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(简体中文|[English](./README.md))
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# 音频相似性检索
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## 介绍
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随着互联网不断发展，电子邮件、社交媒体照片、直播视频、客服语音等非结构化数据已经变得越来越普遍。如果想要使用计算机来处理这些数据，需要使用 embedding 技术将这些数据转化为向量 vector，然后进行存储、建索引、并查询。
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但是，当数据量很大，比如上亿条音频要做相似度搜索，就比较困难了。穷举法固然可行，但非常耗时。针对这种场景，该 demo 将介绍如何使用开源向量数据库 Milvus 搭建音频相似度检索系统。
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音频检索（如演讲、音乐、说话人等检索）实现了在海量音频数据中查询并找出相似声音（或相同说话人）片段。音频相似性检索系统可用于识别相似的音效、最大限度减少知识产权侵权等，还可以快速的检索声纹库、帮助企业控制欺诈和身份盗用等。在音频数据的分类和统计分析中，音频检索也发挥着重要作用。
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在本 demo 中，你将学会如何构建一个音频检索系统，用来检索相似的声音片段。使用基于 PaddleSpeech 预训练模型（音频分类模型，说话人识别模型等）将上传的音频片段转换为向量数据，并存储在 Milvus 中。Milvus 自动为每个向量生成唯一的 ID，然后将 ID 和 相应的音频信息（音频id，音频的说话人id等等）存储在 MySQL，这样就完成建库的工作。用户在检索时，上传测试音频，得到向量，然后在 Milvus 中进行向量相似度搜索，Milvus 返回的检索结果为向量 ID，通过 ID 在 MySQL 内部查询相应的音频信息即可。
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注：该 demo 使用 [CN-Celeb](http://openslr.org/82/) 数据集，包括至少 650000 条音频，3000 个说话人，来建立音频向量库（音频特征，或音频说话人特征），然后通过预设的距离计算方式进行音频（或说话人）检索，这里面数据集也可以使用其他的，根据需要调整，如Librispeech，VoxCeleb，UrbanSound，GloVe，MNIST等。
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## 使用方法
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### 1. PaddleSpeech 安装
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音频向量的提取需要用到基于 PaddleSpeech 训练的模型，所以请确保在运行之前已经安装了 PaddleSpeech，具体安装步骤，详见[安装文档](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install_cn.md)。
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你可以从 easy，medium，hard 三种方式中选择一种方式安装。
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### 2. MySQL 和 Milvus 安装
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音频相似性的检索需要用到 Milvus, MySQL 服务。 我们可以通过 [docker-compose.yaml](./docker-compose.yaml) 一键启动这些容器，所以请确保在运行之前已经安装了 [Docker Engine](https://docs.docker.com/engine/install/) 和 [Docker Compose](https://docs.docker.com/compose/install/)。 即
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```bash
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## 先进入到 audio_searching 目录，如下示例
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cd ~/PaddleSpeech/demos/audio_searching/
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## 然后启动容器内的相关服务
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docker-compose -f docker-compose.yaml up -d
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```
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你会看到所有的容器都被创建:
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```bash
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Creating network "quick_deploy_app_net" with driver "bridge"
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Creating milvus-minio ... done
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Creating milvus-etcd ... done
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Creating audio-mysql ... done
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Creating milvus-standalone ... done
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Creating audio-webclient ... done
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```
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可以采用'docker ps'来显示所有的容器，还可以使用'docker logs audio-mysql'来获取服务器容器的日志：
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```bash
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CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
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b2bcf279e599 milvusdb/milvus:v2.0.1 "/tini -- milvus run…" 22 hours ago Up 22 hours 0.0.0.0:19530->19530/tcp milvus-standalone
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d8ef4c84e25c mysql:5.7 "docker-entrypoint.s…" 22 hours ago Up 22 hours 0.0.0.0:3306->3306/tcp, 33060/tcp audio-mysql
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8fb501edb4f3 quay.io/coreos/etcd:v3.5.0 "etcd -advertise-cli…" 22 hours ago Up 22 hours 2379-2380/tcp milvus-etcd
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ffce340b3790 minio/minio:RELEASE.2020-12-03T00-03-10Z "/usr/bin/docker-ent…" 22 hours ago Up 22 hours (healthy) 9000/tcp milvus-minio
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15c84a506754 paddlepaddle/paddlespeech-audio-search-client:2.3 "/bin/bash -c '/usr/…" 22 hours ago Up 22 hours (healthy) 0.0.0.0:8068->80/tcp audio-webclient
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```
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### 3. 配置并启动 API 服务
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启动系统服务程序，它会提供基于 HTTP 后端服务。
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- 安装服务依赖的 python 基础包
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```bash
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pip install -r requirements.txt
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```
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- 修改配置(本地运行情况下，一般不用修改，可以跳过该步骤)
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```bash
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## 方法一：修改源码文件
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vim src/config.py
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## 方法二：修改环境变量，如下所示
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export MILVUS_HOST=127.0.0.1
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export MYSQL_HOST=127.0.0.1
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```
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这里列出了一些需要设置的参数，更多信息请参考 [config.py](./src/config.py)
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| **参数** | **描述** | **默认设置** |
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| ---------------- | -------------------- | ------------------- |
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| MILVUS_HOST | Milvus 服务的 IP 地址 | 127.0.0.1 |
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| MILVUS_PORT | Milvus 服务的端口号 | 19530 |
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| VECTOR_DIMENSION | 特征向量的维度 | 192 |
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| MYSQL_HOST | Mysql 服务的 IP 地址 | 127.0.0.1 |
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| MYSQL_PORT | Mysql 服务的端口号 | 3306 |
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| DEFAULT_TABLE | 默认存储的表名 | audio_table |
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- 运行程序
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启动用 Fastapi 构建的服务
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```bash
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export PYTHONPATH=$PYTHONPATH:./src
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python src/audio_search.py
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```
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然后你会看到应用程序启动:
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```bash
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INFO: Started server process [13352]
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2022-03-26 22:45:30,838 ｜ INFO ｜ server.py ｜ serve ｜ 75 ｜ Started server process [13352]
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INFO: Waiting for application startup.
