor/vad_functor.py
+++ b/src/functor/vad_functor.py
@@ -1,60 +1,276 @@
-from funasr import AutoModel
-from typing import List, Dict, Any
-from src.models import VADResponse
-from src.models import AudioBinary_Config
-from src.functor.audiochunk import AudioChunk
-from src.models import AudioBinary_Chunk
-from typing import Callable
+"""
+VADFunctor
+负责对音频片段进行VAD处理, 以VAD_Result进行callback
+"""
-class VAD:
+import threading
+from queue import Empty, Queue
+from typing import List, Any, Callable
+import numpy
+import time
+from datetime import datetime
+from src.models import (
+ VAD_Functor_result,
+ AudioBinary_Config,
+ AudioBinary_data_list,
+)
+from src.functor.base import BaseFunctor
- def __init__(self,
- VAD_model = None,
- audio_config : AudioBinary_Config = None,
- callback: Callable = None,
- ):
- # vad model
- self.VAD_model = VAD_model
- if self.VAD_model is None:
- self.VAD_model = AutoModel(model="fsmn-vad", model_revision="v2.0.4", disable_update=True)
- # audio config
- self.audio_config = audio_config
- # vad result
- self.vad_result = VADResponse(time_chunk_index_callback=callback)
- # audio binary poll
- self.audio_chunk = AudioChunk(
- audio_config=self.audio_config
- )
- self.cache = {}
-
- def push_binary_data(self,
- binary_data: bytes,
- ):
- # 压入二进制数据
- self.audio_chunk.add_chunk(binary_data)
- # 处理音频块
- res = self.VAD_model.generate(input=binary_data,
- cache=self.cache,
- chunk_size=self.audio_config.chunk_size,
- is_final=False)
- # print("VAD generate", res)
- if len(res[0]["value"]):
- self.vad_result += VADResponse.from_raw(res)
-
- def set_callback(self,
- callback: Callable,
- ):
- self.vad_result.time_chunk_index_callback = callback
+# 日志
+from src.utils.logger import get_module_logger
- def process_vad_result(self, callback: Callable = None):
- # 处理VAD结果
- callback = callback if callback is not None else self.vad_result.time_chunk_index_callback
- self.vad_result.process_time_chunk(
- lambda x : callback(
- AudioBinary_Chunk(
- start_time=x["start_time"],
- end_time=x["end_time"],
- chunk=self.audio_chunk.get_chunk(x["start_time"], x["end_time"])
- )
+logger = get_module_logger(__name__)
+
+
+class VADFunctor(BaseFunctor):
+ """
+ VADFunctor
+ 负责对音频片段进行VAD处理, 以VAD_Result进行callback
+ 需要配置好 _model, _callback, _input_queue, _audio_config, _audio_binary_data_list
+ 否则无法run()启动线程
+
+ 运行中, 使用reset_cache()重置缓存, 准备下次任务
+
+ 使用stop()停止线程, 但需要等待input_queue为空
+ """
+
+ def __init__(self) -> None:
+ super().__init__()
+ # 资源与配置
+ self._model: dict = {} # 模型
+ self._callback: List[Callable] = [] # 回调函数
+ self._input_queue: Queue = None # 输入队列
+ self._audio_config: AudioBinary_Config = None # 音频配置
+ self._audio_binary_data_list: AudioBinary_data_list = None # 音频数据列表
+
+ # flag
+ # 此处用到两个锁，但都是为了截断_run线程，考虑后续优化
+ self._is_running: bool = False
+ self._stop_event: bool = False
+
+ # 线程资源
+ self._thread: threading.Thread = None
+
+ # 状态锁
+ self._status_lock: threading.Lock = threading.Lock()
+
+ # 缓存
+ self._audio_cache: numpy.ndarray = None
+ self._audio_cache_preindex: int = 0
+ self._model_cache: dict = {}
+ self._cache_result_list = []
+ self._audiobinary_cache = None
+
+ def reset_cache(self) -> None:
+ """
+ 重置缓存, 用于任务完成后清理缓存数据, 准备下次任务
+ """
+ self._audio_cache = None
+ self._audio_cache_preindex = 0
+ self._model_cache = {}
+ self._cache_result_list = []
+ self._audiobinary_cache = None
+
+ def set_input_queue(self, queue: Queue) -> None:
+ """
+ 设置监听的输入消息队列
+ """
+ self._input_queue = queue
+
+ def set_model(self, model: dict) -> None:
+ """
+ 设置推理模型
+ """
+ self._model = model
+
+ def set_audio_config(self, audio_config: AudioBinary_Config) -> None:
+ """
+ 设置音频配置
+ """
+ self._audio_config = audio_config
+ logger.debug("VADFunctor设置音频配置: %s", self._audio_config)
+
+ def set_audio_binary_data_list(
+ self, audio_binary_data_list: AudioBinary_data_list
+ ) -> None:
+ """
+ 设置音频数据列表, 为Class AudioBinary_data_list类型
+ AudioBinary_data_list包含binary_data_list, 为list[_AudioBinary_data]类型
+ _AudioBinary_data包含binary_data, 为bytes/numpy.ndarray类型
+ """
+ self._audio_binary_data_list = audio_binary_data_list
+
+ def add_callback(self, callback: Callable) -> None:
+ """
+ 向自身的_callback: List[Callable]回调函数列表中添加回调函数
+ """
+ if not isinstance(self._callback, list):
+ self._callback = []
+ self._callback.append(callback)
+
+ def _do_callback(self, result: List[List[int]]) -> None:
+ """
+ 回调函数
+ VADFunctor包装结果, 存储到AudioBinary中, 并向队列中添加AudioBinary_Slice
+
+ 输入:
+ result: List[[start,end]] 处理所得VAD端点
+ 其中若start==-1, 则表示前无端点, 若end==-1, 则表示后无端点
+ 当处理得到一个完成片段时, 存入AudioBinary中, 并向队列中添加AudioBinary_Slice
+ 输出:
+ None
+ """
+ # 持久化缓存结果队列
+ for pair in result:
+ [start, end] = pair
+ # 若无前端点, 则向缓存队列中合并
+ if start == -1:
+ self._cache_result_list[-1][1] = end
+ else:
+ self._cache_result_list.append(pair)
+ logger.debug(f"VADFunctor结果: {self._cache_result_list}")
+ while len(self._cache_result_list) > 0 and self._cache_result_list[0][1] != -1:
+ logger.debug(f"VADFunctor结果: {self._cache_result_list}")
+ logger.debug(f"VADFunctor list[0][1]: {self._cache_result_list[0][1]}")
+ # 创建VAD片段
+ # 计算开始帧
+ start_frame = self._audio_config.ms2frame(self._cache_result_list[0][0])
+ start_frame -= self._audio_cache_preindex
+ # 计算结束帧
+ end_frame = self._audio_config.ms2frame(self._cache_result_list[0][1])
+ end_frame -= self._audio_cache_preindex
+ # 计算开始时间
+ timestamp = time.time()
+ format_time = datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S')
+ logger.debug(f"{format_time}创建VAD片段: {start_frame} - {end_frame}")
+ vad_result = VAD_Functor_result.create_from_push_data(
+ audiobinary_data_list=self._audio_binary_data_list,
+ data=self._audiobinary_cache[start_frame:end_frame],
+ start_time=self._cache_result_list[0][0],
+ end_time=self._cache_result_list[0][1],
)
- )
\ No newline at end of file
+ logger.debug(f"{format_time}创建VAD片段成功: {vad_result}")
+ self._audio_cache_preindex += end_frame
+ self._audiobinary_cache = self._audiobinary_cache[end_frame:]
+ for callback in self._callback:
+ callback(vad_result)
+ self._cache_result_list.pop(0)
+
+ def _predeal_data(self, data: Any) -> None:
+ """
+ 预处理数据, 将数据缓存到_audio_cache和_audiobinary_cache中
+ """
+ if self._audio_cache is None:
+ self._audio_cache = data
+ else:
+ # 拼接音频数据
+ if isinstance(self._audio_cache, numpy.ndarray):
+ self._audio_cache = numpy.concatenate((self._audio_cache, data))
+ elif isinstance(self._audio_cache, list):
+ self._audio_cache.append(data)
+
+ if self._audiobinary_cache is None:
+ self._audiobinary_cache = data
+ else:
+ # 拼接音频数据
+ if isinstance(self._audiobinary_cache, numpy.ndarray):
+ self._audiobinary_cache = numpy.concatenate(
+ (self._audiobinary_cache, data)
+ )
+ elif isinstance(self._audiobinary_cache, list):
+ self._audiobinary_cache.append(data)
+
+ def _process(self, data: Any):
+ """
+ 处理数据
+ 使用model进行生成, 并使用_do_callback进行回调
+ """
+ if data is None:
+ result = self._model["vad"].generate(
+ input=self._audio_cache,
+ cache=self._model_cache,
+ chunk_size=self._audio_config.chunk_size,
+ is_final=True,
+ )
+ self._do_callback(result[0]["value"])
+ return
+
+ self._predeal_data(data)
+ if len(self._audio_cache) >= self._audio_config.chunk_stride:
+ result = self._model["vad"].generate(
+ input=self._audio_cache,
+ cache=self._model_cache,
+ chunk_size=self._audio_config.chunk_size,
+ max_end_silence_time = 300,
+ is_final=False,
+ )
+ if len(result[0]["value"]) > 0:
+ self._do_callback(result[0]["value"])
+ logger.debug(f"VADFunctor结果: {result[0]['value']}")
+ self._audio_cache = None
+
+ def _run(self):
+ """
+ 线程运行逻辑
+ 监听输入队列, 当有数据时, 处理数据
+ 当输入队列为空时, 间隔1s检测是否进入停止事件。
+ """
+ # 刷新运行状态
+ with self._status_lock:
+ self._is_running = True
+ self._stop_event = False
+ # 运行逻辑
+ while self._is_running:
+ try:
+ data = self._input_queue.get(True, timeout=1)
+ # logger.debug("[VADFunctor]获取到的数据length: %s", len(data))
+ self._process(data)
+ self._input_queue.task_done()
+ if data is None:
+ break
+ # 当队列为空时, 间隔1s检测是否进入停止事件。
+ except Empty:
+ if self._stop_event:
+ break
+ continue
+ # 其他异常
+ except Exception as e:
+ logger.error("VADFunctor运行时发生错误: %s", e)
+ raise e
+
+ def run(self):
+ """
+ 启动 _run 线程, 并返回线程对象
+ """
+ self._pre_check()
+ self._thread = threading.Thread(target=self._run, daemon=True)
+ self._thread.start()
+ return self._thread
+
+ def _pre_check(self) -> bool:
+ """
+ 检测硬性资源是否都已设置
+ """
+ if self._model is None:
+ raise ValueError("模型未设置")
+ if self._audio_config is None:
+ raise ValueError("音频配置未设置")
+ if self._audio_binary_data_list is None:
+ raise ValueError("音频数据列表未设置")
+ if self._input_queue is None:
+ raise ValueError("输入队列未设置")
+ if self._callback is None:
+ raise ValueError("回调函数未设置")
+ return True
+
+ def stop(self):
+ """
+ 停止线程
+ 通过设置_stop_event为True, 来在input_queue.get()循环为空时退出
+ """
+ with self._status_lock:
+ self._stop_event = True
+ self._thread.join()
+ with self._status_lock:
+ self._is_running = False
+ return not self._thread.is_alive()
\ No newline at end of file
diff --git a/src/logic_trager.py b/src/logic_trager.py
index 18a3210..bf92bdb 100644
--- a/src/logic_trager.py
+++ b/src/logic_trager.py
@@ -6,34 +6,36 @@
from src.utils.logger import get_module_logger
from typing import Any, Dict, Type, Callable
+
# 配置日志
logger = get_module_logger(__name__, level="INFO")
+
class AutoAfterMeta(type):
"""
自动调用__after__函数的元类
实现单例模式
"""
-
+
_instances: Dict[Type, Any] = {} # 存储单例实例
-
+
def __new__(cls, name, bases, attrs):
# 遍历所有属性
for attr_name, attr_value in attrs.items():
# 如果是函数且不是以_开头
- if callable(attr_value) and not attr_name.startswith('__'):
+ if callable(attr_value) and not attr_name.startswith("__"):
# 获取原函数
original_func = attr_value
-
+
# 创建包装函数
def make_wrapper(func):
def wrapper(self, *args, **kwargs):
# 执行原函数
result = func(self, *args, **kwargs)
-
+
# 构建_after_函数名
after_func_name = f"__after__{func.__name__}"
-
+
# 检查是否存在对应的_after_函数
if hasattr(self, after_func_name):
after_func = getattr(self, after_func_name)
@@ -43,17 +45,18 @@ class AutoAfterMeta(type):
after_func()
except Exception as e:
logger.error(f"调用{after_func_name}时出错: {e}")
-
+
return result
+
return wrapper
-
+
# 替换原函数
attrs[attr_name] = make_wrapper(original_func)
-
+
# 创建类
new_class = super().__new__(cls, name, bases, attrs)
return new_class
-
+
def __call__(cls, *args, **kwargs):
"""
重写__call__方法实现单例模式
@@ -65,9 +68,10 @@ class AutoAfterMeta(type):
logger.info(f"创建{cls.__name__}的新实例")
else:
logger.debug(f"返回{cls.__name__}的现有实例")
-
+
return cls._instances[cls]
+
"""
整体识别的处理逻辑：
1.压入二进制音频信息
@@ -88,10 +92,12 @@ from src.models import AudioBinary_Config
from src.models import AudioBinary_Chunk
from typing import List
+
class LogicTrager(metaclass=AutoAfterMeta):
"""逻辑触发器类"""
-
- def __init__(self,
+
+ def __init__(
+ self,
audio_chunk_max_size: int = 1024 * 1024 * 10,
audio_config: AudioBinary_Config = None,
result_callback: Callable = None,
@@ -99,46 +105,51 @@ class LogicTrager(metaclass=AutoAfterMeta):
):
"""初始化"""
# 存储音频块
- self._audio_chunk : List[AudioBinary_Chunk] = []
+ self._audio_chunk: List[AudioBinary_Chunk] = []
# 存储二进制数据
- self._audio_chunk_binary = b''
+ self._audio_chunk_binary = b""
self._audio_chunk_max_size = audio_chunk_max_size
# 音频参数
- self._audio_config = audio_config if audio_config is not None else AudioBinary_Config()
+ self._audio_config = (
+ audio_config if audio_config is not None else AudioBinary_Config()
+ )
# 结果队列
self._result_queue = []
# 聚合结果回调函数
self._aggregate_result_callback = result_callback
# 组件
- self._vad = VAD(VAD_model = models.get("vad"), audio_config = self._audio_config)
+ self._vad = VAD(VAD_model=models.get("vad"), audio_config=self._audio_config)
self._vad.set_callback(self.push_audio_chunk)
