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安装和运行语音服务容器Install and run Speech Service containers

语音容器使客户能够构建一种经过优化的语音应用程序体系结构, 以利用强大的云功能和边缘区域。Speech containers enable customers to build one speech application architecture that is optimized to take advantage of both robust cloud capabilities and edge locality.

这两个语音容器是语音到文本文本到语音转换。The two speech containers are speech-to-text and text-to-speech.

函数Function 功能Features 最新Latest
语音转文本Speech-to-text
  • 转录连续实时语音或批处理音频记录到带有中间结果的文本中。Transcribes continuous real-time speech or batch audio recordings into text with intermediate results.
  • 1.2.01.2.0
    文本到语音转换Text-to-Speech
  • 将文本转换为自然发音的语音。Converts text to natural-sounding speech. 带有纯文本输入或语音合成标记语言 (SSML)。with plain text input or Speech Synthesis Markup Language (SSML).
  • 1.2.01.2.0

    如果没有 Azure 订阅,请在开始之前创建一个免费帐户If you don't have an Azure subscription, create a free account before you begin.

    先决条件Prerequisites

    使用语音容器之前, 必须满足以下先决条件:You must meet the following prerequisites before using Speech containers:

    必填Required 用途Purpose
    Docker 引擎Docker Engine 需要在主计算机上安装 Docker 引擎。You need the Docker Engine installed on a host computer. Docker 提供用于在 macOSWindowsLinux 上配置 Docker 环境的包。Docker provides packages that configure the Docker environment on macOS, Windows, and Linux. 有关 Docker 和容器的基础知识,请参阅 Docker 概述For a primer on Docker and container basics, see the Docker overview.

    必须将 Docker 配置为允许容器连接 Azure 并向其发送账单数据。Docker must be configured to allow the containers to connect with and send billing data to Azure.

    在 Windows 上,还必须将 Docker 配置为支持 Linux 容器。On Windows, Docker must also be configured to support Linux containers.

    熟悉 DockerFamiliarity with Docker 应对 Docker 概念有基本的了解,例如注册表、存储库、容器和容器映像,以及基本的 docker 命令的知识。You should have a basic understanding of Docker concepts, like registries, repositories, containers, and container images, as well as knowledge of basic docker commands.
    语音资源Speech resource 若要使用这些容器,必须具有:In order to use these containers, you must have:

    用于获取关联的 API 密钥和终结点 URI 的 Azure_语音_资源。An Azure Speech resource to get the associated API key and endpoint URI. Azure 门户的 "语音概述" 和 "密钥" 页上都有这两个值。Both values are available on the Azure portal's Speech Overview and Keys pages. 它们都是启动容器所必需的。They are both required to start the container.

    {API_KEY} :"密钥" 页上有两个可用的资源键之一{API_KEY}: One of the two available resource keys on the Keys page

    {ENDPOINT_URI} :"概述" 页中提供的终结点{ENDPOINT_URI}: The endpoint as provided on the Overview page

    正在收集所需参数Gathering required parameters

    所有认知服务容器都需要三个主要参数。There are three primary parameters for all Cognitive Services' containers that are required. 最终用户许可协议(EULA)的值accept必须为。The end-user license agreement (EULA) must be present with a value of accept. 此外,需要终结点 URL 和 API 密钥。Additionally, both an Endpoint URL and API Key are needed.

    备注

    这三个必需参数的唯一例外是将容器视为 "脱机" 容器。The only exception to these three required parameters is when containers are considered "Offline" containers. 脱机容器不报告使用情况,不按流量计费,并遵循其他计费方法。Offline containers do not report usage, are not metered and follow a different billing methodology.

    终结点 URI{ENDPOINT_URI}Endpoint URI {ENDPOINT_URI}

    "终结点URI" 值在相应认知服务资源的 "Azure 门户概述" 页上可用。The Endpoint URI value is available on the Azure portal Overview page of the corresponding Cognitive Service resource. 导航到 "概述" 页,将鼠标悬停在终结点Copy to clipboard上,将显示一个图标。Navigate to the Overview page, hover over the Endpoint, and a Copy to clipboard icon will appear. 复制并在需要时使用。Copy and use where needed.

