Update app.py

app.py

@@ -1,31 +1,82 @@
 import gradio as gr
 from transformers import pipeline, WhisperProcessor, WhisperForConditionalGeneration
+import logging
 
-# 加载模型和处理器
+# 设置日志
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# 指定模型ID
 model_id = "ZizaM/whisperfinetune_shanxi"
-processor = WhisperProcessor.from_pretrained(model_id)
-model = WhisperForConditionalGeneration.from_pretrained(model_id)
 
-# 创建pipeline
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor
-)
+try:
+    logger.info(f"开始加载模型: {model_id}")
+    # 加载模型和处理器
+    processor = WhisperProcessor.from_pretrained(model_id)
+    model = WhisperForConditionalGeneration.from_pretrained(model_id)
+    logger.info("模型加载成功")
+    
+    # 创建pipeline
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model=model,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor
+    )
+    logger.info("Pipeline 创建成功")
+
+    def transcribe(audio):
+        if audio is None:
+            logger.warning("没有接收到音频输入")
+            return "请先录制音频"
+        
+        try:
+            logger.info("开始处理音频...")
+            # 确保音频路径是字符串类型
+            if isinstance(audio, tuple):
+                audio_path = audio[1]  # 如果是元组，取第二个元素（文件路径）
+            else:
+                audio_path = audio
+                
+            logger.info(f"处理音频文件: {audio_path}")
+            result = pipe(audio_path, generate_kwargs={"language": "zh"})
+            
+            if not result or "text" not in result:
+                logger.error("模型返回结果格式错误")
+                return "识别失败：模型返回结果格式错误"
+                
+            transcribed_text = result["text"].strip()
+            if not transcribed_text:
+                logger.warning("识别结果为空")
+                return "未能识别出有效内容，请重试"
+                
+            logger.info(f"识别成功: {transcribed_text}")
+            return transcribed_text
+            
+        except Exception as e:
+            logger.error(f"识别过程中出错: {str(e)}", exc_info=True)
+            return f"识别失败: {str(e)}"
 
-def transcribe(audio):
-    # 进行语音识别并返回中文结果
-    result = pipe(audio, generate_kwargs={"language": "zh"})
-    return result["text"]
+    # 创建 Gradio 接口
+    iface = gr.Interface(
+        fn=transcribe,
+        inputs=gr.Audio(
+            sources=["microphone"],
+            type="filepath",
+            label="点击麦克风录音"
+        ),
+        outputs=gr.Textbox(
+            label="识别结果",
+            placeholder="这里将显示识别结果..."
+        ),
+        title="语音识别系统",
+        description="点击麦克风图标开始录音，再次点击停止录音。等待几秒钟后会显示识别结果。",
+        examples=None,
+        cache_examples=False
+    )
 
-# 创建 Gradio 接口
-iface = gr.Interface(
-    fn=transcribe,
-    inputs=gr.Audio(sources=["microphone"]),
-    outputs="text",
-    title="语音识别",
-    description="点击麦克风图标开始录音，再次点击停止录音"
-)
+    iface.launch()
 
-iface.launch()
+except Exception as e:
+    logger.error(f"应用启动失败: {str(e)}", exc_info=True)
+    raise e