-
logger.info("初始化LogicTrager")
-
+
def push_binary_data(self, chunk: bytes) -> None:
"""
压入音频块至VAD模块
-
+
参数:
chunk: 音频数据块
"""
# print("LogicTrager push_binary_data", len(chunk))
self._vad.push_binary_data(chunk)
self.__after__push_binary_data()
-
+
def __after__push_binary_data(self) -> None:
"""
添加音频块后处理
"""
# print("LogicTrager __after__push_binary_data")
self._vad.process_vad_result()
-
+
def push_audio_chunk(self, chunk: AudioBinary_Chunk) -> None:
"""
音频处理
"""
- logger.info("LogicTrager push_audio_chunk [{}ms:{}ms] (len={})".format(chunk.start_time, chunk.end_time, len(chunk.chunk)))
+ logger.info(
+ "LogicTrager push_audio_chunk [{}ms:{}ms] (len={})".format(
+ chunk.start_time, chunk.end_time, len(chunk.chunk)
+ )
+ )
self._audio_chunk.append(chunk)
def __after__push_audio_chunk(self) -> None:
@@ -162,4 +173,4 @@ class LogicTrager(metaclass=AutoAfterMeta):
def __call__(self):
"""调用函数"""
- pass
\ No newline at end of file
+ pass
diff --git a/src/models/__init__.py b/src/models/__init__.py
index d64a40a..b460b17 100644
--- a/src/models/__init__.py
+++ b/src/models/__init__.py
@@ -1,3 +1,11 @@
-from .audiobinary import AudioBinary_Config, AudioBinary_Chunk
-from .vad import VADResponse
-__all__ = ["AudioBinary_Config", "AudioBinary_Chunk", "VADResponse"]
\ No newline at end of file
+from .audio import AudioBinary_Config, AudioBinary_data_list, _AudioBinary_data
+from .vad import VAD_Functor_result
+
+from .spk import SpeakerCreate, SpeakerResponse
+
+__all__ = [
+ "AudioBinary_Config",
+ "AudioBinary_data_list",
+ "_AudioBinary_data",
+ "VAD_Functor_result",
+]
diff --git a/src/models/audio.py b/src/models/audio.py
new file mode 100644
index 0000000..a8bbef6
--- /dev/null
+++ b/src/models/audio.py
@@ -0,0 +1,158 @@
+from pydantic import BaseModel, Field, validator
+from typing import List, Any
+import numpy
+
+from src.utils import get_module_logger
+
+logger = get_module_logger(__name__)
+
+binary_data_types = (bytes, numpy.ndarray)
+
+
+class AudioBinary_Config(BaseModel):
+ """二进制音频块配置信息"""
+
+ class Config:
+ arbitrary_types_allowed = True
+
+ audio_data: binary_data_types = Field(description="音频数据", default=None)
+ chunk_size: int = Field(description="块大小", default=100)
+ chunk_stride: int = Field(description="块步长", default=1600)
+ sample_rate: int = Field(description="采样率", default=16000)
+ sample_width: int = Field(description="采样位宽", default=2)
+ channels: int = Field(description="通道数", default=1)
+
+ # 从Dict中加载
+ @classmethod
+ def AudioBinary_Config_from_dict(cls, data: dict):
+ return cls(**data)
+
+ def ms2frame(self, ms: int) -> int:
+ """
+ 将毫秒转换为帧
+ """
+ return int(ms * self.sample_rate / 1000)
+
+ def frame2ms(self, frame: int) -> int:
+ """
+ 将帧转换为毫秒
+ """
+ return int(frame * 1000 / self.sample_rate)
+
+
+class _AudioBinary_data(BaseModel):
+ """音频数据"""
+
+ binary_data: binary_data_types = Field(description="音频二进制数据", default=None)
+
+ class Config:
+ arbitrary_types_allowed = True
+
+ @validator("binary_data")
+ def validate_binary_data(cls, v):
+ """
+ 验证音频数据
+ Args:
+ v: 音频数据
+ Returns:
+ binary_data_types: 音频数据
+ """
+ if not isinstance(v, (bytes, numpy.ndarray)):
+ logger.warning(
+ "[%s]binary_data不是bytes, numpy.ndarray类型, 而是%s类型, 请检查",
+ cls.__class__.__name__,
+ type(v),
+ )
+ return v
+
+ def __len__(self):
+ """
+ 获取音频数据长度
+ Returns:
+ int: 音频数据长度
+ """
+ return len(self.binary_data)
+
+ def __init__(self, binary_data: binary_data_types):
+ """
+ 初始化音频数据
+ Args:
+ binary_data: 音频数据
+ """
+ logger.debug(
+ "[%s]初始化音频数据, 数据类型为%s",
+ self.__class__.__name__,
+ type(binary_data),
+ )
+ super().__init__(binary_data=binary_data)
+
+ def __getitem__(self):
+ """
+ 当获取数据时, 直接返回binary_data
+ Returns:
+ binary_data_types: 音频数据
+ """
+ return self.binary_data
+
+
+class AudioBinary_data_list(BaseModel):
+ """音频数据列表"""
+
+ binary_data_list: List[_AudioBinary_data] = Field(
+ description="音频数据列表", default=[]
+ )
+
+ class Config:
+ arbitrary_types_allowed = True
+
+ def push_data(self, data: binary_data_types) -> int:
+ """
+ 添加音频数据
+ Args:
+ data: 音频数据
+ Returns:
+ int: 数据在binary_data_list中的索引
+ """
+ self.binary_data_list.append(_AudioBinary_data(binary_data=data))
+ return len(self.binary_data_list) - 1
+
+ def __getitem__(self, index: int):
+ """
+ 获取音频数据
+ Args:
+ index: 音频数据在binary_data_list中的索引
+ Returns:
+ _AudioBinary_data: 音频数据
+ """
+ return self.binary_data_list[index]
+
+ def __len__(self):
+ """
+ 获取音频数据列表长度
+ Returns:
+ int: 音频数据列表长度
+ """
+ return len(self.binary_data_list)
+
+
+# class AudioBinary_Slice(BaseModel):
+# """音频块切片"""
+# target_Binary: AudioBinary_data_list = Field(description="目标音频块", default=None)
+# start_index: int = Field(description="开始位置", default=0)
+# end_index: int = Field(description="结束位置", default=-1)
+
+# @validator('start_index')
+# def validate_start_index(cls, v):
+# if v < 0:
+# raise ValueError("start_index必须大于0")
+# return v
+
+# @validator('end_index')
+# def validate_end_index(cls, v):
+# if v < cls.start_index:
+# logger.debug("[%s]end_index小于start_index, 将end_index设置为start_index", cls.__class__.__name__)
+# v = cls.start_index
+# return v
+
+# def __call__(self):
+# return self.target_Binary(self.start_index, self.end_index)
diff --git a/src/models/audiobinary.py b/src/models/audiobinary.py
deleted file mode 100644
index 3ad0d26..0000000
--- a/src/models/audiobinary.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from pydantic import BaseModel, Field
-
-class AudioBinary_Config(BaseModel):
- """二进制音频块配置信息"""
- audio_data: bytes = Field(description="音频数据", default=None)
- chunk_size: int = Field(description="块大小", default=100)
- chunk_stride: int = Field(description="块步长", default=1600)
- sample_rate: int = Field(description="采样率", default=16000)
- sample_width: int = Field(description="采样位宽", default=2)
- channels: int = Field(description="通道数", default=1)
-
-class AudioBinary_Chunk(BaseModel):
- """音频块"""
- start_time: int = Field(description="开始时间", default=0)
- end_time: int = Field(description="结束时间", default=0)
- chunk: bytes = Field(description="音频块", default=None)
diff --git a/src/models/spk.py b/src/models/spk.py
new file mode 100644
index 0000000..1497454
--- /dev/null
+++ b/src/models/spk.py
@@ -0,0 +1,70 @@
+"""
+src/models/spk.py
+------------------------
+此模块定义与说话人（speakers）表对应的 Pydantic 模型，用于 API 数据验证和序列化。
+
+模型说明：
+- SpeakerBase：定义说话人的基础字段，包括姓名与描述。
+- SpeakerCreate：用于创建说话人时的数据验证，直接继承 SpeakerBase。
+- SpeakerUpdate：用于更新说话人信息时，所有字段均为可选。
+- SpeakerResponse：返回给客户端时使用，包含数据库生成的字段（如 speaker_id、created_at 等）。
+"""
+
+from datetime import datetime
+from typing import Optional, List
+from uuid import UUID
+from pydantic import BaseModel, Field
+from src.utils import get_module_logger
+
+logger = get_module_logger(__name__)
+
+# 基础模型，定义说话人的核心属性
+class SpeakerBase(BaseModel):
+ speaker_id: UUID = Field(
+ ...,
+ description="说话人唯一标识符"
+ )
+ speaker_name: str = Field(
+ ...,
+ max_length=255,
+ description="说话人姓名，必填，最多255字符"
+ )
+ speaker_description: Optional[str] = Field(
+ None,
+ description="说话人描述信息，可选"
+ )
+ speaker_embs: Optional[List[float]] = Field(
+ None,
+ description="说话人embedding，可选"
+ )
+
+# 用于创建说话人时的数据模型，直接继承 SpeakerBase
+class SpeakerCreate(SpeakerBase):
+ pass
+
+# 用于更新说话人时的数据模型，所有字段都是可选的
+class SpeakerUpdate(BaseModel):
+ speaker_name: Optional[str] = Field(
+ None,
+ max_length=255,
+ description="说话人姓名"
+ )
+ speaker_description: Optional[str] = Field(
+ None,
+ description="说话人描述信息"
+ )
+
+# 用于 API 返回的说话人响应模型，扩展基础模型以包含数据库生成的字段
+class SpeakerResponse(SpeakerBase):
+ speaker_id: UUID = Field(
+ ...,
+ description="说话人的唯一标识符（UUID）"
+ )
+ created_at: datetime = Field(
+ ...,
+ description="记录创建时间"
+ )
+ updated_at: datetime = Field(
+ ...,
+ description="最近更新时间"
+ )
\ No newline at end of file
diff --git a/src/models/vad.py b/src/models/vad.py
index 46f087a..30be717 100644
--- a/src/models/vad.py
+++ b/src/models/vad.py
@@ -1,143 +1,91 @@
from pydantic import BaseModel, Field, validator
-from typing import List, Optional, Callable
+from typing import List, Optional, Callable, Any
+from .audio import AudioBinary_data_list, _AudioBinary_data
-class VADSegment(BaseModel):
- """VAD片段"""
- start: int = Field(description="开始时间(ms)")
- end: int = Field(description="结束时间(ms)")
-class VADResult(BaseModel):
- """VAD结果"""
- key: str = Field(description="音频标识")
- value: List[VADSegment] = Field(description="VAD片段列表")
+class VAD_Functor_result(BaseModel):
+ """
+ VADFunctor结果
+ """
-class VADResponse(BaseModel):
- """VAD响应"""
- results: List[VADResult] = Field(description="VAD结果列表", default_factory=list)
- time_chunk: List[VADSegment] = Field(description="时间块", default_factory=list)
- time_chunk_index: int = Field(description="当前处理时间块索引", default=0)
- time_chunk_index_callback: Optional[Callable[[int], None]] = Field(
- description="时间块索引回调函数",
- default=None
+ audiobinary_data_list: AudioBinary_data_list = Field(description="音频数据列表")
+ audiobinary_index: int = Field(description="音频数据索引")
+ audiobinary_data: _AudioBinary_data = Field(
+ description="音频数据, 指向AudioBinary_data"
)
-
- @validator('time_chunk')
- def validate_time_chunk(cls, v):
- """验证时间块的有效性"""
- if not v:
- return v
-
- # 检查时间顺序
- for i in range(len(v) - 1):
- if v[i].end >= v[i + 1].start:
- raise ValueError(f"时间块{i}的结束时间({v[i].end})大于等于下一个时间块的开始时间({v[i + 1].start})")
+ start_time: int = Field(description="开始时间", is_required=True)
+ end_time: int = Field(description="结束时间", is_required=True)
+
+ @validator("audiobinary_data_list")
+ def validate_audiobinary_data_list(cls, v):
+ if not isinstance(v, AudioBinary_data_list):
+ raise ValueError("audiobinary_data_list必须是AudioBinary_data_list类型")
+ return v
+
+ @validator("audiobinary_index")
+ def validate_audiobinary_index(cls, v):
+ if not isinstance(v, int):
+ raise ValueError("audiobinary_index必须是int类型")
+ if v < 0:
+ raise ValueError("audiobinary_index必须大于0")
+ return v
+
+ @validator("audiobinary_data")
+ def validate_audiobinary_data(cls, v):
+ if not isinstance(v, _AudioBinary_data):
+ raise ValueError("audiobinary_data必须是AudioBinary_data类型")
+ return v
+
+ @validator("start_time")
+ def validate_start_time(cls, v):
+ if not isinstance(v, int):
+ raise ValueError("start_time必须是int类型")
+ if v < 0:
+ raise ValueError("start_time必须大于0")
+ return v
+
+ @validator("end_time")
+ def validate_end_time(cls, v, values):
+ if not isinstance(v, int):
+ raise ValueError("end_time必须是int类型")
+ if "start_time" in values and v <= values["start_time"]:
+ raise ValueError("end_time必须大于start_time")
return v
-
- # 回调未处理的时间块
- def process_time_chunk(self, callback: Callable[[int], None] = None) -> None:
- """处理时间块"""
- # print("Enter process_time_chunk", self.time_chunk_index, len(self.time_chunk))
- while self.time_chunk_index < len(self.time_chunk) - 1:
- index = self.time_chunk_index
- if self.time_chunk[index].end != -1:
- x = {
- "start_time": self.time_chunk[index].start,
- "end_time": self.time_chunk[index].end
- }
- if callback is not None:
- callback(x)
- elif self.time_chunk_index_callback is not None:
- self.time_chunk_index_callback(x)
- else:
- print("[Warning] No callback available")
- self.time_chunk_index += 1
-
- def __add__(self, other: 'VADResponse') -> 'VADResponse':
- """合并两个VADResponse"""
- if not self.results:
- self.results = other.results
- self.time_chunk = other.time_chunk
- return self
-
- # 检查是否可以合并最后一个结果
- last_result = self.results[-1]
- first_other = other.results[0]
-
- if last_result.value[-1].end == first_other.value[0].start:
- # 合并相邻的时间段
- last_result.value[-1].end = first_other.value[0].end
- first_other.value.pop(0)
-
- # 更新time_chunk
- self.time_chunk[-1].end = other.time_chunk[0].end
