NeMo Speaker Recognition Configuration Files

User Guide (Latest Version)

This page covers NeMo configuration file setup that is specific to speaker recognition models. For general information about how to set up and run experiments that is common to all NeMo models (e.g. experiment manager and PyTorch Lightning trainer parameters), see the NeMo Models page.

The model section of NeMo speaker recognition configuration files will generally require information about the dataset(s) being used, the preprocessor for audio files, parameters for any augmentation being performed, as well as the model architecture specification. The sections on this page cover each of these in more detail.

Example configuration files for all of the Speaker related scripts can be found in the config directory of the examples {NEMO_ROOT/examples/speaker_tasks/recognition/conf}.

Training, validation, and test parameters are specified using the train_ds, validation_ds, and test_ds sections of your configuration file, respectively. Depending on the task, you may have arguments specifying the sample rate of your audio files, max time length to consider for each audio file , whether or not to shuffle the dataset, and so on. You may also decide to leave fields such as the manifest_filepath blank, to be specified via the command line at run time.

Any initialization parameters that are accepted for the Dataset class used in your experiment can be set in the config file.

An example TitaNet train and validation configuration could look like ({NEMO_ROOT}examples/speaker_tasks/recognition/conf/titanet-large.yaml):


model: train_ds: manifest_filepath: ??? sample_rate: 16000 labels: None # finds labels based on manifest file batch_size: 32 trim_silence: False shuffle: True validation_ds: manifest_filepath: ??? sample_rate: 16000 labels: None # Keep None, to match with labels extracted during training batch_size: 32 shuffle: False # No need to shuffle the validation data

If you would like to use tarred dataset, have a look at Datasets Configuration.

Preprocessor helps to compute MFCC or mel spectrogram features that are given as inputs to model. For details on how to write this section, refer to Preprocessor Configuration

For TitaNet training we use on-the-fly augmentations with MUSAN and RIR impulses using noise augmentor section

The following example sets up musan augmentation with audio files taken from manifest path and minimum and maximum SNR specified with min_snr and max_snr respectively. This section can be added to train_ds part in model


model: ... train_ds: ... augmentor: noise: manifest_path: /path/to/musan/manifest_file prob: 0.2 # probability to augment the incoming batch audio with augmentor data min_snr_db: 5 max_snr_db: 15

See the API section for more details.

Each configuration file should describe the model architecture being used for the experiment. Models in the NeMo ASR collection need a encoder section and a decoder section, with the _target_ field specifying the module to use for each.

The following sections go into more detail about the specific configurations of each model architecture.

For more information about the TitaNet Encoder models, see the Models page.

After features have been computed from TitaNet encoder, we pass these features to the decoder to compute embeddings and then to compute log probabilities for training models.


model: ... decoder: _target_: nemo.collections.asr.modules.SpeakerDecoder feat_in: *enc_feat_out num_classes: 7205 # Total number of classes in voxceleb1,2 training manifest file pool_mode: attention # xvector, attention emb_sizes: 192 # number of intermediate emb layers. can be comma separated for additional layers like 512,512 angular: true # if true then loss will be changed to angular softmax loss and consider scale and margin from loss section else train with cross-entropy loss loss: scale: 30 margin 0.2

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