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# Create Speaker Templates

Training speaker templates involves providing a set of audio samples that contain speech for a specific speaker. Speech Server uses these samples to create a speaker template. There are two main approaches to presenting data for speaker template training.

1. Use a single audio file that contains speech from the speaker.

2. Use a set of audio files that contain speech from the speaker to create a set of audio template feature files, which are then used to generate the speaker template.

Both approaches take a base model, and adapt this to better fit a specific speaker based on the example audio that you provide. The base model is typically the Universal Background Model (UBM) provided in the Speaker ID pack. The template generated is an adaptation of the UBM that uses the training audio that you supply to better represent the speaker.

## Approach 1: Single Audio Source

The simplest approach is to carry out speaker template training in a single step. You can do this by using the `SpkIdTrainWav` task, which takes a single audio file and produces a new speaker template file. Alternatively, if the audio source is a stream rather than a file, use the `SpkIdTrainStream` task.

If you use this approach, you can use only a single audio file to train the template.

## Approach 2: Multiple Audio Files

Alternatively, you can carry out the training in two steps:

1. Use the `SpkIdFeature` task to generate an audio feature file for each audio file that is to be used for training a speaker template.

2. Take the set of feature files generated in Step 1, and use these to train the new speaker template file. The `SpkIdTrain` task is used for this step, which takes a list file listing all the feature files generated in Step 1 as input, and produces a single speaker template file.

The key advantage to this approach is that you can use multiple audio files to train a single speaker template, which is important when considering Iterative Training.

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