- other.time_chunk.pop(0)
-
- # 添加剩余的结果
- if first_other.value:
- self.results.extend(other.results)
- self.time_chunk.extend(other.time_chunk)
- else:
- # 直接添加所有结果
- self.results.extend(other.results)
- self.time_chunk.extend(other.time_chunk)
-
- return self
@classmethod
- def from_raw(cls, raw_data: List[dict]) -> "VADResponse":
+ def create_from_push_data(
+ cls,
+ audiobinary_data_list: AudioBinary_data_list,
+ data: Any,
+ start_time: int,
+ end_time: int,
+ ):
"""
- 从原始数据创建VADResponse
-
- 参数:
- raw_data: 原始数据，格式如 [{'key': 'xxx', 'value': [[-1, 59540], [59820, -1]]}]
-
- 返回:
- VADResponse: 解析后的VAD响应
+ 创建VAD片段
"""
- results = []
- time_chunk = []
- for item in raw_data:
- segments = [
- VADSegment(start=seg[0], end=seg[1])
- for seg in item['value']
- ]
- results.append(VADResult(
- key=item['key'],
- value=segments
- ))
- time_chunk.extend(segments)
- return cls(results=results, time_chunk=time_chunk)
-
- def to_raw(self) -> List[dict]:
+ index = audiobinary_data_list.push_data(data)
+
+ return cls(
+ audiobinary_data_list=audiobinary_data_list,
+ audiobinary_index=index,
+ audiobinary_data=audiobinary_data_list[index],
+ start_time=start_time,
+ end_time=end_time,
+ )
+
+ def __len__(self):
"""
- 转换为原始数据格式
-
- 返回:
- List[dict]: 原始数据格式
+ 获取音频数据长度
"""
- return [
- {
- 'key': result.key,
- 'value': [[seg.start, seg.end] for seg in result.value]
- }
- for result in self.results
- ]
+ return len(self.audiobinary_data.binary_data)
def __str__(self):
- result_str = "VADResponse:\n"
- for result in self.results:
- for value_item in result.value:
- result_str += f"[{value_item.start}:{value_item.end}]\n"
- return result_str
-
- def __iter__(self):
- return iter(self.time_chunk)
-
- def __next__(self):
- return next(self.time_chunk)
-
- def __len__(self):
- return len(self.time_chunk)
-
- def __getitem__(self, index):
- return self.time_chunk[index]
\ No newline at end of file
+ """
+ 字符串展示内容
+ """
+ tostr = f"audiobinary_data_index: {self.audiobinary_index}\n"
+ tostr += f"start_time: {self.start_time}\n"
+ tostr += f"end_time: {self.end_time}\n"
+ tostr += f"data_length: {len(self.audiobinary_data.binary_data)}\n"
+ tostr += f"data_type: {type(self.audiobinary_data.binary_data)}\n"
+ return tostr
diff --git a/src/pipeline/ASRpipeline.py b/src/pipeline/ASRpipeline.py
new file mode 100644
index 0000000..399bd23
--- /dev/null
+++ b/src/pipeline/ASRpipeline.py
@@ -0,0 +1,280 @@
+from src.pipeline.base import PipelineBase
+from typing import Dict, Any, Callable
+from queue import Queue, Empty
+from src.utils import get_module_logger
+from src.models import AudioBinary_data_list
+import threading
+
+logger = get_module_logger(__name__)
+
+
+class ASRPipeline(PipelineBase):
+ """
+ 管道类
+ 实现具体的处理逻辑
+ """
+
+ def __init__(self, *args, **kwargs):
+ """
+ 初始化管道
+ """
+ super().__init__(*args, **kwargs)
+ self._config: Dict[str, Any] = {}
+ self._functor_dict: Dict[str, Any] = {}
+ self._subqueue_dict: Dict[str, Any] = {}
+ self._is_baked: bool = False
+ self._input_queue: Queue = None
+ self._audio_binary_data_list: AudioBinary_data_list = None
+
+ self._status_lock = threading.Lock()
+ self._is_running: bool = False
+ self._stop_event: bool = False
+ self._callback: Callable = None
+
+ def set_config(self, config: Dict[str, Any]) -> None:
+ """
+ 设置配置
+ 参数:
+ config: Dict[str, Any] 配置
+ """
+ self._config = config
+
+ def get_config(self) -> Dict[str, Any]:
+ """
+ 获取配置
+ 返回:
+ Dict[str, Any] 配置
+ """
+ return self._config
+
+ def set_audio_binary(self, audio_binary: AudioBinary_data_list) -> None:
+ """
+ 设置音频二进制存储单元
+ 参数:
+ audio_binary: 音频二进制
+ """
+ self._audio_binary = audio_binary
+
+ def set_models(self, models: Dict[str, Any]) -> None:
+ """
+ 设置模型
+ """
+ self._models = models
+
+ def set_input_queue(self, input_queue: Queue) -> None:
+ """
+ 设置输入队列
+ """
+ self._input_queue = input_queue
+
+ def set_callback(self, callback: Callable) -> None:
+ """
+ 设置回调函数
+ """
+ self._callback = callback
+
+ def bake(self) -> None:
+ """
+ 烘焙管道
+ """
+ self._pre_check_resource()
+ self._init_functor()
+ self._is_baked = True
+
+ def _pre_check_resource(self) -> None:
+ """
+ 预检查资源
+ """
+ if self._input_queue is None:
+ raise RuntimeError("[ASRpipeline]输入队列未设置")
+ if self._functor_dict is None:
+ raise RuntimeError("[ASRpipeline]functor字典未设置")
+ if self._subqueue_dict is None:
+ raise RuntimeError("[ASRpipeline]子队列字典未设置")
+ if self._audio_binary is None:
+ raise RuntimeError("[ASRpipeline]音频存储单元未设置")
+
+ def _init_functor(self) -> None:
+ """
+ 初始化函数
+ 自身的functor流程图如下
+ self.input_queue(self.run检测输入到subqueue["original"])->vad
+ ->vad2asr ->asrend
+ ->vad2spk ->spkend
+ ->asrend+spkend->resultbinder
+ ->self.callback
+ """
+ try:
+ from src.functor import FunctorFactory
+
+ # 加载VAD、asr、spk functor
+ logger.debug(f"使用FunctorFactory创建vad functor")
+ self._functor_dict["vad"] = FunctorFactory.make_functor(
+ functor_name="vad", config=self._config, models=self._models
+ )
+ logger.debug(f"使用FunctorFactory创建asr functor")
+ self._functor_dict["asr"] = FunctorFactory.make_functor(
+ functor_name="asr", config=self._config, models=self._models
+ )
+ logger.debug(f"使用FunctorFactory创建spk functor")
+ self._functor_dict["spk"] = FunctorFactory.make_functor(
+ functor_name="spk", config=self._config, models=self._models
+ )
+ logger.debug(f"使用FunctorFactory创建resultbinder functor")
+ self._functor_dict["resultbinder"] = FunctorFactory.make_functor(
+ functor_name="resultbinder", config=self._config, models=self._models
+ )
+
+ # 创建音频数据存储单元
+ self._audio_binary_data_list = AudioBinary_data_list()
+
+ self._functor_dict["vad"].set_audio_binary_data_list(
+ self._audio_binary_data_list
+ )
+
+ # 初始化子队列
+ self._subqueue_dict["original"] = Queue()
+ self._subqueue_dict["vad2asr"] = Queue()
+ self._subqueue_dict["vad2spk"] = Queue()
+ self._subqueue_dict["asrend"] = Queue()
+ self._subqueue_dict["spkend"] = Queue()
+ # 输出队列
+ self._subqueue_dict["OUTPUT"] = Queue()
+
+ # 设置子队列的输入队列
+ self._functor_dict["vad"].set_input_queue(self._subqueue_dict["original"])
+ self._functor_dict["asr"].set_input_queue(self._subqueue_dict["vad2asr"])
+ self._functor_dict["spk"].set_input_queue(self._subqueue_dict["vad2spk"])
+ # 设置resultbinder的输入队列
+ # 汇总 asr语音识别结果 和 说话人识别结果
+ self._functor_dict["resultbinder"].add_input_queue(
+ "asr", self._subqueue_dict["asrend"]
+ )
+ self._functor_dict["resultbinder"].add_input_queue(
+ "spk", self._subqueue_dict["spkend"]
+ )
+
+ # 设置回调函数——放置到对应队列中
+ self._functor_dict["vad"].add_callback(self._subqueue_dict["vad2asr"].put)
+ self._functor_dict["vad"].add_callback(self._subqueue_dict["vad2spk"].put)
+ # 设置asr与spk的回调函数
+ self._functor_dict["asr"].add_callback(self._subqueue_dict["asrend"].put)
+ self._functor_dict["spk"].add_callback(self._subqueue_dict["spkend"].put)
+ # 设置resultbinder的回调函数 为 自身被设置的回调函数，用于和外界交互
+ self._functor_dict["resultbinder"].add_callback(self._callback)
+
+ except ImportError as e:
+ raise ImportError(f"functorFactory引入失败,ASRPipeline无法完成初始化: {str(e)}")
+ except Exception as e:
+ raise Exception(f"ASRPipeline初始化失败: {str(e)}")
+
+ def _check_result(self, result: Any) -> None:
+ """
+ 检查结果
+ """
+ # 若asr和spk队列中都有数据，则合并数据
+ if (
+ self._subqueue_dict["asrend"].qsize()
+ & self._subqueue_dict["spkend"].qsize()
+ ):
+ asr_data = self._subqueue_dict["asrend"].get()
+ spk_data = self._subqueue_dict["spkend"].get()
+ # 合并数据
+ result = {"asr_data": asr_data, "spk_data": spk_data}
+ # 通知回调函数
+ self._notify_callbacks(result)
+
+ def run(self) -> threading.Thread:
+ """
+ 运行管道
+ Returns:
+ threading.Thread: 返回已运行线程实例
+ """
+ # 检查运行资源是否准备完毕
+ self._pre_check()
+ self._thread = threading.Thread(target=self._run, daemon=True)
+ self._thread.start()
+ logger.info("[ASRpipeline]管道开始运行")
+ return self._thread
+
+ def _pre_check(self) -> None:
+ """
+ 预检查
+ """
+ if self._is_baked is False:
+ raise RuntimeError("[ASRpipeline]管道未烘焙，无法运行")
+
+ for functor_name, functor in self._functor_dict.items():
+ if functor is None:
+ raise RuntimeError(f"[ASRpipeline]functor{functor_name}异常")
+
+ for subqueue_name, subqueue in self._subqueue_dict.items():
+ if subqueue is None:
+ raise RuntimeError(f"[ASRpipeline]子队列{subqueue_name}异常")
+
+ def _run(self) -> None:
+ """
+ 真实的运行逻辑
+ """
+ # 运行所有functor
+ for functor_name, functor in self._functor_dict.items():
+ logger.info(f"[ASRpipeline]运行{functor_name}functor")
+ self._functor_dict[functor_name].run()
+
+ # 设置管道运行状态
+ with self._status_lock:
+ self._is_running = True
+ self._stop_event = False
+
+ while self._is_running and not self._stop_event:
+ try:
+ data = self._input_queue.get(timeout=self._queue_timeout)
+ # logger.debug("[ASRpipeline]获取到的数据length: %s", len(data))
+ # 检查是否是结束信号
+ if data is None:
+ logger.info("收到结束信号，管道准备停止")
+ self._input_queue.task_done() # 标记结束信号已处理
+ break
+
+ # 处理数据
+ self._process(data)
+
+ # 标记任务完成
+ self._input_queue.task_done()
+
+ except Empty:
+ # 队列获取超时，继续等待
+ continue
+ except Exception as e:
+ logger.error(f"[ASRpipeline]管道处理数据出错: {str(e)}")
+ break
+
+ logger.info("[ASRpipeline]管道停止运行")
+
+ def _process(self, data: Any) -> Any:
+ """
+ 处理数据
+ 参数:
+ data: 输入数据
+ 返回:
+ 处理结果
+ """
+ # 子类实现具体的处理逻辑
+ self._subqueue_dict["original"].put(data)
+
+ def stop(self) -> None:
+ """
+ 停止管道
+ """
+ with self._status_lock:
+ self._is_running = False
+ self._stop_event = True
+ for functor_name, functor in self._functor_dict.items():
+ # logger.info(f"停止{functor_name}functor")
+ if functor.stop():
+ logger.info(f"[ASRpipeline]子Functor[{functor_name}]停止")
+ else:
+ logger.error(f"[ASRpipeline]子Functor[{functor_name}]停止失败")
+ self._thread.join()
+ logger.info("[ASRpipeline]管道停止")
+ return True
diff --git a/src/pipeline/__init__.py b/src/pipeline/__init__.py
new file mode 100644
index 0000000..d1a171d
--- /dev/null
+++ b/src/pipeline/__init__.py
@@ -0,0 +1,3 @@
+from src.pipeline.base import PipelineBase, PipelineFactory
+
+__all__ = ["PipelineBase", "PipelineFactory"]
diff --git a/src/pipeline/base.py b/src/pipeline/base.py
new file mode 100644
index 0000000..89b4c4a
--- /dev/null
+++ b/src/pipeline/base.py
@@ -0,0 +1,151 @@
+from abc import ABC, abstractmethod
+from queue import Queue, Empty
+from typing import List, Callable, Any, Optional
+import logging
+import threading
+import time
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+
+class PipelineBase(ABC):
+ """
+ 管道基类
+ 定义了管道的基本接口和通用功能
+ """
+
+ def __init__(self, input_queue: Optional[Queue] = None):
+ """
+ 初始化管道
+ 参数:
+ input_queue: 输入队列，用于接收数据
+ """
+ self._input_queue = input_queue
+ self._callbacks: List[Callable] = []
+ self._is_running = False
+ self._stop_event = False
+ self._thread: Optional[threading.Thread] = None
+ self._stop_timeout = 5 # 默认停止超时时间（秒）
+ self._queue_timeout = 1 # 队列获取超时时间（秒）
+
+ def set_input_queue(self, queue: Queue) -> None:
+ """
+ 设置输入队列
+ 参数:
+ queue: 输入队列
+ """
+ self._input_queue = queue
+
+ def add_callback(self, callback: Callable) -> None:
+ """
+ 添加回调函数
+ 参数:
+ callback: 回调函数，接收处理结果
+ """
+ self._callbacks.append(callback)
+
+ def _notify_callbacks(self, result: Any) -> None:
+ """
+ 通知所有回调函数
+ 参数:
+ result: 处理结果
+ """
+ for callback in self._callbacks:
+ try:
+ callback(result)
+ except Exception as e:
+ logger.error(f"回调函数执行出错: {str(e)}")
+
+ @abstractmethod
+ def _process(self, data: Any) -> Any:
+ """
+ 处理数据
+ 参数:
+ data: 输入数据
+ 返回:
+ 处理结果
+ """
+ pass
+
+ @abstractmethod
+ def _run(self) -> None:
+ """
+ 运行管道
+ 从输入队列获取数据并处理
+ """
+ pass
+
+ def stop(self, timeout: Optional[float] = None) -> bool:
+ """
+ 停止管道
+ 参数:
+ timeout: 停止超时时间（秒），None表示使用默认超时时间
+ 返回:
+ bool: 是否成功停止
+ """
+ if not self._is_running:
+ return True
+
+ logger.info("正在停止管道...")
