During training, EMA maintains a moving average of the trained parameters. EMA parameters can produce significantly better results and faster convergence for a variety of different domains and models.

EMA is a simple calculation. EMA Weights are pre-initialized with the model weights at the start of training.

Every training update, the EMA weights are updated based on the new model weights.

\[ema_w = ema_w * decay + model_w * (1-decay)\]

Enabling EMA is straightforward. We can pass the additional argument to the experiment manager at runtime.


python examples/asr/asr_ctc/ \ model.train_ds.manifest_filepath=/path/to/my/train/manifest.json \ model.validation_ds.manifest_filepath=/path/to/my/validation/manifest.json \ trainer.devices=2 \ trainer.accelerator='gpu' \ trainer.max_epochs=50 \ exp_manager.ema.enable=True # pass this additional argument to enable EMA

To change the decay rate, pass the additional argument.


python examples/asr/asr_ctc/ \ ... exp_manager.ema.enable=True \ exp_manager.ema.decay=0.999

We also offer other helpful arguments.



exp_manager.ema.validate_original_weights=True Validate the original weights instead of EMA weights.
exp_manager.ema.every_n_steps=2 Apply EMA every N steps instead of every step.
exp_manager.ema.cpu_offload=True Offload EMA weights to CPU. May introduce significant slow-downs.
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