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Content Moderation

Detect sensitive content in your audio files

For the complete documentation index, see llms.txt

cURL quickstart — Content Moderation

bash

# Step 1: Submit transcription with content_safety enabled
curl  \
  --header "Authorization: YOUR_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "audio_url": "",
    "speech_models": ["universal-3-pro", "universal-2"],
    "language_detection": true,
    "content_safety": true
  }'

# Step 2: Poll for result (replace TRANSCRIPT_ID)
curl  \
  --header "Authorization: YOUR_API_KEY"

The response includes content_safety_labels with per-segment results, a summary, and severity scores.

US & EU

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

The model pinpoints sensitive discussions in spoken data and their severity.

Quickstart

Enable Content Moderation by setting content_safety to True in the JSON payload.

python

import requests
import time

base_url = ""

headers = {
    "authorization": "<YOUR_API_KEY>"
}

with open("./local_file.mp3", "rb") as f:
    response = requests.post(base_url + "/v2/upload",
                            headers=headers,
                            data=f)

upload_url = response.json()["upload_url"]

data = {
    "audio_url": upload_url, # You can also use a URL to an audio or video file on the web
    "speech_models": ["universal-3-pro", "universal-2"],
    "language_detection": True,
    "content_safety": True
}

url = base_url + "/v2/transcript"
response = requests.post(url, json=data, headers=headers)

transcript_id = response.json()['id']
polling_endpoint = base_url + "/v2/transcript/" + transcript_id

print(f"Transcript ID: {transcript_id}")

while True:
    transcription_result = requests.get(polling_endpoint, headers=headers).json()

    if transcription_result['status'] == 'completed':

        for result in transcription_result['content_safety_labels']['results']:
            print(result['text'])
            print(f"Timestamp: {result['timestamp']['start']} - {result['timestamp']['end']}")

            # Get category, confidence, and severity.
            for label in result['labels']:
                print(f"{label['label']} - {label['confidence']} - {label['severity']}")  # content safety category

        # Get the confidence of the most common labels in relation to the entire audio file.
        for label, confidence in transcription_result['content_safety_labels']['summary'].items():
            print(f"{confidence * 100}% confident that the audio contains {label}")

        # Get the overall severity of the most common labels in relation to the entire audio file.
        for label, severity_confidence in transcription_result['content_safety_labels']['severity_score_summary'].items():
            print(f"{severity_confidence['low'] * 100}% confident that the audio contains low-severity {label}")
            print(f"{severity_confidence['medium'] * 100}% confident that the audio contains medium-severity {label}")
            print(f"{severity_confidence['high'] * 100}% confident that the audio contains high-severity {label}")
        break
    elif transcription_result['status'] == 'error':
        raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
    else:
        time.sleep(3)

Enable Content Moderation by setting content_safety 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 = ""

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    content_safety=True
)

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

print(f"Transcript ID: {transcript.id}")

for result in transcript.content_safety.results:
    print(result.text)
    print(f"Timestamp: {result.timestamp.start} - {result.timestamp.end}")

    # Get category, confidence, and severity.
    for label in result.labels:
        print(f"{label.label} - {label.confidence} - {label.severity}")  # content safety category

# Get the confidence of the most common labels in relation to the entire audio file.
for label, confidence in transcript.content_safety.summary.items():
    print(f"{confidence * 100}% confident that the audio contains {label}")

# Get the overall severity of the most common labels in relation to the entire audio file.
for label, severity_confidence in transcript.content_safety.severity_score_summary.items():
    print(f"{severity_confidence.low * 100}% confident that the audio contains low-severity {label}")
    print(f"{severity_confidence.medium * 100}% confident that the audio contains medium-severity {label}")
    print(f"{severity_confidence.high * 100}% confident that the audio contains high-severity {label}")

Enable Content Moderation by setting content_safety to true in the JSON payload.