+ self._stop_event = True
+ self._is_running = False
+
+ # 等待线程结束
+ if self._thread and self._thread.is_alive():
+ timeout = timeout if timeout is not None else self._stop_timeout
+ self._thread.join(timeout=timeout)
+
+ # 检查是否成功停止
+ if self._thread.is_alive():
+ logger.warning(f"管道停止超时（{timeout}秒），强制终止")
+ return False
+ else:
+ logger.info("管道已成功停止")
+ return True
+
+ return True
+
+ def force_stop(self) -> None:
+ """
+ 强制停止管道
+ 注意：这可能会导致资源未正确释放
+ """
+ logger.warning("强制停止管道")
+ self._stop_event = True
+ self._is_running = False
+ # 注意：Python的线程无法被强制终止，这里只是设置标志
+ # 实际终止需要依赖操作系统的进程管理
+
+
+class PipelineFactory:
+ """
+ 管道工厂类
+ 用于创建管道实例
+ """
+
+ from src.pipeline.ASRpipeline import ASRPipeline
+ def _create_pipeline_ASRpipeline(*args, **kwargs) -> ASRPipeline:
+ """
+ 创建ASR管道实例
+ """
+ from src.pipeline.ASRpipeline import ASRPipeline
+ pipeline = ASRPipeline()
+ pipeline.set_config(kwargs["config"])
+ pipeline.set_models(kwargs["models"])
+ pipeline.set_audio_binary(kwargs["audio_binary"])
+ pipeline.set_input_queue(kwargs["input_queue"])
+ pipeline.set_callback(kwargs["callback"])
+ # pipeline.bake()
+ return pipeline
+
+ @classmethod
+ def create_pipeline(cls, pipeline_name: str, *args, **kwargs) -> Any:
+ """
+ 创建管道实例
+ """
+ if pipeline_name == "ASRpipeline":
+ return cls._create_pipeline_ASRpipeline(*args, **kwargs)
+ else:
+ raise ValueError(f"不支持的管道类型: {pipeline_name}")
+
diff --git a/src/pipeline/test.py b/src/pipeline/test.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/runner/ASRRunner.py b/src/runner/ASRRunner.py
new file mode 100644
index 0000000..dd50257
--- /dev/null
+++ b/src/runner/ASRRunner.py
@@ -0,0 +1,231 @@
+"""
+-*- encoding: utf-8 -*-
+
+ASRRunner
+继承RunnerBase
+专属pipeline为ASRPipeline
+"""
+
+from src.pipeline.ASRpipeline import ASRPipeline
+from src.pipeline import PipelineFactory
+from src.models import AudioBinary_data_list, AudioBinary_Config
+from src.core.model_loader import ModelLoader
+from src.config import DefaultConfig
+import asyncio
+from queue import Queue
+import soundfile
+import time
+from typing import List, Optional
+import uuid
+from threading import Thread
+from src.utils.mock_websocket import MockWebSocketClient as WebSocketClient
+from .runner import RunnerBase
+
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+OVAERWATCH = False
+
+model_loader = ModelLoader()
+
+class ASRRunner(RunnerBase):
+ """
+ 运行器类
+ 负责管理资源和协调Pipeline的运行
+ """
+ class SenderAndReceiver:
+ """
+ 对于单个pipeline的管理
+ 包含 发送者 和 接收者
+ _sender: 发送者 唯一
+ _receiver: 接收者 可以有多个
+ _pipeline: 对应管道 唯一
+ """
+ def __init__(self, *args, **kwargs):
+ # 可选传入参数，
+ self._name: str = kwargs.get("name", "")
+ self._sender: Optional[WebSocketClient] = kwargs.get("sender", None)
+ self._receiver: List[WebSocketClient] = kwargs.get("receiver", [])
+
+ # 资源
+ self._audio_config: AudioBinary_Config = kwargs.get("audio_config", DefaultConfig.audio_config)
+ self._models: dict = kwargs.get("models", None)
+ self._audio_binary: AudioBinary_data_list = AudioBinary_data_list()
+ # id唯一标识
+ self._id: str = str(uuid.uuid4())
+ # 输入队列
+ self._input_queue: Queue = Queue()
+ self._pipeline: Optional[ASRPipeline] = None
+ self._task: Optional[asyncio.Task] = None
+
+ def set_name(self, name: str):
+ self._name = name
+
+ def set_id(self, id: str):
+ self._id = id
+
+ def set_sender(self, sender: WebSocketClient):
+ self._sender = sender
+
+ def set_pipeline(self, pipeline: ASRPipeline):
+ self._pipeline = pipeline
+ config = {
+ "audio_config": self._audio_config,
+ }
+ self._pipeline.set_config(config)
+ self._pipeline.set_models(self._models)
+ self._pipeline.set_audio_binary(self._audio_binary)
+ self._pipeline.set_input_queue(self._input_queue)
+
+ # --- 异步-同步桥梁 ---
+ # 创建一个线程安全的回调函数，用于从Pipeline的线程中调用Runner的异步方法
+ loop = asyncio.get_running_loop()
+ def thread_safe_callback(message):
+ asyncio.run_coroutine_threadsafe(self.deal_message(message), loop)
+
+ self._pipeline.set_callback(thread_safe_callback)
+ self._pipeline.bake()
+
+ def append_receiver(self, receiver: WebSocketClient):
+ self._receiver.append(receiver)
+
+ def delete_receiver(self, receiver: WebSocketClient):
+ self._receiver.remove(receiver)
+
+ async def deal_message(self, message: str):
+ await self.broadcast(message)
+
+ async def broadcast(self, message: str):
+ """
+ 广播发送给所有接收者
+ """
+ logger.info("[ASRRunner][SAR-%s]广播发送给所有接收者: 消息长度：%s", self._name, len(message))
+ logger.info(f"SAR-{self._name} 的接收者列表: {self._receiver}")
+ tasks = [receiver.send(message) for receiver in self._receiver]
+ await asyncio.gather(*tasks)
+
+ async def _run(self):
+ """
+ 运行SAR
+ """
+ self._pipeline.run()
+ loop = asyncio.get_running_loop()
+ while True:
+ try:
+ data = await self._sender.recv()
+ if data is None:
+ # `None` is used as a signal to end the stream
+ await loop.run_in_executor(None, self._input_queue.put, None)
+ break
+ # logger.debug("[ASRRunner][SAR-%s]接收到的数据length: %s", self._name, len(data))
+ await loop.run_in_executor(None, self._input_queue.put, data)
+ except Exception as e:
+ logger.error(f"[ASRRunner][SAR-{self._name}] _run loop error: {e}")
+ break
+ await self.stop()
+
+ def run(self):
+ """
+ 运行SAR
+ """
+ self._task = asyncio.create_task(self._run())
+
+ async def stop(self):
+ """
+ 停止SAR
+ """
+ logger.info(f"Stopping SAR: {self._name}")
+ self._pipeline.stop()
+
+ # Close all receiver websockets
+ receiver_tasks = [ws.close() for ws in self._receiver]
+ await asyncio.gather(*receiver_tasks, return_exceptions=True)
+
+ # Close the sender websocket
+ if self._sender:
+ await self._sender.close()
+
+ # Cancel the main task if it's still running
+ if self._task and not self._task.done():
+ self._task.cancel()
+ logger.info(f"SAR stopped: {self._name}")
+
+ def __init__(self,*args,**kwargs):
+ """
+ """
+ # 接收资源
+ self._default_audio_config = kwargs.get("audio_config", DefaultConfig.audio_config)
+ # self._audio_binary_list = args.get("audio_binary_list", None)
+ self._default_models = kwargs.get("models", None)
+ self._SAR_list: List[self.SenderAndReceiver] = []
+
+ def set_default_config(self, *args, **kwargs):
+ """
+ 设置配置
+ """
+ self._default_audio_config = kwargs.get("audio_config", self._default_audio_config)
+ self._default_models = kwargs.get("models", self._default_models)
+
+ def new_SAR(
+ self,
+ ws: "WebSocketClient",
+ name: str = "",
+ audio_config: "AudioBinary_Config" = None,
+ models: dict = None
+ ) -> uuid.UUID:
+ """
+ 创建新的SAR SenderAndReceiver
+ """
+ if audio_config is None:
+ audio_config = self._default_audio_config
+ if models is None:
+ models = self._default_models
+
+ try:
+ new_SAR = self.SenderAndReceiver(
+ name=name,
+ audio_config=audio_config,
+ models=models
+ )
+ new_pipeline = ASRPipeline()
+ new_SAR.set_pipeline(new_pipeline)
+ # new_SAR.set_pipeline()
+ logger.info("创建新的SAR: name %s, id %s", new_SAR._name, new_SAR._id)
+ new_SAR.set_sender(ws)
+ new_SAR.append_receiver(ws)
+ new_SAR.run()
+ self._SAR_list.append(new_SAR)
+ return new_SAR._id
+ except Exception as e:
+ logger.error("创建管道失败: %s", e)
+ return None
+
+ def join_SAR(
+ self,
+ ws: "WebSocketClient",
+ name: Optional[str] = None,
+ id: Optional[str] = None,
+ ) -> bool:
+ """
+ 加入SAR的Receiver
+ """
+ # 使用next获取迭代器下一个元素，生成pipeline_list迭代器，按id停止
+ if id:
+ exist_pipeline = next((pipeline for pipeline in self._SAR_list if pipeline._id == id), None)
+ if name:
+ exist_pipeline = next((pipeline for pipeline in self._SAR_list if pipeline._name == name), None)
+ if exist_pipeline:
+ exist_pipeline.append_receiver(ws)
+ return True
+ return False
+
+ async def shutdown(self):
+ """
+ 优雅地关闭所有SAR会话
+ """
+ logger.info("Shutting down all SAR instances...")
+ tasks = [sar.stop() for sar in self._SAR_list]
+ await asyncio.gather(*tasks, return_exceptions=True)
+ logger.info("All SAR instances have been shut down.")
diff --git a/src/runner/__init__.py b/src/runner/__init__.py
new file mode 100644
index 0000000..3372b8e
--- /dev/null
+++ b/src/runner/__init__.py
@@ -0,0 +1,3 @@
+from .ASRRunner import ASRRunner
+
+__all__ = ["ASRRunner"]
diff --git a/src/runner/runner.py b/src/runner/runner.py
new file mode 100644
index 0000000..6966c6f
--- /dev/null
+++ b/src/runner/runner.py
@@ -0,0 +1,105 @@
+"""
+-*- encoding: utf-8 -*-
+
+Runner类
+所有的Runner都对应一个fastapi的endpoint,
+Runner需要处理:
+1.新的websocket 进来后放到 unknow_websocket_pool中
+2.收到特定消息后, 将消息转发给特定的pipeline处理
+3.管理pipeline与websocket对应关系, 管理pipeline的ID
+4.管理pipeline的启动和停止
+5.管理所有pipeline用到的资源, 管理pipeline的存活时间。
+"""
+
+from abc import ABC, abstractmethod
+from typing import Dict, Any, List
+from threading import Thread, Lock
+from queue import Queue
+import traceback
+import time
+
+from src.audio_chunk import AudioChunk
+from src.pipeline import PipelineFactory
+from src.core.model_loader import ModelLoader
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+
+audio_chunk = AudioChunk()
+models_loaded = ModelLoader()
+
+
+class RunnerBase(ABC):
+ """
+ 运行器基类
+ 定义了运行器的基本接口
+ """
+ def __init__(self, *args, **kwargs):
+ pass
+
+class STTRunnerFactory:
+ """
+ STT Runner工厂类
+ 用于创建运行器实例
+ """
+
+ @staticmethod
+ def _create_runner(
+ audio_binary_name: str,
+ model_name_list: List[str],
+ pipeline_name_list: List[str],
+ ) -> RunnerBase:
+ """
+ 创建运行器
+ 参数:
+ audio_binary_name: 音频二进制名称
+ model_name_list: 模型名称列表
+ pipeline_name_list: 管道名称列表
+ 返回:
+ Runner实例
+ """
+ audio_binary = audio_chunk.get_audio_binary(audio_binary_name)
+ models: Dict[str, Any] = {
+ model_name: models_loaded.models[model_name]
+ for model_name in model_name_list
+ }
+ pipelines: List[Pipeline] = [
+ PipelineFactory.create_pipeline(pipeline_name)
+ for pipeline_name in pipeline_name_list
+ ]
+ return RunnerBase(
+ audio_binary_list=[audio_binary], models=models, pipeline_list=pipelines
+ )
+
+ @classmethod
+ def create_runner_from_config(
+ cls,
+ config: Dict[str, Any],
+ ) -> RunnerBase:
+ """
+ 从配置创建运行器
+ 参数:
+ config: 配置字典
+ 返回:
+ Runner实例
+ """
+ audio_binary_name = config["audio_binary_name"]
+ model_name_list = config["model_name_list"]
+ pipeline_name_list = config["pipeline_name_list"]
+ return cls._create_runner(
+ audio_binary_name, model_name_list, pipeline_name_list
+ )
+
+ @classmethod
+ def create_runner_normal(cls) -> RunnerBase:
+ """
+ 创建默认运行器
+ 返回:
+ Runner实例
+ """
+ audio_binary_name = None
+ model_name_list = list(models_loaded.models.keys())
+ pipeline_name_list = None
+ return cls._create_runner(
+ audio_binary_name, model_name_list, pipeline_name_list
+ )
diff --git a/src/server.py b/src/server.py
index 9b27763..062b9b3 100644
--- a/src/server.py
+++ b/src/server.py
@@ -10,233 +10,80 @@ import json
import websockets
import ssl
import argparse
-from config import parse_args
-from models import load_models
-from service import ASRService
+from src.runner import ASRRunner
+from src.config import DefaultConfig
+from src.config import AudioBinary_Config
+from src.websockets.router import websocket_router
+from src.core import ModelLoader
+import uvicorn
+from fastapi import FastAPI
+from contextlib import asynccontextmanager
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
# 全局变量，存储当前连接的WebSocket客户端
websocket_users = set()
-
-async def ws_reset(websocket):
- """重置WebSocket连接状态并关闭连接"""
- print(f"重置WebSocket连接，当前连接数: {len(websocket_users)}")
-
- # 重置状态字典
- websocket.status_dict_asr_online["cache"] = {}
- websocket.status_dict_asr_online["is_final"] = True
- websocket.status_dict_vad["cache"] = {}
- websocket.status_dict_vad["is_final"] = True
- websocket.status_dict_punc["cache"] = {}
-
- # 关闭连接
- await websocket.close()
-
-
-async def clear_websocket():
- """清理所有WebSocket连接"""
- for websocket in websocket_users:
- await ws_reset(websocket)
- websocket_users.clear()
-
-
-async def ws_serve(websocket, path):
+# 使用 lifespan 上下文管理器来管理应用的生命周期
+@asynccontextmanager
+async def lifespan(app: FastAPI):
"""
- WebSocket服务主函数，处理客户端连接和消息
-
- 参数:
- websocket: WebSocket连接对象
- path: 连接路径
+ 在应用启动时加载模型和初始化ASRRunner，
+ 在应用关闭时优雅地关闭ASRRunner。
"""
- frames = [] # 存储所有音频帧
- frames_asr = [] # 存储用于离线ASR的音频帧
- frames_asr_online = [] # 存储用于在线ASR的音频帧
+ logger.info("应用启动，开始加载模型和初始化Runner...")
