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Add torchcodec install for datasets 4.x audio decoding
jefffffff9 +1: Claude Opus 4.7datasets >= 4.0 uses torchcodec as the audio backend; prepare_dataset would fail with ImportError on batch['audio'] without it. - scripts/runpod_setup.sh: pip install torchcodec with torch-version pinning fallback (0.1/0.2/0.3/0.4 → torch 2.4/2.5/2.6/2.7+) - kaggle_master_trainer.ipynb Cell 2: defensive try/except install so the notebook is self-healing on any fresh environment Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>bb78cbf -
Prepare training stack for RunPod: env-aware notebook + bootstrap script
jefffffff9 +1: Claude Sonnet 4.6Cell 3 now auto-detects Kaggle (/kaggle/working), Colab (/content), RunPod (/workspace), or local — and adjusts defaults per environment. On RunPod the base model defaults to whisper-large-v3-turbo (128 mel bins, matches the Space and fits in 24 GB VRAM); on Kaggle T4 it stays on whisper-small to avoid OOM. Batch size / grad accumulation tuned per card (8x4 on 24 GB vs 16x2 on T4, both yield effective batch 32). TRAIN_LANG, HF_USERNAME, and WHISPER_MODEL_ID are now env-var overridable so the notebook can be driven headlessly from a shell. scripts/runpod_setup.sh bootstraps a fresh pod: - clones the Space repo into /workspace/sahel-voice - installs training deps (peft, evaluate, tensorboard, etc.) that are intentionally absent from the HF Spaces runtime requirements.txt - sources .env for HF_TOKEN; falls back to prompting the user - creates persistent checkpoint dirs on the Volume disk so adapters survive pod stop/start - prints GPU / VRAM sanity check at the end C…9049ef3