Struct CreateLanguageModelInput

#[non_exhaustive]
pub struct CreateLanguageModelInput {
    pub language_code: Option<ClmLanguageCode>,
    pub base_model_name: Option<BaseModelName>,
    pub model_name: Option<String>,
    pub input_data_config: Option<InputDataConfig>,
    pub tags: Option<Vec<Tag>>,
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

language_code: Option<ClmLanguageCode>

The language code that represents the language of your 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.

A custom language model can only be used to transcribe files in the same language as the model. For example, if you create a custom language model using US English (en-US), you can only apply this model to files that contain English audio.

base_model_name: Option<BaseModelName>

The Amazon Transcribe standard language model, or base model, used to create your custom language model. Amazon Transcribe offers two options for base models: Wideband and Narrowband.

If the audio you want to transcribe has a sample rate of 16,000 Hz or greater, choose WideBand. To transcribe audio with a sample rate less than 16,000 Hz, choose NarrowBand.

model_name: Option<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. If you try to create a new custom language model with the same name as an existing custom language model, you get a ConflictException error.

input_data_config: Option<InputDataConfig>

Contains the Amazon S3 location of the training data you want to use to create a new custom language model, and permissions to access this location.

When using InputDataConfig, you must include these sub-parameters: S3Uri, which is the Amazon S3 location of your training data, and DataAccessRoleArn, which is the Amazon Resource Name (ARN) of the role that has permission to access your specified Amazon S3 location. You can optionally include TuningDataS3Uri, which is the Amazon S3 location of your tuning data. If you specify different Amazon S3 locations for training and tuning data, the ARN you use must have permissions to access both locations.

tags: Option<Vec<Tag>>

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

To learn more about using tags with Amazon Transcribe, refer to Tagging resources.

Implementations

impl CreateLanguageModelInput

pub fn language_code(&self) -> Option<&ClmLanguageCode>

The language code that represents the language of your 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.

A custom language model can only be used to transcribe files in the same language as the model. For example, if you create a custom language model using US English (en-US), you can only apply this model to files that contain English audio.

pub fn base_model_name(&self) -> Option<&BaseModelName>

The Amazon Transcribe standard language model, or base model, used to create your custom language model. Amazon Transcribe offers two options for base models: Wideband and Narrowband.

If the audio you want to transcribe has a sample rate of 16,000 Hz or greater, choose WideBand. To transcribe audio with a sample rate less than 16,000 Hz, choose NarrowBand.

pub fn model_name(&self) -> Option<&str>

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. If you try to create a new custom language model with the same name as an existing custom language model, you get a ConflictException error.

pub fn input_data_config(&self) -> Option<&InputDataConfig>

Contains the Amazon S3 location of the training data you want to use to create a new custom language model, and permissions to access this location.

When using InputDataConfig, you must include these sub-parameters: S3Uri, which is the Amazon S3 location of your training data, and DataAccessRoleArn, which is the Amazon Resource Name (ARN) of the role that has permission to access your specified Amazon S3 location. You can optionally include TuningDataS3Uri, which is the Amazon S3 location of your tuning data. If you specify different Amazon S3 locations for training and tuning data, the ARN you use must have permissions to access both locations.

pub fn tags(&self) -> &[Tag]

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

To learn more about using tags with Amazon Transcribe, refer to Tagging resources.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .tags.is_none().

impl CreateLanguageModelInput

pub fn builder() -> CreateLanguageModelInputBuilder

Creates a new builder-style object to manufacture CreateLanguageModelInput.

Trait Implementations

impl Clone for CreateLanguageModelInput

fn clone(&self) -> CreateLanguageModelInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateLanguageModelInput

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

Formats the value using the given formatter.

impl PartialEq for CreateLanguageModelInput

fn eq(&self, other: &CreateLanguageModelInput) -> bool

Tests for self and other values to be equal, and is used by ==.

const fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for CreateLanguageModelInput