README.md

metadata

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:
      - split: train
        path: data/train/*.parquet
      - split: validation
        path: data/validation/*.parquet
      - split: test
        path: data/test/*.parquet

NE ASR Augmented Dataset -- Tagin (tgj)

Augmented automatic speech recognition dataset for Tagin (tgj), a Tibeto-Burman language spoken in Arunachal Pradesh, India.

Source

Augmented from sulabhkatiyar/ne-asr-tgj (original transcribed speech data from the ARTPARK-IISc Vaani project).

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

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

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
• Project: ARTPARK-IISc Vaani
• License: CC-BY-4.0

Citation

If you use this dataset, please cite the Vaani project and acknowledge the augmentation pipeline.