Update dataset card for Tagin (tgj)

README.md

@@ -1,4 +1,16 @@
 ---
+language:
+- tgj
+license: cc-by-4.0
+task_categories:
+- automatic-speech-recognition
+pretty_name: "NE ASR Augmented Dataset -- Tagin (tgj)"
+tags:
+- augmented
+- ne-india
+- low-resource
+- speech
+- asr
 configs:
   - config_name: default
     data_files:
@@ -10,11 +22,92 @@ configs:
         path: data/test/*.parquet
 ---
 
-# sulabhkatiyar/ne-asr-tgj-aug
+# NE ASR Augmented Dataset -- Tagin (tgj)
 
-Augmented ASR dataset for language `tgj` (Tier A).
+Augmented automatic speech recognition dataset for **Tagin** (`tgj`),
+a Tibeto-Burman language spoken in Arunachal Pradesh, India.
 
-- Source: `sulabhkatiyar/ne-asr-tgj`
-- Augmentation factor: 3x (1 original + 2 speed + 0 pitch)
-- Training samples: 258 (from 86 originals)
-- Shards: 1
+## Source
+
+Augmented from [`sulabhkatiyar/ne-asr-tgj`](https://huggingface.co/datasets/sulabhkatiyar/ne-asr-tgj)
+(original transcribed speech data from the [ARTPARK-IISc Vaani project](https://vaani.iisc.ac.in/)).
+
+## Language Information
+
+| Property | Value |
+|----------|-------|
+| Language | Tagin |
+| ISO 639-3 | `tgj` |
+| Family | Tibeto-Burman |
+| Region | Arunachal Pradesh, India |
+| Tonal | Yes |
+| Tier | A (0.12h original data) |
+
+## Dataset Statistics
+
+- **Original training samples**: 86
+- **Augmented training samples**: 258 (3x augmentation)
+- **Train shards**: 1
+- **Estimated original duration**: ~0.1 hours
+- **Estimated augmented duration**: ~0.4 hours
+
+| Split | Samples |
+|-------|--------:|
+| train | 258 |
+| validation | 5 |
+| test | 2 |
+
+## Transformations Applied
+
+Each original training sample produces **3 samples** (1 original + 2 speed + 0 pitch):
+
+- **Speed perturbation**: 0.9x, 1.1x (2 variants per sample)
+- **Pitch shift**: Disabled (tonal language -- pitch shift would alter lexical meaning)
+- **Noise augmentation**: Not applied
+
+### SpecAugment Parameters (for training, NOT in this dataset)
+
+These parameters are consumed by the training script and are **not** baked into the audio files:
+
+- `mask_time_prob`: 0.03
+- `mask_time_length`: 10
+- `mask_feature_prob`: 0.0
+- `mask_feature_length`: 10
+- `layerdrop`: 0.0
+
+Full augmentation config: [`configs/augmentation_config.yaml`](https://github.com/sulabhkatiyar/ne_asr/blob/main/configs/augmentation_config.yaml)
+
+## Dataset Format
+
+- **Audio**: 16kHz mono WAV (stored as Parquet with audio bytes)
+- **Text**: Transcriptions
+- **Features**: `audio`, `text`, `language`, `augmentation`
+- **Augmentation labels**: `original`, `speed_0.9`, `speed_1.1`
+
+## How to Use
+
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+ds = load_dataset("sulabhkatiyar/ne-asr-tgj-aug")
+
+# Load only the training split
+train = load_dataset("sulabhkatiyar/ne-asr-tgj-aug", split="train")
+
+# Filter to only original (non-augmented) samples
+original_only = train.filter(lambda x: x["augmentation"] == "original")
+
+# Filter to a specific augmentation type
+speed_09 = train.filter(lambda x: x["augmentation"] == "speed_0.9")
+```
+
+## Original Data
+
+- Source dataset: [`sulabhkatiyar/ne-asr-tgj`](https://huggingface.co/datasets/sulabhkatiyar/ne-asr-tgj)
+- Project: [ARTPARK-IISc Vaani](https://vaani.iisc.ac.in/)
+- License: CC-BY-4.0
+
+## Citation
+
+If you use this dataset, please cite the Vaani project and acknowledge the augmentation pipeline.