javascript

import fs from "fs-extra";

const baseUrl = "";

const headers = {
  authorization: "<YOUR_API_KEY>",
};

const path = "./my-audio.mp3";
const audioData = await fs.readFile(path);
let res = await fetch(`${baseUrl}/v2/upload`, {
  method: "POST",
  headers,
  body: audioData,
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const uploadResponse = await res.json();
const uploadUrl = uploadResponse.upload_url;

const data = {
  audio_url: uploadUrl, // You can also use a URL to an audio or video file on the web
  speech_models: ["universal-3-pro", "universal-2"],
  language_detection: true,
  content_safety: true,
};

const url = `${baseUrl}/v2/transcript`;
res = await fetch(url, {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify(data),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const response = await res.json();

const transcriptId = response.id;
console.log("Transcript ID: ", transcriptId);

const pollingEndpoint = `${baseUrl}/v2/transcript/${transcriptId}`;

while (true) {
  res = await fetch(pollingEndpoint, { headers });
  if (!res.ok) throw new Error(`Error: ${res.status}`);
  const transcriptionResult = await res.json();

  if (transcriptionResult.status === "completed") {
    const contentSafetyLabels = transcriptionResult.content_safety_labels;
    for (const result of contentSafetyLabels.results) {
      console.log(result.text);
      console.log(
        `Timestamp: ${result.timestamp.start} - ${result.timestamp.end}`
      );

      // Get category, confidence, and severity.
      for (const label of result.labels) {
        console.log(`${label.label} - ${label.confidence} - ${label.severity}`); // content safety category
      }
    }
    // Get the confidence of the most common labels in relation to the entire audio file
    for (const [label, confidence] of Object.entries(
      contentSafetyLabels.summary
    )) {
      console.log(
        `${confidence * 100}% confident that the audio contains ${label}`
      );
    }
    // Get the confidence of the most common labels in relation to the entire audio file.
    for (const [label, severity_confidence] of Object.entries(
      contentSafetyLabels.severity_score_summary
    )) {
      console.log(
        `${severity_confidence.low * 100}% confident that the audio contains low-severity ${label}`
      );
      console.log(
        `${severity_confidence.medium * 100}% confident that the audio contains medium-severity ${label}`
      );
      console.log(
        `${severity_confidence.high * 100}% confident that the audio contains high-severity ${label}`
      );
    }

    break;
  } else if (transcriptionResult.status === "error") {
    throw new Error(`Transcription failed: ${transcriptionResult.error}`);
  } else {
    await new Promise((resolve) => setTimeout(resolve, 3000));
  }
}

Enable Content Moderation by setting content_safety to true in the transcription config.

javascript

import { AssemblyAI } from "assemblyai";

const client = new AssemblyAI({
  apiKey: "<YOUR_API_KEY>",
});

// const audioFile = './local_file.mp3'
const audioFile = "";

const params = {
  audio: audioFile,
  speech_models: ["universal-3-pro", "universal-2"],
  language_detection: true,
  content_safety: true,
};

const run = async () => {
  const transcript = await client.transcripts.transcribe(params);
  console.log(`Transcript ID: ${transcript.id}`);
  const contentSafetyLabels = transcript.content_safety_labels;

  // Get the parts of the transcript which were flagged as sensitive
  for (const result of contentSafetyLabels.results) {
    console.log(result.text);
    console.log(
      `Timestamp: ${result.timestamp.start} - ${result.timestamp.end}`
    );

    // Get category, confidence, and severity
    for (const label of result.labels) {
      console.log(`${label.label} - ${label.confidence} - ${label.severity}`);
    }
  }

  // Get the confidence of the most common labels in relation to the entire audio file
  for (const [label, confidence] of Object.entries(
    contentSafetyLabels.summary
  )) {
    console.log(
      `${confidence * 100}% confident that the audio contains ${label}`
    );
  }

  // Get the overall severity of the most common labels in relation to the entire audio file
  for (const [label, severity_confidence] of Object.entries(
    contentSafetyLabels.severity_score_summary
  )) {
    console.log(
      `${severity_confidence.low * 100}% confident that the audio contains low-severity ${label}`
    );
    console.log(
      `${severity_confidence.medium * 100}% confident that the audio contains medium-severity ${label}`
    );
    console.log(
      `${severity_confidence.high * 100}% confident that the audio contains high-severity ${label}`
    );
  }
};
run();

Example output

plain

Smoke from hundreds of wildfires in Canada is triggering air quality alerts throughout the US. Skylines...
Timestamp: 250 - 28920
disasters - 0.8141 - 0.4014

So what is it about the conditions right now that have caused this round of wildfires to...
Timestamp: 29290 - 56190
disasters - 0.9217 - 0.5665

So what is it in this haze that makes it harmful? And I'm assuming it is...
Timestamp: 56340 - 88034
health_issues - 0.9358 - 0.8906

...

