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whisper_stt.py

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+import whisper
+import logging
+from typing import Optional, Dict, Any
+import torch
+
+# 設定日誌
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def transcribe_audio_whisper(
+    file_path: str,
+    model_name: str = "base",
+    language: Optional[str] = None,
+    initial_prompt: Optional[str] = None,
+    task: str = "transcribe"
+) -> Optional[Dict[str, Any]]:
+    """
+    使用 Whisper 模型進行音訊轉文字
+    
+    Args:
+        file_path: 音訊檔案路徑
+        model_name: Whisper 模型名稱 ("tiny", "base", "small", "medium", "large")
+        language: 音訊語言（ISO 639-1 代碼，如 "zh" 表示中文）
+        initial_prompt: 初始提示詞
+        task: 任務類型 ("transcribe" 或 "translate")
+    
+    Returns:
+        包含轉錄結果的字典，如果失敗則返回 None
+    """
+    try:
+        # 檢查 CUDA 是否可用
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        logger.info(f"使用設備: {device}")
+        
+        # 載入模型
+        logger.info(f"載入 Whisper {model_name} 模型...")
+        model = whisper.load_model(model_name, device=device)
+        
+        # 轉錄選項
+        options = {
+            "task": task,
+            "verbose": True
+        }
+        if language:
+            options["language"] = language
+        if initial_prompt:
+            options["initial_prompt"] = initial_prompt
+            
+        # 執行轉錄
+        logger.info("開始轉錄...")
+        result = model.transcribe(file_path, **options)
+        
+        # 整理結果
+        response = {
+            "text": result["text"],
+            "language": result.get("language", "unknown"),
+            "segments": result.get("segments", [])
+        }
+        
+        logger.info("轉錄完成")
+        return response
+        
+    except Exception as e:
+        logger.error(f"轉錄失敗：{str(e)}")
+        return None
+
+def get_available_models() -> list:
+    """
+    取得可用的 Whisper 模型列表
+    """
+    return ["tiny", "base", "small", "medium", "large"]
+
+def get_model_description(model_name: str) -> str:
+    """
+    取得模型描述
+    """
+    descriptions = {
+        "tiny": "最小的模型，速度最快但準確度較低",
+        "base": "基礎模型，平衡速度和準確度",
+        "small": "小型模型，準確度較好",
+        "medium": "中型模型，準確度高",
+        "large": "最大的模型，準確度最高但需要較多資源"
+    }
+    return descriptions.get(model_name, "未知模型")