---

name: pytorch-training description: "Routes Distil-Whisper pseudo-labelling, student initialization, distillation, and evaluation workflows in the PyTorch stack." disable-model-invocation: true metadata: disco-role: operating license: MIT

PyTorch Training

Purpose

Use this sub-skill when the user wants to generate pseudo-labels, initialize a student Whisper model, run teacher-student distillation, or evaluate the PyTorch training scripts under training/.

Include here

• Pseudo-labelling with training/run_pseudo_labelling.py.
• Student initialization with training/create_student_model.py.
• Distillation with training/run_distillation.py.
• Evaluation with training/run_eval.py.
• Dataset mixing, WER filtering, Hub pushes, and Accelerate-driven launch patterns.

Exclude or route elsewhere

• Direct checkpoint inference belongs in inference.
• JAX/Flax reproduction belongs in flax-reproduction.
• TPU-specific conversion helpers belong in flax-reproduction unless the user explicitly wants the PyTorch stack only.

Read next

• references/workflows.md for the full PyTorch training flow.
• references/troubleshooting.md for dataset, Hub, and training-config failures.
• ../../references/model-overview.md for checkpoint choices that affect student initialization.
• ../../scripts/check-env.py before importing the stack.
• scripts/create_student_model.py when the user wants a bundled initialization helper.

How to route

• "Pseudo-label this dataset" -> start here.
• "Initialize a smaller Whisper student" -> start here.
• "Train the distilled model" -> start here.
• "Run evaluation / compute WER" -> start here.