Train your voice model
In this article, you learn how to train a custom neural voice through the Speech Studio portal.
Custom Neural Voice training is currently only available in some regions. After your voice model is trained in a supported region, you can copy it to a Speech resource in another region as needed. See footnotes in the regions table for more information.
Training duration varies depending on how much data you're training. It takes about 40 compute hours on average to train a custom neural voice. Standard subscription (S0) users can train four voices simultaneously. If you reach the limit, wait until at least one of your voice models finishes training, and then try again.
Although the total number of hours required per training method will vary, the same unit price applies to each. For more information, see the Custom Neural training pricing details.
Choose a training method
After you validate your data files, you can use them to build your Custom Neural Voice model. When you create a custom neural voice, you can choose to train it with one of the following methods:
Neural: Create a voice in the same language of your training data, select Neural method.
Neural - cross lingual (Preview): Create a secondary language for your voice model to speak a different language from your training data. For example, with the
zh-CNtraining data, you can create a voice that speaks
en-US. The language of the training data and the target language must both be one of the languages that are supported for cross lingual voice training. You don't need to prepare training data in the target language, but your test script must be in the target language.
Neural - multi style (Preview): Create a custom neural voice that speaks in multiple styles and emotions, without adding new training data. Multi-style voices are particularly useful for video game characters, conversational chatbots, audiobooks, content readers, and more. To create a multi-style voice, you just need to prepare a set of general training data (at least 300 utterances), and select one or more of the preset target speaking styles. You can also create up to 10 custom styles by providing style samples (at least 100 utterances per style) as additional training data for the same voice.
The language of the training data must be one of the languages that are supported for custom neural voice neural, cross-lingual, or multi-style training.
Train your Custom Neural Voice model
To create a custom neural voice in Speech Studio, follow these steps for one of the following methods:
- Sign in to the Speech Studio.
- Select Custom Voice > Your project name > Train model > Train a new model.
- Select Neural as the training method for your model and then select Next. To use a different training method, see Neural - cross lingual or Neural - multi style.
- Select a version of the training recipe for your model. The latest version is selected by default. The supported features and training time can vary by version. Normally, the latest version is recommended for the best results. In some cases, you can choose an older version to reduce training time.
- Select the data that you want to use for training. Duplicate audio names will be removed from the training. Make sure the data you select don't contain the same audio names across multiple .zip files. Only successfully processed datasets can be selected for training. Check your data processing status if you do not see your training set in the list.
- Select a speaker file with the voice talent statement that corresponds to the speaker in your training data.
- Select Next.
- Optionally, you can check the box next to Add my own test script and select test scripts to upload. Each training generates 100 sample audio files automatically, to help you test the model with a default script. You can also provide your own test script with up to 100 utterances for the default style. The generated audio files are a combination of the automatic test scripts and custom test scripts. For more information, see test script requirements.
- Enter a Name and Description to help you identify the model. Choose a name carefully. The model name will be used as the voice name in your speech synthesis request via the SDK and SSML input. Only letters, numbers, and a few punctuation characters are allowed. Use different names for different neural voice models.
- Optionally, enter the Description to help you identify the model. A common use of the description is to record the names of the data that you used to create the model.
- Select Next.
- Select Submit to start training the model.
The Train model table displays a new entry that corresponds to this newly created model. The status reflects the process of converting your data to a voice model, as described in this table:
|Processing||Your voice model is being created.|
|Succeeded||Your voice model has been created and can be deployed.|
|Failed||Your voice model has failed in training. The cause of the failure might be, for example, unseen data problems or network issues.|
|Canceled||The training for your voice model was canceled.|
While the model status is Processing, you can select Cancel training to cancel your voice model. You're not charged for this canceled training.
After you finish training the model successfully, you can review the model details and test the model.
You can use the Audio Content Creation tool in Speech Studio to create audio and fine-tune your deployed voice. If applicable for your voice, one of multiple styles can also be selected.
Rename your model
If you want to rename the model you built, you can select Clone model to create a clone of the model with a new name in the current project.
Enter the new name on the Clone voice model window, then select Submit. The text 'Neural' will be automatically added as a suffix to your new model name.
Test your voice model
After your voice model is successfully built, you can use the generated sample audio files to test it before deploying it for use.
The quality of the voice depends on many factors, such as:
- The size of the training data.
- The quality of the recording.
- The accuracy of the transcript file.
- How well the recorded voice in the training data matches the personality of the designed voice for your intended use case.
Select DefaultTests under Testing to listen to the sample audios. The default test samples include 100 sample audios generated automatically during training to help you test the model. In addition to these 100 audios provided by default, your own test script (at most 100 utterances) provided during training are also added to DefaultTests set. You're not charged for the testing with DefaultTests.
If you want to upload your own test scripts to further test your model, select Add test scripts to upload your own test script.
Before uploading test script, check the test script requirements. You'll be charged for the additional testing with the batch synthesis based on the number of billable characters. See pricing page.
On Add test scripts window, select Browse for a file to select your own script, then select Add to upload it.
Test script requirements
The test script must be a .txt file, less than 1 MB. Supported encoding formats include ANSI/ASCII, UTF-8, UTF-8-BOM, UTF-16-LE, or UTF-16-BE.
Unlike the training transcription files, the test script should exclude the utterance ID (filenames of each utterance). Otherwise, these IDs are spoken.
Here's an example set of utterances in one .txt file:
This is the waistline, and it's falling. We have trouble scoring. It was Janet Maslin.
Each paragraph of the utterance results in a separate audio. If you want to combine all sentences into one audio, make them a single paragraph.
The generated audio files are a combination of the automatic test scripts and custom test scripts.
Update engine version for your voice model
Azure Text-to-Speech engines are updated from time to time to capture the latest language model that defines the pronunciation of the language. After you've trained your voice, you can apply your voice to the new language model by updating to the latest engine version.
When a new engine is available, you're prompted to update your neural voice model.
Go to the model details page, select Update at the top to display Update window.
Then select Update to update your model to the latest engine version.
You're not charged for engine update. The previous versions are still kept. You can check all engine versions for the model from Engine version drop-down list, or remove one if you don't need it anymore.
The updated version is automatically set as default. But you can change the default version by selecting a version from the drop-down list and selecting Set as default.
If you want to test each engine version of your voice model, you can select a version from the drop-down list, then select DefaultTests under Testing to listen to the sample audios. If you want to upload your own test scripts to further test your current engine version, first make sure the version is set as default, then follow the testing steps above.
After you've updated the engine version for your voice model, you need to redeploy this new version. You can only deploy the default version.
For more information, learn more about the capabilities and limits of this feature, and the best practice to improve your model quality.
Copy your voice model to another project
You can copy your voice model to another project for the same region or another region. For example, you can copy a neural voice model that was trained in one region, to a project for another region.
Custom Neural Voice training is currently only available in some regions. But you can easily copy a neural voice model from those regions to other regions. For more information, see the regions for Custom Neural Voice.
To copy your custom neural voice model to another project:
On the Train model tab, select a voice model that you want to copy, and then select Copy to project.
Select the Region, Speech resource, and Project where you want to copy the model. You must have a speech resource and project in the target region, otherwise you need to create them first.
Select Submit to copy the model.
Select View model under the notification message for copy success.
Navigate to the project where you copied the model to deploy the model copy.
Submit and view feedback for