robot-project/wake_word_module/config.py

"""
语音唤醒配置类
"""

from pathlib import Path
from typing import Optional


class WakeWordConfig:
    """语音唤醒配置"""

    def __init__(
        self,
        model_path: str = "models",
        sample_rate: int = 16000,
        num_threads: int = 4,
        provider: str = "cpu",
        max_active_paths: int = 2,
        keywords_score: float = 1.8,
        keywords_threshold: float = 0.2,
        num_trailing_blanks: int = 1,
        detection_cooldown: float = 1.5,
    ):
        """
        初始化语音唤醒配置

        Args:
            model_path: 模型文件目录路径
            sample_rate: 音频采样率
            num_threads: 线程数
            provider: 计算提供者 (cpu/cuda)
            max_active_paths: 最大激活路径数
            keywords_score: 关键词分数
            keywords_threshold: 关键词阈值
            num_trailing_blanks: 尾部空白数
            detection_cooldown: 检测冷却时间(秒)
        """
        self.model_path = Path(model_path)
        self.sample_rate = sample_rate
        self.num_threads = num_threads
        self.provider = provider
        self.max_active_paths = max_active_paths
        self.keywords_score = keywords_score
        self.keywords_threshold = keywords_threshold
        self.num_trailing_blanks = num_trailing_blanks
        self.detection_cooldown = detection_cooldown

    def validate(self) -> bool:
        """验证配置参数"""
        if not self.model_path.exists():
            raise FileNotFoundError(f"模型目录不存在: {self.model_path}")

        required_files = [
            "encoder.onnx",
            "decoder.onnx",
            "joiner.onnx",
            "tokens.txt",
            "keywords.txt",
        ]

        for file_name in required_files:
            if not (self.model_path / file_name).exists():
                raise FileNotFoundError(f"模型文件不存在: {self.model_path / file_name}")

        if not 0.1 <= self.keywords_threshold <= 1.0:
            raise ValueError(f"关键词阈值 {self.keywords_threshold} 超出范围 [0.1, 1.0]")

        if not 0.1 <= self.keywords_score <= 10.0:
            raise ValueError(f"关键词分数 {self.keywords_score} 超出范围 [0.1, 10.0]")

        return True