    收集终结点 uri 供以后使用

    {API_KEY}Keys {API_KEY}

    此密钥用于启动容器,并在相应认知服务资源的 Azure 门户 "密钥" 页上可用。This key is used to start the container, and is available on the Azure portal's Keys page of the corresponding Cognitive Service resource. 导航到 "密钥" 页,然后单击Copy to clipboard 图标。Navigate to the Keys page, and click on the Copy to clipboard icon.

    获取两个密钥之一供以后使用

    重要

    这些订阅密钥用于访问认知服务 API。These subscription keys are used to access your Cognitive Service API. 不要共享你的密钥。Do not share your keys. 安全地存储它们,例如,使用 Azure Key Vault。Store them securely, for example, using Azure Key Vault. 我们还建议定期重新生成这些密钥。We also recommend regenerating these keys regularly. 只需一个密钥就能进行 API 调用。Only one key is necessary to make an API call. 重新生成第一个密钥时,可以使用第二个密钥来继续访问该服务。When regenerating the first key, you can use the second key for continued access to the service.

    请求访问容器注册表Request access to the container registry

    必须先完成并提交认知服务语音容器请求表单, 才能请求访问容器。You must first complete and submit the Cognitive Services Speech Containers Request form to request access to the container.

    通过该表单请求有关你、你的公司以及要使用该容器的用户方案的信息。The form requests information about you, your company, and the user scenario for which you'll use the container. 提交表单后,Azure 认知服务团队可以检查它,确保你满足访问专用容器注册表的条件。After you've submitted the form, the Azure Cognitive Services team reviews it to ensure that you meet the criteria for access to the private container registry.

    重要

    必须使用与表单中的 Microsoft 帐户 (MSA) 或 Azure Active Directory (Azure AD) 帐户关联的电子邮件地址。You must use an email address that's associated with either a Microsoft Account (MSA) or Azure Active Directory (Azure AD) account in the form.

    如果请求获得批准,则你会收到一封电子邮件,其中说明了如何获取凭据和访问专用容器注册表。If your request is approved, you'll receive an email with instructions that describe how to obtain your credentials and access the private container registry.

    使用 Docker CLI 对专用容器注册表进行身份验证Use the Docker CLI to authenticate the private container registry

    可通过多种方法中的任何一种使用认知服务容器的专用容器注册表进行身份验证,但建议的方法是在 Docker CLI 中使用命令行。You can authenticate with the private container registry for Cognitive Services Containers in any of several ways, but the recommended method from the command line is to use the Docker CLI.

    使用 docker login 命令(如以下示例所示)登录到 containerpreview.azurecr.io,即认知服务容器的专用容器注册表。Use the docker login command, as shown in the following example, to log in to containerpreview.azurecr.io, the private container registry for Cognitive Services Containers. 将 <username> 替换为用户名,将 <password> 替换为从 Azure 认知服务团队收到的凭据中提供的密码 。Replace <username> with the user name and <password> with the password that's provided in the credentials you received from the Azure Cognitive Services team.

    docker login containerpreview.azurecr.io -u <username> -p <password>
    

    如果已在文本文件中保护了凭据,则可以使用 cat 命令将该文本文件的内容连接到 docker login 命令,如以下示例所示。If you've secured your credentials in a text file, you can concatenate the contents of that text file, by using the cat command, to the docker login command, as shown in the following example. 将 <passwordFile> 替换为包含密码的文本文件的路径和名称,将 <username> 替换为凭据中提供的用户名 。Replace <passwordFile> with the path and name of the text file that contains the password and <username> with the user name that's provided in your credentials.

    cat <passwordFile> | docker login containerpreview.azurecr.io -u <username> --password-stdin
    

    主计算机The host computer

    主机是运行 Docker 容器且基于 x64 的计算机。The host is a x64-based computer that runs the Docker container. 它可以是本地计算机或 Azure 中的 Docker 托管服务,例如:It can be a computer on your premises or a Docker hosting service in Azure, such as:

    高级矢量扩展支持Advanced Vector Extension support

    主机是运行 docker 容器的计算机。The host is the computer that runs the docker container. 主机必须支持高级矢量扩展(AVX2)。The host must support Advanced Vector Extensions (AVX2). 可以通过以下命令在 Linux 主机上查看此支持:You can check this support on Linux hosts with the following command:

    grep -q avx2 /proc/cpuinfo && echo AVX2 supported || echo No AVX2 support detected
    

    容器要求和建议Container requirements and recommendations

    下表介绍了为每个语音容器分配的最低和推荐的 CPU 内核和内存。The following table describes the minimum and recommended CPU cores and memory to allocate for each Speech container.

