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README.md
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size_categories:
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**Data Source
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[Kaggle Medical Speech, Transcription, and Intent](https://www.kaggle.com/datasets/paultimothymooney/medical-speech-transcription-and-intent "Visit Original Dataset Page on Kaggle")
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**Context
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>8.5 hours of audio utterances paired with text for common medical symptoms
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**Content
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>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
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>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
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>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
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>This dataset contains both the audio utterances and corresponding transcriptions
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**What's new
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*The data is clean from all columns except for the file_path and
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*All Audios are loaded into the DatasetDict as an 1D array,
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*All Audios are resampled into
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size_categories:
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- 1K<n<10K
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---
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**Data Source**<br>
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[Kaggle Medical Speech, Transcription, and Intent](https://www.kaggle.com/datasets/paultimothymooney/medical-speech-transcription-and-intent "Visit Original Dataset Page on Kaggle")<br>
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**Context**<br>
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>8.5 hours of audio utterances paired with text for common medical symptoms.<br>
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**Content**<br>
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>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>
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>
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>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>
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>
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>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>
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>
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+
>This dataset contains both the audio utterances and corresponding transcriptions.<br>
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**What's new**<br>
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*The data is clean from all columns except for the file_path and phrase<br>
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*All Audios are loaded into the DatasetDict as an 1D array, float32<br>
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*All Audios are resampled into 16K<br>
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