Changelog

• Improved the API's ability to handle audio/video files with a duration over 8 hours.

• Further improved transcription processing times by 12%.
• Fixed an edge case in our responses for dual channel audio files where if speaker 2 interrupted speaker 1,  the text from speaker 2 would cause the text from speaker 1 to be split into multiple turns, rather than contextually keeping all of speaker 1's text together.

• Today, we're happy to announce the release of our most accurate Speech Recognition model for asynchronous transcription to date—version 8 (v8).
• This new model dramatically improves overall accuracy (up to 19% relative), and proper noun accuracy as well (up to 25% relative).
• You can read more about our v8 model in our blog here.

• Fixed an edge case where a small percentage of short (<60 seconds in length) dual-channel audio files, with the same audio on each channel, resulted in repeated words in the transcription.

• Launched our v2 Real-Time Streaming Transcription model (read more on our blog).
• This new model improves accuracy of our Real-Time Streaming Transcription by ~10%.
• Launched our Topic Detection v4 model, with an accuracy boost of ~8.37% over v3 (read more on our blog).

• Released our v3 Topic Detection model.
• This model dramatically improves the Topic Detection feature's ability to accurately detect topics based on context.
• For example, in the following text, the model was able to accurately predict "Rugby" without the mention of the sport directly, due to the mention of "Ed Robinson" (a Rugby coach).

• PII Redaction has been improved to better identify (and redact) phone numbers even when they are not explicitly referred to as a phone number.

• Released a fix for PII Redaction that corrects an issue where the model would sometimes detect phone numbers as credit card numbers or social security numbers.

• The API now returns a severity score along with the confidence and label keys when using the Content Safety feature.
• The severity score measures how intense a detected Content Safety label is on a scale of 0 to 1.
• For example, a natural disaster that leads to mass casualties will have a score of 1.0, while a small storm that breaks a mailbox will only be 0.1.

• Fixed an edge case where a small number of transcripts with Automatic Transcript Highlights turned on were not returning any results.

• Fixed an edge case where higher sample rates would occasionally trigger a Client sent audio too fast error from the Real-Time Streaming WebSocket API.
• Fixed an edge case where some streams from Real-Time Streaming WebSocket API were held open after a customer idled their session.
• Fixed an edge case in the /v2/stream endpoint, where large periods of silence would occasionally cause automatic punctuation to fail.
• Improved error handling when non-JSON input is sent to the /v2/transcript endpoint.

• v3 Punctuation Model released.
• v3 brings improved accuracy to automatic punctuation and casing for both async (/v2/transcript) and real-time (WebSocket API) transcripts.
• Released an all-new Word Search feature that will allow developers to search for words in a completed transcript.
• This new feature returns how many times the word was spoken, the index of that word in the transcript's JSON response word list/array, and the associated timestamps for each matched word.

• Fixed an issue causing a small subset of words not to be filtered when profanity filtering was turned on.

• Fixed a bug with PII Redaction, where sometimes dollar amount and date tokens were not being properly redacted.
• AssemblyAI now supports even more audio/video file formats thanks to improvements to our audio transcoding pipeline!
• Fixed a rare bug where a small percentage of transcripts (0.01%) would incorrectly sit in a status of "queued" for up to 60 seconds.

Today we've released a major improvement to our ITN (Inverse Text Normalization) model. This results in better formatting for entities within the transcription, such as phone numbers, money amounts, and dates.

For example:

Money:

• Spoken: "Hey, do you have five dollars?"
• Model output with ITN: "Hey, do you have $5?"

Years:

• Spoken: "Yes, I believe it was back in two thousand eight"
• Model output with ITN: "Yes, I believe it was back in 2008."

Today we've released an updated Automatic Punctuation and Casing Restoration model (Punctuation v2.5)! This update results in improved capitalization of proper nouns in transcripts, reduces over-capitalization issues where some words like were being incorrectly capitalized, and improves some edge cases around words with commas around them. For example:

• "....in the Us" now becomes "....in the US."
• "whatsapp," now becomes "WhatsApp,"

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!