Virtual Lab: Speaker Diarization with x-vectors

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EXPLORER

📄 main.py

📄 model_arch.py

📁 datasets

Architecture Overview

Interactive Editor

root@phd-lab-vbox:~# python main.py

root@phd-gpu-cluster:~# python run_speaker_diarization_with_xvectors_experiment.py --use_cuda=True [INFO] Initializing distributed training environment... [INFO] Loading PhD-level module: Speaker Diarization with x-vectors [METRIC] CUDA Memory Allocated: 38 GB [METRIC] TFLOPS Achieved: 138.4 [SUCCESS] Model converged successfully. Gradients stable.

root@phd-lab-vbox:~#