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--- |
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dataset_info: |
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features: |
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- name: truth |
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dtype: string |
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- name: error |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 947772001 |
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num_examples: 2030784 |
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- name: test |
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num_bytes: 105304042 |
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num_examples: 225643 |
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download_size: 512288701 |
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dataset_size: 1053076043 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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license: mit |
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task_categories: |
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- text-classification |
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- token-classification |
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- translation |
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language: |
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- dv |
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tags: |
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- dhivehi |
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- thaana |
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- thaana-typo |
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- dhivehi-typo |
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pretty_name: dhivehi-typo |
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size_categories: |
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- 1M<n<10M |
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--- |
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# Dhivehi Text Errors Dataset |
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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**. |
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## Dataset Structure |
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* **train**: 90% of the data for training or error analysis |
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* **test**: 10% of the data reserved for evaluation |
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## Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("alakxender/dhivehi-asr-errors-ext") |
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# Inspect an example |
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example = dataset["train"][0] |
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print("Truth :", example["truth"]) |
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print("Error :", example["error"]) |
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``` |
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### Example Output |
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``` |
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Truth : މިކަމުގައި ޝިރުކު ހިމެނޭ ބައެއް ގޮތްތަކަކީ: ތަޢާލާއަށް ޚާއްޞަވެގެންވާ ޣައިބުގެ ޢިލްމު އެނގޭކަމަށް އެފަދަ މީހުން ދަޢުވާކުރުން |
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Error : މިކަމުގައި ޝިރުކު ހިމެނޭ ބައެއް ވައްތަކަކީ ތަޢާލާއަށް ޚާއްސަ ވެގެން ވާގައިބުގެ އިލްމު އިނގޭ ކަމަށް އެފަދަ މީހުން ދައުވާ ކުރުން |
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``` |
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## Data Fields |
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* **truth**: Accurate human transcription of the spoken Dhivehi audio |
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* **error**: Machine-generated transcription from fine-tuned ASR models (Whisper, MMS, or Wav2Vec2) |
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## Applications |
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* **ASR Evaluation**: Benchmark Dhivehi ASR models by measuring error patterns and rates |
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* **Error Analysis**: Identify systematic mistakes made by Whisper, MMS, and Wav2Vec2 fine-tunes |
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* **Text Correction**: Develop or fine-tune models for Dhivehi error correction and post-processing |
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* **NLP Research**: Support Dhivehi text quality improvement tasks such as grammar checking and normalization |
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## Notes |
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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. |
