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PaddleSpeech/demos/audio_searching/README_cn.md

13 KiB

(简体中文|English)

音频相似性检索

介绍

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。

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。

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。

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。

音频检索流程图

注:该 demo 使用 CN-Celeb 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LibrispeechVoxCelebUrbanSoundGloVeMNIST等。

使用方法

1. PaddleSpeech 安装

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

你可以从 easymediumhard 三中方式中选择一种方式安装。

2. MySQL 和 Milvus 安装

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 一键启动这些容器,所以请确保在运行之前已经安装了 Docker EngineDocker Compose。 即

docker-compose -f docker-compose.yaml up -d

然后你会看到所有的容器都被创建:

Creating network "quick_deploy_app_net" with driver "bridge"
Creating milvus-minio    ... done
Creating milvus-etcd     ... done
Creating audio-mysql     ... done
Creating milvus-standalone ... done
Creating audio-webclient     ... done

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:

CONTAINER ID  IMAGE COMMAND CREATED STATUS  PORTS NAMES
b2bcf279e599  milvusdb/milvus:v2.0.1  "/tini -- milvus run…"  22 hours ago  Up 22 hours 0.0.0.0:19530->19530/tcp  milvus-standalone
d8ef4c84e25c  mysql:5.7 "docker-entrypoint.s…"  22 hours ago  Up 22 hours 0.0.0.0:3306->3306/tcp, 33060/tcp audio-mysql
8fb501edb4f3  quay.io/coreos/etcd:v3.5.0  "etcd -advertise-cli…"  22 hours ago  Up 22 hours 2379-2380/tcp milvus-etcd
ffce340b3790  minio/minio:RELEASE.2020-12-03T00-03-10Z  "/usr/bin/docker-ent…"  22 hours ago  Up 22 hours (healthy) 9000/tcp  milvus-minio
15c84a506754  qingen1/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

3. 配置并启动 API 服务

启动系统服务程序,它会提供基于 HTTP 后端服务。

  • 安装服务依赖的 python 基础包

    pip install -r requirements.txt
    
  • 修改配置

    vim src/config.py
    

    请根据实际环境进行修改。 这里列出了一些需要设置的参数,更多信息请参考 config.py

    Parameter Description Default setting
    MILVUS_HOST The IP address of Milvus, you can get it by ifconfig. If running everything on one machine, most likely 127.0.0.1 127.0.0.1
    MILVUS_PORT Port of Milvus. 19530
    VECTOR_DIMENSION Dimension of the vectors. 2048
    MYSQL_HOST The IP address of Mysql. 127.0.0.1
    MYSQL_PORT Port of Milvus. 3306
    DEFAULT_TABLE The milvus and mysql default collection name. audio_table
  • 运行程序

    启动用 Fastapi 构建的服务

    export PYTHONPATH=$PYTHONPATH:./src:../../paddleaudio
    python src/main.py
    

    然后你会看到应用程序启动:

    INFO:     Started server process [13352]
    2022-03-26 22:45:30,838  INFO  server.py  serve  75  Started server process [13352]
    INFO:     Waiting for application startup.
    2022-03-26 22:45:30,839  INFO  on.py  startup  45  Waiting for application startup.
    INFO:     Application startup complete.
    2022-03-26 22:45:30,839  INFO  on.py  startup  59  Application startup complete.
    INFO:     Uvicorn running on http://0.0.0.0:8002 (Press CTRL+C to quit)
    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)
    

4. 测试方法

  • 准备数据

    wget -c https://www.openslr.org/resources/82/cn-celeb_v2.tar.gz && tar -xvf cn-celeb_v2.tar.gz 
    

    注:如果希望快速搭建 demo可以采用 ./src/test_main.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

  • 脚本测试(推荐)

    python ./src/test_main.py
    

    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

    输出:

