Product improvements

We have renewed our SOC2 Type 2 certification, and expanded it to include Processing Integrity. Our SOC2 Type 2 certification now covers all five Trust Services Criteria (TSCs). 

Our SOC2 Type 2 report is available in our Trust Center to organizations with an NDA.

We have obtained our inaugural ISO 27001:2022 certification, which is an internationally recognized standard for managing information security. It provides a systematic framework for protecting sensitive information through risk management, policies, and procedures. 

Our ISO 27001:2022 report is available in our Trust Center to organizations with an NDA.

We've improved our timestamp algorithm, yielding higher accuracy for long numerical strings like credit card numbers, phone numbers, etc.

We've released a fix for no-space languages like Japanese and Chinese. While transcripts for these languages correctly contain no spaces in responses from our API, the text attribute of the utterances key previously contained spaces. These extraneous spaces have been removed.

We've improved Universal-2's formatting for punctuation, lowering the likelihood of consecutive punctuation characters such as ?'.

We now offer multichannel transcription, allowing users to transcribe files with up to 32 separate audio channels, making speaker identification easier in situations like virtual meetings.

You can enable multichannel transcription via the `multichannel` parameter when making API requests. Here's how you can do it with our Python SDK:

import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY" 
audio_file = "path/to/your/file.mp3"

config = aai.TranscriptionConfig(multichannel=True)

transcriber = aai.Transcriber(config=config)
transcript = transcriber.transcribe(audio_url)

print(transcript.json_response["audio_channels"])
print(transcript.utterances)

You can learn more about multichannel transcription in our Docs.

Last week we released Universal-2, our latest Speech-to-Text model. Universal-2 builds upon our previous model Universal-1 to make significant improvements in "last mile" challenges critical to real-world use cases - proper nouns, formatting, and alphanumerics.

Comparison of error rates for Universal-2 vs Universal-1 across overall performance (Standard ASR) and four last-mile areas, each measured by the appropriate metric

Universal-2 is now the default model for English files sent to our `v2/transcript` endpoint for async processing. You can read more about Universal-2 in our announcement blog or research blog, or you can try it out now on our Playground.