本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 使用有毒語音偵測
<a name="toxicity-using"></a>

## 在批次轉錄中使用有毒語音偵測
<a name="toxicity-using-batch"></a>

若要將有毒語音偵測與批次轉錄搭配使用，請參閱下列範例：

### AWS 管理主控台
<a name="toxicity-using-console-batch"></a>

1. 登入 [AWS 管理主控台](https://console.aws.amazon.com/transcribe/)。

1. 在導覽窗格中，選擇**轉錄作業**，然後選擇**建立作業**(右上角)。這會開啟**指定作業詳細資訊‭**頁面。  
![Amazon Transcribe 主控台螢幕擷取畫面：「指定任務詳細資訊」頁面。](http://docs.aws.amazon.com/zh_tw/transcribe/latest/dg/images/toxicity-batch-details-1.png)

1.  在**指定作業詳細資訊‭**頁面上，您也可以視需要啟用 PII 修訂。請注意，毒性偵測不支援其他列出的選項。選擇**下一步**。這會引導您前往**設定工作 - 選擇性**頁面。在**音訊設定**面板中，選擇**毒性偵測**。  
![Amazon Transcribe 主控台螢幕擷取畫面：「設定任務」頁面。](http://docs.aws.amazon.com/zh_tw/transcribe/latest/dg/images/toxicity-batch-details-2.png)

1. 選擇**建立作業**以執行轉錄作業。

1. 轉錄作業完成後，您可以從轉錄作業詳細資訊頁面的**下載**下拉式功能表下載您的文字記錄。

### AWS CLI
<a name="toxicity-using-cli-batch"></a>

此範例使用 [start-transcription-job](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/transcribe/start-transcription-job.html) 指令和 `ToxicityDetection` 參數。如需詳細資訊，請參閱[https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartTranscriptionJob.html](https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartTranscriptionJob.html)及[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
```

以下是使用 [start-transcription-job](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/transcribe/start-transcription-job.html) 指令的另一個範例，以及包含替代毒性偵測的請求內文。

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

檔案 *my-first-toxicity-job.json* 包含以下請求內文。

```
{
  "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" ]
      }
   ]
}
```

### 適用於 Python (Boto3) 的 AWS SDK
<a name="toxicity-using-python-batch"></a>

 此範例使用 適用於 Python (Boto3) 的 AWS SDK 為 [start\_transcription\_job](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/transcribe.html#TranscribeService.Client.start_transcription_job) `ToxicityDetection` 方法啟用 。如需詳細資訊，請參閱[https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartTranscriptionJob.html](https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartTranscriptionJob.html)及[https://docs.aws.amazon.com/transcribe/latest/APIReference/Welcome.html](https://docs.aws.amazon.com/transcribe/latest/APIReference/Welcome.html)。

如需使用 AWS SDKs 的其他範例，包括功能特定、案例和跨服務範例，請參閱 [使用 AWS SDKs 的 Amazon Transcribe 程式碼範例](service_code_examples.md)章節。

```
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)
```

## 範例輸出
<a name="toxicity-using-output"></a>

 在您的轉錄輸出中標記有毒語音並進行分類。有毒語音的每個執行個體都會分類並指定可信度分數 (介於 0 和 1 之間的值)。可信度值較高表示內容在指定類別中是有毒語音的可能性越高。

### 範例輸出(JSON)
<a name="toxicity-using-output-json"></a>

 以下是 JSON 格式的範例輸出，顯示具有相關可信度分數的分類有毒語音。

```
{
    "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"
}
```