{
  config,
  lib,
  pkgs,
  ...
}:
with lib; let
  cfg = config.cli.stt-ptt;

  # Build stt-ptt package with the selected whisper package
  sttPttPackage = pkgs.stt-ptt.override {
    whisper-cpp = cfg.whisperPackage;
  };

  modelDir = "${config.xdg.dataHome}/stt-ptt/models";
  modelPath = "${modelDir}/${cfg.model}.bin";

  # HuggingFace URL for whisper.cpp models
  modelUrl = "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/${cfg.model}.bin";
in {
  options.cli.stt-ptt = {
    enable = mkEnableOption "Push to Talk Speech to Text using Whisper";

    whisperPackage = mkOption {
      type = types.package;
      default = pkgs.whisper-cpp;
      description = ''
        The whisper-cpp package to use. Available options:

        Pre-built variants:
        - `pkgs.whisper-cpp` - CPU-based inference (default)
        - `pkgs.whisper-cpp-vulkan` - Vulkan GPU acceleration

        Override options (can be combined):
        - `cudaSupport` - NVIDIA CUDA support
        - `rocmSupport` - AMD ROCm support
        - `vulkanSupport` - Vulkan support
        - `coreMLSupport` - Apple CoreML (macOS only)
        - `metalSupport` - Apple Metal (macOS ARM only)

        Example overrides:
        - `pkgs.whisper-cpp.override { cudaSupport = true; }` - NVIDIA GPU
        - `pkgs.whisper-cpp.override { rocmSupport = true; }` - AMD GPU
        - `pkgs.whisper-cpp.override { vulkanSupport = true; }` - Vulkan
      '';
      example = literalExpression "pkgs.whisper-cpp.override { cudaSupport = true; }";
    };

    model = mkOption {
      type = types.str;
      default = "ggml-large-v3-turbo";
      description = ''
        The Whisper model to use. Models are downloaded from HuggingFace.

        Available models (sorted by size/quality):
        - `ggml-tiny` / `ggml-tiny.en` - 75MB, fastest, lowest quality
        - `ggml-base` / `ggml-base.en` - 142MB, fast, basic quality
        - `ggml-small` / `ggml-small.en` - 466MB, balanced
        - `ggml-medium` / `ggml-medium.en` - 1.5GB, good quality
        - `ggml-large-v1` - 2.9GB, high quality (original)
        - `ggml-large-v2` - 2.9GB, high quality (improved)
        - `ggml-large-v3` - 2.9GB, highest quality
        - `ggml-large-v3-turbo` - 1.6GB, high quality, optimized speed (recommended)

        Models ending in `.en` are English-only and slightly faster for English.
        Quantized versions (q5_0, q5_1, q8_0) are also available for reduced size.
      '';
      example = "ggml-base.en";
    };

    notifyTimeout = mkOption {
      type = types.int;
      default = 3000;
      description = ''
        Notification timeout in milliseconds for the recording indicator.
        Set to 0 for persistent notifications.
      '';
      example = 5000;
    };

    language = mkOption {
      type = types.enum [
        "auto"
        "en"
        "es"
        "fr"
        "de"
        "it"
        "pt"
        "ru"
        "zh"
        "ja"
        "ko"
        "ar"
        "hi"
        "tr"
        "pl"
        "nl"
        "sv"
        "da"
        "fi"
        "no"
        "vi"
        "th"
        "id"
        "uk"
        "cs"
      ];
      default = "auto";
      description = ''
        Language for speech recognition. Use "auto" for automatic language detection,
        or specify a language code (ISO 639-1 standard) for better accuracy.

        Auto-detection analyzes the audio to determine the spoken language automatically.
        Specifying a language can improve accuracy if you know the language in advance.

        Common language codes:
        - en: English
        - es: Spanish
        - fr: French
        - de: German
        - zh: Chinese
        - ja: Japanese
        - ko: Korean

        whisper.cpp supports 100+ languages. See whisper.cpp documentation for the full list.
      '';
      example = "en";
    };
  };

  config = mkIf cfg.enable {
    home.packages = [sttPttPackage];

    home.sessionVariables = {
      STT_MODEL = modelPath;
      STT_LANGUAGE = cfg.language;
      STT_NOTIFY_TIMEOUT = toString cfg.notifyTimeout;
    };

    # Create model directory and download model if not present
    home.activation.downloadWhisperModel = lib.hm.dag.entryAfter ["writeBoundary"] ''
      MODEL_DIR="${modelDir}"
      MODEL_PATH="${modelPath}"
      MODEL_URL="${modelUrl}"

      $DRY_RUN_CMD mkdir -p "$MODEL_DIR"

      if [ ! -f "$MODEL_PATH" ]; then
        echo "Downloading Whisper model: ${cfg.model}..."
        $DRY_RUN_CMD ${pkgs.curl}/bin/curl -L -o "$MODEL_PATH" "$MODEL_URL" || {
          echo "Failed to download model from $MODEL_URL"
          echo "Please download manually and place at: $MODEL_PATH"
        }
      fi
    '';
  };
}