- global websocket_users
- # await clear_websocket() # 清理现有连接(目前注释掉，允许多客户端)
+ # 1. 加载模型
+ # 这里的参数可以从配置文件或环境变量中获取
+ args = {
+ "asr_model": "paraformer-zh",
+ "asr_model_revision": "v2.0.4",
+ "vad_model": "fsmn-vad",
+ "vad_model_revision": "v2.0.4",
+ "spk_model": "cam++",
+ "spk_model_revision": "v2.0.2",
+ }
+ model_loader = ModelLoader()
+ models = model_loader.load_models(args)
- # 添加到用户集合
- websocket_users.add(websocket)
-
- # 初始化连接状态
- websocket.status_dict_asr = {}
- websocket.status_dict_asr_online = {"cache": {}, "is_final": False}
- websocket.status_dict_vad = {"cache": {}, "is_final": False}
- websocket.status_dict_punc = {"cache": {}}
- websocket.chunk_interval = 10
- websocket.vad_pre_idx = 0
- websocket.is_speaking = True # 默认用户正在说话
-
- # 语音检测状态
- speech_start = False
- speech_end_i = -1
-
- # 初始化配置
- websocket.wav_name = "microphone"
- websocket.mode = "2pass" # 默认使用两阶段识别模式
-
- print("新用户已连接", flush=True)
+ # 2. 初始化 ASRRunner
+ _audio_config = AudioBinary_Config(
+ chunk_size=200, # ms
+ sample_rate=16000,
+ sample_width=2, # 16-bit
+ channels=1,
+ )
+ _audio_config.chunk_stride = int(_audio_config.chunk_size * _audio_config.sample_rate / 1000)
- try:
- # 持续接收客户端消息
- async for message in websocket:
- # 处理JSON配置消息
- if isinstance(message, str):
- try:
- messagejson = json.loads(message)
-
- # 更新各种配置参数
- if "is_speaking" in messagejson:
- websocket.is_speaking = messagejson["is_speaking"]
- websocket.status_dict_asr_online["is_final"] = not websocket.is_speaking
- if "chunk_interval" in messagejson:
- websocket.chunk_interval = messagejson["chunk_interval"]
- if "wav_name" in messagejson:
- websocket.wav_name = messagejson.get("wav_name")
- if "chunk_size" in messagejson:
- chunk_size = messagejson["chunk_size"]
- if isinstance(chunk_size, str):
- chunk_size = chunk_size.split(",")
- websocket.status_dict_asr_online["chunk_size"] = [int(x) for x in chunk_size]
- if "encoder_chunk_look_back" in messagejson:
- websocket.status_dict_asr_online["encoder_chunk_look_back"] = messagejson["encoder_chunk_look_back"]
- if "decoder_chunk_look_back" in messagejson:
- websocket.status_dict_asr_online["decoder_chunk_look_back"] = messagejson["decoder_chunk_look_back"]
- if "hotword" in messagejson:
- websocket.status_dict_asr["hotword"] = messagejson["hotwords"]
- if "mode" in messagejson:
- websocket.mode = messagejson["mode"]
- except json.JSONDecodeError:
- print(f"无效的JSON消息: {message}")
+ asr_runner = ASRRunner()
+ asr_runner.set_default_config(
+ audio_config=_audio_config,
+ models=models,
+ )
- # 根据chunk_interval更新VAD的chunk_size
- websocket.status_dict_vad["chunk_size"] = int(
- websocket.status_dict_asr_online.get("chunk_size", [0, 10])[1] * 60 / websocket.chunk_interval
- )
-
- # 处理音频数据
- if len(frames_asr_online) > 0 or len(frames_asr) >= 0 or not isinstance(message, str):
- if not isinstance(message, str): # 二进制音频数据
- # 添加到帧缓冲区
- frames.append(message)
- duration_ms = len(message) // 32 # 计算音频时长
- websocket.vad_pre_idx += duration_ms
+ # 3. 将 asr_runner 实例存储在 app.state 中
+ app.state.asr_runner = asr_runner
+ logger.info("模型加载和Runner初始化完成。")
- # 处理在线ASR
- frames_asr_online.append(message)
- websocket.status_dict_asr_online["is_final"] = speech_end_i != -1
-
- # 达到chunk_interval或最终帧时处理在线ASR
- if (len(frames_asr_online) % websocket.chunk_interval == 0 or
- websocket.status_dict_asr_online["is_final"]):
- if websocket.mode == "2pass" or websocket.mode == "online":
- audio_in = b"".join(frames_asr_online)
- try:
- await asr_service.async_asr_online(websocket, audio_in)
- except Exception as e:
- print(f"在线ASR处理错误: {e}")
- frames_asr_online = []
-
- # 如果检测到语音开始，收集帧用于离线ASR
- if speech_start:
- frames_asr.append(message)
-
- # VAD处理 - 语音活动检测
- try:
- speech_start_i, speech_end_i = await asr_service.async_vad(websocket, message)
- except Exception as e:
- print(f"VAD处理错误: {e}")
-
- # 检测到语音开始
- if speech_start_i != -1:
- speech_start = True
- # 计算开始偏移并收集前面的帧
- beg_bias = (websocket.vad_pre_idx - speech_start_i) // duration_ms
- frames_pre = frames[-beg_bias:] if beg_bias < len(frames) else frames
- frames_asr = []
- frames_asr.extend(frames_pre)
-
- # 处理离线ASR (语音结束或用户停止说话)
- if speech_end_i != -1 or not websocket.is_speaking:
- if websocket.mode == "2pass" or websocket.mode == "offline":
- audio_in = b"".join(frames_asr)
- try:
- await asr_service.async_asr(websocket, audio_in)
- except Exception as e:
- print(f"离线ASR处理错误: {e}")
-
- # 重置状态
- frames_asr = []
- speech_start = False
- frames_asr_online = []
- websocket.status_dict_asr_online["cache"] = {}
-
- # 如果用户停止说话，完全重置
- if not websocket.is_speaking:
- websocket.vad_pre_idx = 0
- frames = []
- websocket.status_dict_vad["cache"] = {}
- else:
- # 保留最近的帧用于下一轮处理
- frames = frames[-20:]
+ yield
- except websockets.ConnectionClosed:
- print(f"连接已关闭...", flush=True)
- await ws_reset(websocket)
- websocket_users.remove(websocket)
- except websockets.InvalidState:
- print("无效的WebSocket状态...")
- except Exception as e:
- print(f"发生异常: {e}")
+ # --- 应用关闭时执行的代码 ---
+ logger.info("应用关闭，开始清理资源...")
+ await app.state.asr_runner.shutdown()
+ logger.info("资源清理完成。")
+# 初始化FastAPI应用，并指定lifespan
+app = FastAPI(lifespan=lifespan)
-def start_server(args, asr_service_instance):
- """
- 启动WebSocket服务器
-
- 参数:
- args: 命令行参数
- asr_service_instance: ASR服务实例
- """
- global asr_service
- asr_service = asr_service_instance
-
- # 配置SSL (如果提供了证书)
- if args.certfile and len(args.certfile) > 0:
- ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
- ssl_context.load_cert_chain(args.certfile, keyfile=args.keyfile)
-
- start_server = websockets.serve(
- ws_serve, args.host, args.port,
- subprotocols=["binary"],
- ping_interval=None,
- ssl=ssl_context
- )
- else:
- start_server = websockets.serve(
- ws_serve, args.host, args.port,
- subprotocols=["binary"],
- ping_interval=None
- )
-
- print(f"WebSocket服务器已启动 - 监听 {args.host}:{args.port}")
-
- # 启动事件循环
- asyncio.get_event_loop().run_until_complete(start_server)
- asyncio.get_event_loop().run_forever()
+# 挂载WebSocket路由
+app.include_router(websocket_router, prefix="/ws")
+@app.get("/")
+async def read_root():
+ return {"message": "FunASR-FastAPI WebSocket Server is running."}
+# 如果需要直接运行此文件进行测试
if __name__ == "__main__":
- # 解析命令行参数
- args = parse_args()
-
- # 加载模型
- print("正在加载模型...")
- models = load_models(args)
- print("模型加载完成！当前仅支持单个客户端同时连接！")
-
- # 创建ASR服务
- asr_service = ASRService(models)
-
- # 启动服务器
- start_server(args, asr_service)
\ No newline at end of file
+ # 注意：在生产环境中，推荐使用Gunicorn + Uvicorn workers
+ uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/src/service.py b/src/service.py
deleted file mode 100644
index 131cfa2..0000000
--- a/src/service.py
+++ /dev/null
@@ -1,127 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-ASR服务模块 - 提供语音识别相关的核心功能
-"""
-
-import json
-
-
-class ASRService:
- """ASR服务类，封装各种语音识别相关功能"""
-
- def __init__(self, models):
- """
- 初始化ASR服务
-
- 参数:
- models: 包含各种预加载模型的字典
- """
- self.model_asr = models["asr"]
- self.model_asr_streaming = models["asr_streaming"]
- self.model_vad = models["vad"]
- self.model_punc = models["punc"]
-
- async def async_vad(self, websocket, audio_in):
- """
- 语音活动检测
-
- 参数:
- websocket: WebSocket连接
- audio_in: 二进制音频数据
-
- 返回:
- tuple: (speech_start, speech_end) 语音开始和结束位置
- """
- # 使用VAD模型分析音频段
- segments_result = self.model_vad.generate(
- input=audio_in,
- **websocket.status_dict_vad
- )[0]["value"]
-
- speech_start = -1
- speech_end = -1
-
- # 解析VAD结果
- if len(segments_result) == 0 or len(segments_result) > 1:
- return speech_start, speech_end
-
- if segments_result[0][0] != -1:
- speech_start = segments_result[0][0]
- if segments_result[0][1] != -1:
- speech_end = segments_result[0][1]
-
- return speech_start, speech_end
-
- async def async_asr(self, websocket, audio_in):
- """
- 离线ASR处理
-
- 参数:
- websocket: WebSocket连接
- audio_in: 二进制音频数据
- """
- if len(audio_in) > 0:
- # 使用离线ASR模型处理音频
- rec_result = self.model_asr.generate(
- input=audio_in,
- **websocket.status_dict_asr
- )[0]
-
- # 如果有标点符号模型且识别出文本，则添加标点
- if self.model_punc is not None and len(rec_result["text"]) > 0:
- rec_result = self.model_punc.generate(
- input=rec_result["text"],
- **websocket.status_dict_punc
- )[0]
-
- # 如果识别出文本，发送到客户端
- if len(rec_result["text"]) > 0:
- mode = "2pass-offline" if "2pass" in websocket.mode else websocket.mode
- message = json.dumps({
- "mode": mode,
- "text": rec_result["text"],
- "wav_name": websocket.wav_name,
- "is_final": websocket.is_speaking,
- })
- await websocket.send(message)
- else:
- # 如果没有音频数据，发送空文本
- mode = "2pass-offline" if "2pass" in websocket.mode else websocket.mode
- message = json.dumps({
- "mode": mode,
- "text": "",
- "wav_name": websocket.wav_name,
- "is_final": websocket.is_speaking,
- })
- await websocket.send(message)
-
- async def async_asr_online(self, websocket, audio_in):
- """
- 在线ASR处理
-
- 参数:
- websocket: WebSocket连接
- audio_in: 二进制音频数据
- """
- if len(audio_in) > 0:
- # 使用在线ASR模型处理音频
- rec_result = self.model_asr_streaming.generate(
- input=audio_in,
- **websocket.status_dict_asr_online
- )[0]
-
- # 在2pass模式下，如果是最终帧则跳过(留给离线ASR处理)
- if websocket.mode == "2pass" and websocket.status_dict_asr_online.get("is_final", False):
- return
-
- # 如果识别出文本，发送到客户端
- if len(rec_result["text"]):
- mode = "2pass-online" if "2pass" in websocket.mode else websocket.mode
- message = json.dumps({
- "mode": mode,
- "text": rec_result["text"],
- "wav_name": websocket.wav_name,
- "is_final": websocket.is_speaking,
- })
- await websocket.send(message)
\ No newline at end of file
diff --git a/src/utils/__init__.py b/src/utils/__init__.py
new file mode 100644
index 0000000..ec586b8
--- /dev/null
+++ b/src/utils/__init__.py
@@ -0,0 +1,6 @@
+from .logger import get_module_logger, setup_root_logger
+
+__all__ = [
+ "get_module_logger",