99.42% confident that the audio contains disasters
92.70% confident that the audio contains health_issues

57.43% confident that the audio contains low-severity disasters
42.56% confident that the audio contains mid-severity disasters
0.0% confident that the audio contains high-severity disasters
23.57% confident that the audio contains low-severity health_issues
30.22% confident that the audio contains mid-severity health_issues
46.19% confident that the audio contains high-severity health_issues

Adjust the confidence threshold

The confidence threshold determines how likely something is to be flagged as inappropriate content. A threshold of 50% (which is the default) means any label with a confidence score of 50% or greater is flagged.

To adjust the confidence threshold for your transcription, include content_safety_confidence in the JSON payload.

python

# Setting the content safety confidence threshold to 60%.
data = {
    "audio_url": upload_url,
    "content_safety": True,
    "content_safety_confidence": 60
}

To adjust the confidence threshold for your transcription, include content_safety_confidence in the transcription config.

python

# Setting the content safety confidence threshold to 60%.
config = aai.TranscriptionConfig(
  content_safety=True,
  content_safety_confidence=60
)

To adjust the confidence threshold for your transcription, include content_safety_confidence in the JSON payload.

javascript

// Setting the content safety confidence threshold to 60%.
const data = {
  audio_url: uploadUrl,
  content_safety: true,
  content_safety_confidence: 60,
};

To adjust the confidence threshold for your transcription, include content_safety_confidence in the transcription config.

javascript

// Setting the content safety confidence threshold to 60%.
const params = {
  audio: audioFile,
  content_safety: true,
  content_safety_confidence: 60,
};

API reference

Request

bash

curl  \
--header "Authorization: <YOUR_API_KEY>" \
--header "Content-Type: application/json" \
--data '{
  "audio_url": "YOUR_AUDIO_URL",
  "content_safety": true,
  "content_safety_confidence": 60
}'

Key	Type	Description
`content_safety`	boolean	Enable Content Moderation.
`content_safety_confidence`	integer	The confidence threshold for content moderation. Values must be between 25 and 100.

Response

Key	Type	Description
`content_safety_labels`	object	An object containing all results of the Content Moderation model.
`content_safety_labels.status`	string	Is either `success`, or `unavailable` in the rare case that the Content Moderation model failed.
`content_safety_labels.results`	array	An array of objects, one for each section in the audio file, that the Content Moderation file flagged.
`content_safety_labels.results[i].text`	string	The transcript of the i-th section flagged by the Content Moderation model.
`content_safety_labels.results[i].labels`	array	An array of objects, one per sensitive topic, that was detected in the i-th section.
`content_safety_labels.results[i].labels[j].label`	string	The label of the sensitive topic.
`content_safety_labels.results[i].labels[j].confidence`	number	The confidence score for the j-th topic being discussed in the i-th section, from 0 to 1.
`content_safety_labels.results[i].labels[j].severity`	number	How severely the j-th topic is discussed in the i-th section, from 0 to 1.
`content_safety_labels.results[i].sentences_idx_start`	number	The sentence index at which the i-th section begins.
`content_safety_labels.results[i].sentences_idx_end`	number	The sentence index at which the i-th section ends.
`content_safety_labels.results[i].timestamp`	object	Timestamp information for the i-th section.
`content_safety_labels.results[i].timestamp.start`	number	The time, in milliseconds, at which the i-th section begins.
`content_safety_labels.results[i].timestamp.end`	number	The time, in milliseconds, at which the i-th section ends.
`content_safety_labels.summary`	object	A summary of the Content Moderation confidence results for the entire audio file.
`content_safety_labels.summary.topic`	number	A confidence score for the presence of the sensitive topic "topic" across the entire audio file.
`content_safety_labels.severity_score_summary`	object	A summary of the Content Moderation severity results for the entire audio file.
`content_safety_labels.severity_score_summary.topic.[low, medium, high]`	number	A distribution across the values "low", "medium", and "high" for the severity of the presence of "topic" in the audio file.

