#card-group {
  background-color: #f8f8f9;
}

.card {
  background-color: transparent !important;
}

#content-container h1,
#content-container h2,
#content-container h3,
#content-container h4,
#content-container h5,
#content-container h6 {
  font-family: 'Monument Grotesk Regular', serif;
}

#home-search-entry {
  outline: 4px solid rgb(229, 231, 235);
}

.dark #home-search-entry {
  outline: 4px solid rgb(21, 21, 21);
}

#content-container p {
  font-size: 1rem;
  line-height: 1.65rem;
}

.wide-table {
  width: 100%;
  overflow-x: auto;
}

.wide-table table {
  width: 175%;
  margin-bottom: 0;
}

/* Prompt Library */
#prompt-library-container {
  margin: 4rem auto;
  max-width: 48rem;
  padding-left: 1.25rem;
  padding-right: 1.25rem;
}

.prompt-library-title {
  font-size: 24px;
  text-align: center;
  font-weight: 700;
  color: #17181d;
}

.dark .prompt-library-title {
  color: #e5e7eb;
}

.prompt-library-description {
  margin-top: 1rem;
  text-align: center;
  color: #374151;
}

.dark .prompt-library-description {
  color: #c0c1c3;
}

.main-content {
  margin-bottom: 10rem;
  max-width: 64rem;
  margin-left: auto;
  margin-right: auto;
  padding-left: 1.25rem;
  padding-right: 1.25rem;
}

.prompt-controllers {
  display: flex;
  gap: 0.5rem;
}

.prompt-search-container {
  position: relative;
  flex: 1 1 0%;
}

.prompt-search-icon-container {
  display: flex;
  position: absolute;
  top: 0;
  bottom: 0;
  left: 0;
  align-items: center;
  padding-left: 0.75rem;
}

.prompt-search-icon {
  margin-left: 0.25rem;
  margin-right: 0.75rem;
  flex: none;
  width: 1rem;
  height: 1rem;
  background-color: #6b7280;
  mask-image: url(https://mintlify.b-cdn.net/v6.5.1/solid/magnifying-glass.svg);
  mask-repeat: no-repeat;
  mask-position: center center;
}

input.prompt-search-bar {
  display: block;
  height: 2.5rem;
  padding-left: 2.5rem;
  border-radius: 0.75rem;
  border-width: 1px;
  background-color: #ffffff;
  width: 100%;
  color: #111827;
  box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
}

.dark input.prompt-search-bar {
  color: #ffffff;
  background-color: rgb(var(--background-dark));
  border-color: #d1d5db1a;
}

input.prompt-search-bar:focus {
  outline-color: rgb(var(--primary));
}

.dark input.prompt-search-bar:focus {
  outline-color: rgb(var(--primary-light));
}

.dark .prompt-search-icon {
  background-color: #ffffff80;
}

#category-select {
  padding-left: 1rem;
  padding-right: 2.5rem;
  height: 2.5rem;
  display: flex;
  align-items: center;
  border-radius: 0.75rem;
  border-width: 1px;
  color: #111827;
  background-color: #ffffff;
  cursor: pointer;
  box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
  white-space: nowrap;
}

.dark #category-select {
  background-color: rgb(var(--background-dark));
  border-color: #d1d5db1a;
  color: #ffffff;
}

#category-select:hover {
  background-color: #f9fafb;
}

.dark #category-select:hover {
  background-color: #ffffff0d;
}

#category-select:focus {
  outline-color: rgb(var(--primary));
}

.dark #category-select:focus {
  outline-color: rgb(var(--primary-light));
}

#categories-dropdown {
  top: calc(100% + 4px);
  padding: 0.5rem 0.5rem;
  display: none;
  position: absolute;
  z-index: 10;
  border-radius: 0.75rem;
  border-width: 1px;
  width: 100%;
  color: #111827;
  background-color: #ffffff;
  box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
}