    容器Container 最低要求Minimum 建议Recommended
    cognitive-services-speech-to-textcognitive-services-speech-to-text 2核心2 core
    2 GB 内存2-GB memory
    4核4 core
    4 GB 内存4-GB memory
    cognitive-services-text-to-speechcognitive-services-text-to-speech 1核, 0.5 GB 内存1 core, 0.5-GB memory 2核, 1 GB 内存2 core, 1-GB memory
    • 每个核心必须至少为 2.6 千兆赫 (GHz) 或更快。Each core must be at least 2.6 gigahertz (GHz) or faster.

    核心和内存对应于 --cpus--memory 设置,用作 docker run 命令的一部分。Core and memory correspond to the --cpus and --memory settings, which are used as part of the docker run command.

    注意;最小和推荐的基于 Docker 限制, 而是主机资源。Note; The minimum and recommended are based off of Docker limits, not the host machine resources. 例如, "语音到文本" 容器内存映射大型语言模型的部分, 并_建议_将整个文件放在内存中, 这是额外的 4-6 GB。For example, speech-to-text containers memory map portions of a large language model, and it is recommended that the entire file fits in memory, which is an additional 4-6 GB. 另外, 由于模型正在分页到内存中, 因此第一次运行的容器可能需要更长时间。Also, the first run of either container may take longer, since models are being paged into memory.

    使用 docker pull 获取容器映像Get the container image with docker pull

    可用的语音容器图像。Container images for Speech are available.

    容器Container 存储库Repository
    cognitive-services-speech-to-textcognitive-services-speech-to-text containerpreview.azurecr.io/microsoft/cognitive-services-speech-to-text:latest
    cognitive-services-text-to-speechcognitive-services-text-to-speech containerpreview.azurecr.io/microsoft/cognitive-services-text-to-speech:latest

    提示

    可以使用 docker images 命令列出下载的容器映像。You can use the docker images command to list your downloaded container images. 例如,以下命令以表格列出每个下载的容器映像的 ID、存储库和标记:For example, the following command lists the ID, repository, and tag of each downloaded container image, formatted as a table:

    docker images --format "table {{.ID}}\t{{.Repository}}\t{{.Tag}}"
    
    IMAGE ID         REPOSITORY                TAG
    <image-id>       <repository-path/name>    <tag-name>
    

    语言区域设置在容器标记中Language locale is in container tag

    标记将提取区域设置和jessarus语音。 en-us latestThe latest tag pulls the en-us locale and jessarus voice.

    语音到文本区域设置Speech to text locales

    除之外latest的所有标记均采用以下格式, <culture>其中指示区域设置容器:All tags, except for latest are in the following format, where the <culture> indicates the locale container:

    <major>.<minor>.<patch>-<platform>-<culture>-<prerelease>
    

    以下标记是以下格式的示例:The following tag is an example of the format:

    1.2.0-amd64-en-us-preview
    

    下表列出了1.2.0 版本的容器中的语音到文本的支持区域设置:The following table lists the supported locales for speech-to-text in the 1.2.0 version of the container:

    语言区域设置Language locale TagsTags
    中文Chinese zh-cn
    英语English en-us
    en-gb
    en-au
    en-in
    法语French fr-ca
    fr-fr
    德语German de-de
    意大利语Italian it-it
    日语Japanese ja-jp
    韩语Korean ko-kr
    葡萄牙语Portuguese pt-br
    西班牙语Spanish es-es
    es-mx

    文本到语音区域设置Text to speech locales

    除之外latest的所有标记均采用以下格式, <culture>其中<voice>指示区域设置, 指示容器的语音:All tags, except for latest are in the following format, where the <culture> indicates the locale and the <voice> indicates the voice of the container:

    <major>.<minor>.<patch>-<platform>-<culture>-<voice>-<prerelease>
    

    以下标记是以下格式的示例:The following tag is an example of the format:

    1.2.0-amd64-en-us-jessarus-preview
    

    下表列出了1.2.0 版本的容器中的文本到语音功能的支持区域设置:The following table lists the supported locales for text-to-speech in the 1.2.0 version of the container:

