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name: distil-whisper description: "Routes Distil-Whisper inference, PyTorch distillation training, and Flax reproduction workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Distil-Whisper

Purpose

Use this skill when the user wants to transcribe audio with Distil-Whisper, choose or compare checkpoints, run the PyTorch distillation stack under training/, or reproduce the original JAX/Flax stack under training/flax/.

First stop

• Read references/model-overview.md to choose a checkpoint and decode path.
• Run scripts/check-env.py to verify the CPU inspection stack before deeper work.
• Read references/repo-provenance.md when deciding whether this skill still matches the current checkout.

Route map

• sub-skills/inference/SKILL.md for checkpoint usage, short-form transcription, long-form transcription, speculative decoding, and inference-speed trade-offs.
• sub-skills/pytorch-training/SKILL.md for pseudo-labelling, student initialization, distillation training, evaluation, and dataset mixing in the PyTorch stack.
• sub-skills/flax-reproduction/SKILL.md for the JAX/Flax package, conversion helpers, long-form transcription, and original reproduction workflows.

Install notes

This repo has two practical dependency surfaces:

• The Flax package under training/flax/.
• The PyTorch training scripts under training/.

For a local checkout, install the Flax package in editable mode and then add the PyTorch training dependencies from the root training/ metadata. The verified inspection environment used a single CPU prefix with Python 3.11, a CPU-only torch wheel, JAX 0.4.18, Flax 0.7.2, SciPy 1.11.4, and the core Hugging Face audio stack. See references/repo-provenance.md for the exact verified versions.

Minimal smoke

1. python scripts/check-env.py
2. python -m pip check
3. Read the relevant sub-skill before running a workflow command.

Runtime boundaries

• Do not tell future agents to open or run files from the original repository checkout when a bundled reference or script exists.
• Do not treat benchmark-only or TPU-host-specific helpers as default user workflows.
• Keep training-scale runs, model downloads, and Hub pushes in the route-specific references unless the user explicitly wants to perform them.