TranscribeService / Client / describe_language_model

describe_language_model#

TranscribeService.Client.describe_language_model(**kwargs)#

Provides information about the specified custom language model.

This operation also shows if the base language model that you used to create your custom language model has been updated. If Amazon Transcribe has updated the base model, you can create a new custom language model using the updated base model.

If you tried to create a new custom language model and the request wasn’t successful, you can use DescribeLanguageModel to help identify the reason for this failure.

Request Syntax

response = client.describe_language_model(
    ModelName='string'
)

Parameters:

ModelName (string) –

[REQUIRED]

The name of the custom language model you want information about. Model names are case sensitive.

Return type:

dict

Returns:

Response Syntax

{
    'LanguageModel': {
        'ModelName': 'string',
        'CreateTime': datetime(2015, 1, 1),
        'LastModifiedTime': datetime(2015, 1, 1),
        'LanguageCode': 'en-US'|'hi-IN'|'es-US'|'en-GB'|'en-AU'|'de-DE'|'ja-JP',
        'BaseModelName': 'NarrowBand'|'WideBand',
        'ModelStatus': 'IN_PROGRESS'|'FAILED'|'COMPLETED',
        'UpgradeAvailability': True|False,
        'FailureReason': 'string',
        'InputDataConfig': {
            'S3Uri': 'string',
            'TuningDataS3Uri': 'string',
            'DataAccessRoleArn': 'string'
        }
    }
}

Response Structure

(dict) –
- LanguageModel (dict) –
  
  Provides information about the specified custom language model.
  
  This parameter also shows if the base language model you used to create your custom language model has been updated. If Amazon Transcribe has updated the base model, you can create a new custom language model using the updated base model.
  
  If you tried to create a new custom language model and the request wasn’t successful, you can use this DescribeLanguageModel to help identify the reason for this failure.
  - ModelName (string) –
    
    A unique name, chosen by you, for your custom language model.
    
    This name is case sensitive, cannot contain spaces, and must be unique within an Amazon Web Services account.
  - CreateTime (datetime) –
    
    The date and time the specified custom language model was created.
    
    Timestamps are in the format YYYY-MM-DD'T'HH:MM:SS.SSSSSS-UTC. For example, 2022-05-04T12:32:58.761000-07:00 represents 12:32 PM UTC-7 on May 4, 2022.
  - LastModifiedTime (datetime) –
    
    The date and time the specified custom language model was last modified.
    
    Timestamps are in the format YYYY-MM-DD'T'HH:MM:SS.SSSSSS-UTC. For example, 2022-05-04T12:32:58.761000-07:00 represents 12:32 PM UTC-7 on May 4, 2022.
  - LanguageCode (string) –
    
    The language code used to create your custom language model. Each custom language model must contain terms in only one language, and the language you select for your custom language model must match the language of your training and tuning data.
    
    For a list of supported languages and their associated language codes, refer to the Supported languages table. Note that US English ( en-US) is the only language supported with Amazon Transcribe Medical.
  - BaseModelName (string) –
    
    The Amazon Transcribe standard language model, or base model, used to create your custom language model.
  - ModelStatus (string) –
    
    The status of the specified custom language model. When the status displays as COMPLETED the model is ready for use.
  - UpgradeAvailability (boolean) –
    
    Shows if a more current base model is available for use with the specified custom language model.
    
    If false, your custom language model is using the most up-to-date base model.
    
    If true, there is a newer base model available than the one your language model is using.
    
    Note that to update a base model, you must recreate the custom language model using the new base model. Base model upgrades for existing custom language models are not supported.
  - FailureReason (string) –
    
    If ModelStatus is FAILED, FailureReason contains information about why the custom language model request failed. See also: Common Errors.
  - InputDataConfig (dict) –
    
    The Amazon S3 location of the input files used to train and tune your custom language model, in addition to the data access role ARN (Amazon Resource Name) that has permissions to access these data.
    - S3Uri (string) –
      
      The Amazon S3 location (URI) of the text files you want to use to train your custom language model.
      
      Here’s an example URI path: s3://DOC-EXAMPLE-BUCKET/my-model-training-data/
    - TuningDataS3Uri (string) –
      
      The Amazon S3 location (URI) of the text files you want to use to tune your custom language model.
      
      Here’s an example URI path: s3://DOC-EXAMPLE-BUCKET/my-model-tuning-data/
    - DataAccessRoleArn (string) –
      
      The Amazon Resource Name (ARN) of an IAM role that has permissions to access the Amazon S3 bucket that contains your input files. If the role that you specify doesn’t have the appropriate permissions to access the specified Amazon S3 location, your request fails.
      
      IAM role ARNs have the format arn:partition:iam::account:role/role-name-with-path. For example: arn:aws:iam::111122223333:role/Admin.
      
      For more information, see IAM ARNs.

Exceptions

TranscribeService.Client.exceptions.BadRequestException
TranscribeService.Client.exceptions.LimitExceededException
TranscribeService.Client.exceptions.InternalFailureException
TranscribeService.Client.exceptions.NotFoundException