如何通过API或SDK创建Microsoft自定义语音 [英] How to create a Microsoft custom voice through the API or SDK

问题描述

我正在评估Microsoft Custom Voice作为潜在的供应商，并想知道如何通过API或SDK编程地创建和训练自定义声音.

经过广泛的搜索，我只找到了说明如何通过其自定义语音门户创建自定义语音的文档.此页面上有一行提示自定义语音培训API.

下面是该段落以及指向该文档页面的链接.

您能帮我弄清楚该怎么做还是确认不存在这样的API?

准备好数据后，就可以开始将其上传到自定义语音门户，或通过自定义语音培训API.

当前，此页面指向Speech 2.0 API，但看起来很快就会有Microsoft推出3.0.如您所见，如果您查看来自语音门户的网络呼叫(

如何使用它们?

您可以通过门户进行操作，并检查API端调用的内容.

以下是该过程的概述:

1. 使用/datasets/upload 操作创建用于模型训练的数据集
2. 成功处理完数据集后，请使用对/models 的POST请求创建模型(请参见操作/endpoints s的POST(请参阅操作https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/how-to-custom-voice-create-voice

   解决方案

   I guess George's answer is not relevant as you are specially talking of "Custom" voice / speech.

   Link to the APIs

   There are APIs for this part but the documentation is not clear, you are right. You can find at https://westus.cris.ai/swagger/ui/index the API and available operations. Note that it exits in several regions, for example West Europe is https://westeurope.cris.ai/swagger/ui

   Currently, this page points to Speech 2.0 API but it looks like there will be a 3.0 from Microsoft soon. As you can see if you look at the network calls from the Speech portal (link), they are in fact already using this 3.0 api (preview):

   How to use them?

   You can have a look to the process by doing it through the portal and checking what is called on the API side.

   Here is an overview of the process:

   1. Create your dataset for model training, using /datasets/upload operation
   2. Once your dataset has been processed successfully, create a model using a POST request to /models (see operation here). This POST has several details in the body: base model, training dataset used etc. It is this operation which will train your model, you don't need another call to start the training
   3. Once training is completed (you can check the status using GET on /models or a specific GET using the model ID to /models/yourModelId), you can "deploy" it. For that, you have to create an endpoint based on this model: it is a POST to /endpointss (see operation here)
   4. Then you can monitor the deployment status by calling GET on /endpoints or GET by ID, like for models

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