Two NEST SSL model-support jobs are failing because the synthetic AudioNoiseBatch used by the generated training-step test produces a non-finite loss.
Failures from run https://github.com/NVIDIA-NeMo/NeMo/actions/runs/27729959354?pr=15802:
Both jobs pass model init and inference; only the direct synthetic training_step() check fails. The test constructs random audio, noise, and independent random noisy_audio, which may not be a valid training sample for these restored NEST SSL artifacts.
Suggested follow-up: replace the synthetic batch with a minimal valid sample for these models, or keep the generated model-support training-step check disabled for these two artifacts while preserving init and inference coverage.
Two NEST SSL model-support jobs are failing because the synthetic
AudioNoiseBatchused by the generated training-step test produces a non-finite loss.Failures from run https://github.com/NVIDIA-NeMo/NeMo/actions/runs/27729959354?pr=15802:
L2_Model_Support_nvidia__ssl_en_nest_large_v1_0tests/e2e_nightly/test_model_support_nvidia__ssl_en_nest_large_v1_0.py::test_model_training_stepAssertionError: Loss is not finite: nanL2_Model_Support_nvidia__ssl_en_nest_xlarge_v1_0tests/e2e_nightly/test_model_support_nvidia__ssl_en_nest_xlarge_v1_0.py::test_model_training_stepAssertionError: Loss is not finite: nanBoth jobs pass model init and inference; only the direct synthetic
training_step()check fails. The test constructs randomaudio,noise, and independent randomnoisy_audio, which may not be a valid training sample for these restored NEST SSL artifacts.Suggested follow-up: replace the synthetic batch with a minimal valid sample for these models, or keep the generated model-support training-step check disabled for these two artifacts while preserving init and inference coverage.