Product improvements

We are happy to announce the release of our most accurate Speech Recognition model to date - version 9 (v9). This updated model delivers increased performance across many metrics on a wide range of audio types.

Word Error Rate, or WER, is the primary quantitative metric by which the performance of an automatic transcription model is measured. Our new v9 model shows significant improvements across a range of different audio types, as seen in the chart below, with a more than 11% improvement on average.

In addition to standard overall WER advancements, the new v9 model shows marked improvements with respect to proper nouns. In the chart below, we can see the relative performance increase of v9 over v8 for various types of audio, with a nearly 15% improvement on average.

The new v9 transcription model is currently live in production. This means that customers will see improved performance with no changes required on their end. The new model will automatically be used for all transcriptions created by our /v2/transcript endpoint going forward, with no need to upgrade for special access.

While our customers enjoy the elevated performance of the v9 model, our AI research team is already hard at work on our v10 model, which is slated to launch in early 2023. Building upon v9, the v10 model is expected to radically improve the state of the art in speech recognition.

Try our new v9 transcription model through your browser using the AssemblyAI Playground. Alternatively, sign up for a free API token to test it out through our API, or schedule a time with our AI experts to learn more.

We are excited to announce that new Summarization models are now available! Developers can now choose between multiple summary models that best fit their use case and customize the output based on the summary length.

The new models are:

• Informative which is best for files with a single speaker, like a presentation or lecture
• Conversational which is best for any multi-person conversation, like customer/agent phone calls or interview/interviewee calls
• Catchy which is best for creating video, podcast, or media titles

Developers can use the summary_model parameter in their POST request to specify which of our summary models they would like to use. This new parameter can be used along with the existing summary_type parameter to allow the developer to customize the summary to their needs.

import requests
endpoint = "https://api.assemblyai.com/v2/transcript"
json = {
    "audio_url": "https://bit.ly/3qDXLG8",
    "summarization": True,
    "summary_model": "informative", # conversational | catchy
    "summary_type": "bullets" # bullets_verbose | gist | headline | paragraph
}
headers = {
	"authorization": "YOUR-API-TOKEN",
    "content-type": "application/json"
}
response = requests.post(endpoint, json=json, headers=headers)
print(response.json())

Check out our latest blog post to learn more about the new Summarization models or head to the AssemblyAI Playground to test Summarization in your browser!

We’ve made updates to our Core Transcription model to improve the transcription accuracy of the word COVID. This improvement is effective immediately for all audio files submitted to AssemblyAI for transcription.

Static IP support for webhooks is now generally available!

Outgoing webhook requests sent from AssemblyAI will now originate from a static IP address 44.238.19.20, rather than a dynamic IP address. This gives you the ability to easily validate that the source of the incoming request is coming from our server. Optionally, you can choose to whitelist this static IP address to add an additional layer of security to your system.

See our walkthrough on how to start receiving webhooks for your transcriptions.

import requests
endpoint = "https://api.assemblyai.com/v2/transcript"
json = {
  "audio_url": "https://bit.ly/3qDXLG8",
    "summarization": True,
    "summary_type": "bullets" # paragraph | headline | gist 
}
headers = {
  "authorization": "YOUR-API-TOKEN",
    "content-type": "application/json"
}
response = requests.post(endpoint, json=json, headers=headers)
print(response.json())

Starting today, you can now transcribe and summarize entire audio files with a single API call.

To enable our new Summarization models, include the following parameter: "summarization": true in your POST request to /v2/transcript. When the transcription finishes, you will see the summary key in the JSON response containing the summary of your transcribed audio or video file.

By default, summaries will be returned in the style of bullet points. You can customize the style of summary by including the optional summary_type parameter in your POST request along with one of the following values: paragraph, headline, or gist. Here is the full list of summary types we support.

// summary_type = "paragraph"

"summary": "Josh Seiden and Brian Donohue discuss the
topic of outcome versus output on Inside Intercom.
Josh Seiden is a product consultant and author who has
just released a book called Outcomes Over Output.
Brian is product management director and he's looking
forward to the chat."

// summary_type = "headline"

"summary": "Josh Seiden and Brian Donohue discuss the
topic of outcomes versus output."

// summary_type = "gist"

"summary": "Outcomes over output"

// summary_type = = "bullets"

"summary": "Josh Seiden and Brian Donohue discuss
the topic of outcome versus output on Inside Intercom.
Josh Seiden is a product consultant and author who has
just released a book called Outcomes Over Output.
Brian is product management director and he's looking
forward to the chat.\n- ..."

Examples of use cases for Summarization include:

• Identify key takeaways from phone calls to speed up post-call review and reduce manual summarization
• Summarize long podcasts into short descriptions so users can preview before they listen.
• Instantly generate meetings summaries to quickly recap virtual meetings and highlight post-meeting actions
• Suggest 3-5 word video titles automatically for user-generated content
• Synthesize long educational courses, lectures, and media broadcasts into their most important points for faster consumption

We're really excited to see what you build with our new Summarization models. To get started, try it out for free in our no-code playground or visit our documentation for more info on how to enable Summarization in your API requests.

We’ve improved our Automatic Casing model and fixed a minor bug that caused over-capitalization in English transcripts. The Automatic Casing model is enabled by default with our Core Transcription API to improve transcript readability for video captions (SRT/VTT). See our documentation for more info on Automatic Casing.

Our Core Transcription model has been fine-tuned to better detect short utterances in English transcripts. Examples of short utterances include one-word answers such as “No.” and “Right.” This update will take effect immediately for all customers.