.dark #categories-dropdown {
  background-color: rgb(var(--background-dark));
  border-color: #d1d5db1a;
  color: #ffffff;
}

#categories-dropdown-clickout {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 0;
}

.dropdown-icon-container {
  display: flex;
  position: absolute;
  top: 0;
  bottom: 0;
  right: 0;
  align-items: center;
  padding-right: 0.25rem;
}

.dropdown-icon {
  margin-left: 0.25rem;
  margin-right: 0.75rem;
  flex: none;
  width: 0.75rem;
  height: 0.75rem;
  background-color: #6b7280;
  mask-image: url(https://mintlify.b-cdn.net/v6.5.1/solid/caret-down.svg);
  mask-repeat: no-repeat;
  mask-position: center center;
}

.dark .dropdown-icon {
  background-color: #ffffff80;
}

#prompts-container {
  grid-template-columns: repeat(1, minmax(0, 1fr));
  gap: 2rem;
}

.dropdown-item {
  padding: 0.25rem 0.5rem;
  border-radius: 0.375rem;
  display: flex;
  align-items: center;
  cursor: pointer;
}

.dropdown-item:hover {
  background-color: #f9fafb;
}

.dark .dropdown-item:hover {
  background-color: #ffffff0d;
}

.check-icon {
  mask-image: url(https://mintlify.b-cdn.net/v6.5.1/solid/check.svg);
  height: 0.875rem;
  width: 1rem;
  background-color: rgb(var(--primary-light));
  mask-repeat: no-repeat;
  mask-position: center center;
}

/* Light mode styles */
.prompt-card {
  display: block;
  border-radius: 1rem;
  transition: all 0.3s ease;
  text-decoration: none;
  overflow: hidden;
  min-height: 200px;
  border: 1px solid rgba(255, 255, 255, 0.1);
  background: linear-gradient(135deg,
    #f5f5f5,  /* Light gray */
    #ffefd5   /* Papaya whip - light warm yellow */
  ) !important;
}

.prompt-title {
  font-size: 1.5rem;
  font-weight: 600;
  color: #0b0b0b !important;
  margin-bottom: 1.5rem;
  line-height: 1.3;
}

/* Dark mode styles */
html.dark .prompt-card {
  background: linear-gradient(135deg,
  #0D1123,  /* Darker blue */
  #1B1728   /* Deep blue */
  ) !important;
  border: 1px solid rgba(255, 255, 255, 0.1);
}

/* Super specific title selectors */
html.dark .prompt-card .prompt-title,
html.dark #prompts-container .prompt-title,
html.dark .prompt-content .prompt-title {
  color: #ffffff !important;
}

/* Remove any conflicting title styles */
.prompt-title,
#prompts-container .prompt-title,
.prompt-content .prompt-title {
  font-size: 1.5rem;
  font-weight: 600;
  margin-bottom: 1.5rem;
  line-height: 1.3;
}

html.dark .prompt-card .author-name {
  color: rgba(255, 255, 255, 0.9);
}

html.dark .prompt-card .prompt-date {
  color: rgba(255, 255, 255, 0.6);
}

.prompt-description {
  font-size: 0.875rem;
  color: #6b7280;
  line-height: 1.25rem;
  margin: 0;
}

/* Dark mode styles */
.dark .prompt-description {
  color: rgba(255, 255, 255, 0.9);
}

#prompts-container {
  display: grid;
  margin-top: 2.5rem;
}

@media (min-width: 640px) {
  #category-select {
    width: 16rem;
  }
}

@media (min-width: 1024px) {
  #prompts-container {
    grid-template-columns: repeat(2, minmax(0, 1fr));
  }
}

/* Utility classes */
.relative {
  position: relative;
}

.flex-1 {
  flex: 1 1 0%;
}

.intro-card {
  border: 1px solid #cdd0d5;
}

.dark .intro-card {
  border: 1px solid #1c1c1d;
}

.grid-container {
  display: grid;
  grid-template-columns: repeat(1, 1fr);
  gap: 1rem;
}

