Changelog

We have released an updated Content Safety Model - v7! Performance for 10 out of all 19 Content Safety labels has been improved, with the biggest improvements being for the Profanity and Natural Disasters labels.

We have just released a major real-time update!

Developers will now be able to use the word_boost parameter in requests to the real-time API, allowing you to introduce your own custom vocabulary to the model for that given session! This custom vocabulary will lead to improved accuracy for the provided words.

General Improvements

We will now be limiting one websocket connection per real-time session to ensure the integrity of a customer's transcription and prevent multiple users/clients from using the websocket same session.

Note: Developers can still have multiple real-time sessions open in parallel, up to the Concurrency Limit on the account. For example, if an account has a Concurrency Limit of 32, that account could have up to 32 concurrent real-time sessions open.

Today we have released v2 of our Topic Detection Model. This new model will predict multiple topics for each paragraph of text, whereas v1 was limited to predicting a single. For example, given the text:

"Elon Musk just released a new Tesla that drives itself!"

v1:

• Automotive>AutoType>DriverlessCars: 1

v2:

• Automotive>AutoType>DriverlessCars: 1
• PopCulture : 0.84
• PopCulture>CelebrityStyle: 0.56

This improvement will result in the visual output looking significantly better, and containing more informative responses for developers!

In this minor improvement, we have increased the number of topics the model can return in the summary key of the JSON response from 10 to 20.

Often times, developers will need to expose their AssemblyAI API Key in their client applications when establishing connections with our real-time streaming transcription API. Now, developers can create a temporary API token that expires in a customizable amount of time (similar to an AWS S3 Temporary Authorization URL) that can safely be exposed in the client applications and front-ends.

This will allow developers to create short-lived API tokens designed to be used securely in the browser, along with authorization within the query string!

For example, authenticating in the query parameters with a temporary token would look like so:

wss://api.assemblyai.com/v2/realtime/ws?sample_rate=16000&token={TEMP_TOKEN}

For more information, you can view our Docs!

In this minor update, we improve the accuracy across all Content Safety labels, and add two new labels for better content categorization. The two new labels are sensitive_social_issues and marijuana.

New label definitions:

• sensitive_social_issues: This category includes content that may be considered insensitive, irresponsible, or harmful to specific groups based on their beliefs, political affiliation, sexual orientation, or gender identity.
• marijuana: This category includes content that discusses marijuana or its usage.

We are pleased to announce the official release of our Real-Time Streaming Transcription API! This API uses WebSockets and a fast Conformer Neural Network architecture that allows for a quick and accurate transcription in real-time.

Find out more in our Docs here!

• Developers can now send in files up to 5.5 GB in size, compared to the previous 4.5 GB.
• More topics have been added to our Topic Detection Model, along with increased speed and accuracy. You can see a complete list of detectable topics in our Docs here!

• An issue with speaker diarization where speakers were being missed, even when speaking long enough to be detected, has been solved!

Today we have released two of our enterprise-level models, Content Safety Detection and Topic Detection, to all users!

Now any developer can make use of these cutting edge models within their applications and products. Explore these new features in our Docs:

• Topic Detection
• Content Safety Detection

With this minor update, our Redaction Model will better detect Social Security Numbers and Medical References for additional security and data protection!

Today we released a new punctuation model that is more extensive than its predecessor, and will drive improvements in punctuation and casing accuracy!

List Historical Transcripts

• Developers can get a list of their historical transcriptions. This list can be filtered by status and date. This new endpoint will allow developers to see if they have any queued, processing, or throttled transcriptions.

Pre-Formatted Paragraphs

• Developers can now get pre-formatted paragraphs by calling our new paragraphs endpoint! The model will attempt to semantically break the transcript up into paragraphs of five sentences or less.

You can explore each feature further in our Docs:

• Historical Transcripts
• Pre-Formatted Paragraphs

Topic Detection Response Improvements

• Now each topic will include timestamps for each segment of classified text. We have also added a new summary key that will contain the confidence of all unique topics detected throughout the entire transcript.

• We have made improvements to our Speaker Diarization Model that increases accuracy over short and long transcripts.

We have released an update to our PII Redaction Model that will now support detecting and redacting additional classes!

• blood_type
• medical_condition
• drug (including vitamins/minerals)
• injury
• medical_process

Entity Definitions:

• blood_type: Blood type
• medical_condition: A medical condition. Includes diseases, syndromes, deficits, disorders. E.g., chronic fatigue syndrome, arrhythmia, depression.
• drug: Medical drug, including vitamins and minerals. E.g., Advil, Acetaminophen, Panadol
• injury: Human injury, e.g., I broke my arm, I have a sprained wrist. Includes mutations, miscarriages, and dislocations.
• medical_process: Medical process, including treatments, procedures, and tests. E.g., "heart surgery," "CT scan."

• We have made a major update to our Speaker Diarization Model that will improve results both in speed and accuracy. This update introduces the UNK speaker label for when a speaker for a word/phrase is unknown. This label is in place to prevent combining the unknown speaker with the dominant speaker, giving the developer more insight into who may or may not be speaking!

Our Content Safety Model has been trained on higher-quality data and now supports the following new labels:

• Company Financials: can detect when things like stock prices or revenue are discussed.
• Natural Disasters: in the past, we used the label Accidents to cover natural disasters and man-made accidents like plane crashes. Now Natural Disasters covers things like hurricanes, and Accidents covers Man-Made Accidents like plane crashes.