+ "setup_root_logger",
+ ]
diff --git a/src/utils/data_format.py b/src/utils/data_format.py
new file mode 100644
index 0000000..110074f
--- /dev/null
+++ b/src/utils/data_format.py
@@ -0,0 +1,122 @@
+"""
+处理各类音频数据与bytes的转换
+"""
+
+import wave
+from pydub import AudioSegment
+import io
+
+
+def wav_to_bytes(wav_path: str) -> bytes:
+ """
+ 将WAV文件读取为bytes数据。
+
+ 参数:
+ wav_path (str): WAV文件的路径。
+
+ 返回:
+ bytes: WAV文件的原始字节数据。
+
+ 异常:
+ FileNotFoundError: 如果WAV文件不存在。
+ wave.Error: 如果文件不是有效的WAV文件。
+ """
+ try:
+ with wave.open(wav_path, "rb") as wf:
+ # 读取所有帧
+ frames = wf.readframes(wf.getnframes())
+ return frames
+ except FileNotFoundError:
+ # 可以选择记录日志或重新抛出，这里为了清晰直接重新抛出
+ raise FileNotFoundError(f"错误: 未找到WAV文件 '{wav_path}'")
+ except wave.Error as e:
+ raise wave.Error(f"错误: 打开或读取WAV文件 '{wav_path}' 失败 - {e}")
+
+
+def bytes_to_wav(
+ bytes_data: bytes, wav_path: str, nchannels: int, sampwidth: int, framerate: int
+):
+ """
+ 将bytes数据写入为WAV文件。
+
+ 参数:
+ bytes_data (bytes): 音频的字节数据。
+ wav_path (str): 保存WAV文件的路径。
+ nchannels (int): 声道数 (例如 1 for mono, 2 for stereo)。
+ sampwidth (int): 采样宽度 (字节数, 例如 2 for 16-bit audio)。
+ framerate (int): 采样率 (例如 44100, 16000)。
+
+ 异常:
+ wave.Error: 如果写入WAV文件失败。
+ """
+ try:
+ with wave.open(wav_path, "wb") as wf:
+ wf.setnchannels(nchannels)
+ wf.setsampwidth(sampwidth)
+ wf.setframerate(framerate)
+ wf.writeframes(bytes_data)
+ except wave.Error as e:
+ raise wave.Error(f"错误: 写入WAV文件 '{wav_path}' 失败 - {e}")
+ except Exception as e:
+ # 捕获其他可能的写入错误
+ raise Exception(f"写入WAV文件 '{wav_path}' 时发生未知错误 - {e}")
+
+
+def mp3_to_bytes(mp3_path: str) -> bytes:
+ """
+ 将MP3文件转换为bytes数据 (原始PCM数据)。
+
+ 参数:
+ mp3_path (str): MP3文件的路径。
+
+ 返回:
+ bytes: MP3文件解码后的原始PCM字节数据。
+
+ 异常:
+ FileNotFoundError: 如果MP3文件不存在。
+ pydub.exceptions.CouldntDecodeError: 如果MP3文件无法解码。
+ """
+ try:
+ audio = AudioSegment.from_mp3(mp3_path)
+ # 获取原始PCM数据
+ return audio.raw_data
+ except FileNotFoundError:
+ raise FileNotFoundError(f"错误: 未找到MP3文件 '{mp3_path}'")
+ except Exception as e: # pydub 可能抛出多种解码相关的错误
+ raise Exception(f"错误: 处理MP3文件 '{mp3_path}' 失败 - {e}")
+
+
+def bytes_to_mp3(
+ bytes_data: bytes,
+ mp3_path: str,
+ frame_rate: int,
+ channels: int,
+ sample_width: int,
+ bitrate: str = "192k",
+):
+ """
+ 将原始PCM bytes数据转换为MP3文件。
+
+ 参数:
+ bytes_data (bytes): 原始PCM字节数据。
+ mp3_path (str): 保存MP3文件的路径。
+ frame_rate (int): 原始PCM数据的采样率 (例如 44100)。
+ channels (int): 原始PCM数据的声道数 (例如 1 for mono, 2 for stereo)。
+ sample_width (int): 原始PCM数据的采样宽度 (字节数, 例如 2 for 16-bit)。
+ bitrate (str): MP3编码的比特率 (例如 "128k", "192k", "320k")。
+
+ 异常:
+ Exception: 如果转换或写入MP3文件失败。
+ """
+ try:
+ # 从原始数据创建AudioSegment对象
+ audio = AudioSegment(
+ data=bytes_data,
+ sample_width=sample_width,
+ frame_rate=frame_rate,
+ channels=channels,
+ )
+ # 导出为MP3
+ audio.export(mp3_path, format="mp3", bitrate=bitrate)
+ except Exception as e:
+ raise Exception(f"错误: 转换或写入MP3文件 '{mp3_path}' 失败 - {e}")
diff --git a/src/utils/logger.py b/src/utils/logger.py
index b8f5f12..444a399 100644
--- a/src/utils/logger.py
+++ b/src/utils/logger.py
@@ -3,6 +3,7 @@ import sys
from pathlib import Path
from typing import Optional
+
def setup_logger(
name: str = None,
level: str = "INFO",
@@ -12,80 +13,79 @@ def setup_logger(
) -> logging.Logger:
"""
设置并返回一个配置好的logger实例
-
+
Args:
name: logger的名称，默认为None（使用root logger）
level: 日志级别，默认为"INFO"
log_file: 日志文件路径，默认为None（仅控制台输出）
log_format: 日志格式
date_format: 日期格式
-
+
Returns:
logging.Logger: 配置好的logger实例
"""
# 获取logger实例
logger = logging.getLogger(name)
-
+
# 设置日志级别
level = getattr(logging, level.upper())
logger.setLevel(level)
-
+
print(f"添加处理器 {name} {log_file} {log_format} {date_format}")
# 创建格式器
formatter = logging.Formatter(log_format, date_format)
-
+
# 添加控制台处理器
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
-
+
# 如果指定了日志文件，添加文件处理器
if log_file:
# 确保日志目录存在
log_path = Path(log_file)
log_path.parent.mkdir(parents=True, exist_ok=True)
-
- file_handler = logging.FileHandler(log_file, encoding='utf-8')
+
+ file_handler = logging.FileHandler(log_file, encoding="utf-8")
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
-
+
# 注意：移除了 propagate = False，允许日志传递
return logger
-def setup_root_logger(
- level: str = "INFO",
- log_file: Optional[str] = None
-) -> None:
+
+def setup_root_logger(level: str = "INFO", log_file: Optional[str] = None) -> None:
"""
配置根日志器
-
+
Args:
level: 日志级别
log_file: 日志文件路径
"""
setup_logger(None, level, log_file)
+
def get_module_logger(
module_name: str,
level: Optional[str] = None, # 改为可选参数
- log_file: Optional[str] = None # 一般不需要单独指定
+ log_file: Optional[str] = None, # 一般不需要单独指定
) -> logging.Logger:
"""
获取模块级别的logger
-
+
Args:
module_name: 模块名称，通常传入__name__
level: 可选的日志级别，如果不指定则继承父级配置
log_file: 可选的日志文件路径，一般不需要指定
"""
logger = logging.getLogger(module_name)
-
+
# 只有显式指定了level才设置
if level:
logger.setLevel(getattr(logging, level.upper()))
-
+
# 只有显式指定了log_file才添加文件处理器
if log_file:
setup_logger(module_name, level or "INFO", log_file)
-
- return logger
\ No newline at end of file
+
+ return logger
diff --git a/src/utils/mock_websocket.py b/src/utils/mock_websocket.py
new file mode 100644
index 0000000..a2bfa9c
--- /dev/null
+++ b/src/utils/mock_websocket.py
@@ -0,0 +1,55 @@
+import queue
+
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+
+class MockWebSocketClient:
+ """A mock WebSocket client to simulate a connection for testing."""
+
+ def __init__(self):
+ self.sent_messages = []
+ self._is_closed = False
+ self.receive_queue = queue.Queue()
+
+ def send(self, message: dict):
+ """Simulates sending a message (which is a dict)."""
+ if self._is_closed:
+ print("Warning: sending message on a closed websocket")
+ return
+ self.sent_messages.append(message)
+ print(f"Mock WS received: {message}")
+
+ def recv(self):
+ """Simulates receiving data from the WebSocket."""
+ if self._is_closed:
+ return None
+ try:
+ # Block until data is available, with a timeout to prevent hanging.
+ data = self.receive_queue.get(timeout=10)
+ if data is None:
+ self._is_closed = True
+ return data
+ except queue.Empty:
+ print("Mock WS recv timeout")
+ self._is_closed = True
+ return None
+
+ def close(self):
+ """Simulates closing the WebSocket connection."""
+ if not self._is_closed:
+ # Put None to unblock any waiting recv call
+ self.receive_queue.put(None)
+ self._is_closed = True
+ print("Mock WS closed")
+
+ def put_for_recv(self, data):
+ """Puts data into the receive queue for the `recv` method to consume."""
+ if data is None:
+ return
+ # logger.debug("Mock WS put_for_recv length: %s", len(data))
+ self.receive_queue.put(data)
+
+ @property
+ def is_closed(self):
+ return self._is_closed
\ No newline at end of file
diff --git a/src/websockets/adapter.py b/src/websockets/adapter.py
new file mode 100644
index 0000000..31e4a8e
--- /dev/null
+++ b/src/websockets/adapter.py
@@ -0,0 +1,66 @@
+import numpy as np
+from fastapi import WebSocket
+from typing import Union
+import uuid
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+
+class FastAPIWebSocketAdapter:
+ """
+ 一个适配器类，用于将FastAPI的WebSocket对象包装成ASRRunner所期望的接口。
+ 同时处理数据类型转换。
+ """
+ def __init__(self, websocket: WebSocket, sample_rate: int = 16000, sample_width: int = 2):
+ self._ws = websocket
+ self._sample_rate = sample_rate
+ self._sample_width = sample_width
+ self._total_received = 0
+ async def recv(self) -> Union[np.ndarray, None]:
+ """
+ 接收来自FastAPI WebSocket的数据。
+ 如果收到的是字节流，将其转换为Numpy数组。
+ 如果收到的是文本"close"，返回None以表示结束。
+ """
+ message = await self._ws.receive()
+ if 'bytes' in message:
+ bytes_data = message['bytes']
+ audio_array = np.frombuffer(bytes_data, dtype=np.int16)
+ # 将int16转为float32
+ audio_array = audio_array.astype(np.float32)
+ # 归一化
+ audio_array = audio_array / 32768.0
+
+ # 使用回车符 \r 覆盖打印进度
+ self._total_received += len(bytes_data)
+ print(f"🎧 [Adapter] 正在接收音频... 总计: {self._total_received / 1024:.2f} KB", end='\r')
+
+ return audio_array
+ elif 'text' in message and message['text'].lower() == 'close':
+ print("\n🏁 [Adapter] 收到 'close' 信号。") # 在收到结束信号时换行
+ return None # 返回 None 来作为结束信号
+ return np.array([]) # 返回空数组以忽略其他类型的消息
+
+ async def send(self, message: dict):
+ """
+ 将字典消息作为JSON发送给客户端。
+ 在发送前，将所有UUID对象转换为字符串以确保可序列化。
+ """
+ def convert_uuids(obj):
+ if isinstance(obj, dict):
+ return {k: convert_uuids(v) for k, v in obj.items()}
+ elif isinstance(obj, list):
+ return [convert_uuids(elem) for elem in obj]
+ elif isinstance(obj, uuid.UUID):
+ return str(obj)
+ return obj
+
+ serializable_message = convert_uuids(message)
+ logger.info(f"[Adapter] 发送消息: {serializable_message}")
+ await self._ws.send_json(serializable_message)
+
+ async def close(self):
+ """
+ 关闭WebSocket连接。
+ """
+ await self._ws.close()
\ No newline at end of file
diff --git a/src/websockets/endpoint/__init__.py b/src/websockets/endpoint/__init__.py
new file mode 100644
index 0000000..5e3a2bf
--- /dev/null
+++ b/src/websockets/endpoint/__init__.py
@@ -0,0 +1,3 @@
+from .asr_endpoint import router as asr_router
+
+__all__ = ["asr_router"]
\ No newline at end of file
diff --git a/src/websockets/endpoint/asr_endpoint.py b/src/websockets/endpoint/asr_endpoint.py
new file mode 100644
index 0000000..042bb3b
--- /dev/null
+++ b/src/websockets/endpoint/asr_endpoint.py
@@ -0,0 +1,89 @@
+from fastapi import APIRouter, WebSocket, WebSocketDisconnect, Query, Request
+from src.websockets.adapter import FastAPIWebSocketAdapter
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+router = APIRouter()
+
+@router.websocket("/asr/{session_id}")
+async def asr_websocket_endpoint(
+ websocket: WebSocket,
+ session_id: str,
+ mode: str = Query(default="sender", enum=["sender", "receiver"])
+):
+ """
+ ASR WebSocket 端点
+
+ - **session_id**: 标识一个识别会话的唯一ID.
+ - **mode**: 客户端模式.
+ - `sender`: 作为音频发送方加入，将创建一个新的识别会话.
+ - `receiver`: 作为结果接收方加入，订阅一个已存在的会话.
