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Product improvements

Check out the AssemblyAI changelog to see weekly accuracy and product improvements our team has been working on.

Powering incredible companies


Significant processing time improvement

We’ve made significant improvements to our transcoding pipeline, resulting in a 98% overall speedup in transcoding time and a 12% overall improvement in processing time for our asynchronous API.

We’ve implemented a caching system for some third-party resources to ensure our continued operations in the event of external resources being down.


Announcing LeMUR - our new framework for applying powerful LLMs to transcribed speech

We’re introducing our new framework LeMUR, which makes it simple to apply Large Language Models (LLMs) to transcripts of audio files up to 10 hours in length.

LLMs unlock a range of impressive capabilities that allow teams to build powerful Generative AI features. However, building these features is difficult due to the limited context windows of modern LLMs, among other challenges that necessitate the development of complicated processing pipelines.

LeMUR circumvents this problem by making it easy to apply LLMs to transcribed speech, meaning that product teams can focus on building differentiating Generative AI features rather than focusing on building infrastructure. Learn more about what LeMUR can do and how it works in our announcement blog, or jump straight to trying LeMUR in our Playground.


Decreased latency and improved password reset

We’ve implemented a range of improvements to our English pipeline, leading to an average 38% improvement in overall latency for asynchronous English transcriptions.

We’ve made improvements to our password reset process, offering greater clarity to users attempting to reset their passwords while still ensuring security throughout the reset process.


Conformer-1 now available for Real-Time transcription, new Speaker Labels parameter, and more

We're excited to announce that our new Conformer-1 Speech Recognition model is now available for real-time English transcriptions, offering a 24.3% relative accuracy improvement.

Effective immediately, this state-of-the-art model will be the default model for all English audio data sent to the wss:// WebSocket API.

The Speaker Labels model now accepts a new optional parameter called speakers_expected. If you have high confidence in the number of speakers in an audio file, then you can specify it with speakers_expected in order to improve Speaker Labels performance, particularly for short utterances.

TLS 1.3 is now available for use with the AssemblyAI API. Using TLS 1.3 can decrease latency when establishing a connection to the API.

Our PII redaction scaling has been improved to increase stability, particularly when processing longer files.

We've improved the quality and accuracy of our Japanese model.

Short transcripts that are unable to be summarized will now return an empty summary and a successful transcript.


New AI Models for Italian / Japanese Punctuation Improvements

Our Content Safety and Topic Detection models are now available for use with Italian audio files.

We’ve made improvements to our Japanese punctuation model, increasing relative accuracy by 11%. These changes are effective immediately for all Japanese audio files submitted to AssemblyAI.