Checkpoints

There are two main ways to load pretrained checkpoints in NeMo as introduced in loading ASR checkpoints. In speaker diarization, the diarizer loads checkpoints that are passed through the config file. For example:

Loading Local Checkpoints

Load VAD model

pretrained_vad_model='/path/to/vad_marblenet.nemo' # local .nemo or pretrained vad model name
...
# pass with hydra config
config.diarizer.vad.model_path=pretrained_vad_model

Load Speaker Embedding model

pretrained_speaker_model='/path/to/ecapa_tdnn.nemo' # local .nemo or pretrained speakernet model name
...
# pass with hydra config
config.diarizer.speaker_embeddings.model_path=pretrained_speaker_model

NeMo will automatically save checkpoints of a model you are training in a .nemo format. You can also manually save your models at any point using model.save_to(<checkpoint_path>.nemo).

Inference

Note

For details and deep understanding, please refer to <NeMo_git_root>/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb.

Check out Datasets for preparing audio files and optional label files.

Run and evaluate speaker diarizer with below command:

# Have a look at the instruction inside the script and pass the arguments you might need.
python <NeMo_git_root>/examples/speaker_tasks/diarization/speaker_diarize.py

NGC Pretrained Checkpoints

The ASR collection has checkpoints of several models trained on various datasets for a variety of tasks. These checkpoints are obtainable via NGC NeMo Automatic Speech Recognition collection. The model cards on NGC contain more information about each of the checkpoints available.

In general, you can load models with model name in the following format,

pretrained_vad_model='vad_telephony_marblenet'
pretrained_speaker_model='ecapa_tdnn'
...
config.diarizer.vad.model_path=retrained_vad_model \
config.diarizer.speaker_embeddings.model_path=pretrained_speaker_model

where the model name is the value under “Model Name” entry in the tables below.

Models for Speaker Diarization

Model Name

Model Base Class

Model Card

vad_marblenet

EncDecClassificationModel

https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_marblenet

vad_telephony_marblenet

EncDecClassificationModel

https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet

ecapa_tdnn

EncDecSpeakerLabelModel

https://ngc.nvidia.com/catalog/models/nvidia:nemo:ecapa_tdnn