Media Server 24.1

# Train a Custom Language Model

To create a new custom language model, use the action `TrainCustomSpeechLanguageModel`.

To train a custom language model

- Train a custom language model using the `TrainCustomSpeechLanguageModel` action. Set the following parameters:

  `identifier` | (Optional) A unique identifier for the custom language model (maximum 254 bytes). If you do not set this parameter, Media Server generates an identifier automatically.
  `languagepack` | The language to base the custom language model on. You cannot train a custom language model with one language and use it with another, so this parameter and the `LanguagePack` parameter in your speech-to-text task must have the same value.
  `textdata` | (Set this or `textpath`, but not both). The text to use for training. Text files must be uploaded as multipart/form-data. For more information about sending data to Media Server, see Send Data by Using a POST Method.
  `textpath` | (Set this or `textdata`, but not both). A comma-separated list of paths to the files that contain the text to use for training. The paths must be absolute, or relative to the Media Server executable file.

  For example:

      curl http://localhost:14000 -F action=TrainCustomSpeechLanguageModel
                                  -F languagepack=ENUK
                                  -F textdata=@sample1.txt,sample2.txt

  Alternatively, the following example provides the paths of the text files, rather than sending the data:

      curl http://localhost:14000 -F action=TrainCustomSpeechLanguageModel
                                  -F languagepack=ENUK
                                  -F textpath=data/sample1.txt,data/sample2.txt

You can list the custom language models that you have trained using the action `ListCustomSpeechLanguageModels`. For more information about the actions that you can use for training custom language models, refer to the Media Server Reference.

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Version 24.1 | Last updated December 2023

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Product: IDOL Media Server 24.1

Topic Title: Train a Custom Language Model