README.md

metadata

language: en
tags:
  - automatic-speech-recognition
  - medical
  - preprocessing
  - whisper
  - wav2vec2
  - huggingface
license: apache-2.0

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.