    语言区域设置Language locale TagsTags 支持的语音Supported voices
    中文Chinese zh-cn huihuirushuihuirus
    kangkang-apollokangkang-apollo
    yaoyao-apolloyaoyao-apollo
    英语English en-au catherinecatherine
    hayleyrushayleyrus
    英语English en-gb george-apollogeorge-apollo
    hazelrushazelrus
    susan-apollosusan-apollo
    英语English en-in heera-apolloheera-apollo
    priyaruspriyarus
    ravi-apolloravi-apollo
    英语English en-us jessarusjessarus
    benjaminrusbenjaminrus
    jessa24krusjessa24krus
    ziraruszirarus
    guy24krusguy24krus
    法语French fr-ca carolinecaroline
    harmonierusharmonierus
    法语French fr-fr hortenserushortenserus
    julie-apollojulie-apollo
    paul-apollopaul-apollo
    德语German de-de heddahedda
    heddarusheddarus
    stefan-apollostefan-apollo
    意大利语Italian it-it cosimo-apollocosimo-apollo
    luciarusluciarus
    日语Japanese ja-jp ayumi-apolloayumi-apollo
    harukarusharukarus
    ichiro-apolloichiro-apollo
    韩语Korean ko-kr heamirusheamirus
    葡萄牙语Portuguese pt-br daniel-apollodaniel-apollo
    heloisarusheloisarus
    西班牙语Spanish es-es elenaruselenarus
    刘娜-apollolaura-apollo
    pablo-apollopablo-apollo
    西班牙语Spanish es-mx hildarushildarus
    raul-apolloraul-apollo

    用于语音容器的 Docker 拉取Docker pull for the speech containers

    语音转文本Speech-to-text

    docker pull containerpreview.azurecr.io/microsoft/cognitive-services-speech-to-text:latest
    

    文本转语音Text-to-speech

    docker pull containerpreview.azurecr.io/microsoft/cognitive-services-text-to-speech:latest
    

    如何使用容器How to use the container

    一旦容器位于主计算机上,请通过以下过程使用容器。Once the container is on the host computer, use the following process to work with the container.

    1. 使用所需的计费设置运行容器Run the container, with the required billing settings. 提供 docker run 命令的多个示例More examples of the docker run command are available.
    2. 查询容器的预测终结点Query the container's prediction endpoint.

    通过 docker run 运行容器Run the container with docker run

    使用 docker run 命令运行容器。Use the docker run command to run the container. 有关如何获取{ENDPOINT_URI}{API_KEY}值的详细信息,请参阅收集必需的参数Refer to gathering required parameters for details on how to get the {ENDPOINT_URI} and {API_KEY} values.

    可用的命令示例 docker runExamples of the docker run command are available.

    备注

    预览期间, 计费设置必须有效才能启动容器, 但不会对使用情况收费。During the preview, the billing settings must be valid to start the container, but you aren't billed for usage.

    文本转语音Text-to-speech

    docker run --rm -it -p 5000:5000 --memory 2g --cpus 1 \
    containerpreview.azurecr.io/microsoft/cognitive-services-text-to-speech \
    Eula=accept \
    Billing={ENDPOINT_URI} \
    ApiKey={API_KEY}
    

    语音转文本Speech-to-text

    docker run --rm -it -p 5000:5000 --memory 2g --cpus 2 \
    containerpreview.azurecr.io/microsoft/cognitive-services-speech-to-text \
    Eula=accept \
    Billing={ENDPOINT_URI} \
    ApiKey={API_KEY}
    

    此命令:This command:

    • 从容器映像运行语音容器Runs a Speech container from the container image
    • 分配2个 CPU 内核和2千兆字节 (GB) 的内存Allocates 2 CPU cores and 2 gigabytes (GB) of memory
    • 公开 TCP 端口 5000,并为容器分配伪 TTYExposes TCP port 5000 and allocates a pseudo-TTY for the container
    • 退出后自动删除容器。Automatically removes the container after it exits. 容器映像在主计算机上仍然可用。The container image is still available on the host computer.

    重要

    必须指定 EulaBillingApiKey 选项运行容器;否则,该容器不会启动。The Eula, Billing, and ApiKey options must be specified to run the container; otherwise, the container won't start. 有关详细信息,请参阅计费For more information, see Billing.

