Transcribing an audio file using a medical custom vocabulary

Use the StartMedicalTranscriptionJob or the AWS Management Console to start a transcription job that uses a custom vocabulary to improve transcription accuracy.

AWS Management Console

1. Sign in to the AWS Management Console.

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

3. Choose Create job.

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

5. Choose Next.

6. Under Customization, enable Custom vocabulary.

7. Under Vocabulary selection, choose a custom vocabulary.

8. Choose Create.

API

To enable speaker partitioning in an audio file 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 your audio file and the language of your vocabulary filter.

  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 whether the audio file is a conversation or a dictation.

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

  7. For the Settings object, specify the following.

     1. VocabularyName – the name of your custom vocabulary.

The following request uses the AWS SDK for Python (Boto3) to start a batch transcription job with a custom vocabulary.

from __future__ import print_function
import time
import boto3
transcribe = boto3.client('transcribe', 'us-west-2')
job_name = "my-first-med-transcription-job"
job_uri = "s3://DOC-EXAMPLE-BUCKET/my-input-files/my-media-file.flac"
transcribe.start_medical_transcription_job(
   MedicalTranscriptionJobName = job_name,
   Media = {
       'MediaFileUri': job_uri
   },
   OutputBucketName = 'DOC-EXAMPLE-BUCKET',
   OutputKey = 'my-output-files/', 
   LanguageCode = 'en-US',
   Specialty = 'PRIMARYCARE',
   Type = 'CONVERSATION',
   Settings = {
       'VocabularyName': 'example-med-custom-vocab'
       }
 )

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)