Topic 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.

Quickstart

Enable Topic Detection by setting iab_categories to true in the transcription config.

Python

import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"

# audio_file = "./local_file.mp3"
audio_file = "https://assembly.ai/wildfires.mp3"

config = aai.TranscriptionConfig(iab_categories=True)

transcript = aai.Transcriber().transcribe(audio_file, config)

# Get the parts of the transcript that were tagged with topics
for result in transcript.iab_categories.results:
    print(result.text)
    print(f"Timestamp: {result.timestamp.start} - {result.timestamp.end}")
    for label in result.labels:
        print(f"{label.label} ({label.relevance})")

# Get a summary of all topics in the transcript
for topic, relevance in transcript.iab_categories.summary.items():
    print(f"Audio is {relevance * 100}% relevant to {topic}")

import { AssemblyAI } from 'assemblyai'

const client = new AssemblyAI({
  apiKey: 'YOUR_API_KEY'
})

// const audioFile = './local_file.mp3'
const audioFile =
  'https://assembly.ai/wildfires.mp3'

const params = {
  audio: audioFile,
  iab_categories: true
}

const run = async () => {
  const transcript = await client.transcripts.transcribe(params)

  // Get the parts of the transcript that were tagged with topics
  for (const result of transcript.iab_categories_result!.results) {
    console.log(result.text)
    console.log(
      `Timestamp: ${result.timestamp?.start} - ${result.timestamp?.end}`
    )
    for (const label of result.labels!) {
      console.log(`${label.label} (${label.relevance})`)
    }
  }

  // Get a summary of all topics in the transcript
  for (const [topic, relevance] of Object.entries(
    transcript.iab_categories_result!.summary
  )) {
    console.log(`Audio is ${relevance * 100} relevant to ${topic}`)
  }
}

run()

package main

import (
    "context"
    "fmt"

    aai "github.com/AssemblyAI/assemblyai-go-sdk"
)

func main() {
    client := aai.NewClient("YOUR_API_KEY")

    // For local files see our Getting Started guides.
    audioURL := "https://assembly.ai/wildfires.mp3"

    ctx := context.Background()

    transcript, _ := client.Transcripts.TranscribeFromURL(ctx, audioURL, &aai.TranscriptOptionalParams{
        IABCategories: aai.Bool(true),
    })

    for _, result := range transcript.IABCategoriesResult.Results {
        fmt.Println(aai.ToString(result.Text))
        fmt.Println("Timestamp:",
            aai.ToInt64(result.Timestamp.Start), "-",
            aai.ToInt64(result.Timestamp.End),
        )

        for _, label := range result.Labels {
            fmt.Printf("%s (%v)", aai.ToString(label.Label), aai.ToFloat64(label.Relevance))
        }
    }

    for topic, relevance := range transcript.IABCategoriesResult.Summary {
        fmt.Printf("Audio is %v%% relevant to %s\n", relevance*100, topic)
    }
}

import com.assemblyai.api.AssemblyAI;
import com.assemblyai.api.resources.transcripts.types.*;

public final class Main {
    public static void main(String... args) throws Exception {

        var client = AssemblyAI.builder()
                .apiKey("YOUR_API_KEY")
                .build();

        // For local files see our Getting Started guides.
        String audioUrl = "https://assembly.ai/wildfires.mp3";

        var params = TranscriptOptionalParams.builder()
                .iabCategories(true)
                .build();

        Transcript transcript = client.transcripts().transcribe(audioUrl, params);

        if (transcript.getStatus().equals(TranscriptStatus.ERROR)) {
            throw new Exception(transcript.getError().get());
        }

        // Get the parts of the transcript that were tagged with topics
        for (TopicDetectionResult result : transcript.getIabCategoriesResult().get().getResults()) {
            System.out.println(result.getText());
            System.out.printf("Timestamp: %d - %d\n", result.getTimestamp().get().getStart(), result.getTimestamp().get().getEnd());
            for (TopicDetectionResultLabelsItem label : result.getLabels().get()) {
                System.out.printf("%s (%.2f)\n", label.getLabel(), label.getRelevance());
            }
            System.out.println();
        }

        System.out.println();

        // Get a summary of all topics in the transcript
        for (var entry : transcript.getIabCategoriesResult().get().getSummary().entrySet()) {
            System.out.printf("Audio is %.2f%% relevant to %s\n", entry.getValue() * 100, entry.getKey());
        }
    }
}

using AssemblyAI;
using AssemblyAI.Transcripts;

var client = new AssemblyAIClient("YOUR_API_KEY");

var transcript = await client.Transcripts.TranscribeAsync(new TranscriptParams
{
    // For local files see our Getting Started guides.
    AudioUrl = "https://assembly.ai/wildfires.mp3",
    IabCategories = true
});

// Get the parts of the transcript that were tagged with topics
foreach (var result in transcript.IabCategoriesResult!.Results)
{
    Console.WriteLine(result.Text);
    Console.WriteLine($"Timestamp: {result.Timestamp?.Start} - {result.Timestamp?.End}");

    foreach (var label in result.Labels!)
    {
        Console.WriteLine($"{label.Label} ({label.Relevance})");
    }
}

