Enabling speaker partitioning in batch transcriptions - Amazon Transcribe

Enabling speaker partitioning in batch transcriptions

You can enable speaker partitioning in a batch transcription job using either the StartMedicalTranscriptionJob 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.

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

Sign in to the AWS Management Console.
In the navigation pane, under Amazon Transcribe Medical, choose Transcription jobs.
Choose Create job.
On the Specify job details page, provide information about your transcription job.
Choose Next.
Enable Audio identification.
For Audio identification type, choose Speaker partitioning.
For Maximum number of speakers, enter the maximum number of speakers that you think are speaking in your audio file.
Choose Create.

AWS Management Console

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

Sign in to the AWS Management Console.
In the navigation pane, under Amazon Transcribe Medical, choose Transcription jobs.
Choose Create job.
On the Specify job details page, provide information about your transcription job.
Choose Next.
Enable Audio identification.
For Audio identification type, choose Speaker partitioning.
For Maximum number of speakers, enter the maximum number of speakers that you think are speaking in your audio file.
Choose Create.

To enable speaker partitioning using a batch transcription job (API)

For the StartMedicalTranscriptionJob API, specify the following.
1. For MedicalTranscriptionJobName, specify a name that is unique in your AWS account.
2. For LanguageCode, specify the language code that corresponds to the language spoken in the audio file.
3. For the MediaFileUri parameter of the Media object, specify the name of the audio file that you want to transcribe.
4. For Specialty, specify the medical specialty of the clinician speaking in the audio file.
5. For Type, specify CONVERSATION.
6. For OutputBucketName, specify the Amazon S3 bucket to store the transcription results.
7. For the Settings object, specify the following.
  1. ShowSpeakerLabels – true.
  2. 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"
}

API

To enable speaker partitioning using a batch transcription job (API)

For the StartMedicalTranscriptionJob API, specify the following.
1. For MedicalTranscriptionJobName, specify a name that is unique in your AWS account.
2. For LanguageCode, specify the language code that corresponds to the language spoken in the audio file.
3. For the MediaFileUri parameter of the Media object, specify the name of the audio file that you want to transcribe.
4. For Specialty, specify the medical specialty of the clinician speaking in the audio file.
5. For Type, specify CONVERSATION.
6. For OutputBucketName, specify the Amazon S3 bucket to store the transcription results.
7. For the Settings object, specify the following.
  1. ShowSpeakerLabels – true.
  2. 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"
}

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
        }
}

AWS CLI

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
        }
}

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Enabling speaker partitioning

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Enabling speaker partitioning in batch transcriptions

AWS Management Console

To enable speaker partitioning using a batch transcription job (API)

API

To enable speaker partitioning using a batch transcription job (API)

To transcribe an audio file of a conversation between a clinician practicing primary care and a patient (AWS CLI)

AWS CLI

To transcribe an audio file of a conversation between a clinician practicing primary care and a patient (AWS CLI)

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