修改--NLP一律使用

修改--先使用DeepL API再使用Gemini API
修改--blockReason: SAFETY時直接調用DeepL結果

Local-Windows/main.py

@@ -11,7 +11,7 @@
 
 
 # 初始化 Faster Whisper 模型
-model = WhisperModel("large-v2", device="cuda", compute_type="int8")  # 使用适合您硬件的模型大小和设备
+model = WhisperModel("large-v2", device="cuda", compute_type="int8")  
 
 #從api_key.txt取得api
 def get_api_key_from_file(file_path='api_key.txt'):
@@ -55,58 +55,49 @@ def call_deepl_api(input_text, deepL_auth_key, target_lang='ZH'):
 
 # 呼叫Gemini的函數
 def call_gemini_api(input_text):
-    # 從文件中取得API KEY
+    # 获取 DeepL API KEY
+    deepl_auth_key = get_deepl_api_key_from_file()
+    if not deepl_auth_key:
+        print("无法获取 DeepL API KEY。")
+        return None
+
+    # 先使用 DeepL API 翻译为简体中文
+    simplified_chinese_text = call_deepl_api(input_text, deepl_auth_key, target_lang='ZH')
+    if not simplified_chinese_text:
+        print("DeepL 翻译失败。")
+        return None
+
+    # 从文件中取得 Gemini API KEY
     api_key = get_api_key_from_file()
     if not api_key:
-        print("API KEY 無效")
+        print("Gemini API KEY 无效")
         return None
-    
-    # APIURL請求標頭
+
+    # Gemini API URL 请求头
     url = "https://palm-proxy.arcelibs.com/v1beta/models/gemini-pro:generateContent?key={}".format(api_key)
     headers = {'Content-Type': 'application/json'}
 
-    # 分段處理
-    segments = detect_and_split_text(input_text, max_length=500)  # 假设 500 是分段长度的上限
-    all_translated_text = []
+    # 格式化输入文本，准备发送给 Gemini API
+    formatted_input = f"請你將下列語句翻譯成繁體中文，忽略任何語句與文法問題:\n{simplified_chinese_text}"
+    data = json.dumps({"contents": [{"parts": [{"text": formatted_input}]}]})
 
-    for segment in segments:
-        formatted_input = f"請你必須將下列語句翻譯成流暢的繁體中文: \n{segment}"
-        data = json.dumps({"contents": [{"parts": [{"text": formatted_input}]}]})
+    # 发送请求到 Gemini API
+    response = requests.post(url, headers=headers, data=data)
+    response_data = response.json()
 
-        response = requests.post(url, headers=headers, data=data)
-        response_data = response.json()
-        response_text = json.dumps(response_data, indent=4)
-
-        # 保存API回應內容
-        save_transcription("gemini_response", response_text, is_api_response=True)
-
-        if response.status_code == 200:
-            if 'blockReason' in response_data and response_data['blockReason'] == 'SAFETY':
-                # 获取 DeepL API KEY
-                deepl_auth_key = get_deepl_api_key_from_file()
-                if deepl_auth_key:
-                    deepl_translated = call_deepl_api(segment, deepl_auth_key)
-                    if deepl_translated:
-                        # 再次尝试使用 Gemini API
-                        gemini_second_try = call_gemini_api(deepl_translated)
-                        all_translated_text.append(gemini_second_try)
-                    else:
-                        print("DeepL 翻译失败。")
-                        all_translated_text.append(f"[翻译段落失败: {segment}]")
-                else:
-                    print("无法获取 DeepL API KEY。")
-                    all_translated_text.append(f"[翻译段落失败: {segment}]")
-            elif 'candidates' in response_data:
-                translated_text = response_data['candidates'][0]['content']['parts'][0]['text']
-                all_translated_text.append(translated_text)
-            else:
-                print("KeyError: 'candidates' not found in response.")
-                all_translated_text.append(f"[翻译段落失败: {segment}]")
+     # 检查响应并处理
+    if response.status_code == 200:
+        if 'blockReason' in response_data and response_data['blockReason'] == 'SAFETY':
+            # 如果 Gemini API 返回 blockReason 为 SAFETY，直接使用 DeepL 翻译结果
+            return simplified_chinese_text
+        elif 'candidates' in response_data:
+            return response_data['candidates'][0]['content']['parts'][0]['text']
         else:
-            print(f"错误: {response.status_code}")
-            all_translated_text.append(f"[翻译段落失败: {segment}]")
-
-    return ' '.join(all_translated_text)
+            print("KeyError: 'candidates' not found in response.")
+            return None
+    else:
+        print(f"错误: {response.status_code}")
+        return None
 
 
 # 使用 yt-dlp 獲取 YouTube 直播媒體位置
@@ -161,7 +152,7 @@ def save_transcription(file_path, text, is_api_response=False):
         file.write(text)
 
 # 新增語言檢測機制並加載模型
-def detect_and_split_text(text, max_length):
+def detect_and_split_text(text, max_length=500):
     language = detect(text)  
     nlp = None
     if language == "en":
@@ -171,7 +162,7 @@ def detect_and_split_text(text, max_length):
     elif language in ["zh-cn", "zh-tw"]:
         nlp = spacy.load("zh_core_web_sm")
     else:
-        # 無法辨識?就用標點符號分段
+        # 如果无法识别语言，则根据标点符号分段
         return split_text_on_punctuation(text, max_length)
 
     return split_text_natural(nlp, text, max_length)
@@ -185,7 +176,7 @@ def split_text_natural(nlp, text, max_length):
         if len(current_segment + sent.text) <= max_length:
             current_segment += sent.text + " "
         else:
-            if current_segment:  # 空字串處理
+            if current_segment:  # 非空字符串
                 segments.append(current_segment.strip())
             current_segment = sent.text + " "
     if current_segment:
@@ -194,10 +185,8 @@ def split_text_natural(nlp, text, max_length):
 
 # 不是中英日文? 就用標點符號來分類
 def split_text_on_punctuation(text, max_length):
-    # 定義分類的標點符號
     punctuations = ".!?\n"
     pattern = f"[{re.escape(punctuations)}]"
-
     segments = []
     current_segment = ""
     for word in re.split(pattern, text):
@@ -209,7 +198,6 @@ def split_text_on_punctuation(text, max_length):
             current_segment = word
     if current_segment:
         segments.append(current_segment.strip())
-
     return segments
 
 # 主流程
@@ -239,7 +227,7 @@ def main(segment_duration, total_duration):
         time.sleep(segment_duration)
 
 # 配置參數
-SEGMENT_DURATION = 10  # 每一段錄製的長度，單位是秒
+SEGMENT_DURATION = 15  # 每一段錄製的長度，單位是秒
 TOTAL_DURATION = 6000   # 總錄製時間，單位是秒
 
 if __name__ == "__main__":