import { Tensor } from "openvino-node";
import { PerfMetrics, VLMPerfMetrics, WhisperPerfMetrics, Text2SpeechPerfMetrics } from "./perfMetrics.js";
import { GenerationFinishReason } from "./utils.js";
/**
 * Structure to store resulting batched text outputs and scores for each batch.
 * @note The first num_return_sequences elements correspond to the first batch element.
 */
export declare class DecodedResults {
    /**
     * @param {string[]} texts - Vector of resulting sequences.
     * @param {number[]} scores - Scores for each sequence.
     * @param {PerfMetrics} perfMetrics - Performance metrics (tpot, ttft, etc.).
     * @param {Record<string, unknown>[]} parsed - The results of parsers processing for each sequence.
     * @param {GenerationFinishReason[]} finishReasons - Finish reasons for each sequence.
     */
    constructor(texts: string[], scores: number[], perfMetrics: PerfMetrics, parsed: Record<string, unknown>[], finishReasons?: GenerationFinishReason[]);
    toString(): string;
    texts: string[];
    scores: number[];
    perfMetrics: PerfMetrics;
    parsed: Record<string, unknown>[];
    finishReasons: GenerationFinishReason[];
}
/**
 * Structure to store VLM resulting batched text outputs and scores for each batch.
 * @note The first num_return_sequences elements correspond to the first batch element.
 */
export declare class VLMDecodedResults extends DecodedResults {
    /**
     * @param {string[]} texts - Vector of resulting sequences.
     * @param {number[]} scores - Scores for each sequence.
     * @param {VLMPerfMetrics} perfMetrics - VLM-specific performance metrics.
     * @param {Record<string, unknown>[]} parsed - The results of parsers processing for each sequence.
     * @param {GenerationFinishReason[]} finishReasons - Finish reasons for each sequence.
     */
    constructor(texts: string[], scores: number[], perfMetrics: VLMPerfMetrics, parsed: Record<string, unknown>[], finishReasons?: GenerationFinishReason[]);
    /** VLM specific performance metrics. */
    perfMetrics: VLMPerfMetrics;
}
/** Whisper decoded result chunk (when return_timestamps or word_timestamps is enabled). */
export type WhisperDecodedResultChunk = {
    text: string;
    startTs: number;
    endTs: number;
};
/** Word-level timing (when word_timestamps is enabled). */
export type WhisperWordTiming = {
    word: string;
    startTs: number;
    endTs: number;
    /** Word token identifiers as `BigInt64Array`. */
    tokenIds?: BigInt64Array;
};
/**
 * Result of WhisperPipeline.generate() with texts, scores, perf metrics, and optional timestamps.
 */
export declare class WhisperDecodedResults extends DecodedResults {
    chunks?: WhisperDecodedResultChunk[] | undefined;
    words?: WhisperWordTiming[] | undefined;
    constructor(texts: string[], scores: number[], perfMetrics: WhisperPerfMetrics, chunks?: WhisperDecodedResultChunk[] | undefined, words?: WhisperWordTiming[] | undefined);
    /** Whisper-specific performance metrics. */
    perfMetrics: WhisperPerfMetrics;
}
/**
 * Result of Text2SpeechPipeline.generate() with audio tensors and perf metrics.
 * Each element in `speeches` is an audio waveform tensor sampled at 16 kHz.
 */
export declare class Text2SpeechDecodedResults {
    constructor(speeches: Tensor[], perfMetrics: Text2SpeechPerfMetrics);
    speeches: Tensor[];
    perfMetrics: Text2SpeechPerfMetrics;
}