Content Moderation

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 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(content_safety=True)

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

# Get the parts of the transcript which were flagged as sensitive.
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
    print()

# 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}")

print()

# 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}")

Example output

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 transcription config.

Python

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

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",
  "content_safety": true,
  "content_safety_confidence": 50
}'

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

{
content_safety_labels:{
    "status": "success",
    "results": [...],
    "summary": {...},
    "severity_score_summary": {...}
  }
}

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

Why is the Content Moderation model not detecting sensitive content in my audio file?

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.

Why is the Content Moderation model flagging content that isn't actually sensitive?

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.

How do I know which specific parts of the audio file contain sensitive content?

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.

Can the Content Moderation model be used in real-time applications?

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.

Why am I receiving an error message when using the Content Moderation model?

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.