sulabhkatiyar/ne-asr-dataset-lus-aug

NE ASR Augmented Dataset -- Mizo (lus)

Augmented automatic speech recognition dataset for Mizo (lus), a Tibeto-Burman language spoken in Mizoram, India.

Source

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

Language Information

Dataset Statistics

• —Original training samples: 9,850
• —Augmented training samples: 29,550 (3x augmentation)
• —Train shards: 50
• —Estimated original duration: ~20.8 hours
• —Estimated augmented duration: ~62.2 hours

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: 0.1
• —mask_time_length: 10
• —mask_feature_prob: 0.05
• —mask_feature_length: 10
• —layerdrop: 0.05

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-lus-aug")

# Load only the training split
train = load_dataset("sulabhkatiyar/ne-asr-lus-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-lus`
• —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.