---
language:
- trp
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
pretty_name: "NE ASR Augmented Dataset -- Kokborok (trp)"
tags:
- augmented
- ne-india
- low-resource
- speech
- asr
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train/*.parquet
      - split: validation
        path: data/validation/*.parquet
      - split: test
        path: data/test/*.parquet
---

# NE ASR Augmented Dataset -- Kokborok (trp)

## v2 (2026-05-29): rebuilt 3x speed-only

The previous **v1 (5x)** revision applied +/-1 semitone pitch-shift augmentation to the train split. **trp (Kokborok) is a tonal language with 2 phonemic tones (H/L)**; pitch-shift augmentation corrupts the tonal contrasts the model must learn. **v1 is therefore DEPRECATED and should not be used for training.**

v2 specs:
- Augmentation: **speed-perturb only** (factors per `configs/augmentation_config.yaml` v2 spec: 0.9, 1.0, 1.1).
- Aug factor: **3x** (was 5x).
- Train rows: **7,533** (= 2,511 x 3).
- Val / test: **unchanged from source** (no augmentation on eval splits).
- Source: [`sulabhkatiyar/ne-asr-trp`](https://huggingface.co/datasets/sulabhkatiyar/ne-asr-trp).
- Tonality fix per `.claude/reports/module6/tonal_audit.md` (root-cause: `configs/augmentation_config.yaml` previously flagged `trp.tonal: false`; corrected to `true` on 2026-05-29 commit `06f9b14`).
- Rebuilt by: `scripts/augment_data.py --config configs/augmentation_config.yaml --lang trp --clean` (Phase B.5.2).

---

Augmented automatic speech recognition dataset for **Kokborok** (`trp`),
a Tibeto-Burman language spoken in Tripura, India.

## Source

Augmented from [`sulabhkatiyar/ne-asr-trp`](https://huggingface.co/datasets/sulabhkatiyar/ne-asr-trp)
(original transcribed speech data from the [ARTPARK-IISc Vaani project](https://vaani.iisc.ac.in/)).

## Language Information

| Property | Value |
|----------|-------|
| Language | Kokborok |
| ISO 639-3 | `trp` |
| Family | Tibeto-Burman |
| Region | Tripura, India |
| Tonal | Yes |
| Tier | C (3.81h original data) |

## Dataset Statistics

- **Original training samples**: 2,511
- **Augmented training samples**: 7,533 (3x augmentation)
- **Train shards**: 6
- **Estimated original duration**: ~3.8 hours
- **Estimated augmented duration**: ~11.4 hours

| Split | Samples |
|-------|--------:|
| train | 7,533 |
| validation | 274 |
| test | 279 |

## 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`: 0.07
- `mask_time_length`: 10
- `mask_feature_prob`: 0.05
- `mask_feature_length`: 10
- `layerdrop`: 0.05

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-trp-aug")

# Load only the training split
train = load_dataset("sulabhkatiyar/ne-asr-trp-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-trp`](https://huggingface.co/datasets/sulabhkatiyar/ne-asr-trp)
- 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.