---
language:
- ccp
license: cc-by-nc-4.0
task_categories:
- automatic-speech-recognition
pretty_name: "NE ASR Augmented Dataset -- Chakma (ccp)"
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
---

<!-- v2-banner -->
## Version 2.0 (rebuilt 2026-05-29) — see VERSION HISTORY below

> **WARNING:** This is **v2.0**, a clean rebuild that supersedes the deprecated
> **v1.0** revision. v1.0 had been built at an unintended 7x expansion (with
> pitch-shift augmentation on a tonal language) and is retracted. Use v2.0 only.

### Version history

#### v2.0 (HEAD = `d66aacc9aafc0eaaf2333eb24f1509b182d7f693`, 2026-05-29) — current

- **Rebuilt at correct 3x augmentation**: speed-perturb only (factors 0.9, 1.0, 1.1).
- **No pitch shift** on this tonal language (pitch shift alters lexical tone contrasts
  in Chakma; disabled per `configs/augmentation_config.yaml` and enforced by a
  belt-and-suspenders override inside `scripts/augment_data.py`).
- **Source**: `sulabhkatiyar/ne-asr-ccp` train split = 32,807 rows -> 32,807 x 3 =
  **98,421 augmented train rows**.
- **Validation split**: 4,513 rows (copied unchanged from source).
- **Test split**: 3,820 rows (copied unchanged from source).
- **License**: CC-BY-NC 4.0 (inherited from the MMS-1B base model used downstream;
  the auto-generated YAML `license: cc-by-4.0` reflects only the source recordings'
  license; once combined with MMS-1B-derived training artifacts, downstream use
  inherits the more restrictive CC-BY-NC 4.0 from MMS).

#### v1.0 (PRE-HEAD = `d3635238c31239e5ec58396ecfd37099f500b965` boundary, retracted) — DO NOT USE

- v1.0 had been built at an **unintended 7x expansion** (`229,649 train rows = 32,807 x 7`),
  including pitch-shift variants (±1 and ±2 semitones) on top of the speed-perturb
  variants. **This violated both the augmentation policy** (Chakma is tonal; pitch shift
  corrupts tonal contrasts) **and CC-BY-NC 4.0 compliance accounting** (the 7x expansion
  was not disclosed in the v1 dataset card).
- The Tier-1 `ccp` adapter that had been fine-tuned on v1.0 is therefore **deprecated**;
  v2.0 supersedes it, and the adapter is being retrained from scratch on this v2.0 data.
- **Users must NOT use v1.0** (the v1.0 commits have been replaced; this repo was
  rebuilt fresh, so v1.0 parquet files are no longer reachable). This note is preserved
  for auditability.

### License (restate)

This augmented dataset is published under **CC-BY-NC 4.0**, inherited from the
MMS-1B base model used in the downstream fine-tuning pipeline. The original Vaani
recordings are CC-BY-4.0; the combined artifact (audio + MMS-derived model outputs)
inherits the more restrictive non-commercial term.

---

# NE ASR Augmented Dataset -- Chakma (ccp)

Augmented automatic speech recognition dataset for **Chakma** (`ccp`),
a Indo-Aryan language spoken in Mizoram, India.

## Source

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

## Language Information

| Property | Value |
|----------|-------|
| Language | Chakma |
| ISO 639-3 | `ccp` |
| Family | Indo-Aryan |
| Region | Mizoram, India |
| Tonal | Yes |
| Tier | E (49.81h original data) |

## Dataset Statistics

- **Original training samples**: 32,807
- **Augmented training samples**: 98,421 (3x augmentation)
- **Train shards**: 66
- **Estimated original duration**: ~49.8 hours
- **Estimated augmented duration**: ~149.4 hours

| Split | Samples |
|-------|--------:|
| train | 98,421 |
| validation | 4,513 |
| test | 3,820 |

## 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`: 0.12
- `mask_time_length`: 12
- `mask_feature_prob`: 0.06
- `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-ccp-aug")

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