@media (min-width: 768px) {
  .grid-container {
    grid-template-columns: repeat(2, 1fr);
  }
}

@media (min-width: 1024px) {
  .grid-container {
    grid-template-columns: repeat(3, 1fr);
  }
}

.prompt-card {
  display: flex;
  padding: 1.5rem;
  border-radius: 0.75rem;
  border: 1px solid #e5e7eb;
  transition: all 0.2s ease;
  text-decoration: none;
}

.dark .prompt-card {
  background: rgb(var(--background-dark));
  border-color: #1c1c1d;
}

.prompt-card:hover {
  transform: translateY(-2px);
  box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
}

.prompt-icon-container {
  flex-shrink: 0;
  width: 48px;
  height: 48px;
  border-radius: 8px;
  background: #f3f4f6;
  display: flex;
  align-items: center;
  justify-content: center;
  margin-right: 1rem;
}

.prompt-content {
  flex: 1;
}

.prompt-title {
  font-size: 1.125rem;
  font-weight: 600;
  color: #1f2937;
  margin-bottom: 0.5rem;
}

.dark .prompt-title {
  color: #e5e7eb;
}

.prompt-description {
  font-size: 0.875rem;
  color: #6b7280;
  line-height: 1.25rem;
  margin: 0;
}

.dark .prompt-description {
  color: #9ca3af;
}

.prompt-card {
  display: flex;
  flex-direction: column;
  padding: 2rem;
  border-radius: 1rem;
  border: 1px solid rgba(255, 255, 255, 0.1);
  transition: all 0.3s ease;
  text-decoration: none;
  min-height: 200px;
}

.prompt-card:hover {
  transform: translateY(-4px);
  box-shadow: 0 8px 16px rgba(0, 0, 0, 0.2);
}

.prompt-content {
  display: flex;
  flex-direction: column;
  height: 100%;
  justify-content: space-between;
}

.prompt-title {
  font-size: 1.5rem;
  font-weight: 600;
  color: #ffffff;
  margin-bottom: 1.5rem;
  line-height: 1.3;
}

.prompt-metadata {
  margin-top: auto;
}

.author-section {
  display: flex;
  align-items: center;
  margin-bottom: 1rem;
}

.author-name {
  color: #1f2937;
  text-decoration: none;
  font-size: 1rem;
  font-weight: 500;
}

.author-name:hover {
  text-decoration: underline;
}

.prompt-info {
  display: flex;
  justify-content: space-between;
  align-items: center;
}

.prompt-date {
  color: rgba(0, 0, 0, 0.6);
  font-size: 0.875rem;
}

.prompt-tags {
  display: flex;
  gap: 0.5rem;
}

.prompt-tag {
  padding: 0.25rem 0.75rem;
  border-radius: 1rem;
  font-size: 0.25rem;
  font-weight: 500;
  background-color: rgba(255, 255, 255, 0.1);
}

/* Update grid layout for better spacing */
.grid-container {
  display: grid;
  grid-template-columns: repeat(1, 1fr);
  gap: 1.5rem;
}

@media (min-width: 768px) {
  .grid-container {
    grid-template-columns: repeat(2, 1fr);
  }
}

@media (min-width: 1024px) {
  .grid-container {
    grid-template-columns: repeat(3, 1fr);
  }
}

/* Make the selector more specific and add !important to override any conflicts */
.prompt-card .prompt-title,
#prompts-container .prompt-title,
.prompt-content .prompt-title {
  font-size: 1.5rem;
  font-weight: 600;
  color: #0b0b0b !important;  /* Force black color */
  margin-bottom: 1.5rem;
  line-height: 1.3;
}

/* Also ensure dark mode isn't interfering */
.dark .prompt-title {
  color: #0b0b0b !important;
}

