Template for Speaker Identification

This folder provides a working, well-documented example for training a speaker identification model from scratch, based on a few hours of data. The data we use is from Mini Librispeech + OpenRIR.

There are four files here:

• train.py: the main code file, outlines the entire training process.
• train.yaml: the hyperparameters file, sets all parameters of execution.
• custom_model.py: A file containing the definition of a PyTorch module.
• mini_librispeech_prepare.py: If necessary, downloads and prepares data manifests.

To train the speaker-id model, just execute the following on the command-line:

python train.py train.yaml

This will automatically download and prepare the data manifest for mini librispeech, and then train a model with dynamically augmented samples.

More details about what each file does and how to make modifications are found within each file. The whole folder can be copied and used as a starting point for developing recipes doing classification tasks similar to speech speaker-id (e.g, language-id, emotion classification, ..). Please reach out to the SpeechBrain team if any errors are found or clarification is needed about how parts of the template work. Good Luck!

For more information, please take a look into the "speaker-id from scratch" tutorial