Finalize GenAI exam: simplify qabilah+q2, add q5 LangChain agent

q2-arabic-tts-stt/arabic-tts-stt-finetune.md

@@ -1,57 +0,0 @@
-# Q2 — Fine-tuning an Arabic TTS or STT
-
-⏱ **Budget: ~15 minutes** · Weight: 25%
-
-## Choose ONE option
-
-- **Option A — TTS.** Fine-tune an open Arabic (or multilingual) text-to-speech model — e.g. Coqui **XTTS-v2**, **VITS**, or **StyleTTS2** — on a single-speaker Arabic dataset.
-- **Option B — STT.** Fine-tune **Whisper** (e.g. `whisper-small`, `large-v3`) on Arabic speech — e.g. Mozilla **Common Voice `ar`**, **MGB-2**, or a custom corpus.
-
-## Produce a runnable guide covering
-
-1. **Model & dataset choice** — justify it; cite dataset sizes, license, and dialect (MSA vs. Egyptian vs. Gulf vs. Maghrebi).
-2. **Data preprocessing** — audio resampling/normalization, text normalization, and the Arabic-specific gotchas: **diacritization (تشكيل)**, letter variants (أ/إ/آ, ة/ه, ى/ي), and (for STT) whether to strip diacritics before computing WER. Train/val split.
-3. **Training setup** — framework (e.g. Hugging Face Trainer, Coqui TTS, NeMo), **LoRA vs. full fine-tune**, key hyperparameters (batch size, LR, epochs, warmup), hardware, and a rough time/cost estimate.
-4. **Evaluation** — for STT: **WER/CER** (and how to compute them fairly for Arabic); for TTS: **MOS**, **speaker similarity (SECS)**, and using an ASR to measure intelligibility. Include the actual metric definitions/commands.
-5. **Deployment on Ghaymah** — GPU sizing, container vs. function, serving stack, latency, and how the model is served behind an API.
-6. **Risks & mitigations** — dialect coverage, hallucination, diacritic fidelity, MSA vs. dialect mismatch, data licensing.
-
-Include **real code snippets** (Python / Hugging Face / CLI). Cite sources inline.
-
-## Deliverable
-
-Fill in the template below. State clearly which option you chose.
-
----
-
-## ANSWER — Arabic TTS/STT Fine-tune
-
-**Option chosen:** A / B  (delete one)
-
-### 1. Model & dataset choice
-
-> _write here_ 
-
-### 2. Data preprocessing
-
-> _write here + code snippets_ 
-
-### 3. Training setup
-
-> _write here + code/config snippets_ 
-
-### 4. Evaluation
-
-> _write here + metric definitions/commands_ 
-
-### 5. Deployment on Ghaymah
-
-> _write here_ 
-
-### 6. Risks & mitigations
-
-| Risk | Mitigation |
-|---|---|
-| | |
-| | |
-| | |

q2-arabic-tts-stt/arabic-tts-stt-proposal.md

@@ -0,0 +1,39 @@
+# Q2 — Propose an Arabic TTS or STT Model + Dataset
+
+⏱ **Budget: ~6 minutes** · Weight: 15%
+
+## Choose ONE option
+
+- **Option A — TTS.** Propose a text-to-speech model to fine-tune for Arabic.
+- **Option B — STT.** Propose a speech-to-text model to fine-tune for Arabic.
+
+## The task
+
+Propose **one model** and **one dataset**, with a short justification. No training, no running code — just a well-reasoned pick. (Short code/CLI snippets are allowed but not required.)
+
+Cover:
+
+1. **Model** — name it, and say why: architecture, license, multilingual/Arabic support.
+2. **Dataset** — name it, and say why: size, dialect (MSA vs. Egyptian vs. Gulf vs. Maghrebi), license, where to get it.
+3. **Why this pairing works for Arabic** — 1–2 sentences (e.g. diacritization handling, dialect coverage).
+4. **One key risk** — 1 sentence.
+
+## ANSWER — Arabic TTS/STT Proposal
+
+**Option chosen:** A / B  (delete one)
+
+### Proposed model
+
+> _write here_ 
+
+### Proposed dataset
+
+> _write here_ 
+
+### Why this pairing works for Arabic
+
+> _write here_ 
+
+### One key risk
+
+> _write here_