.prompt-metadata {
  margin-top: auto;
}

.author-name {
  display: block;
  color: #1f2937;
  font-size: 1rem;
  font-weight: 500;
  margin-bottom: 0.5rem;
}

.prompt-date {
  color: rgba(0, 0, 0, 0.6);
  font-size: 0.875rem;
}

.prompt-info {
  display: flex;
  justify-content: space-between;
  align-items: center;
  margin-top: 1rem;
}

.prompt-tags {
  display: flex;
  gap: 0.5rem;
}

.prompt-tag {
  padding: 0.5rem 1rem;
  border-radius: 0.5rem;
  font-size: 0.875rem;
  font-weight: 500;
}

/* Dark mode styles */
.dark .author-name {
  color: rgba(255, 255, 255, 0.9);
}

.dark .prompt-date {
  color: rgba(255, 255, 255, 0.6);
}

/* Section specific styles */
.content-section {
  margin-top: 2rem;
}

.section-title {
  font-size: 1.5rem;
  font-weight: 600;
  margin-bottom: 0rem;
  color: #111827;
}

.dark .section-title {
  color: #e5e7eb;
}

#new-apis-container,
#popular-container,
#all-prompts-container {
  margin-top: 0.5rem;
}

Question & Answer allows you to ask free-form questions about a single transcript or a group of transcripts.
The questions can be any whose answers you find useful, such as judging whether a caller is likely to become a customer or whether all items on a meeting's agenda were covered.


Ask questions using LeMUR

The model that is used for the final prompt after compression is performed.


Legacy - deprecating on 02/06/25. Claude 2.0 is a legacy model that has good complex reasoning. It offers more nuanced responses and improved contextual comprehension.


Legacy - deprecating on 02/06/25. Claude 2.1 is a legacy model similar to Claude 2.0. The key difference is that it minimizes model hallucination and system prompts, has a larger context window, and performs better in citations.


Claude 3.5 Sonnet is Anthropic's most intelligent model to date, outperforming Claude 3 Opus on a wide range of evaluations, with the speed and cost of Claude 3 Sonnet.


Claude 3 Haiku is the fastest model that can execute lightweight actions.


Claude 3 Opus is good at handling complex analysis, longer tasks with many steps, and higher-order math and coding tasks.


Claude 3 Sonnet is a legacy model with a balanced combination of performance and speed for efficient, high-throughput tasks.


Legacy - deprecating on 10/28/24. Claude Instant is a legacy model that is optimized for speed and cost. Claude Instant can complete requests up to 20% faster than Claude 2.0.


LeMUR Mistral 7B is an LLM self-hosted by AssemblyAI. It's the fastest and cheapest of the LLM options. We recommend it for use cases like basic summaries and factual Q&A.


Legacy - deprecating on 10/28/24. The same as Claude Instant.

Legacy - deprecating on 02/06/25. The same as Claude 2.0.

How you want the answer to be returned. This can be any text. Can't be used with answer_options. Examples: "short sentence", "bullet points"


What discrete options to return. Useful for precise responses. Can't be used with answer_format. Example: ["Yes", "No"]


Any context about the transcripts you wish to provide. This can be a string or any object.

The question you wish to ask. For more complex questions use default model.

An answer generated by LeMUR and its question

The number of input tokens used by the model

The number of output tokens generated by the model

Params to ask questions about the transcripts

ApiKey

An error occurred while processing the request

AssemblyAI Docs

Home

Overview

Getting started

Learn how to transcribe and analyze an audio file.

Transcribe an Audio File

Learn how to leverage LLMs for speech using LeMUR.

Apply LLMs to audio files

Webhooks are custom HTTP callbacks that you can define to get notified when your transcripts are ready

Webhooks

Learn what languages are supported by AssemblyAI

Supported Languages

To ensure a smooth experience for all users, certain operations have per-account usage limits.

Usage limits

Library

Convert spoken audio into text using our Speech-to-Text models.

