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README.md

@@ -97,6 +97,24 @@ Fine-tuned on [`oddadmix/dialectal-arabic-lahgtna-v2-smaller-augmented`](https:/
   (fp32 weights + generate)
 - **Metric**: WER/CER on cleaned references (clean-text normalized)
 
+## Fine-tuning (reproduce)
+
+The exact fine-tuning code is bundled in this repo (`train.py`, `normalize.py`, `evaluate_model.py`) plus `requirements.txt`. Trained on `oddadmix/dialectal-arabic-lahgtna-v2-smaller-augmented` (**private**) — swap in any HF audio dataset with `audio` + `text` columns. `normalize.py` is the shared text cleaning (strip tashkil + non-verbal tags, keep dialectal letters گ ڨ چ).
+
+```bash
+pip install -r requirements.txt
+huggingface-cli login          # for the (private) dataset
+
+python train.py \
+  --base_model openai/whisper-medium --run_name my-run \
+  --per_device_train_batch_size 8 --gradient_accumulation_steps 4 \
+  --learning_rate 1e-5 --warmup_steps 500 --max_steps 6000
+
+python evaluate_model.py --model runs/my-run          # WER / CER
+```
+Note: some checkpoints (e.g. large-v3-turbo) ship in fp16 — `train.py` force-loads
+fp32 so `generate()` doesn't crash at eval. bf16 autocast is used for training.
+
 ## Usage
 ```python
 import torch, torchaudio