# Enabling speaker partitioning in batch transcriptions
<a name="conversation-diarization-batch-med"></a>

You can enable speaker partitioning in a batch transcription job using either the [https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartMedicalTranscriptionJob.html](https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartMedicalTranscriptionJob.html) API or the AWS Management Console. This enables you to partition the text per speaker in a clinician-patient conversation and determine who said what in the transcription output.

## AWS Management Console
<a name="conversation-diarization-batch-med-console"></a>

To use the AWS Management Console to enable speaker diarization in your transcription job, you enable audio identification and then speaker partitioning.

1. Sign in to the [AWS Management Console](https://console.aws.amazon.com/transcribe/).

1. In the navigation pane, under Amazon Transcribe Medical, choose **Transcription jobs**.

1. Choose **Create job**.

1. On the **Specify job details** page, provide information about your transcription job.

1. Choose **Next**.

1. Enable **Audio identification**.

1. For **Audio identification type**, choose **Speaker partitioning**.

1. For **Maximum number of speakers**, enter the maximum number of speakers that you think are speaking in your audio file.

1. Choose **Create**.

## API
<a name="conversation-diarization-batch-med-api"></a>

**To enable speaker partitioning using a batch transcription job (API)**
+ For the [https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartMedicalTranscriptionJob.html](https://docs.aws.amazon.com/transcribe/latest/APIReference/API_StartMedicalTranscriptionJob.html) API, specify the following.

  1. For `MedicalTranscriptionJobName`, specify a name that is unique in your AWS account.

  1. For `LanguageCode`, specify the language code that corresponds to the language spoken in the audio file.

  1. For the `MediaFileUri` parameter of the `Media` object, specify the name of the audio file that you want to transcribe.

  1. For `Specialty`, specify the medical specialty of the clinician speaking in the audio file.

  1. For `Type`, specify `CONVERSATION`.

  1. For `OutputBucketName`, specify the Amazon S3 bucket to store the transcription results.

  1. For the `Settings` object, specify the following.

     1. `ShowSpeakerLabels` – `true`.

     1. `MaxSpeakerLabels` – An integer between 2 and 10 to indicate the number of speakers that you think are speaking in your audio.

The following request uses the AWS SDK for Python (Boto3) to start a batch transcription job of a primary care clinician patient dialogue with speaker partitioning enabled.

```
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_medical_transcription_job(
    MedicalTranscriptionJobName = job_name,
    Media={
        'MediaFileUri': job_uri
    },
    OutputBucketName = '{{amzn-s3-demo-bucket}}',
    OutputKey = '{{my-output-files}}/', 
    LanguageCode = 'en-US',
    Specialty = 'PRIMARYCARE',
    Type = 'CONVERSATION',
    OutputBucketName = '{{amzn-s3-demo-bucket}}',
Settings = {'ShowSpeakerLabels': True,
         'MaxSpeakerLabels': 2
         }
         )
while True:
    status = transcribe.get_medical_transcription_job(MedicalTranscriptionJobName = job_name)
    if status['MedicalTranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']:
        break
    print("Not ready yet...")
    time.sleep(5)
print(status)
```

The following example code shows the transcription results of a transcription job with speaker partitioning enabled.

```
{
    "jobName": "job ID",
    "accountId": "111122223333",
    "results": {
        "transcripts": [
            {
                "transcript": "Professional answer."
            }
        ],
        "speaker_labels": {
            "speakers": 1,
            "segments": [
                {
                    "start_time": "0.000000",
                    "speaker_label": "spk_0",
                    "end_time": "1.430",
                    "items": [
                        {
                            "start_time": "0.100",
                            "speaker_label": "spk_0",
                            "end_time": "0.690"
                        },
                        {
                            "start_time": "0.690",
                            "speaker_label": "spk_0",
                            "end_time": "1.210"
                        }
                    ]
                }
            ]
        },
        "items": [
            {
                "start_time": "0.100",
                "end_time": "0.690",
                "alternatives": [
                    {
                        "confidence": "0.8162",
                        "content": "Professional"
                    }
                ],
                "type": "pronunciation"
            },
            {
                "start_time": "0.690",
                "end_time": "1.210",
                "alternatives": [
                    {
                        "confidence": "0.9939",
                        "content": "answer"
                    }
                ],
                "type": "pronunciation"
            },
            {
                "alternatives": [
                    {
                        "content": "."
                    }
                ],
                "type": "punctuation"
            }
        ]
    },
    "status": "COMPLETED"
}
```

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

**To transcribe an audio file of a conversation between a clinician practicing primary care and a patient (AWS CLI)**
+ Run the following code.

  ```
                      
  aws transcribe start-transcription-job \
  --region {{us-west-2}} \
  --cli-input-json file://{{example-start-command}}.json
  ```

  The following code shows the contents of `example-start-command.json`.

  ```
  {
      "MedicalTranscriptionJobName": "{{my-first-med-transcription-job}}",       
       "Media": {
            "MediaFileUri": "s3://{{amzn-s3-demo-bucket}}/{{my-input-files}}/{{my-audio-file}}.{{flac}}"
        },
        "OutputBucketName": "{{amzn-s3-demo-bucket}}",
        "OutputKey": "{{my-output-files}}/", 
        "LanguageCode": "en-US",
        "Specialty": "PRIMARYCARE",
        "Type": "CONVERSATION",
        "Settings":{
            "ShowSpeakerLabels": true,
            "MaxSpeakerLabels": 2
          }
  }
  ```