使用毒性言论检测

在批量转录中使用毒性言论检测

要将毒性言论检测与批量转录一起使用，请查看以下示例：

AWS Management Console

1. 登录到 AWS Management Console。

2. 在导航窗格中，选择转录作业，然后选择创建作业（右上角）。这将打开指定作业详细信息页面。

   

3. 在指定作业详细信息页面上，您也可以根据需要启用 PII 编辑。请注意，毒性检测不支持列出的其它选项。选择下一步。此时您将会看到配置作业 - 可选页面。在音频设置 面板中，选择毒性检测。

   

4. 选择创建作业以运行您的转录作业。

5. 转录作业完成后，您可以从转录作业详情页面的下载下拉菜单中下载转录。

AWS CLI

此示例使用start-transcription-job命令和ToxicityDetection参数。有关更多信息，请参阅StartTranscriptionJob 和ToxicityDetection。

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
    

这是另一个使用start-transcription-job命令的示例，以及包含毒性检测的请求正文。

aws transcribe start-transcription-job \
--region us-west-2 \
--cli-input-json file://filepath/my-first-toxicity-job.json     
    

my-first-toxicity-job.json 文件包含以下请求正文。

{
  "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)

此示例使用启用 start_t ransc ToxicityDetection ription_job 方法。 AWS SDK for Python (Boto3) 有关更多信息，请参阅StartTranscriptionJob 和ToxicityDetection。

有关使用的其他示例 AWS SDKs，包括特定功能、场景和跨服务示例，请参阅本章。使用 Amazon Transcribe 的代码示例 AWS SDKs

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)
    

示例输出

在您的转录输出中，会对毒性言论进行标记和分类。对每个毒性言论进行分类并分配置信度分数（介于 0 和 1 之间的值）。置信度值越大，表示内容属于指定类别的毒性言论的可能性越大。

示例输出 (JSON)

以下是 JSON 格式的示例输出，显示了经过分类的毒性言论以及相关的置信度分数。

{
    "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"
}