/**
 * Inference code generated from the JSON schema spec in ./spec
 *
 * Using src/scripts/inference-codegen
 */
/**
 * Inputs for Automatic Speech Recognition inference
 */
export interface AutomaticSpeechRecognitionInput {
    /**
     * The input audio data as a base64-encoded string. If no `parameters` are provided, you can
     * also provide the audio data as a raw bytes payload.
     */
    inputs: string;
    /**
     * Additional inference parameters
     */
    parameters?: AutomaticSpeechRecognitionParameters;
    [property: string]: unknown;
}
/**
 * Additional inference parameters
 *
 * Additional inference parameters for Automatic Speech Recognition
 */
export interface AutomaticSpeechRecognitionParameters {
    /**
     * Parametrization of the text generation process
     */
    generation_parameters?: GenerationParameters;
    /**
     * Whether to output corresponding timestamps with the generated text
     */
    return_timestamps?: boolean;
    [property: string]: unknown;
}
/**
 * Parametrization of the text generation process
 *
 * Ad-hoc parametrization of the text generation process
 */
export interface GenerationParameters {
    /**
     * Whether to use sampling instead of greedy decoding when generating new tokens.
     */
    do_sample?: boolean;
    /**
     * Controls the stopping condition for beam-based methods.
     */
    early_stopping?: EarlyStoppingUnion;
    /**
     * If set to float strictly between 0 and 1, only tokens with a conditional probability
     * greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
     * 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
     * Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
     */
    epsilon_cutoff?: number;
    /**
     * Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
     * float strictly between 0 and 1, a token is only considered if it is greater than either
     * eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
     * term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
     * the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
     * See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
     * for more details.
     */
    eta_cutoff?: number;
    /**
     * The maximum length (in tokens) of the generated text, including the input.
     */
    max_length?: number;
    /**
     * The maximum number of tokens to generate. Takes precedence over max_length.
     */
    max_new_tokens?: number;
    /**
     * The minimum length (in tokens) of the generated text, including the input.
     */
    min_length?: number;
    /**
     * The minimum number of tokens to generate. Takes precedence over min_length.
     */
    min_new_tokens?: number;
    /**
     * Number of groups to divide num_beams into in order to ensure diversity among different
     * groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
     */
    num_beam_groups?: number;
    /**
     * Number of beams to use for beam search.
     */
    num_beams?: number;
    /**
     * The value balances the model confidence and the degeneration penalty in contrastive
     * search decoding.
     */
    penalty_alpha?: number;
    /**
     * The value used to modulate the next token probabilities.
     */
    temperature?: number;
    /**
     * The number of highest probability vocabulary tokens to keep for top-k-filtering.
     */
    top_k?: number;
    /**
     * If set to float < 1, only the smallest set of most probable tokens with probabilities
     * that add up to top_p or higher are kept for generation.
     */
    top_p?: number;
    /**
     * Local typicality measures how similar the conditional probability of predicting a target
     * token next is to the expected conditional probability of predicting a random token next,
     * given the partial text already generated. If set to float < 1, the smallest set of the
     * most locally typical tokens with probabilities that add up to typical_p or higher are
     * kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
     */
    typical_p?: number;
    /**
     * Whether the model should use the past last key/values attentions to speed up decoding
     */
    use_cache?: boolean;
    [property: string]: unknown;
}
/**
 * Controls the stopping condition for beam-based methods.
 */
export type EarlyStoppingUnion = boolean | "never";
/**
 * Outputs of inference for the Automatic Speech Recognition task
 */
export interface AutomaticSpeechRecognitionOutput {
    /**
     * When returnTimestamps is enabled, chunks contains a list of audio chunks identified by
     * the model.
     */
    chunks?: AutomaticSpeechRecognitionOutputChunk[];
    /**
     * The recognized text.
     */
    text: string;
    [property: string]: unknown;
}
export interface AutomaticSpeechRecognitionOutputChunk {
    /**
     * A chunk of text identified by the model
     */
    text: string;
    /**
     * The start and end timestamps corresponding with the text
     */
    timestamps: number[];
    [property: string]: unknown;
}
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