// Get a summary of all topics in the transcript
foreach (var summary in transcript.IabCategoriesResult.Summary)
{
    Console.WriteLine($"Audio is {summary.Value * 100} relevant to {summary.Key}");
}

require 'assemblyai'

client = AssemblyAI::Client.new(api_key: 'YOUR_API_KEY')

# For local files see our Getting Started guides.
audio_url = 'https://assembly.ai/wildfires.mp3'

transcript = client.transcripts.transcribe(
  audio_url: audio_url,
  iab_categories: true
)

# Get the parts of the transcript that were tagged with topics
transcript.iab_categories_result.results.each do |result|
  puts result.text
  printf("Timestamp: %<start>d - %<end>d\n", start: result.timestamp.start, end: result.timestamp.end_)
  result.labels.each do |label|
    printf("%<label>s (%<relevance>f)\n", label: label.label, relevance: label.relevance)
  end
  puts
end

puts

# Get a summary of all topics in the transcript
transcript.iab_categories_result.summary.each_pair do |topic, relevance|
  printf(
    "Audio is %<relevance>d%% relevant to %<topic>s\n",
    relevance: relevance * 100,
    topic: topic
  )
end

Example output

Smoke from hundreds of wildfires in Canada is triggering air quality alerts throughout the US. Skylines...
Timestamp: 250 - 28920
Home&Garden>IndoorEnvironmentalQuality (0.9881)
NewsAndPolitics>Weather (0.5561)
MedicalHealth>DiseasesAndConditions>LungAndRespiratoryHealth (0.0042)
...
Audio is 100.0% relevant to NewsAndPolitics>Weather
Audio is 93.78% relevant to Home&Garden>IndoorEnvironmentalQuality
...

API reference​

Request​

curl https://api.assemblyai.com/v2/transcript \
--header "Authorization: YOUR_API_KEY" \
--header "Content-Type: application/json" \
--data '{
  "audio_url": "YOUR_AUDIO_URL",
  "iab_categories": true
}'

Key	Type	Description
iab_categories	boolean	Enable Topic Detection.

Response

{
  iab_categories:true,
  iab_categories_result:{
  status:"success",
  results:[...],
  summary:{...}
  }
}

Key	Type	Description
iab_categories_result	object	The result of the Topic Detection model.
iab_categories_result.status	string	Is either success, or unavailable in the rare case that the Content Moderation model failed.
iab_categories_result.results	array	An array of the Topic Detection results.
iab_categories_result.results[i].text	string	The text in the transcript in which the i-th instance of a detected topic occurs.
iab_categories_result.results[i].labels[j].relevance	number	How relevant the j-th detected topic is in the i-th instance of a detected topic.
iab_categories_result.results[i].labels[j].label	string	The IAB taxonomical label for the j-th label of the i-th instance of a detected topic, where > denotes supertopic/subtopic relationship.
iab_categories_result.results[i].timestamp.start	number	The starting time in the audio file at which the i-th detected topic instance is discussed.
iab_categories_result.results[i].timestamp.end	number	The ending time in the audio file at which the i-th detected topic instance is discussed.
iab_categories_result.summary	object	Summary where each property is a detected topic.
iab_categories_result.summary.topic	number	The overall relevance of topic to the entire audio file.

The response also includes the request parameters used to generate the transcript.

Frequently asked questions

How does the Topic Detection model handle misspelled or unrecognized words?

The Topic Detection model uses natural language processing and machine learning to identify related words and phrases even if they are misspelled or unrecognized. However, the accuracy of the detection may depend on the severity of the misspelling or the obscurity of the word.

Can I use the Topic Detection model to identify entities that aren't part of the IAB Taxonomy?

No, the Topic Detection model can only identify entities that are part of the IAB Taxonomy. The model is optimized for contextual targeting use cases, so using the predefined IAB categories ensures the most accurate results.

Why am I not getting any topic predictions for my audio file?

There could be several reasons why you aren’t getting any topic predictions for your audio file. One possible reason is that the audio file doesn’t contain enough relevant content for the model to analyze. Additionally, the accuracy of the predictions may be affected by factors such as background noise, low-quality audio, or a low confidence threshold for topic detection. It’s recommended to review and adjust the model’s configuration parameters and to provide high-quality, relevant audio files for analysis.

Why am I getting inaccurate or irrelevant topic predictions for my audio file?

There could be several reasons why you’re getting inaccurate or irrelevant topic predictions for your audio file. One possible reason is that the audio file contains background noise or other non-relevant content that’s interfering with the model’s analysis. Additionally, the accuracy of the predictions may be affected by factors such as low-quality audio, a low confidence threshold for topic detection, or insufficient training data. It’s recommended to review and adjust the model’s configuration parameters, to provide high-quality, relevant audio files for analysis, and to consider adding additional training data to the model.

Is AssemblyAI associated with IAB?

As of 2023, AssemblyAI is a partner with the Interactive Advertising Bureau (IAB), a certification and community for advertising across the internet. AssemblyAI built Topic Detection using the IAB Taxonomy, which is a blueprint of the approximately 700 topics used to categorize ads.