The Speech Recognition model enables you to transcribe spoken words into written text and is the foundation of all AssemblyAI products.

Speech Recognition

Learn how to detect multiple speakers in an audio

Speaker Diarization

Learn how to transcribe live audio streams

Streaming

Apply Large Language Models to spoken data. A Large Language Model (LLM) is a machine learning model that uses natural language processing (NLP) to generate text. LeMUR is a framework that lets you apply LLMs to audio transcripts, for example to ask questions about a call, or to summarize a meeting.

Learn how to summarize your audio using LeMUR

Summarize Your Audio Data

In this guide, you'll learn how to use LeMUR to ask questions and get answers about your audio data.

Ask questions about your audio data

Learn about different use cases for LeMUR with these examples.

Create Custom LLM Prompts

Learn how you can customize LeMUR parameters to alter the outcome.

Change model and parameters

Learn how to get better results from  by using prompting techniques to optimize your prompt. This page walks you through the parts of a prompt and gives you tips on how to use them to improve your prompts.

Prompt engineering

Learn about each of our Audio Intelligence models.

Audio Intelligence

Distill important information by summarizing your audio files.

Summarization

The Content Moderation model lets you detect inappropriate content in audio files to ensure that your content is safe for all audiences.

Content Moderation

The Sentiment Analysis model detects the sentiment of each spoken sentence in the transcript text. Use Sentiment Analysis to get a detailed analysis of the positive, negative, or neutral sentiment conveyed in the audio, along with a confidence score for each result.

Sentiment Analysis

The Entity Detection model lets you automatically identify and categorize key information in transcribed audio content.

Entity Detection

The Topic Detection model lets you identify different topics in the transcript. The model uses the , a standardized language for content description which consists of 698 comprehensive topics.

Topic Detection

The Auto Chapters model summarizes audio data over time into chapters. Chapters makes it easy for users to navigate and find specific information.

Auto Chapters

The Key Phrases model identifies significant words and phrases in your transcript and lets you extract the most important concepts or highlights from your audio or video file.

Key Phrases

The PII Redaction model lets you minimize sensitive information about individuals by automatically identifying and removing it from your transcript.

PII Redaction

This page describes how to perform common operations with the REST API

Upload a media file to AssemblyAI's servers.

Upload a media file

Create a transcript from a media file that is accessible via a URL.

Transcribe audio

Get the transcript resource. The transcript is ready when the "status" is "completed".

Get transcript

Get the transcript split by sentences. The API will attempt to semantically segment the transcript into sentences to create more reader-friendly transcripts.

Get sentences in transcript

Get the transcript split by paragraphs. The API will attempt to semantically segment your transcript into paragraphs to create more reader-friendly transcripts.

Get paragraphs in transcript

Export your transcript in SRT or VTT format to use with a video player for subtitles and closed captions.

Get subtitles for transcript

Retrieve the redacted audio object containing the status and URL to the redacted audio.

Get redacted audio

Search through the transcript for keywords. You can search for individual words, numbers, or phrases containing up to five words or numbers.

Search words in transcript

Retrieve a list of transcripts you created.
Transcripts are sorted from newest to oldest. The previous URL always points to a page with older transcripts.


List transcripts

Remove the data from the transcript and mark it as deleted.

Delete transcript

Use the LeMUR task endpoint to input your own LLM prompt.

Run a task using LeMUR

Custom Summary allows you to distill a piece of audio into a few impactful sentences.
You can give the model context to obtain more targeted results while outputting the results in a variety of formats described in human language.


Summarize a transcript using LeMUR

Use LeMUR to generate a list of action items from a transcript

Extract action items

Retrieve a LeMUR response that was previously generated.


Retrieve LeMUR response

Delete the data for a previously submitted LeMUR request.
The LLM response data, as well as any context provided in the original request will be removed.


