Using a custom language model

Once you've created your custom language model, you can include it in your transcription requests; refer to the following sections for examples.

The language of the model you're including in your request must match the language code you specify for your media. If the languages don't match, your custom language model is not applied to your transcription and there are no warnings or errors.

Using a custom language model in a batch transcription

To use a custom language model with a batch transcription, see the following for examples:

AWS Management Console

1. Sign in to the AWS Management Console.

2. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page.

3. In the Job settings panel under Model type, select the Custom language model box.

   

   You must also select an input language from the dropdown menu.

   

4. Under Custom model selection, select an existing custom language model from the dropdown menu or Create a new one.

   Add the Amazon S3 location of your input file in the Input data panel.

5. Select Next for additional configuration options.

   Select Create job to run your transcription job.

AWS CLI

This example uses the start-transcription-job command and ModelSettings parameter with the VocabularyName sub-parameter. For more information, see StartTranscriptionJob and ModelSettings.

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 \
--model-settings LanguageModelName=my-first-language-model

Here's another example using the start-transcription-job command, and a request body that includes your custom language model with that job.

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

The file my-first-model-job.json contains the following request body.

{
  "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",
  "ModelSettings": {
        "LanguageModelName": "my-first-language-model"
   }
}

AWS SDK for Python (Boto3)

This example uses the AWS SDK for Python (Boto3) to include a custom language model using the ModelSettings argument for the start_transcription_job method. For more information, see StartTranscriptionJob and ModelSettings.

For additional examples using the AWS SDKs, including feature-specific, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter.

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', 
    ModelSettings = {
        'LanguageModelName': 'my-first-language-model' 
   }
)

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)

Using a custom language model in a streaming transcription

To use a custom language model with a streaming transcription, see the following for examples:

AWS Management Console

1. Sign into the AWS Management Console.

2. In the navigation pane, choose Real-time transcription. Scroll down to Customizations and expand this field if it is minimized.

   

3. Toggle on Custom language model and select a model from the dropdown menu.

   

   Include any other settings you want to apply to your stream.

4. You're now ready to transcribe your stream. Select Start streaming and begin speaking. To end your dictation, select Stop streaming.

HTTP/2 stream

This example creates an HTTP/2 request that includes your custom language model. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers specific to Amazon Transcribe, see StartStreamTranscription.

POST /stream-transcription HTTP/2
host: transcribestreaming.us-west-2.amazonaws.com
X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription
Content-Type: application/vnd.amazon.eventstream
X-Amz-Content-Sha256: string
X-Amz-Date: 20220208T235959Z
Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz-target;x-amz-security-token, Signature=string
x-amzn-transcribe-language-code: en-US
x-amzn-transcribe-media-encoding: flac
x-amzn-transcribe-sample-rate: 16000      
x-amzn-transcribe-language-model-name: my-first-language-model
transfer-encoding: chunked

Parameter definitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section.

WebSocket stream

This example creates a presigned URL that applies your custom language model to a WebSocket stream. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartStreamTranscription.

GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription-websocket?
&X-Amz-Algorithm=AWS4-HMAC-SHA256
&X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus-west-2%2Ftranscribe%2Faws4_request
&X-Amz-Date=20220208T235959Z
&X-Amz-Expires=300
&X-Amz-Security-Token=security-token
&X-Amz-Signature=string
&X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date
&language-code=en-US
&media-encoding=flac
&sample-rate=16000    
&language-model-name=my-first-language-model

Parameter definitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section.