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

Supported labels

Label	Description	Model output	Severity
Accidents	Any man-made incident that happens unexpectedly and results in damage, injury, or death.	`accidents`	Yes
Alcohol	Content that discusses any alcoholic beverage or its consumption.	`alcohol`	Yes
Company Financials	Content that discusses any sensitive company financial information.	`financials`	No
Crime Violence	Content that discusses any type of criminal activity or extreme violence that is criminal in nature.	`crime_violence`	Yes
Drugs	Content that discusses illegal drugs or their usage.	`drugs`	Yes
Gambling	Includes gambling on casino-based games such as poker, slots, etc. as well as sports betting.	`gambling`	Yes
Hate Speech	Content that's a direct attack against people or groups based on their sexual orientation, gender identity, race, religion, ethnicity, national origin, disability, etc.	`hate_speech`	Yes
Health Issues	Content that discusses any medical or health-related problems.	`health_issues`	Yes
Manga	Mangas are comics or graphic novels originating from Japan with some of the more popular series being "Pokemon", "Naruto", "Dragon Ball Z", "One Punch Man", and "Sailor Moon".	`manga`	No
Marijuana	This category includes content that discusses marijuana or its usage.	`marijuana`	Yes
Natural Disasters	Phenomena that happens infrequently and results in damage, injury, or death. Such as hurricanes, tornadoes, earthquakes, volcano eruptions, and firestorms.	`disasters`	Yes
Negative News	News content with a negative sentiment which typically occur in the third person as an unbiased recapping of events.	`negative_news`	No
NSFW (Adult Content)	Content considered "Not Safe for Work" and consists of content that a viewer would not want to be heard/seen in a public environment.	`nsfw`	No
Pornography	Content that discusses any sexual content or material.	`pornography`	Yes
Profanity	Any profanity or cursing.	`profanity`	Yes
Sensitive Social Issues	This category includes content that may be considered insensitive, irresponsible, or harmful to certain groups based on their beliefs, political affiliation, sexual orientation, or gender identity.	`sensitive_social_issues`	No
Terrorism	Includes terrorist acts as well as terrorist groups. Examples include bombings, mass shootings, and ISIS. Note that many texts corresponding to this topic may also be classified into the crime violence topic.	`terrorism`	Yes
Tobacco	Text that discusses tobacco and tobacco usage, including e-cigarettes, nicotine, vaping, and general discussions about smoking.	`tobacco`	Yes
Weapons	Text that discusses any type of weapon including guns, ammunition, shooting, knives, missiles, torpedoes, etc.	`weapons`	Yes

Frequently asked questions

There could be a few reasons for this. First, make sure that the audio file contains speech, and not just background noise or music. Additionally, the model may not have been trained on the specific type of sensitive content you're looking for. If you believe the model should be able to detect the content but it's not, you can reach out to AssemblyAI's support team for assistance.

The model may occasionally flag content as sensitive that isn't actually problematic. This can happen if the model isn't trained on the specific context or nuances of the language being used. In these cases, you can manually review the flagged content and determine if it's actually sensitive or not. If you believe the model is consistently flagging content incorrectly, you can contact AssemblyAI's support team to report the issue.

The Content Moderation model provides segment-level results that pinpoint where in the audio the sensitive content was discussed, as well as the degree to which it was discussed. You can access this information in the results key of the API response. Each result in the list contains a text key that shows the sensitive content, and a labels key that shows the detected sensitive topics along with their confidence and severity scores.

The model is designed to process batches of segments in significantly less than 1 second, making it suitable for real-time applications. However, keep in mind that the actual processing time depends on the length of the audio file and the number of segments it's divided into. Additionally, the model may occasionally require additional time to process particularly complex or long segments.

If you receive an error message, it may be due to an issue with your request format or parameters. Double-check that your request includes the correct audio_url parameter. If you continue to experience issues, you can reach out to AssemblyAI's support team for assistance.

Content Moderation ​

Quickstart ​

Example output ​

Adjust the confidence threshold ​

API reference ​

Request ​

Response ​

Supported labels ​

Frequently asked questions ​

Content Moderation

Quickstart

Example output

Adjust the confidence threshold

API reference

Request

Response

Supported labels

Frequently asked questions