Dataset Viewer
The dataset could not be loaded because the splits use different data file formats, which is not supported. Read more about the splits configuration. Click for more details.
Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('validation'): ('json', {}), NamedSplit('test'): ('arrow', {})}
Error code:   FileFormatMismatchBetweenSplitsError

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

prepared_medical_speech

Dataset Summary

prepared_medical_speech holds the Hugging Face Hani89/medical_asr_recording_dataset split into train/validation/test subsets with normalized transcripts and mono 16 kHz waveforms that both Whisper and Wav2Vec2 pipelines share. Each sample pairs a floating-point waveform array with a lowercased, whitespace-normalized medical sentence covering symptoms, conditions, and complaints.

Supported Tasks and Leaderboards

  • Task: Automatic Speech Recognition (ASR)
  • Models: Whisper (encoder-decoder) & Wav2Vec2 (CTC)
  • Intended use: fine-tuning, evaluation, and contextual prompting benchmarks targeting medical vocabulary

Languages

  • English (medical, symptom-focused)

Dataset Structure

Each split contains examples with two fields:

  • audio: dictionary with array (list of float samples), sampling_rate (int, 16 000), and path (optional)
  • sentence: normalized transcript (lowercased, collapsed whitespace)

Splits:

  • train: used for fine-tuning (after carving 10 % for validation)
  • validation: sampled from the original training split (seeded split for reproducibility)
  • test: preserved Hugging Face test split (unchanged content)

Data Collection

Derived from the publicly available Medical Speech, Transcription, and Intent dataset. Audio and transcripts are untouched except for resampling/reshaping and standardized transcript casing.

Preprocessing

  1. Cast every sample to mono and assert 16 kHz sampling rate to satisfy Whisper/Wav2Vec2 requirements.
  2. Normalize sentence by trimming, lowercasing, and squashing extra whitespace.
  3. Split the original training split into train vs validation with a 90/10 split (seed 42) while preserving Hugging Face test.
  4. Save the resulting DatasetDict to prepared_medical_speech for reuse.

Provenance

Source: Hani89/medical_asr_recording_dataset on Hugging Face (dataset includes about 8.5 hours, 6,661 utterances).

Licensing

Original dataset: import license from Hugging Face (check Hani89/medical_asr_recording_dataset). This prepared split inherits the same terms; confirm any restrictions before redistribution.

Citation

If you use this prepared data, cite the original dataset authors via the Hugging Face page and mention that it was preprocessed for Whisper/Wav2Vec2 experiments.

Downloads last month
41