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README.md

@@ -19,6 +19,7 @@ Bahraini Arabic ASR fine-tune based on `MohamedRashad/Arabic-Whisper-CodeSwitchi
 
 - `bahraini_asr_codeswitching`
 - this public package is the Bahraini ASR release bundle kept under a stable name instead of a raw checkpoint folder
+- tokenizer config was normalized for direct `WhisperProcessor.from_pretrained(...)` loading
 - interactive listening page: [`Hishambarakat/Bahraini_ASR_Review`](https://huggingface.co/spaces/Hishambarakat/Bahraini_ASR_Review)
 
 ## Inference
@@ -48,11 +49,20 @@ print(result["text"])
 ## What was trained
 
 - Base model: `MohamedRashad/Arabic-Whisper-CodeSwitching-Edition`
+- Baseline pretraining data included `MohamedRashad/arabic-english-code-switching`
 - Private Bahraini speech corpus with manual transcript cleanup
 - Dialect-specific correction passes for Bahraini phrasing
 - Review-driven checkpoint selection from unseen clips
 - The public release is packaged under `bahraini_asr_codeswitching`
 
+## Why Bahraini fine-tuning matters
+
+The baseline model is useful, but its upstream code-switching data includes a broader Arabic mix and is not specialized for Bahraini dialect preservation. In practice, that means some outputs can be semantically plausible while still drifting away from the speaker's local wording.
+
+This matters for Bahraini ASR because a transcript can look "close enough" in standard Arabic or another dialect while still failing to preserve the actual form that was spoken.
+
+![Bahraini ASR overview](assets/Bahraini_ASR.png)
+
 ## Evaluation summary
 
 This checkpoint was selected from a manual unseen-clip review rather than a formal WER/CER benchmark.
@@ -79,6 +89,38 @@ Important note:
 - some later local proxy review files had labeling inconsistencies, so the example table is treated as the more conservative public-facing evidence set
 - in other words: the counts below are from the original 1250 review pass, while the example rows were hand-pruned to avoid overstating wins
 
+## Dialect preservation candidates
+
+In addition to the manual 50-clip review, we ran a separate text-only mining pass over the private training corpus to look for likely dialect-preservation patterns between:
+
+- `text`
+- `text_asr_v5_raw`
+
+This is not a benchmark and should not be read as ASR accuracy. It is a heuristic analysis meant to surface likely cases where a local Bahraini form was shifted toward a more common outside-dialect form.
+
+Topline from that pass:
+
+- total train rows scanned: `52,695`
+- changed transcript rows (`text != text_asr_v5_raw`): `50,335`
+- heuristic dialect-shift candidates: `360`
+- strict local-to-common substitution cases: `37`
+
+Conservative interpretation:
+
+- heuristic dialect-shift candidates account for about `0.72%` of changed rows
+- the stricter local-to-common subset accounts for about `0.07%` of changed rows
+- these are small percentages in absolute terms, but they are high-value because they target the exact dialect-preservation failure mode we care about
+
+Most common strict patterns found:
+
+- `ليش -> ليه`: `19`
+- `مب -> مش`: `18`
+
+Useful framing:
+
+- some baseline outputs are semantically plausible, but they do not preserve the speaker's original dialect wording
+- this analysis is best treated as **dialect preservation candidate mining**, not formal detection accuracy
+
 ## Review examples
 
 These clips are the strongest remaining `ours` wins from the corrected local review file after removing rows that we manually judged as baseline, tie, or both_bad.