+ """
+ await websocket.accept()
+
+ # 从websocket.app.state获取全局的ASRRunner实例
+ asr_runner = websocket.app.state.asr_runner
+
+ # 创建WebSocket适配器
+ # 注意：这里的audio_config应该与ASRRunner中的默认配置一致
+ audio_config = asr_runner._default_audio_config
+ adapter = FastAPIWebSocketAdapter(
+ websocket,
+ sample_rate=audio_config.sample_rate,
+ sample_width=audio_config.sample_width
+ )
+
+ if mode == "sender":
+ logger.info(f"客户端 {websocket.client} 作为 'sender' 加入会话: {session_id}")
+ # 创建一个新的SAR会话
+ sar_id = asr_runner.new_SAR(ws=adapter, name=session_id)
+ if sar_id is None:
+ logger.error(f"为会话 {session_id} 创建SAR失败")
+ await websocket.close(code=1011, reason="Failed to create ASR session")
+ return
+
+ sar = next((s for s in asr_runner._SAR_list if s._id == sar_id), None)
+ try:
+ # 端点函数等待后台任务完成。
+ # 真正的接收逻辑在SAR的_run方法中，该方法由new_SAR作为后台任务启动。
+ # 当客户端断开连接时，adapter.recv()会抛出异常，
+ # _run任务会捕获它，然后停止并清理，最后任务结束。
+ if sar and sar._task:
+ await sar._task
+ else:
+ # 如果任务没有被创建，记录一个错误并关闭连接
+ logger.error(f"SAR任务未能在会话 {session_id} 中启动")
+ await websocket.close(code=1011, reason="Failed to start ASR task")
+
+ except Exception as e:
+ # 捕获任何意外的错误
+ logger.error(f"会话 {session_id} 的 'sender' 端点发生未知错误: {e}")
+ finally:
+ logger.info(f"'sender' {websocket.client} 在会话 {session_id} 的连接处理已结束")
+
+ elif mode == "receiver":
+ logger.info(f"客户端 {websocket.client} 作为 'receiver' 加入会话: {session_id}")
+ # 加入一个已存在的SAR会话
+ joined = asr_runner.join_SAR(ws=adapter, name=session_id)
+ if not joined:
+ logger.warning(f"无法找到会话 {session_id}，'receiver' {websocket.client} 加入失败")
+ await websocket.close(code=1011, reason=f"Session '{session_id}' not found")
+ return
+
+ try:
+ # Receiver只需要保持连接，等待从SAR广播过来的消息
+ # 这个循环也用于检测断开
+ while True:
+ await websocket.receive_text()
+ except WebSocketDisconnect:
+ logger.info(f"'receiver' {websocket.client} 在会话 {session_id} 中断开连接")
+ # Receiver断开时，需要将其从SAR的接收者列表中移除
+ sar = next((s for s in asr_runner._SAR_list if s._name == session_id), None)
+ if sar:
+ sar.delete_receiver(adapter)
+ logger.info(f"已从会话 {session_id} 中移除 'receiver' {websocket.client}")
+
+ else:
+ # 理论上，由于FastAPI的enum校验，这里的代码不会被执行
+ logger.error(f"无效的模式: {mode}")
+ await websocket.close(code=1003, reason="Invalid mode specified")
\ No newline at end of file
diff --git a/src/websockets/router.py b/src/websockets/router.py
new file mode 100644
index 0000000..489b616
--- /dev/null
+++ b/src/websockets/router.py
@@ -0,0 +1,9 @@
+from fastapi import APIRouter
+from .endpoint import asr_endpoint
+
+websocket_router = APIRouter()
+
+# 包含ASR端点路由
+websocket_router.include_router(asr_endpoint.router)
+
+__all__ = ["websocket_router"]
\ No newline at end of file
diff --git a/test_main.py b/test_main.py
index 596385f..c7a697a 100644
--- a/test_main.py
+++ b/test_main.py
@@ -1,22 +1,26 @@
-from src.utils.logger import get_module_logger, setup_root_logger
-from tests.modelsuse import vad_model_use_online_logic, asr_model_use_offline
-import json
-setup_root_logger(level="INFO",log_file="logs/test_main.log")
+"""
+测试主函数
+请在tests目录下创建测试文件, 并在此文件中调用
+"""
+from tests.pipeline.asr_test import test_asr_pipeline
+from src.utils.logger import get_module_logger, setup_root_logger
+from tests.runner.asr_runner_test import test_asr_runner
+import asyncio
+
+setup_root_logger(level="INFO", log_file="logs/test_main.log")
logger = get_module_logger(__name__)
-logger.info("开始测试")
-vad_result = vad_model_use_online_logic("tests/vad_example.wav")
-logger.info("测试结束")
-if vad_result is None:
- logger.warning("VAD结果为空")
-else:
- logger.info(f"VAD结果: {vad_result}")
+# 清空logs/test_main.log文件
+with open("logs/test_main.log", "w") as f:
+ f.truncate()
-asr_result = asr_model_use_offline("tests/vad_example.wav")
-# asr_result str->dict
-setup_root_logger(level="INFO",log_file="logs/test_main.log")
-result = asr_result[0]['sentence_info']
-for item in result:
- #[{'start': 70, 'end': 2340, 'sentence': '试 错 的 过 程 很 简 单', 'timestamp': [[380, 620], [640, 740], [740, 940], [940, 1020], [1020, 1260], [1500, 1740], [1740, 1840], [1840, 2135]], 'spk': 0}
- logger.info(f"spk[{item['spk']}] [{item['start']}ms:{item['end']}ms] {item['sentence'].replace(' ', '')}")
\ No newline at end of file
+# from tests.functor.vad_test import test_vad_functor
+# logger.info("开始测试VAD函数器")
+# test_vad_functor()
+
+# logger.info("开始测试ASR管道")
+# test_asr_pipeline()
+
+logger.info("开始测试ASRRunner")
+asyncio.run(test_asr_runner())
diff --git a/tests/XT_ZZY.wav b/tests/XT_ZZY.wav
new file mode 100644
index 0000000..f83d78a
Binary files /dev/null and b/tests/XT_ZZY.wav differ
diff --git a/tests/XT_ZZY_denoise.wav b/tests/XT_ZZY_denoise.wav
new file mode 100644
index 0000000..30ae0ba
Binary files /dev/null and b/tests/XT_ZZY_denoise.wav differ
diff --git a/tests/__init__.py b/tests/__init__.py
index 4ffb53d..259625c 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -1 +1 @@
-"""FunASR WebSocket服务测试模块"""
\ No newline at end of file
+"""FunASR WebSocket服务测试模块"""
diff --git a/tests/functor/vad_test.py b/tests/functor/vad_test.py
new file mode 100644
index 0000000..a8cfd13
--- /dev/null
+++ b/tests/functor/vad_test.py
@@ -0,0 +1,124 @@
+"""
+Functor测试
+VAD测试
+"""
+
+from src.functor.vad_functor import VADFunctor
+from src.functor.asr_functor import ASRFunctor
+from src.functor.spk_functor import SPKFunctor
+from queue import Queue, Empty
+from src.core.model_loader import ModelLoader
+from src.models import AudioBinary_Config, AudioBinary_data_list
+from src.utils.data_format import wav_to_bytes
+import time
+from src.utils.logger import get_module_logger
+from pydub import AudioSegment
+import soundfile
+
+# 观察参数
+OVERWATCH = False
+
+logger = get_module_logger(__name__)
+
+model_loader = ModelLoader()
+
+
+def test_vad_functor():
+ # 加载模型
+ args = {
+ "asr_model": "paraformer-zh",
+ "asr_model_revision": "v2.0.4",
+ "vad_model": "fsmn-vad",
+ "vad_model_revision": "v2.0.4",
+ "auto_update": False,
+ }
+ model_loader.load_models(args)
+ # 加载数据
+ f_data, sample_rate = soundfile.read("tests/vad_example.wav")
+ audio_config = AudioBinary_Config(
+ chunk_size=200,
+ chunk_stride=1600,
+ sample_rate=sample_rate,
+ sample_width=16,
+ channels=1,
+ )
+ chunk_stride = int(audio_config.chunk_size * sample_rate / 1000)
+ audio_config.chunk_stride = chunk_stride
+ # 创建输入队列
+ input_queue = Queue()
+ vad2asr_queue = Queue()
+ vad2spk_queue = Queue()
+ # 创建音频数据列表
+ audio_binary_data_list = AudioBinary_data_list()
+
+ # 创建VAD函数器
+ vad_functor = VADFunctor()
+ # 设置输入队列
+ vad_functor.set_input_queue(input_queue)
+ # 设置音频配置
+ vad_functor.set_audio_config(audio_config)
+ # 设置音频数据列表
+ vad_functor.set_audio_binary_data_list(audio_binary_data_list)
+ # 设置回调函数
+ vad_functor.add_callback(lambda x: print(f"vad callback: {x}"))
+ vad_functor.add_callback(lambda x: vad2asr_queue.put(x))
+ vad_functor.add_callback(lambda x: vad2spk_queue.put(x))
+ # 设置模型
+ vad_functor.set_model({"vad": model_loader.models["vad"]})
+ # 启动VAD函数器
+ vad_functor.run()
+
+ # 创建ASR函数器
+ asr_functor = ASRFunctor()
+ # 设置输入队列
+ asr_functor.set_input_queue(vad2asr_queue)
+ # 设置音频配置
+ asr_functor.set_audio_config(audio_config)
+ # 设置回调函数
+ asr_functor.add_callback(lambda x: print(f"asr callback: {x}"))
+ # 设置模型
+ asr_functor.set_model({"asr": model_loader.models["asr"]})
+ # 启动ASR函数器
+ asr_functor.run()
+
+ # 创建SPK函数器
+ spk_functor = SPKFunctor()
+ # 设置输入队列
+ spk_functor.set_input_queue(vad2spk_queue)
+ # 设置音频配置
+ spk_functor.set_audio_config(audio_config)
+ # 设置回调函数
+ spk_functor.add_callback(lambda x: print(f"spk callback: {x}"))
+ # 设置模型
+ spk_functor.set_model(
+ {
+ # 'spk': model_loader.models['spk']
+ "spk": "fake_spk"
+ }
+ )
+ # 启动SPK函数器
+ spk_functor.run()
+
+ f_binary = f_data
+ audio_clip_len = 200
+ print(
+ f"f_binary: {len(f_binary)}, audio_clip_len: {audio_clip_len}, clip_num: {len(f_binary) // audio_clip_len}"
+ )
+ for i in range(0, len(f_binary), audio_clip_len):
+ binary_data = f_binary[i : i + audio_clip_len]
+ input_queue.put(binary_data)
+ # 等待VAD函数器结束
+
+ vad_functor.stop()
+ print("[vad_test] VAD函数器结束")
+
+ asr_functor.stop()
+ print("[vad_test] ASR函数器结束")
+
+ # 保存音频数据
+ if OVERWATCH:
+ for index in range(len(audio_binary_data_list)):
+ save_path = f"tests/vad_test_output_{index}.wav"
+ soundfile.write(
+ save_path, audio_binary_data_list[index].binary_data, sample_rate
+ )
diff --git a/tests/modelsuse.py b/tests/modelsuse.py
index 0554e0c..f66a746 100644
--- a/tests/modelsuse.py
+++ b/tests/modelsuse.py
@@ -1,10 +1,21 @@
+"""
+模型使用测试
+此处主要用于各类调用模型的处理数据与输出格式
+请在主目录下test_main.py中调用
+将需要测试的模型定义在函数中进行测试, 函数名称需要与测试内容匹配。
+"""
+
from funasr import AutoModel
from typing import List, Dict, Any
from src.models import VADResponse
import time
+
def vad_model_use_online(file_path: str) -> List[Dict[str, Any]]:
- chunk_size = 100 # ms
+ """
+ 在线VAD模型使用
+ """
+ chunk_size = 100 # ms
model = AutoModel(model="fsmn-vad", model_revision="v2.0.4", disable_update=True)
vad_result = VADResponse()
@@ -16,12 +27,14 @@ def vad_model_use_online(file_path: str) -> List[Dict[str, Any]]:
chunk_stride = int(chunk_size * sample_rate / 1000)
cache = {}
- total_chunk_num = int(len((speech)-1)/chunk_stride+1)
+ total_chunk_num = int(len((speech) - 1) / chunk_stride + 1)
for i in range(total_chunk_num):
time.sleep(0.1)
- speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
+ speech_chunk = speech[i * chunk_stride : (i + 1) * chunk_stride]
is_final = i == total_chunk_num - 1
- res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
+ res = model.generate(
+ input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size
+ )
if len(res[0]["value"]):
vad_result += VADResponse.from_raw(res)
for item in res[0]["value"]:
@@ -32,42 +45,64 @@ def vad_model_use_online(file_path: str) -> List[Dict[str, Any]]:
# print(item)
return vad_result
+
def vad_model_use_online_logic(file_path: str) -> List[Dict[str, Any]]:
+ """
+ 在线VAD模型使用
+ 测试LogicTrager
+ 在Rebuild版本后LogicTrager中已弃用
+ """
from src.logic_trager import LogicTrager
import soundfile
from src.config import parse_args
+
args = parse_args()
- from src.functor.model_loader import load_models
- models = load_models(args)
+ # from src.functor.model_loader import load_models
+ # models = load_models(args)
+ from src.core.model_loader import ModelLoader
- chunk_size = 200 # ms
+ models = ModelLoader(args)
+
+ chunk_size = 200 # ms
from src.models import AudioBinary_Config
import soundfile
speech, sample_rate = soundfile.read(file_path)
chunk_stride = int(chunk_size * sample_rate / 1000)
- audio_config = AudioBinary_Config(sample_rate=sample_rate, sample_width=2, channels=1, chunk_size=chunk_size)
+ audio_config = AudioBinary_Config(
+ sample_rate=sample_rate, sample_width=2, channels=1, chunk_size=chunk_size
+ )
logic_trager = LogicTrager(models=models, audio_config=audio_config)
- for i in range(len(speech)//chunk_stride+1):
- speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
+ for i in range(len(speech) // chunk_stride + 1):
+ speech_chunk = speech[i * chunk_stride : (i + 1) * chunk_stride]
logic_trager.push_binary_data(speech_chunk)
# for item in items:
# print(item)
return None
+
def asr_model_use_offline(file_path: str) -> List[Dict[str, Any]]:
+ """
+ ASR模型使用
+ 离线ASR模型使用
+ """
from funasr import AutoModel
- model = AutoModel(model="paraformer-zh", model_revision="v2.0.4",
- vad_model="fsmn-vad", vad_model_revision="v2.0.4",
- # punc_model="ct-punc-c", punc_model_revision="v2.0.4",
- spk_model="cam++", spk_model_revision="v2.0.2",
- spk_mode="vad_segment",
- auto_update=False,
- )
+
+ model = AutoModel(
+ model="paraformer-zh",
+ model_revision="v2.0.4",
+ vad_model="fsmn-vad",
+ vad_model_revision="v2.0.4",
+ # punc_model="ct-punc-c", punc_model_revision="v2.0.4",
+ spk_model="cam++",
+ spk_model_revision="v2.0.2",
+ spk_mode="vad_segment",
+ auto_update=False,
+ )
import soundfile
@@ -78,7 +113,9 @@ def asr_model_use_offline(file_path: str) -> List[Dict[str, Any]]:
result = model.generate(speech)
return result
-if __name__ == "__main__":
- # vad_result = vad_model_use_online("tests/vad_example.wav")
- vad_result = vad_model_use_online_logic("tests/vad_example.wav")
- # print(vad_result)
\ No newline at end of file
+
+# if __name__ == "__main__":
+# 请在主目录下调用test_main.py文件进行测试
+# vad_result = vad_model_use_online("tests/vad_example.wav")
+# vad_result = vad_model_use_online_logic("tests/vad_example.wav")
+# print(vad_result)
diff --git a/tests/pipeline/asr_test.py b/tests/pipeline/asr_test.py
new file mode 100644
index 0000000..1efd6b8
--- /dev/null
+++ b/tests/pipeline/asr_test.py
@@ -0,0 +1,94 @@
+"""
+Pipeline测试
+VAD+ASR+SPK(FAKE)
+"""
+
+from src.pipeline.ASRpipeline import ASRPipeline
+from src.pipeline import PipelineFactory
+from src.models import AudioBinary_data_list, AudioBinary_Config
+from src.core.model_loader import ModelLoader
+from queue import Queue
+import soundfile
+import time
+
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+OVAERWATCH = False
+
+model_loader = ModelLoader()
+
+
+def test_asr_pipeline():
+ # 加载模型
+ args = {
+ "asr_model": "paraformer-zh",
+ "asr_model_revision": "v2.0.4",
+ "vad_model": "fsmn-vad",
+ "vad_model_revision": "v2.0.4",
+ "spk_model": "cam++",
+ "spk_model_revision": "v2.0.2",
+ "audio_update": False,
+ }
+ models = model_loader.load_models(args)
+ audio_data, sample_rate = soundfile.read("tests/vad_example.wav")
+ audio_config = AudioBinary_Config(
+ chunk_size=200,
+ chunk_stride=1600,
+ sample_rate=sample_rate,
+ sample_width=16,
+ channels=1,
+ )
+ chunk_stride = int(audio_config.chunk_size * sample_rate / 1000)
+ audio_config.chunk_stride = chunk_stride
+
+ # 创建参数Dict
+ config = {
+ "audio_config": audio_config,
+ }
+
+ # 创建音频数据列表
+ audio_binary_data_list = AudioBinary_data_list()
+
+ input_queue = Queue()
+
+ # 创建Pipeline
+ # asr_pipeline = ASRPipeline()
+ # asr_pipeline.set_models(models)
+ # asr_pipeline.set_config(config)
+ # asr_pipeline.set_audio_binary(audio_binary_data_list)
+ # asr_pipeline.set_input_queue(input_queue)
+ # asr_pipeline.add_callback(lambda x: print(f"pipeline callback: {x}"))
+ # asr_pipeline.bake()
+ asr_pipeline = PipelineFactory.create_pipeline(
+ pipeline_name = "ASRpipeline",
+ models=models,
+ config=config,
+ audio_binary=audio_binary_data_list,
+ input_queue=input_queue,
+ callback=lambda x: print(f"pipeline callback: {x}")
+ )
+
+ asr_pipeline.bake()
+ # 运行Pipeline
+ asr_instance = asr_pipeline.run()
+
+
+ audio_clip_len = 200
+ print(
+ f"audio_data: {len(audio_data)}, audio_clip_len: {audio_clip_len}, clip_num: {len(audio_data) // audio_clip_len}"
+ )
+ for i in range(0, len(audio_data), audio_clip_len):
+ input_queue.put(audio_data[i : i + audio_clip_len])
+
+ # time.sleep(10)
+ # input_queue.put(None)
+
+ # 等待Pipeline结束
+ # asr_instance.join()
+
+ time.sleep(5)
+ asr_pipeline.stop()
+ # asr_pipeline.stop()
diff --git a/tests/runner/asr_runner_test.py b/tests/runner/asr_runner_test.py
new file mode 100644
index 0000000..00fd866
--- /dev/null
+++ b/tests/runner/asr_runner_test.py
@@ -0,0 +1,132 @@
+"""
+ASRRunner test
+"""
+import asyncio
+import soundfile
+import numpy as np
+from src.runner.ASRRunner import ASRRunner
+from src.core.model_loader import ModelLoader
+from src.models import AudioBinary_Config
+from asyncio import Queue as AsyncQueue
+
+from src.utils.logger import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+class AsyncMockWebSocketClient:
+ """一个用于测试目的的异步WebSocket客户端模拟器。"""
+
+ def __init__(self):
+ self._recv_q = AsyncQueue()
+ self._send_q = AsyncQueue()
+
+ def put_for_recv(self, item):
+ """允许测试将数据送入模拟的WebSocket中。"""
+ self._recv_q.put_nowait(item)
+
+ async def get_from_send(self):
+ """允许测试从模拟的WebSocket中获取结果。"""
+ return await self._send_q.get()
+
+ async def recv(self):
+ """ASRRunner将调用此方法来获取数据。"""
+ return await self._recv_q.get()
+
+ async def send(self, item):
+ """ASRRunner将通过回调调用此方法来发送结果。"""
+ logger.info(f"Mock WS 收到结果: {item}")
+ await self._send_q.put(item)
+
+ async def close(self):
+ """一个模拟的关闭方法。"""
+ pass
+
+
+async def test_asr_runner():
+ """
+ 针对ASRRunner的端到端测试，已适配异步操作。
+ 1. 加载模型.
