Changelog

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.

We've fixed a bug with the Historical Transcript endpoint that was causing null to appear as the value of the completed key.

Today, we’re releasing our new Japanese transcription model to help you transcribe and analyze your Japanese audio and video files using our cutting-edge AI.

Now you can automatically convert any Japanese audio or video file to text by including "language_code": "ja" in your POST request to our /v2/transcript endpoint.

In conjunction with transcription, we’ve also added Japanese support for our AI models including Custom Vocabulary (Word Boost), Custom Spelling, Automatic Punctuation / Casing, Profanity Filtering, and more. This means you can boost transcription accuracy with more granularity based on your use case. See the full list of supported models available for Japanese transcriptions here.

To get started, visit our walkthrough on Specifying a Language on our AssemblyAI documentation page or try it out now in our Sandbox!

We’ve released our new Hindi transcription model to help you transcribe and analyze your Hindi audio and video files.

Now you can automatically convert any Hindi audio or video file to text by including "language_code": "hi" in your POST request to our /v2/transcript endpoint.

We’ve also added Hindi support for our AI models including Custom Vocabulary (Word Boost), Custom Spelling, Automatic Punctuation / Casing, Profanity Filtering, and more. See the full list of supported models available for Hindi transcriptions here.

To get started with Hindi transcription, visit our walkthrough on Specifying a Language on our AssemblyAI documentation page.

Our Webhook service now supports the use of Custom Headers for authentication.

A Custom Header can be used for added security to authenticate webhook requests from AssemblyAI. This feature allows a developer to optionally provide a value to be used as an authorization header on the returning webhook from AssemblyAI, giving the ability to validate incoming webhook requests.

To use a Custom Header, you will include two additional parameters in your POST request to /v2/transcript: webhook_auth_header_name and webhook_auth_header_value. The webhook_auth_header_name parameter accepts a string containing the header's name which will be inserted into the webhook request. The webhook_auth_header_value parameter accepts a string with the value of the header that will be inserted into the webhook request. See our Using Webhooks documentation to learn more and view our code examples.

• Improved the overall accuracy of the Speaker Labels feature and the model’s ability to segment speakers.

• Fix a small edge case that would occasionally cause some transcripts to complete with NULL as the language_code value.

• Content Moderation and Topic Detection now available for the Portuguese language.

• Improved Inverse Text Normalization of money amounts in transcript text.

• Addressed an issue with Real-Time Transcription that would occasionally cause variance in timestamps over the course of a session.
• Fixed an edge case with transcripts including Filler Words that would occasionally cause server errors.

• Automatic Language Detection now supports detecting Dutch and Portuguese.

• Accuracy of the Automatic Language Detection model improved on files with large amounts of silence.
• Improved speaker segmentation accuracy for Speaker Labels.

• Dutch and Portuguese transcription is now generally available for our /v2/transcript endpoint. See our documentation for more information on specifying a language in your POST request.

• Content Moderation and Topic Detection features are now available for French, German, and Spanish languages.

• Improved redaction accuracy for credit_card_number, credit_card_expiration, and credit_card_cvv policies in our PII Redaction feature.

• Fixed an edge case that would occasionally affect the capitalization of words in transcripts when disfluencies was set to true.

• French, German, and Italian transcription is now publicly available. Check out our documentation for more information on Specifying a Language in your POST request.

• Released v2 of our Spanish model, improving absolute accuracy by ~4%.
• Automatic Language Detection now supports French, German, and Italian.
• Reduced the volume of the beep used to redact PII information in redacted audio files.

• Fixed an edge case that would occasionally affect timestamps for a small number of words when disfluencies was set to true.
• Fixed an edge case where PII audio redaction would occasionally fail when using local files.

• Added two new policies for PII Redaction and Entity Detection: drivers_license and banking_information.

• Spanish transcription is now publicly available. Check out our documentation for more information on Specifying a Language in your POST request.
• Automatic Language Detection is now available for our /v2/transcript endpoint. This feature can identify the dominant language that’s spoken in an audio file and route the file to the appropriate model for the detected language.
• Our new Custom Spelling feature gives you the ability to specify how words are spelled or formatted in the transcript text. For example, Custom Spelling could be used to change all instances "CS 50" to "CS50".

• Released Auto Chapters v6, improving the summarization of longer chapters.

• Auto Chapters v5 released, improving headline and gist generation and quote formatting in the summary key.

• Fixed an edge case in Dual-Channel files where initial words in an audio file would occasionally be missed in the transcription.

• Region-specific spelling improved for en_uk and en_au language codes.
• Improved the formatting of “MP3” in transcripts.
• Improved Real-Time transcription error handling for corrupted audio files.

• Released v3 of our Real-Time Transcription model, improving overall accuracy by 18% and proper noun recognition by 23% relative to the v2 model.

• Improved PII Redaction and Entity Detection for CREDIT_CARD_CVV and LOCATION.

• Added an Auto Retry feature, which automatically retries transcripts that fail with a Server error, developers have been alerted message. This feature is enabled by default. To disable it, visit the Account tab in your Developer Dashboard.

• Auto Chapters v4 released, improving chapter summarization in the summary key.
• Added a trailing period for the gist key in the Auto Chapters feature.