Purge LeMUR request data

Create a temporary authentication token for Streaming Speech-to-Text

Create temporary authentication token for Streaming STT

Streaming Speech-to-Text

Integrate AssemblyAI with your favorite tools and services.

Semantic Kernel is an SDK for multiple programming languages to develop applications with . However, LLMs only operate on textual data and don't understand what is said in audio files. With the , you can use AssemblyAI's transcription models using the  to transcribe your audio and video files.

Semantic Kernel

Semantic Kernel Integration for AssemblyAI

Activepieces is an open-source, no-code automation platform that enables users to streamline workflows by connecting various applications and automating tasks.

Activepieces

Integrate Activepieces with AssemblyAI

Make (formerly Integromat) is a workflow automation tool that lets you integrate various services together without requiring coding knowledge.

Make

Integrate Make with AssemblyAI

Postman is a user-friendly tool for testing API endpoints. The AssemblyAI API Postman collection contains all the HTTP requests you can make to the AssemblyAI API, so you don't need to write them yourself.

Postman

The Postman collection for the AssemblyAI API

Microsoft Power Automate is a low-code workflow automation platform with a rich collection of connectors to Microsoft's first-party services and third-party services.  is the equivalent service built for developers and IT pros.

Power Automate

Integrate Power Automate with AssemblyAI

Zapier is a workflow automation tool that lets you integrate various services together without requiring coding knowledge. You can use our AI models to process audio data by transcribing it with speech recognition models and analyzing it with audio intelligence models. You can supply audio to the AssemblyAI app and connect the output of our models to other services in your Zaps.

Zapier

Zapier Integration with AssemblyAI

Rivet is an open-source visual AI programming environment. Through a collaboration between AssemblyAI and Rivet, you can use AssemblyAI speech-to-text and LeMUR capabilities in Rivet.

Rivet

AssemblyAI plugin for Rivet

Haystack (2.x) is an open-source Python framework for building custom LLM applications. The Haystack Integration for AssemblyAI seamlessly integrates with Haystack to use audio files in LLM pipelines.

Haystack

Haystack Integration for AssemblyAI

Twilio is a programmable communication platform for voice, messaging, and email. By combining Twilio with AssemblyAI, you can transcribe voice calls in , and voice recordings and voice messages . Combine transcription with our  and  to analyze the calls and messages.

Twilio

Integrate Twilio with AssemblyAI

Cloudfare

Recall

Follow along to see weekly accuracy and product improvements.

Changelog

Blog

Playground

Documentation

Cookbooks

Speech-to-Text

LeMUR - LLMs for speech

API Reference

Integrations

Concepts

Get Started

Support

Learn how to transcribe streaming audio from a microphone.

Learn how to transcribe streaming audio in Python.

Python

Transcribe streaming audio from a microphone in Python

Learn how to transcribe streaming audio in TypeScript.

TypeScript

Transcribe streaming audio from a microphone in TypeScript

Learn how to transcribe streaming audio in Go.

Transcribe streaming audio from a microphone in Go

Learn how to transcribe streaming audio in C#.

Transcribe audio from a microphone in C#

Learn how to transcribe streaming audio in Java.

Java

Transcribe streaming audio from a microphone in Java

To apply LLMs to speech, you first need to transcribe the audio to text, which is what the AssemblyAI integration for LangChain helps you with.

🦜️🔗 LangChain Python Integration with AssemblyAI

JavaScript

🦜️🔗 LangChain JavaScript Integration with AssemblyAI

You can use  to transcribe audio files inside your LlamaIndex applications.

LlamaIndex Python Integration with AssemblyAI

You can use the AssemblyAI readers from LlamaIndex.TS to transcribe audio files inside your LlamaIndex applications.

LlamaIndex TypeScript Integration with AssemblyAI

Integrations built by the AssemblyAI community. Not supported by AssemblyAI.

Api Reference

Files

transcript

LeMUR

streaming

Ask questions using LeMUR

Authorizations

Body

Response