README.md

---
language:
- multilingual
- as
- br
- cy
- et
- eu
- gl
- hu
- hy
- ka
- kk
- lt
- lv
- mk
- mt
- oc
- sk
- sl
- sw
- ta
- tk
- tt
license: mit
task_categories:
- automatic-speech-recognition
---

# Whisper 3 Large Evaluation on Mozilla Common Voice 17 Rare Languages (Enhanced Metrics)

## Dataset Description

This enhanced dataset contains comprehensive evaluation results of OpenAI's Whisper 3 Large model on rare languages from Mozilla Common Voice 17, with extensive additional metrics for thorough ASR evaluation.

### Key Features

**Enhanced Error Metrics:**
- **WER** (Word Error Rate): Standard word-level error measurement
- **CER** (Character Error Rate): Character-level error measurement
- **MER** (Match Error Rate): Alternative error rate calculation
- **WIL** (Word Information Lost): Information loss measurement

**Edit Distance Analysis:**
- Word-level and character-level edit distances
- Normalized edit distance metrics
- Comprehensive distance analysis

**Length and Structure Metrics:**
- Word, character, and sentence counts
- Length ratios and differences
- Average word length analysis
- Sentence structure preservation

**Script-Specific Analysis:**
- Latin, Cyrillic, Armenian, Georgian, Tamil, Bengali character ratios
- Punctuation preservation analysis
- Script-specific performance metrics

**Statistical Metrics:**
- Jaccard similarity for vocabulary overlap
- Frequency correlation analysis
- Vocabulary union and overlap metrics
- Unique word analysis

### Dataset Statistics

- **Total samples**: 111,507
- **Languages**: 21 rare languages
- **Total metrics**: 56 comprehensive evaluation metrics
- **Scripts covered**: Latin, Cyrillic, Armenian, Georgian, Tamil, Bengali

### Language Coverage

| Language | Code | Script | Sample Count |
|----------|------|--------|--------------|
| Assamese | as | Bengali | ~551 |
| Breton | br | Latin | ~2,212 |
| Welsh | cy | Latin | ~5,379 |
| Estonian | et | Latin | ~2,653 |
| Basque | eu | Latin | ~13,630 |
| Galician | gl | Latin | ~9,990 |
| Hungarian | hu | Latin | ~11,435 |
| Armenian | hy | Armenian | ~4,281 |
| Georgian | ka | Georgian | ~12,618 |
| Kazakh | kk | Cyrillic | ~514 |
| Lithuanian | lt | Latin | ~4,753 |
| Latvian | lv | Latin | ~6,752 |
| Macedonian | mk | Cyrillic | ~1,097 |
| Maltese | mt | Latin | ~1,662 |
| Occitan | oc | Latin | ~254 |
| Slovak | sk | Latin | ~5,000 |
| Slovenian | sl | Latin | ~1,242 |
| Swahili | sw | Latin | ~12,253 |
| Tamil | ta | Tamil | ~12,074 |
| Turkmen | tk | Latin | ~546 |
| Tatar | tt | Cyrillic | ~4,964 |

### Performance Highlights

**Top Performing Languages (by WER):**
1. Hungarian (hu): WER = 0.1822
2. Galician (gl): WER = 0.2027
3. Slovenian (sl): WER = 0.2205
4. Macedonian (mk): WER = 0.2762
5. Latvian (lv): WER = 0.3021

### Usage

```python
from datasets import load_dataset

# Load the enhanced dataset
dataset = load_dataset("norbertm/whisper-eval-rare-languages-csv")

# Access comprehensive metrics
print(dataset['train'][0])
```

### Research Applications

This enhanced dataset enables:

1. **Comprehensive ASR Evaluation**: Multiple error metrics for thorough analysis
2. **Script-Specific Analysis**: Understanding performance across different writing systems
3. **Statistical Analysis**: Vocabulary and frequency correlation studies
4. **Length Analysis**: Understanding how text length affects recognition
5. **Cross-Language Comparison**: Detailed performance comparison across 21 languages

### Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{whisper_eval_enhanced_2024,
  title={Whisper 3 Large Evaluation on Mozilla Common Voice 17 Rare Languages (Enhanced Metrics)},
  author={norbertm},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/norbertm/whisper-eval-rare-languages-csv}
}
```

### License

This dataset is licensed under the MIT License.

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*This enhanced version includes 46 additional metrics beyond the original WER and CER, providing unprecedented depth for ASR evaluation research.*