The Entity Detection model lets you automatically identify and categorize key information in transcribed audio content.
Here are a few examples of what you can detect:
- Names of people
- Phone numbers
- Medical data
- Social security numbers
For the full list of entities that you can detect, check out Supported entities.
curl https://api.assemblyai.com/v2/transcript \
--header "Authorization: <YOUR_API_KEY>" \
--header "Content-Type: application/json" \
|boolean||Enable Entity Detection.|
|array||An array of detected entities.|
|string||The type of entity for the i-th detected entity.|
|string||The text for the i-th detected entity.|
|number||The starting time, in milliseconds, at which the i-th detected entity appears in the audio file.|
|number||The ending time, in milliseconds, for the i-th detected entity in the audio file.|
The response also includes the request parameters used to generate the transcript.
The model is designed to automatically detect and classify various types of entities within the transcription text. The detected entities and their corresponding types is listed individually in the entities key of the response object, ordered by when they first appear in the transcript.
|Banking information, including account and routing numbers.|
|Blood type (e.g., O-, AB positive).|
|Credit card verification code (e.g., CVV: 080).|
|Expiration date of a credit card.|
|Credit card number.|
|Specific calendar date (e.g., December 18).|
|Date of Birth (e.g., Date of Birth: March 7, 1961).|
|Driver's license number (e.g., DL #356933-540).|
|Medications, vitamins, or supplements (e.g., Advil, Acetaminophen, Panadol).|
|Email address (e.g., email@example.com).|
|Name of an event or holiday (e.g., Olympics, Yom Kippur).|
|Bodily injury (e.g., I broke my arm, I have a sprained wrist).|
|Name of a natural language (e.g., Spanish, French).|
|Any location reference including mailing address, postal code, city, state, province, or country.|
|Name of a medical condition, disease, syndrome, deficit, or disorder (e.g., chronic fatigue syndrome, arrhythmia, depression).|
|Medical process, including treatments, procedures, and tests (e.g., heart surgery, CT scan).|
|Name and/or amount of currency (e.g., 15 pesos, $94.50).|
|Terms indicating nationality, ethnicity, or race (e.g., American, Asian, Caucasian).|
|Job title or profession (e.g., professor, actors, engineer, CPA).|
|Name of an organization (e.g., CNN, McDonalds, University of Alaska).|
|Account passwords, PINs, access keys, or verification answers (e.g., 27%alfalfa, temp1234, My mother's maiden name is Smith).|
|Number associated with an age (e.g., 27, 75).|
|Name of a person, such as "Bob" and "Doug Jones".|
|Telephone or fax number.|
|Terms referring to a political party, movement, or ideology. For example, "Republican" and "Liberal".|
|Terms indicating religious affiliation, such as "Hindu" and "Catholic".|
|Expressions indicating clock times, such as "19:37:28" and "10pm EST".|
|Internet addresses, such as "www.assemblyai.com".|
|Social Security Number or equivalent.|
Frequently asked questions
The model is capable of identifying entities with variations in spelling or formatting. However, the accuracy of the detection may depend on the severity of the variation or misspelling.
No, the Entity Detection model currently doesn't support the detection of custom entity types. However, the model is capable of detecting a wide range of predefined entity types, including people, organizations, locations, dates, times, addresses, phone numbers, medical data, and banking information, among others.
To improve the accuracy of the Entity Detection model, it's recommended to provide high-quality audio files with clear and distinct speech. In addition, it's important to ensure that the audio content is relevant to the use case and that the entities being detected are relevant to the intended analysis. Finally, it may be helpful to review and adjust the model's configuration parameters, such as the confidence threshold for entity detection, to optimize the results.