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2022-03-26 22:45:30,839 ｜ INFO ｜ on.py ｜ startup ｜ 45 ｜ Waiting for application startup.
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INFO: Application startup complete.
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2022-03-26 22:45:30,839 ｜ INFO ｜ on.py ｜ startup ｜ 59 ｜ Application startup complete.
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INFO: Uvicorn running on http://0.0.0.0:8002 (Press CTRL+C to quit)
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2022-03-26 22:45:30,840 ｜ INFO ｜ server.py ｜ _log_started_message ｜ 206 ｜ Uvicorn running on http://0.0.0.0:8002 (Press CTRL+C to quit)
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```
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### 4. 测试方法
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- 准备数据
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```bash
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wget -c https://www.openslr.org/resources/82/cn-celeb_v2.tar.gz && tar -xvf cn-celeb_v2.tar.gz
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```
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**注**：如果希望快速搭建 demo，可以采用 ./src/test_audio_search.py:download_audio_data 内部的 20 条音频，另外后续结果展示以该集合为例
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- 准备模型（如果使用默认模型，可以跳过此步骤）
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```bash
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## 修改模型配置参数，目前 model 仅支持 ecapatdnn_voxceleb12，后续将支持多种类型
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vim ./src/encode.py
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```
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- 脚本测试（推荐）
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```bash
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python ./src/test_audio_search.py
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```
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注：内部将依次下载数据，加载 paddlespeech 模型，提取 embedding，存储建库，检索，删库
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输出：
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```bash
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Downloading https://paddlespeech.bj.bcebos.com/vector/audio/example_audio.tar.gz ...
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...
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Unpacking ./example_audio.tar.gz ...
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[2022-03-26 22:50:54,987] [ INFO] - checking the aduio file format......
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[2022-03-26 22:50:54,987] [ INFO] - The sample rate is 16000
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[2022-03-26 22:50:54,987] [ INFO] - The audio file format is right
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[2022-03-26 22:50:54,988] [ INFO] - device type: cpu
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[2022-03-26 22:50:54,988] [ INFO] - load the pretrained model: ecapatdnn_voxceleb12-16k
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[2022-03-26 22:50:54,990] [ INFO] - Downloading sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_0.tar.gz from https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_0.tar.gz
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...
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[2022-03-26 22:51:17,285] [ INFO] - start to dynamic import the model class
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[2022-03-26 22:51:17,285] [ INFO] - model name ecapatdnn
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[2022-03-26 22:51:23,864] [ INFO] - start to set the model parameters to model
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[2022-03-26 22:54:08,115] [ INFO] - create the model instance success
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[2022-03-26 22:54:08,116] [ INFO] - Preprocess audio file: /home/zhaoqingen/PaddleSpeech/demos/audio_
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searching/example_audio/knife_hit_iron3.wav
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[2022-03-26 22:54:08,116] [ INFO] - load the audio sample points, shape is: (11012,)
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[2022-03-26 22:54:08,150] [ INFO] - extract the audio feat, shape is: (80, 69)
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[2022-03-26 22:54:08,152] [ INFO] - feats shape: [1, 80, 69]
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[2022-03-26 22:54:08,154] [ INFO] - audio extract the feat success
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[2022-03-26 22:54:08,155] [ INFO] - start to do backbone network model forward
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[2022-03-26 22:54:08,155] [ INFO] - feats shape:[1, 80, 69], lengths shape: [1]
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[2022-03-26 22:54:08,433] [ INFO] - embedding size: (192,)
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Extracting feature from audio No. 1 , 20 audios in total
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[2022-03-26 22:54:08,435] [ INFO] - checking the aduio file format......
