Training Pipeline
Export approved + starred audio to HuggingFace, then fine-tune Whisper. Runs on RunPod.
← Back to workbench
1. Export Dataset
2. Train Whisper
Source filters
Library
jemedia (JEM Media — main archive)
satmar
training
Include
50-hour collection (
is_selected_50hr = true
) + approved + non-benchmark
My starred audio (
user_favorites
) + approved + non-benchmark
Stage
Both — Stage 1 (folders) + Stage 2 (parquet) + push to HF
Stage 1 only — staging folders, no parquet
Stage 2 only — re-pack existing folders
Behavior
Resume (skip already-exported)
Force (re-export everything)
Limit (smoke test)
Output
Format
Whisper (ivrit-ai 30s slices, parquet)
Gemini tuning (JSONL) — coming soon
HF dataset repo
JEM repo ref
The export runs on the JEM repo at the given git ref. Use a branch name to test a feature export before merging.
Start export
Inputs
Base config
jem_lora_v1 — LoRA (faster, cheaper)
jem_full_v1 — Full fine-tune
Base model (HF)
Dataset repo (HF)
Output model name
Run name (W&B)
Hyperparameters
Method
LoRA
QLoRA
Full fine-tune
Epochs
Learning rate
Per-device batch size
Gradient accumulation
Warmup steps
Weight decay
Eval steps
Save steps
Max steps (smoke)
Eval
Predict WER during eval
Start training
A100 80GB · ~$1.89/hr