# Using toxic speech detection
## Using toxic speech detection in a batch transcription
To use toxic speech detection with a batch transcription, see the following for examples:
### AWS Management Console
1. Sign in to the [AWS Management Console](https://console.aws.amazon.com/transcribe/).
1. In the navigation pane, choose **Transcription jobs**, then select **Create job** (top right). This opens the **Specify job details** page.
![\[Amazon Transcribe console screenshot: the 'specify job details' page.\]](http://docs.aws.amazon.com/transcribe/latest/dg/images/toxicity-batch-details-1.png)
1. On the **Specify job details** page, you can also enable PII redaction if you want. Note that the other listed options are not supported with Toxicity detection. Select **Next**. This takes you to the **Configure job - optional** page. In the **Audio settings** panel, select **Toxicity detection**.
![\[Amazon Transcribe console screenshot: the 'configure job' page.\]](http://docs.aws.amazon.com/transcribe/latest/dg/images/toxicity-batch-details-2.png)
1. Select **Create job** to run your transcription job.
1. Once your transcription job is complete, you can download your transcript from the **Download** drop-down menu in the transcription job's detail page.
### AWS CLI
This example uses the [start-transcription-job](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/transcribe/start-transcription-job.html) command and `ToxicityDetection` parameter. For more information, see [https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartTranscriptionJob.html](https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartTranscriptionJob.html) and [https://docs.aws.amazon.com/transcribe/latest/APIReference/API_ToxicityDetection.html](https://docs.aws.amazon.com/transcribe/latest/APIReference/API_ToxicityDetection.html).
```
aws transcribe start-transcription-job \
--region us-west-2 \
--transcription-job-name my-first-transcription-job \
--media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \
--output-bucket-name amzn-s3-demo-bucket \
--output-key my-output-files/ \
--language-code en-US \
--toxicity-detection ToxicityCategories=ALL
```
Here's another example using the [start-transcription-job](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/transcribe/start-transcription-job.html) command, and a request body that includes toxicity detection.
```
aws transcribe start-transcription-job \
--region us-west-2 \
--cli-input-json file://filepath/my-first-toxicity-job.json
```
The file *my-first-toxicity-job.json* contains the following request body.
```
{
"TranscriptionJobName": "my-first-transcription-job",
"Media": {
"MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac"
},
"OutputBucketName": "amzn-s3-demo-bucket",
"OutputKey": "my-output-files/",
"LanguageCode": "en-US",
"ToxicityDetection": [
{
"ToxicityCategories": [ "ALL" ]
}
]
}
```
### AWS SDK for Python (Boto3)
This example uses the AWS SDK for Python (Boto3) to enable `ToxicityDetection` for the [start\$1transcription\$1job](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/transcribe.html#TranscribeService.Client.start_transcription_job) method. For more information, see [https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartTranscriptionJob.html](https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartTranscriptionJob.html) and [https://docs.aws.amazon.com/transcribe/latest/APIReference/Welcome.html](https://docs.aws.amazon.com/transcribe/latest/APIReference/Welcome.html).
For additional examples using the AWS SDKs, including feature-specific, scenario, and cross-service examples, refer to the [Code examples for Amazon Transcribe using AWS SDKs](service_code_examples.md) chapter.
```
from __future__ import print_function
import time
import boto3
transcribe = boto3.client('transcribe', 'us-west-2')
job_name = "my-first-transcription-job"
job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac"
transcribe.start_transcription_job(
TranscriptionJobName = job_name,
Media = {
'MediaFileUri': job_uri
},
OutputBucketName = 'amzn-s3-demo-bucket',
OutputKey = 'my-output-files/',
LanguageCode = 'en-US',
ToxicityDetection = [
{
'ToxicityCategories': ['ALL']
}
]
)
while True:
status = transcribe.get_transcription_job(TranscriptionJobName = job_name)
if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']:
break
print("Not ready yet...")
time.sleep(5)
print(status)
```
## Example output
Toxic speech is tagged and categorized in your transcription output. Each instance of toxic speech is categorized and assigned a confidence score (a value between 0 and 1). A larger confidence value indicates a greater likelihood that the content is toxic speech within the specified category.
### Example output (JSON)
The following is an example output in JSON format showing categorized toxic speech with associated confidence scores.
```
{
"jobName": "my-toxicity-job",
"accountId": "111122223333",
"results": {
"transcripts": [...],
"items":[...],
"toxicity_detection": [
{
"text": "What the * are you doing man? That's why I didn't want to play with your * . man it was a no, no I'm not calming down * man. I well I spent I spent too much * money on this game.",
"toxicity": 0.7638,
"categories": {
"profanity": 0.9913,
"hate_speech": 0.0382,
"sexual": 0.0016,
"insult": 0.6572,
"violence_or_threat": 0.0024,
"graphic": 0.0013,
"harassment_or_abuse": 0.0249
},
"start_time": 8.92,
"end_time": 21.45
},
Items removed for brevity
{
"text": "What? Who? What the * did you just say to me? What's your address? What is your * address? I will pull up right now on your * * man. Take your * back to , tired of this **.",
"toxicity": 0.9816,
"categories": {
"profanity": 0.9865,
"hate_speech": 0.9123,
"sexual": 0.0037,
"insult": 0.5447,
"violence_or_threat": 0.5078,
"graphic": 0.0037,
"harassment_or_abuse": 0.0613
},
"start_time": 43.459,
"end_time": 54.639
},
]
},
...
"status": "COMPLETED"
}
```