/**
 * Whisper adapter using @xenova/transformers (v2)
 *
 * Uses Whisper models running in the browser via WASM.
 * V2 uses a simpler, more stable pipeline API.
 * Requires downloading model files (~40MB-150MB depending on model size).
 */
import type { InitState, OnProgress } from '../../core/types.js';
import type { STTAdapter, STTCapabilities, STTOptions, STTResult, WhisperWasmSTTOptions } from './types.js';
/**
 * Whisper adapter for high-accuracy speech recognition
 * Uses transformers.js v2 pipeline API
 */
export declare class WhisperWasmSTTAdapter implements STTAdapter {
    readonly type: "whisper-wasm";
    private _initState;
    private options;
    private _isListening;
    private mediaRecorder;
    private audioChunks;
    private stream;
    private resultListeners;
    private errorListeners;
    private startListeners;
    private endListeners;
    private transcriber;
    private processingPromise;
    private processingResolve;
    constructor(options?: Partial<WhisperWasmSTTOptions>);
    get initState(): InitState;
    ensureInitialized(onProgress?: OnProgress): Promise<void>;
    private transformersModule;
    private importTransformers;
    /**
     * Configure transformers.js environment settings
     */
    private configureTransformersEnv;
    getCapabilities(): STTCapabilities;
    start(_options?: STTOptions): Promise<void>;
    private getSupportedMimeType;
    private processRecording;
    stop(): Promise<void>;
    abort(): void;
    isListening(): boolean;
    onResult(callback: (result: STTResult) => void): () => void;
    onError(callback: (error: Error) => void): () => void;
    onStart(callback: () => void): () => void;
    onEnd(callback: () => void): () => void;
    dispose(): Promise<void>;
}
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