Product improvements

We’ve implemented changes that yield between a 43% to 200% increase in processing speed for our Summarization models, depending on which model is selected, with no measurable impact on the quality of results.

We have standardized the response from our API for automatically detected languages that do not support requested features. In particular, when Automatic Language Detection is used and the detected language does not support a feature requested in the transcript request, our API will return null in the response for that feature.

We've released LeMUR - our framework for applying LLMs to spoken data - for general availability. LeMUR is optimized for high accuracy on specific tasks:

1. Custom Summary allows users to automatically summarize files in a flexible way
2. Question & Answer allows users to ask specific questions about audio files and receive answers to these questions
3. Action Items allows users to automatically generate a list of action items from virtual or in-person meetings

Additionally, LeMUR can be applied to groups of transcripts in order to simultaneously analyze a set of files at once, allowing users to, for example, summarize many podcast episode or ask questions about a series of customer calls.

Our Python SDK allows users to work with LeMUR in just a few lines of code:

# version 0.15 or greater
import assemblyai as aai

# set your API key
aai.settings.api_key = f"{API_TOKEN}"

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

# generate and print action items
result = transcript.lemur.action_items(
    context="A GitLab meeting to discuss logistics",
    answer_format="**<topic header>**\n<relevant action items>\n",
)

print(result.response)

Learn more about LeMUR in our blog post, or jump straight into the code in our associated Colab notebook.

We’ve introduced a new, optional speech_threshold parameter, allowing users to only transcribe files that contain at least a specified percentage of spoken audio, represented as a ratio in the range [0, 1].

You can use the speech_threshold parameter with our Python SDK as below:

import assemblyai as aai

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

config = aai.TranscriptionConfig(speech_threshold=0.1)

file_url = "https://github.com/AssemblyAI-Examples/audio-examples/raw/main/20230607_me_canadian_wildfires.mp3"

transcriber = aai.Transcriber()
transcript = transcriber.transcribe(file_url, config)

print(transcript.text)

Smoke from hundreds of wildfires in Canada is triggering air quality alerts throughout the US. Skylines from ...

If the percentage of speech in the audio file does not meet or surpass the provided threshold, then the value of transcript.text will be None and you will receive an error:

if not transcript.text:
	print(transcript.error)

Audio speech threshold 0.9461 is below the requested speech threshold value 1.0

As usual, you can also include the speech_threshold parameter in the JSON of raw HTTP requests for any language.

We’ve fixed a bug in which timestamps could sometimes be incorrectly reported for our Topic Detection and Content Safety models.

We’ve made improvements to detect and remove a hallucination that would sometimes occur with specific audio patterns.

We’ve fixed an issue in which the last character in an alphanumeric sequence could fail to be transcribed. The fix is effective immediately and constitutes a 95% reduction in errors of this type.

We’ve fixed an issue in which consecutive identical numbers in a long number sequence could fail to be transcribed. This fix is effective immediately and constitutes a 66% reduction in errors of this type.

We’ve made improvements to the Speaker Labels model, adjusting the impact of the speakers_expected parameter to better allow the model to determine the correct number of unique speakers, especially in cases where one or more speakers talks substantially less than others.

We’ve expanded our caching system to include additional third-party resources to help further ensure our continued operations in the event of external resources being down.