    查询容器的预测终结点Query the container's prediction endpoint

    容器Container 终结点Endpoint
    语音转文本Speech-to-text ws://localhost:5000/speech/recognition/dictation/cognitiveservices/v1ws://localhost:5000/speech/recognition/dictation/cognitiveservices/v1
    文本转语音Text-to-speech http://localhost:5000/speech/synthesize/cognitiveservices/v1

    语音转文本Speech-to-text

    容器提供基于 websocket 的查询终结点 Api, 可通过语音 SDK进行访问。The container provides websocket-based query endpoint APIs, that are accessed through the Speech SDK.

    默认情况下, 语音 SDK 使用联机语音服务。By default, the Speech SDK uses online speech services. 若要使用该容器,需要更改初始化方法。To use the container, you need to change the initialization method. 请参阅下面的示例。See the examples below.

    对于 C#For C#

    请从使用此 Azure 云初始化调用:Change from using this Azure-cloud initialization call:

    var config = SpeechConfig.FromSubscription("YourSubscriptionKey", "YourServiceRegion");
    

    更改为使用容器终结点发出此调用:to this call using the container endpoint:

    var config = SpeechConfig.FromEndpoint(
        new Uri("ws://localhost:5000/speech/recognition/dictation/cognitiveservices/v1"),
        "YourSubscriptionKey");
    

    对于 PythonFor Python

    请从使用此 Azure 云初始化调用Change from using this Azure-cloud initialization call

    speech_config = speechsdk.SpeechConfig(
        subscription=speech_key, region=service_region)
    

    更改为使用容器终结点发出此调用:to this call using the container endpoint:

    speech_config = speechsdk.SpeechConfig(
        subscription=speech_key, endpoint="ws://localhost:5000/speech/recognition/dictation/cognitiveservices/v1")
    

    文本转语音Text-to-speech

    容器提供了 REST 终结点 Api, 可以在此处找到,此处提供了示例。The container provides REST endpoint APIs that can be found here and samples can be found here.

    验证容器是否正在运行Validate that a container is running

    有几种方法可用于验证容器是否正在运行。There are several ways to validate that the container is running. 找到相关容器的外部 IP地址和公开端口, 并打开你喜欢的 web 浏览器。Locate the External IP address and exposed port of the container in question, and open your favorite web browser. 使用以下各种请求 Url 来验证容器是否正在运行。Use the various request URLs below to validate the container is running. 下面列出的示例请求 url 为http://localhost:5000, 但特定容器可能会有所不同。The example request URLs listed below are http://localhost:5000, but your specific container may vary. 请记住, 你要依赖容器的外部 IP地址和公开的端口。Keep in mind that you're to rely on your container's External IP address and exposed port.

    请求 URLRequest URL 用途Purpose
    http://localhost:5000/ 容器提供主页。The container provides a home page.
    http://localhost:5000/status 使用 HTTP GET 请求, 用于验证容器是否正在运行而不会导致终结点查询。Requested with an HTTP GET, to validate that the container is running without causing an endpoint query. 此请求可用于 Kubernetes 运行情况和就绪情况探测This request can be used for Kubernetes liveness and readiness probes.
    http://localhost:5000/swagger 该容器提供了一套完整的端点文档和一项试用功能。The container provides a full set of documentation for the endpoints and a Try it out feature. 使用此功能可以将设置输入到基于 Web 的 HTML 表单并进行查询,而无需编写任何代码。With this feature, you can enter your settings into a web-based HTML form and make the query without having to write any code. 查询返回后,将提供示例 CURL 命令,用于演示所需的 HTTP 标头和正文格式。After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that's required.

    容器的主页

    停止容器Stop the container

    若要关闭的情况下该容器,其中容器正在运行,在命令行环境中选择Ctrl + CTo shut down the container, in the command-line environment where the container is running, select Ctrl+C.

    疑难解答Troubleshooting

    运行该容器时,该容器将使用 stdoutstderr 来输出信息,这些信息有助于排查启动或运行容器时发生的问题。When you run the container, the container uses stdout and stderr to output information that is helpful to troubleshoot issues that happen while starting or running the container.

    帐单Billing

    语音容器使用 Azure 帐户中的_语音_资源将计费信息发送到 azure。The Speech containers send billing information to Azure, using a Speech resource on your Azure account.

    对该容器的查询在用于 <ApiKey> 的 Azure 资源的定价层计费。Queries to the container are billed at the pricing tier of the Azure resource that's used for the <ApiKey>.

