Update dataset card with train/eval/test split info

README.md

@@ -49,7 +49,7 @@ dataset_info:
   dataset_size: 6450725712.362
 ---
 
-# Common Voice Georgian — Cleaned for mostly TTS/STT
+# Common Voice Georgian — Cleaned for TTS/STT
 
 A high-quality subset of [Mozilla Common Voice Georgian](https://commonvoice.mozilla.org/en/datasets) cleaned and filtered specifically for text-to-speech fine-tuning.
 
@@ -57,14 +57,22 @@ A high-quality subset of [Mozilla Common Voice Georgian](https://commonvoice.moz
 
 | | |
 |---|---|
-| **Samples** | 21,421 |
-| **Duration** | 35.0 hours |
+| **Total samples** | 21,421 |
+| **Total duration** | 35.0 hours |
 | **Speakers** | 12 |
 | **Sample rate** | 24 kHz mono WAV |
 | **Language** | Georgian (kat) |
 | **Source** | Mozilla Common Voice 19.0 |
 | **License** | CC-0 (public domain) |
 
+## Splits
+
+| Split | Samples | Description |
+|-------|---------|-------------|
+| `train` | 20,300 | Training data |
+| `eval` | 1,001 | Validation data |
+| `test` | 120 | Best quality speaker references (top NISQA scores) |
+
 ## Quality Pipeline
 
 The dataset was cleaned from ~71K raw Common Voice recordings through a 6-stage pipeline:
@@ -114,11 +122,17 @@ from datasets import load_dataset
 
 ds = load_dataset("NMikka/Common-Voice-Geo-Cleaned")
 
-# Access a sample
-sample = ds["train"][0]
-print(sample["text"])       # Georgian text
-print(sample["audio"])      # {'array': [...], 'sampling_rate': 24000}
-print(sample["speaker_id"]) # Speaker ID (0-11)
+# Training
+for sample in ds["train"]:
+    print(sample["text"], sample["duration"])
+
+# Validation
+for sample in ds["eval"]:
+    print(sample["text"])
+
+# Best speaker references (for TTS inference/voice cloning)
+for sample in ds["test"]:
+    print(sample["text"], sample["speaker_id"])
 ```
 
 ## Citation
@@ -132,4 +146,4 @@ If you use this dataset, please cite Mozilla Common Voice:
   booktitle={LREC},
   year={2020}
 }
-```
+```