task_categories:automatic-speech-recognition size_categories:1K<n<10K language:en license:apache-2.0 medical region:us
---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: audio
    struct:
    - name: array
      sequence:
        sequence: float32
    - name: path
      dtype: string
    - name: sampling_rate
      dtype: int64
  - name: sentence
    dtype: string
  splits:
  - name: train
    num_bytes: 3128740048
    num_examples: 5328
  - name: test
    num_bytes: 776455056
    num_examples: 1333
  download_size: 3882364624
  dataset_size: 3905195104
license: apache-2.0
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
---
**Data Source**<br>
[Kaggle Medical Speech, Transcription, and Intent](https://www.kaggle.com/datasets/paultimothymooney/medical-speech-transcription-and-intent "Visit Original Dataset Page on Kaggle")<br>

**Context**<br>
>8.5 hours of audio utterances paired with text for common medical symptoms.<br>

**Content**<br>
>This data contains thousands of audio utterances for common medical symptoms like “knee pain” or “headache,” totaling more than 8 hours in aggregate. Each utterance was created by individual human contributors based on a given symptom. These audio snippets can be used to train conversational agents in the medical field.<br>
>
>This Figure Eight dataset was created via a multi-job workflow. The first involved contributors writing text phrases to describe symptoms given. For example, for “headache,” a contributor might write “I need help with my migraines.” Subsequent jobs captured audio utterances for accepted text strings.<br>
>
>Note that some of the labels are incorrect and some of the audio files have poor quality. I would recommend cleaning the dataset before training any machine learning models.<br>
>
>This dataset contains both the audio utterances and corresponding transcriptions.<br>

**What's new**<br>
*The data is clean from all columns except for the file_path and phrase<br>
*All Audios are loaded into the DatasetDict as an 1D array, float32<br>
*All Audios are resampled into 16K<br>
*The new structure :<br>
        train = {<br>
            'audio': {<br>
                'path': file_path, *the mp3 files is not included here, please visit the kaggle to dowload em*<br>
                'array': waveform_np,<br>
                'sampling_rate': 16000<br>
            },<br>
            'sentence': the text transcription<br>
        }