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[2022-03-26 22:54:08,435] [ INFO] - The sample rate is 16000
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[2022-03-26 22:54:08,436] [ INFO] - The audio file format is right
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[2022-03-26 22:54:08,436] [ INFO] - device type: cpu
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[2022-03-26 22:54:08,436] [ INFO] - Model has been initialized
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[2022-03-26 22:54:08,436] [ INFO] - Preprocess audio file: /home/zhaoqingen/PaddleSpeech/demos/audio_searching/example_audio/sword_wielding.wav
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[2022-03-26 22:54:08,436] [ INFO] - load the audio sample points, shape is: (6391,)
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[2022-03-26 22:54:08,452] [ INFO] - extract the audio feat, shape is: (80, 40)
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[2022-03-26 22:54:08,454] [ INFO] - feats shape: [1, 80, 40]
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[2022-03-26 22:54:08,454] [ INFO] - audio extract the feat success
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[2022-03-26 22:54:08,454] [ INFO] - start to do backbone network model forward
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[2022-03-26 22:54:08,455] [ INFO] - feats shape:[1, 80, 40], lengths shape: [1]
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[2022-03-26 22:54:08,633] [ INFO] - embedding size: (192,)
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Extracting feature from audio No. 2 , 20 audios in total
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...
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2022-03-26 22:54:15,892 ｜ INFO ｜ audio_search.py ｜ load_audios ｜ 85 ｜ Successfully loaded data, total count: 20
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2022-03-26 22:54:15,908 ｜ INFO ｜ audio_search.py ｜ count_audio ｜ 148 ｜ Successfully count the number of data!
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[2022-03-26 22:54:15,916] [ INFO] - checking the aduio file format......
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[2022-03-26 22:54:15,916] [ INFO] - The sample rate is 16000
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[2022-03-26 22:54:15,916] [ INFO] - The audio file format is right
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[2022-03-26 22:54:15,916] [ INFO] - device type: cpu
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[2022-03-26 22:54:15,916] [ INFO] - Model has been initialized
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[2022-03-26 22:54:15,916] [ INFO] - Preprocess audio file: /home/zhaoqingen/PaddleSpeech/demos/audio_searching/example_audio/test.wav
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[2022-03-26 22:54:15,917] [ INFO] - load the audio sample points, shape is: (8456,)
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[2022-03-26 22:54:15,923] [ INFO] - extract the audio feat, shape is: (80, 53)
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[2022-03-26 22:54:15,924] [ INFO] - feats shape: [1, 80, 53]
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[2022-03-26 22:54:15,924] [ INFO] - audio extract the feat success
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[2022-03-26 22:54:15,924] [ INFO] - start to do backbone network model forward
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[2022-03-26 22:54:15,924] [ INFO] - feats shape:[1, 80, 53], lengths shape: [1]
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[2022-03-26 22:54:16,051] [ INFO] - embedding size: (192,)
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...
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2022-03-26 22:54:16,086 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/test.wav, score 100.0
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2022-03-26 22:54:16,087 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/knife_chopping.wav, score 29.182177782058716
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2022-03-26 22:54:16,087 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/knife_cut_into_body.wav, score 22.73637056350708
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...
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2022-03-26 22:54:16,088 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 136 ｜ Successfully searched similar audio!
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2022-03-26 22:54:17,164 ｜ INFO ｜ audio_search.py ｜ drop_tables ｜ 160 ｜ Successfully drop tables in Milvus and MySQL!
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```
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- 前端测试（可选）
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在浏览器中输入 127.0.0.1:8068 访问前端页面
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**注**：如果浏览器和服务不在同一台机器上，那么 IP 需要修改成服务所在的机器 IP，并且 docker-compose.yaml 中相应的 API_URL 也要修改，然后重新执行 docker-compose.yaml 文件，使修改生效。
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- 上传音频
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在服务端下载数据并解压到一文件夹，假设为 /home/speech/data/，那么在上传页面地址栏输入 /home/speech/data/ 进行数据上传
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- 检索相似音频
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选择左上角放大镜，点击 “Default Target Audio File” 按钮，从客户端上传测试音频，接着你将看到检索结果
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### 5. 结果
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机器配置：
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- 操作系统: CentOS release 7.6
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- 内核：4.17.11-1.el7.elrepo.x86_64
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- 处理器：Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
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- 内存：132G
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数据集：
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- CN-Celeb, 训练集 65万, 测试集 1万，向量维度 192，距离计算方式 L2
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召回和耗时统计如下图：
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基于 Milvus 的检索框架在召回率 90% 的前提下，检索耗时约 2.9 毫秒，加上特征提取(Embedding)耗时约 500 毫秒(测试音频时长约 5 秒)，即单条音频测试总共耗时约 503 毫秒，可以满足大多数应用场景。
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### 6. 预训练模型
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以下是 PaddleSpeech 提供的预训练模型列表：
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| 模型 | 采样率
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| :--- | :---:
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| ecapa_tdnn| 16000
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