README.md

metadata

dataset_info:
  features:
    - name: truth
      dtype: string
    - name: error
      dtype: string
  splits:
    - name: train
      num_bytes: 947772001
      num_examples: 2030784
    - name: test
      num_bytes: 105304042
      num_examples: 225643
  download_size: 512288701
  dataset_size: 1053076043
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
license: mit
task_categories:
  - text-classification
  - token-classification
  - translation
language:
  - dv
tags:
  - dhivehi
  - thaana
  - thaana-typo
  - dhivehi-typo
pretty_name: dhivehi-typo
size_categories:
  - 1M<n<10M

Dhivehi Text Errors Dataset

This dataset provides truth–error pairs for Dhivehi (Maldivian) text, intended for evaluating and analyzing Automatic Speech Recognition (ASR) systems. The focus is on common transcription errors made by models fine-tuned on Whisper, MMS, and Wav2Vec2.

Dataset Structure

• train: 90% of the data for training or error analysis
• test: 10% of the data reserved for evaluation

Usage

from datasets import load_dataset

dataset = load_dataset("alakxender/dhivehi-asr-errors-ext")

# Inspect an example
example = dataset["train"][0]
print("Truth :", example["truth"])
print("Error :", example["error"])

Example Output

Truth : މިކަމުގައި ޝިރުކު ހިމެނޭ ބައެއް ގޮތްތަކަކީ: ތަޢާލާއަށް ޚާއްޞަވެގެންވާ ޣައިބުގެ ޢިލްމު އެނގޭކަމަށް އެފަދަ މީހުން ދަޢުވާކުރުން
Error : މިކަމުގައި ޝިރުކު ހިމެނޭ ބައެއް ވައްތަކަކީ ތަޢާލާއަށް ޚާއްސަ ވެގެން ވާގައިބުގެ އިލްމު އިނގޭ ކަމަށް އެފަދަ މީހުން ދައުވާ ކުރުން

Data Fields

• truth: Accurate human transcription of the spoken Dhivehi audio
• error: Machine-generated transcription from fine-tuned ASR models (Whisper, MMS, or Wav2Vec2)

Applications

• ASR Evaluation: Benchmark Dhivehi ASR models by measuring error patterns and rates
• Error Analysis: Identify systematic mistakes made by Whisper, MMS, and Wav2Vec2 fine-tunes
• Text Correction: Develop or fine-tune models for Dhivehi error correction and post-processing
• NLP Research: Support Dhivehi text quality improvement tasks such as grammar checking and normalization

Notes

This dataset contains text-level errors only (no audio). It is best used as a complementary resource for improving Dhivehi ASR systems and building correction pipelines tailored to common failure cases.