    Downloading https://paddlespeech.bj.bcebos.com/vector/audio/example_audio.tar.gz ...
    ...
    Unpacking ./example_audio.tar.gz ...
    [2022-03-26 22:50:54,987] [    INFO] - checking the aduio file format......
    [2022-03-26 22:50:54,987] [    INFO] - The sample rate is 16000
    [2022-03-26 22:50:54,987] [    INFO] - The audio file format is right
    [2022-03-26 22:50:54,988] [    INFO] - device type: cpu
    [2022-03-26 22:50:54,988] [    INFO] - load the pretrained model: ecapatdnn_voxceleb12-16k
    [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
    ...
    [2022-03-26 22:51:17,285] [    INFO] - start to dynamic import the model class
    [2022-03-26 22:51:17,285] [    INFO] - model name ecapatdnn
    [2022-03-26 22:51:23,864] [    INFO] - start to set the model parameters to model
    [2022-03-26 22:54:08,115] [    INFO] - create the model instance success
    [2022-03-26 22:54:08,116] [    INFO] - Preprocess audio file: /home/zhaoqingen/PaddleSpeech/demos/audio_
    searching/example_audio/knife_hit_iron3.wav
    [2022-03-26 22:54:08,116] [    INFO] - load the audio sample points, shape is: (11012,)
    [2022-03-26 22:54:08,150] [    INFO] - extract the audio feat, shape is: (80, 69)
    [2022-03-26 22:54:08,152] [    INFO] - feats shape: [1, 80, 69]
    [2022-03-26 22:54:08,154] [    INFO] - audio extract the feat success
    [2022-03-26 22:54:08,155] [    INFO] - start to do backbone network model forward
    [2022-03-26 22:54:08,155] [    INFO] - feats shape:[1, 80, 69], lengths shape: [1]
    [2022-03-26 22:54:08,433] [    INFO] - embedding size: (192,)
    Extracting feature from audio No. 1 , 20 audios in total
    [2022-03-26 22:54:08,435] [    INFO] - checking the aduio file format......
    [2022-03-26 22:54:08,435] [    INFO] - The sample rate is 16000
    [2022-03-26 22:54:08,436] [    INFO] - The audio file format is right
    [2022-03-26 22:54:08,436] [    INFO] - device type: cpu
    [2022-03-26 22:54:08,436] [    INFO] - Model has been initialized
    [2022-03-26 22:54:08,436] [    INFO] - Preprocess audio file: /home/zhaoqingen/PaddleSpeech/demos/audio_searching/example_audio/sword_wielding.wav
    [2022-03-26 22:54:08,436] [    INFO] - load the audio sample points, shape is: (6391,)
    [2022-03-26 22:54:08,452] [    INFO] - extract the audio feat, shape is: (80, 40)
    [2022-03-26 22:54:08,454] [    INFO] - feats shape: [1, 80, 40]
    [2022-03-26 22:54:08,454] [    INFO] - audio extract the feat success
    [2022-03-26 22:54:08,454] [    INFO] - start to do backbone network model forward
    [2022-03-26 22:54:08,455] [    INFO] - feats shape:[1, 80, 40], lengths shape: [1]
    [2022-03-26 22:54:08,633] [    INFO] - embedding size: (192,)
    Extracting feature from audio No. 2 , 20 audios in total
    ...
    2022-03-26 22:54:15,892  INFO  main.py  load_audios  85  Successfully loaded data, total count: 20
    2022-03-26 22:54:15,908  INFO  main.py  count_audio  148  Successfully count the number of data!
    [2022-03-26 22:54:15,916] [    INFO] - checking the aduio file format......
    [2022-03-26 22:54:15,916] [    INFO] - The sample rate is 16000
    [2022-03-26 22:54:15,916] [    INFO] - The audio file format is right
    [2022-03-26 22:54:15,916] [    INFO] - device type: cpu
    [2022-03-26 22:54:15,916] [    INFO] - Model has been initialized
    [2022-03-26 22:54:15,916] [    INFO] - Preprocess audio file: /home/zhaoqingen/PaddleSpeech/demos/audio_searching/example_audio/test.wav
    [2022-03-26 22:54:15,917] [    INFO] - load the audio sample points, shape is: (8456,)
    [2022-03-26 22:54:15,923] [    INFO] - extract the audio feat, shape is: (80, 53)
    [2022-03-26 22:54:15,924] [    INFO] - feats shape: [1, 80, 53]
    [2022-03-26 22:54:15,924] [    INFO] - audio extract the feat success
    [2022-03-26 22:54:15,924] [    INFO] - start to do backbone network model forward
    [2022-03-26 22:54:15,924] [    INFO] - feats shape:[1, 80, 53], lengths shape: [1]
    [2022-03-26 22:54:16,051] [    INFO] - embedding size: (192,)
    ...
    2022-03-26 22:54:16,086  INFO  main.py  search_local_audio  132  search result http://testserver/data?audio_path=./example_audio/test.wav, score 100.0
    2022-03-26 22:54:16,087  INFO  main.py  search_local_audio  132  search result http://testserver/data?audio_path=./example_audio/knife_chopping.wav, score 29.182177782058716
    2022-03-26 22:54:16,087  INFO  main.py  search_local_audio  132  search result http://testserver/data?audio_path=./example_audio/knife_cut_into_body.wav, score 22.73637056350708
    ...
    2022-03-26 22:54:16,088  INFO  main.py  search_local_audio  136  Successfully searched similar audio!
    2022-03-26 22:54:17,164  INFO  main.py  drop_tables  160  Successfully drop tables in Milvus and MySQL!
    
  • 前端测试(可选)

    在浏览器中输入 127.0.0.1:8068 访问前端页面

    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。

    • 上传音频

      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

    • 检索相似音频

      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

5. 结果

机器配置:

  • 操作系统: CentOS release 7.6
  • 内核4.17.11-1.el7.elrepo.x86_64
  • 处理器Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
  • 内存132G

数据集:

  • CN-Celeb, 训练集 65万, 测试集 1万向量维度 192距离计算方式 L2

召回和耗时统计如下图:

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。

6. 预训练模型

以下是 PaddleSpeech 提供的预训练模型列表:

模型 采样率
ecapa_tdnn 16000
panns_cnn6 32000
panns_cnn10 32000
panns_cnn14 32000