    如果未连接到计费终结点进行计量,则 Azure 认知服务容器不会被许可运行。Azure Cognitive Services containers aren't licensed to run without being connected to the billing endpoint for metering. 必须始终让容器可以向计费终结点传送计费信息。You must enable the containers to communicate billing information with the billing endpoint at all times. 认知服务容器不会将客户数据(例如,正在分析的图像或文本)发送给 Microsoft。Cognitive Services containers don't send customer data, such as the image or text that's being analyzed, to Microsoft.

    连接到 AzureConnect to Azure

    容器需要计费参数值才能运行。The container needs the billing argument values to run. 这些值使容器可以连接到计费终结点。These values allow the container to connect to the billing endpoint. 容器约每 10 到 15 分钟报告一次使用情况。The container reports usage about every 10 to 15 minutes. 如果容器未在允许的时间范围内连接到 Azure,容器将继续运行,但不会为查询提供服务,直到计费终结点恢复。If the container doesn't connect to Azure within the allowed time window, the container continues to run but doesn't serve queries until the billing endpoint is restored. 尝试连接按 10 到 15 分钟的相同时间间隔进行 10 次。The connection is attempted 10 times at the same time interval of 10 to 15 minutes. 如果无法在 10 次尝试内连接到计费终结点,容器将停止运行。If it can't connect to the billing endpoint within the 10 tries, the container stops running.

    计费参数Billing arguments

    必须使用有效值指定所有以下三个选项,才能使 docker run 命令启动容器:For the docker run command to start the container, all three of the following options must be specified with valid values:

    OptionOption 描述Description
    ApiKey 用于跟踪计费信息的认知服务资源的 API 密钥。The API key of the Cognitive Services resource that's used to track billing information.
    必须将此选项的值设置为 Billing 中指定的已预配资源的 API 密钥。The value of this option must be set to an API key for the provisioned resource that's specified in Billing.
    Billing 用于跟踪计费信息的认知服务资源的终结点。The endpoint of the Cognitive Services resource that's used to track billing information.
    必须将此选项的值设置为已预配的 Azure 资源的终结点 URI。The value of this option must be set to the endpoint URI of a provisioned Azure resource.
    Eula 表示已接受容器的许可条款。Indicates that you accepted the license for the container.
    此选项的值必须设置为 acceptThe value of this option must be set to accept.

    有关这些选项的详细信息,请参阅配置容器For more information about these options, see Configure containers.

    博客文章Blog posts

    开发人员示例Developer samples

    可在 GitHub 存储库中查看开发人员示例。Developer samples are available at our GitHub repository.

    观看网络研讨会View webinar

    加入网络研讨会了解:Join the webinar to learn about:

    • 如何将认知服务部署到任何使用 Docker 的计算机How to deploy Cognitive Services to any machine using Docker
    • 如何将认知服务部署到 AKSHow to deploy Cognitive Services to AKS

    总结Summary

    本文介绍了用于下载、安装和运行语音容器的概念和工作流。In this article, you learned concepts and workflow for downloading, installing, and running Speech containers. 综上所述:In summary:

    • 语音为 Docker 提供两个 Linux 容器, 将语音封装到文本和文本到语音。Speech provides two Linux containers for Docker, encapsulating speech to text and text to speech.
    • 可从 Azure 中的专用容器注册表下载容器映像。Container images are downloaded from the private container registry in Azure.
    • 容器映像在 Docker 中运行。Container images run in Docker.
    • 您可以使用 REST API 或 SDK 通过指定容器的主机 URI 来调用语音容器中的操作。You can use either the REST API or SDK to call operations in Speech containers by specifying the host URI of the container.
    • 在实例化容器时, 需要提供计费信息。You're required to provide billing information when instantiating a container.

    重要

    如果未连接到 Azure 进行计量,则无法授权并运行认知服务容器。Cognitive Services containers are not licensed to run without being connected to Azure for metering. 客户需要始终让容器向计量服务传送账单信息。Customers need to enable the containers to communicate billing information with the metering service at all times. 认知服务容器不会将客户数据(例如,正在分析的图像或文本)发送给 Microsoft。Cognitive Services containers do not send customer data (e.g., the image or text that is being analyzed) to Microsoft.

    后续步骤Next steps