Product improvements

We’ve released new Punctuation and Truecasing models, achieving significant improvements for acronyms, mixed-case words, and more.

Below is a visual comparison between our previous Punctuation Restoration and Truecasing models (red) and the new models (green):

Going forward, the new Punctuation Restoration and Truecasing models will automatically be used for async and real-time transcriptions, with no need to upgrade for special access. Use the parameters punctuate and format_text, respectively, to enable/disable the models in a request (enabled by default).

Read more about our new models here.

Our real-time transcription service now supports PCM Mu-law, an encoding used primarily in the telephony industry. This encoding is set by using the `encoding` parameter in requests to our API. You can read more about our PCM Mu-law support here.

We have improved internal reporting for our transcription service, which will allow us to better monitor traffic.

Users can now directly pass in custom text inputs into LeMUR through the input_text parameter as an alternative to transcript IDs. This gives users the ability to use any information from the async API, formatted however they want, with LeMUR for maximum flexibility.

For example, users can assign action items per user by inputting speaker-labeled transcripts, or pull citations by inputting timestamped transcripts. Learn more about the new input_text parameter in our LeMUR API reference, or check out examples of how to use the input_text parameter in the AssemblyAI Cookbook.

We’ve made improvements that reduce hallucinations which sometimes occurred from transcribing hold music on phone calls. This improvement is effective immediately with no changes required by users.

We’ve fixed an issue that would sometimes yield an inability to fulfill a request when XML was returned by LeMUR /task endpoint.

We’ve made improvements to our file downloading pipeline which reduce transcription latency. Latency has been reduced by at least 3 seconds for all audio files, with greater improvements for large audio files provided via external URLs.

We’ve improved error messaging for increased clarity in the case of internal server errors.

We have released the beta for our new usage dashboard. You can now see a usage summary broken down by async transcription, real-time transcription, Audio Intelligence, and LeMUR. Additionally, you can see charts of usage over time broken down by model.

We have added support for AWS marketplace on the dashboard/account management pages of our web application.

We have fixed an issue in which LeMUR would sometimes fail when handling extremely short transcripts.

We have added a new parameter to LeMUR that allows users to specify a temperature for LeMUR generation. Temperature refers to how stochastic the generated text is and can be a value from 0 to 1, inclusive, where 0 corresponds to low creativity and 1 corresponds to high creativity. Lower values are preferred for tasks like multiple choice, and higher values are preferred for tasks like coming up with creative summaries of clips for social media.

Here is an example of how to set the temperature parameter with our Python SDK (which is available in version 0.18.0 and up):

import assemblyai as aai

aai.settings.api_key = f"{API_TOKEN}"

transcriber = aai.Transcriber()
transcript = transcriber.transcribe("https://storage.googleapis.com/aai-web-samples/meeting.mp4")

result = transcript.lemur.summarize(
	temperature=0.25
)

print(result.response)

We have added a new endpoint that allows users to delete the data for a previously submitted LeMUR request. The response data as well as any context provided in the original request will be removed. Continuing the example from above, we can see how to delete LeMUR data using our Python SDK:

request_id = result.request_id

deletion_result = aai.Lemur.purge_request_data(request_id)
print(deletion_result)

We have improved the error messaging for our Word Search functionality. Each phrase used in a Word Search functionality must be 5 words or fewer. We have improved the clarity of the error message when a user makes a request which contains a phrase that exceeds this limit.

We have fixed an edge case error that would occur when both disfluencies and Auto Chapters were enabled for audio files that contained non-fluent English.