+ 2. 配置并初始化ASRRunner.
+ 3. 创建一个异步的模拟WebSocket客户端.
+ 4. 在Runner中启动一个新的SenderAndReceiver (SAR)实例.
+ 5. 通过模拟的WebSocket流式传输音频数据.
+ 6. 等待处理任务完成并断言其无错误运行.
+ """
+ # 1. 加载模型
+ model_loader = ModelLoader()
+ args = {
+ "asr_model": "paraformer-zh",
+ "asr_model_revision": "v2.0.4",
+ "vad_model": "fsmn-vad",
+ "vad_model_revision": "v2.0.4",
+ "spk_model": "cam++",
+ "spk_model_revision": "v2.0.2",
+ }
+ models = model_loader.load_models(args)
+ audio_file_path = "tests/XT_ZZY_denoise.wav"
+ audio_data, sample_rate = soundfile.read(audio_file_path, dtype='float32')
+ logger.info(
+ f"加载数据: {audio_file_path} , audio_data_length: {len(audio_data)}, audio_data_type: {type(audio_data)}, sample_rate: {sample_rate}"
+ )
+ # 进一步详细打印audio_data数据类型
+ # 详细打印audio_data的类型和结构信息，便于调试
+ logger.info(f"audio_data 类型: {type(audio_data)}")
+ logger.info(f"audio_data dtype: {getattr(audio_data, 'dtype', '未知')}")
+ logger.info(f"audio_data shape: {getattr(audio_data, 'shape', '未知')}")
+ logger.info(f"audio_data ndim: {getattr(audio_data, 'ndim', '未知')}")
+ logger.info(f"audio_data 示例前10个值: {audio_data[:10] if hasattr(audio_data, '__getitem__') else '不可切片'}")
+
+ # 2. 配置音频
+ audio_config = AudioBinary_Config(
+ chunk_size=200, # ms
+ sample_rate=sample_rate,
+ sample_width=2, # 16-bit
+ channels=1,
+ )
+ audio_config.chunk_stride = int(audio_config.chunk_size * sample_rate / 1000)
+
+ # 3. 设置ASRRunner
+ asr_runner = ASRRunner()
+ asr_runner.set_default_config(
+ audio_config=audio_config,
+ models=models,
+ )
+
+ # 4. 创建模拟WebSocket并启动SAR
+ mock_ws = AsyncMockWebSocketClient()
+ sar_id = asr_runner.new_SAR(
+ ws=mock_ws,
+ name="test_sar",
+ )
+ assert sar_id is not None, "创建新的SAR实例失败"
+
+ # 获取SAR实例以等待其任务
+ sar = next((s for s in asr_runner._SAR_list if s._id == sar_id), None)
+ assert sar is not None, "无法从Runner中获取SAR实例。"
+ assert sar._task is not None, "SAR任务未被创建。"
+
+ # 5. 在后台任务中模拟流式音频
+ async def feed_audio():
+ logger.info("Feeder任务已启动：开始流式传输音频数据...")
+ # 每次发送100ms的音频
+ audio_clip_len = int(sample_rate * 0.1)
+ for i in range(0, len(audio_data), audio_clip_len):
+ chunk = audio_data[i : i + audio_clip_len].astype(np.float32)
+ if chunk.size == 0:
+ break
+ mock_ws.put_for_recv(chunk)
+ await asyncio.sleep(0.1) # 模拟实时流
+
+ # 发送None来表示音频流结束
+ mock_ws.put_for_recv(None)
+ logger.info("Feeder任务已完成：所有音频数据已发送。")
+
+ feeder_task = asyncio.create_task(feed_audio())
+
+ # 6. 等待SAR处理完成
+ # SAR任务在从模拟WebSocket接收到None后会结束
+ await sar._task
+ await feeder_task # 确保feeder也已完成
+
+ logger.info("ASRRunner测试成功完成。")
diff --git a/tests/spkverify_use.py b/tests/spkverify_use.py
new file mode 100644
index 0000000..f1a457e
--- /dev/null
+++ b/tests/spkverify_use.py
@@ -0,0 +1,25 @@
+from modelscope.pipelines import pipeline
+sv_pipeline = pipeline(
+ task='speaker-verification',
+ model='iic/speech_campplus_sv_zh-cn_16k-common',
+ model_revision='v1.0.0'
+)
+speaker1_a_wav = 'https://modelscope.cn/api/v1/models/damo/speech_campplus_sv_zh-cn_16k-common/repo?Revision=master&FilePath=examples/speaker1_a_cn_16k.wav'
+speaker1_b_wav = 'https://modelscope.cn/api/v1/models/damo/speech_campplus_sv_zh-cn_16k-common/repo?Revision=master&FilePath=examples/speaker1_b_cn_16k.wav'
+speaker2_a_wav = 'https://modelscope.cn/api/v1/models/damo/speech_campplus_sv_zh-cn_16k-common/repo?Revision=master&FilePath=examples/speaker2_a_cn_16k.wav'
+# 相同说话人语音
+result = sv_pipeline([speaker1_a_wav, speaker1_b_wav])
+print(result)
+# 不同说话人语音
+result = sv_pipeline([speaker1_a_wav, speaker2_a_wav])
+print(result)
+# 可以自定义得分阈值来进行识别，阈值越高，判定为同一人的条件越严格
+result = sv_pipeline([speaker1_a_wav, speaker1_a_wav], thr=0.6)
+print(result)
+# 可以传入output_emb参数，输出结果中就会包含提取到的说话人embedding
+result = sv_pipeline([speaker1_a_wav, speaker2_a_wav], output_emb=True)
+print(result['embs'], result['outputs'])
+# result1 = sv_pipeline([speaker1_a_wav], output_emb=True)
+# print(result1['embs'], result1['outputs'])
+# 可以传入save_dir参数，提取到的说话人embedding会存储在save_dir目录中
+result = sv_pipeline([speaker1_a_wav, speaker2_a_wav], save_dir='savePath/')
\ No newline at end of file
diff --git a/tests/test_config.py b/tests/test_config.py
index 6710998..4540910 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -10,23 +10,23 @@ import os
from unittest.mock import patch
# 将src目录添加到路径
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from src.config import parse_args
def test_default_args():
"""测试默认参数值"""
- with patch('sys.argv', ['script.py']):
+ with patch("sys.argv", ["script.py"]):
args = parse_args()
-
+
# 检查服务器参数
assert args.host == "0.0.0.0"
assert args.port == 10095
-
+
# 检查SSL参数
assert args.certfile == ""
assert args.keyfile == ""
-
+
# 检查模型参数
assert "paraformer" in args.asr_model
assert args.asr_model_revision == "v2.0.4"
@@ -36,7 +36,7 @@ def test_default_args():
assert args.vad_model_revision == "v2.0.4"
assert "punc" in args.punc_model
assert args.punc_model_revision == "v2.0.4"
-
+
# 检查硬件配置
assert args.ngpu == 1
assert args.device == "cuda"
@@ -46,19 +46,26 @@ def test_default_args():
def test_custom_args():
"""测试自定义参数值"""
test_args = [
- 'script.py',
- '--host', 'localhost',
- '--port', '8080',
- '--certfile', 'cert.pem',
- '--keyfile', 'key.pem',
- '--asr_model', 'custom_model',
- '--ngpu', '0',
- '--device', 'cpu'
+ "script.py",
+ "--host",
+ "localhost",
+ "--port",
+ "8080",
+ "--certfile",
+ "cert.pem",
+ "--keyfile",
+ "key.pem",
+ "--asr_model",
+ "custom_model",
+ "--ngpu",
+ "0",
+ "--device",
+ "cpu",
]
-
- with patch('sys.argv', test_args):
+
+ with patch("sys.argv", test_args):
args = parse_args()
-
+
# 检查自定义参数
assert args.host == "localhost"
assert args.port == 8080
@@ -66,4 +73,4 @@ def test_custom_args():
assert args.keyfile == "key.pem"
assert args.asr_model == "custom_model"
assert args.ngpu == 0
- assert args.device == "cpu"
\ No newline at end of file
+ assert args.device == "cpu"
diff --git a/tests/websocket/websocket_asr.py b/tests/websocket/websocket_asr.py
new file mode 100644
index 0000000..6157e77
--- /dev/null
+++ b/tests/websocket/websocket_asr.py
@@ -0,0 +1,110 @@
+import asyncio
+import websockets
+import soundfile as sf
+import uuid
+
+# --- 配置 ---
+HOST = "localhost"
+PORT = 11096
+SESSION_ID = str(uuid.uuid4())
+SENDER_URI = f"ws://{HOST}:{PORT}/ws/asr/{SESSION_ID}?mode=sender"
+RECEIVER_URI = f"ws://{HOST}:{PORT}/ws/asr/{SESSION_ID}?mode=receiver"
+
+AUDIO_FILE_PATH = "tests/XT_ZZY_denoise.wav" # 确保此测试文件存在且为 16kHz, 16-bit, 单声道
+CHUNK_DURATION_MS = 100 # 每次发送100ms的音频数据
+CHUNK_SIZE = int(16000 * 2 * CHUNK_DURATION_MS / 1000) # 3200 bytes
+
+async def run_receiver():
+ """作为接收者连接，并打印收到的所有消息。"""
+ print(f"▶️ [Receiver] 尝试连接到: {RECEIVER_URI}")
+ try:
+ async with websockets.connect(RECEIVER_URI) as websocket:
+ print("✅ [Receiver] 连接成功，等待消息...")
+ try:
+ while True:
+ message = await websocket.recv()
+ print(f"🎧 [Receiver] 收到结果: {message}")
+ except websockets.exceptions.ConnectionClosed as e:
+ print(f"✅ [Receiver] 连接已由服务器正常关闭: {e.reason}")
+ except Exception as e:
+ print(f"❌ [Receiver] 连接失败: {e}")
+
+async def run_sender():
+ """
+ 作为发送者连接，同时负责发送音频和接收自己会话的广播结果。
+ """
+ await asyncio.sleep(1) # 等待receiver有机会先连接
+ print(f"▶️ [Sender] 尝试连接到: {SENDER_URI}")
+ try:
+ async with websockets.connect(SENDER_URI) as websocket:
+ print("✅ [Sender] 连接成功。")
+
+ # --- 并行任务：接收消息 ---
+ async def receive_task():
+ print("▶️ [Sender-Receiver] 开始监听广播消息...")
+ try:
+ while True:
+ message = await websocket.recv()
+ print(f"🎧 [Sender-Receiver] 收到结果: {message}")
+ except websockets.exceptions.ConnectionClosed:
+ print("✅ [Sender-Receiver] 连接已关闭，停止监听。")
+
+ receiver_sub_task = asyncio.create_task(receive_task())
+
+ # --- 主任务：发送音频 ---
+ try:
+ print("▶️ [Sender] 准备发送音频...")
+ audio_data, sample_rate = sf.read(AUDIO_FILE_PATH, dtype='int16')
+ if sample_rate != 16000:
+ print(f"❌ [Sender] 错误：音频文件采样率必须是 16kHz。")
+ receiver_sub_task.cancel()
+ return
+
+ total_samples = len(audio_data)
+ chunk_samples = CHUNK_SIZE // 2
+ samples_sent = 0
+ print(f"音频加载成功，总长度: {total_samples} samples。开始分块发送...")
+
+ for i in range(0, total_samples, chunk_samples):
+ chunk = audio_data[i:i + chunk_samples]
+ if len(chunk) == 0:
+ break
+ await websocket.send(chunk.tobytes())
+ samples_sent += len(chunk)
+ print(f"🎧 [Sender] 正在发送: {samples_sent}/{total_samples} samples", end="\r")
+ await asyncio.sleep(CHUNK_DURATION_MS / 1000)
+
+ print()
+ print("🏁 [Sender] 音频流发送完毕，发送 'close' 信号。")
+ await websocket.send("close")
+
+ except FileNotFoundError:
+ print(f"❌ [Sender] 错误：找不到音频文件 {AUDIO_FILE_PATH}")
+ except Exception as e:
+ print(f"❌ [Sender] 发送过程中发生错误: {e}")
+
+ # 等待接收任务自然结束（当连接关闭时）
+ await receiver_sub_task
+
+ except Exception as e:
+ print(f"❌ [Sender] 连接失败: {e}")
+
+async def main():
+ """同时运行 sender 和 receiver 任务。"""
+ print("--- 开始 WebSocket ASR 服务端到端测试 ---")
+ print(f"会话 ID: {SESSION_ID}")
+
+ # 创建 receiver 和 sender 任务
+ sender_task = asyncio.create_task(run_sender())
+ await asyncio.sleep(7)
+ receiver_task = asyncio.create_task(run_receiver())
+
+ # 等待两个任务完成
+ await asyncio.gather(receiver_task, sender_task)
+
+ print("--- 测试结束 ---")
+
+if __name__ == "__main__":
+ # 在运行此脚本前，请确保 FastAPI 服务器正在运行。
+ # python main.py
+ asyncio.run(main())
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..ea52bc5
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,3332 @@
+version = 1
+revision = 1
+requires-python = ">=3.9.21"
+resolution-markers = [
+ "python_full_version >= '3.13'",
+ "python_full_version == '3.12.*'",
+ "python_full_version == '3.11.*'",
+ "python_full_version == '3.10.*'",
+ "python_full_version < '3.10'",
+]
+
+[[package]]
+name = "addict"
+version = "2.4.0"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/85/ef/fd7649da8af11d93979831e8f1f8097e85e82d5bfeabc8c68b39175d8e75/addict-2.4.0.tar.gz", hash = "sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494", size = 9186 }
+wheels = [
+ { url = "https://pypi.tuna.tsinghua.edu.cn/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl", hash = "sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc", size = 3832 },
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
+wheels = [
+ { url = "https://pypi.tuna.tsinghua.edu.cn/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
+]
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