Product improvements

We’ve improved our Automatic Casing model and fixed a minor bug that caused over-capitalization in English transcripts. The Automatic Casing model is enabled by default with our Core Transcription API to improve transcript readability for video captions (SRT/VTT). See our documentation for more info on Automatic Casing.

Our Core Transcription model has been fine-tuned to better detect short utterances in English transcripts. Examples of short utterances include one-word answers such as “No.” and “Right.” This update will take effect immediately for all customers.

Over the next few weeks, we will begin rolling out Static IP support for webhooks to customers in stages.

Outgoing webhook requests sent from AssemblyAI will now originate from a static IP address 44.238.19.20, rather than a dynamic IP address. This gives you the ability to easily validate that the source of the incoming request is coming from our server. Optionally, you can choose to whitelist this static IP address to add an additional layer of security to your system.

See our walkthrough on how to start receiving webhooks for your transcriptions.

We’ve made improvements to our Core Transcription model to better identify and transcribe numbers present in your audio files.

Accurate number transcription is critical for customers that need to redact Personally Identifiable Information (PII) that gets exchanged during phone calls. Examples of PII include credit card numbers, addresses, phone numbers, and social security numbers.

In order to help you handle sensitive user data at scale, our PII Redaction model automatically detects and removes sensitive info from transcriptions. For example, when PII redaction is enabled, a phone number like 412-412-4124 would become ###-###-####.

To learn more, check out our blog that covers all of our PII Redaction Policies or try our PII Redaction model in our Sandbox here!

We've updated our Disfluency Detection model to improve the accuracy of timestamps for disfluency words.

By default, disfluencies such as "um" or "uh" and "hm" are automatically excluded from transcripts. However, we allow customers to include these filler words by simply setting the disfluencies parameter to true in their POST request to /v2/transcript, which enables our Disfluency Detection model.

More info and code examples can be found here.

We've improved the Speaker Label model’s ability to identify unique speakers for single word or short utterances.