sulabhkatiyar/ne-asr-dataset-nag-aug

NE ASR Augmented Dataset -- Nagamese (nag)

Augmented automatic speech recognition dataset for Nagamese (nag), a Assamese-based creole language spoken in Nagaland, India.

Source

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

Language Information

Dataset Statistics

• —Original training samples: 12,862
• —Augmented training samples: 90,034 (7x augmentation)
• —Train shards: 258
• —Estimated original duration: ~23.8 hours
• —Estimated augmented duration: ~166.3 hours

Transformations Applied

Each original training sample produces 7 samples (1 original + 2 speed + 4 pitch):

• —Speed perturbation: 0.9x, 1.1x (2 variants per sample)
• —Pitch shift: -2, -1, +1, +2 semitones (4 variants per sample)
• —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, pitch_-2, pitch_-1, pitch_+1, pitch_+2

How to Use

from datasets import load_dataset

# Load the full dataset
ds = load_dataset("sulabhkatiyar/ne-asr-nag-aug")

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