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Datasets:
norbertm
/
whisper-eval-rare-languages-csv

Tasks:
Automatic Speech Recognition
Languages:
multilingual
Assamese
Breton
License:
Dataset card Data Studio Files
xet
Community
Dataset Preview
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowNotImplementedError
Message:      Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 642, in write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 457, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1847, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 661, in finalize
                  self._build_writer(self.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 457, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1456, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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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 

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:

@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.

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

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