Struct aws_sdk_transcribe::client::fluent_builders::CreateLanguageModel

pub struct CreateLanguageModel<C = DynConnector, M = DefaultMiddleware, R = Standard> { /* private fields */ }

Fluent builder constructing a request to CreateLanguageModel.

Creates a new custom language model. Use Amazon S3 prefixes to provide the location of your input files. The time it takes to create your model depends on the size of your training data.

Implementations

impl<C, M, R> CreateLanguageModel<C, M, R> where
    C: SmithyConnector,
    M: SmithyMiddleware<C>,
    R: NewRequestPolicy, 

pub async fn send(
    self
) -> Result<CreateLanguageModelOutput, SdkError<CreateLanguageModelError>> where
    R::Policy: SmithyRetryPolicy<CreateLanguageModelInputOperationOutputAlias, CreateLanguageModelOutput, CreateLanguageModelError, CreateLanguageModelInputOperationRetryAlias>, 

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn language_code(self, input: ClmLanguageCode) -> Self

The language of the input text you're using to train your custom language model.

pub fn set_language_code(self, input: Option<ClmLanguageCode>) -> Self

The language of the input text you're using to train your custom language model.

pub fn base_model_name(self, input: BaseModelName) -> Self

The Amazon Transcribe standard language model, or base model used to create your custom language model.

If you want to use your custom language model to transcribe audio with a sample rate of 16,000 Hz or greater, choose Wideband.

If you want to use your custom language model to transcribe audio with a sample rate that is less than 16,000 Hz, choose Narrowband.

pub fn set_base_model_name(self, input: Option<BaseModelName>) -> Self

The Amazon Transcribe standard language model, or base model used to create your custom language model.

If you want to use your custom language model to transcribe audio with a sample rate of 16,000 Hz or greater, choose Wideband.

If you want to use your custom language model to transcribe audio with a sample rate that is less than 16,000 Hz, choose Narrowband.

pub fn model_name(self, input: impl Into<String>) -> Self

The name you choose for your custom language model when you create it.

pub fn set_model_name(self, input: Option<String>) -> Self

The name you choose for your custom language model when you create it.

pub fn input_data_config(self, input: InputDataConfig) -> Self

Contains the data access role and the Amazon S3 prefixes to read the required input files to create a custom language model.

pub fn set_input_data_config(self, input: Option<InputDataConfig>) -> Self

Contains the data access role and the Amazon S3 prefixes to read the required input files to create a custom language model.

pub fn tags(self, input: Tag) -> Self

Appends an item to Tags.

To override the contents of this collection use set_tags.

Adds one or more tags, each in the form of a key:value pair, to a new language model at the time you create this new model.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

Adds one or more tags, each in the form of a key:value pair, to a new language model at the time you create this new model.

Trait Implementations

impl<C: Clone, M: Clone, R: Clone> Clone for CreateLanguageModel<C, M, R>

fn clone(&self) -> CreateLanguageModel<C, M, R>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<C: Debug, M: Debug, R: Debug> Debug for CreateLanguageModel<C, M, R>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.