fix any

nodes.py

@@ -1,86 +1,51 @@
 import requests
 import io
-import librosa # Changed from librosa.core
+import librosa.core as core
 import torch
-import numpy # For type hinting, though librosa.load returns numpy array
-import warnings
 
 class AudioLoadPath:
     @classmethod
-    def INPUT_TYPES(cls): # Changed s to cls for convention
-        return {
-            "required": {
-                "path": ("STRING", {"default": "X://insert/path/here.mp4"}),
-                "sample_rate": ("INT", {"default": 22050, "min": 6000, "max": 192000, "step": 1}),
-                "offset": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1e6, "step": 0.001}),
-                "duration": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1e6, "step": 0.001})
-            }
-        }
+    def INPUT_TYPES(s):
+        return {"required": { "path": ("STRING", {"default": "X://insert/path/here.mp4"}),
+                              "sample_rate": ("INT", {"default": 22050, "min": 6000, "max": 192000, "step": 1}),
+                              "offset": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1e6, "step": 0.001}),
+                              "duration": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1e6, "step": 0.001})}}
 
-    RETURN_TYPES = ("AUDIO",)
-    CATEGORY = "EasyAI" # Or your preferred category
+    RETURN_TYPES = ("AUDIO", )
+    CATEGORY = "EasyAI"
     FUNCTION = "load"
 
-    def load(self, path: str, sample_rate: int, offset: float, duration: float | None):
+    def load(self, path: str, sample_rate: int, offset: float, duration: float|None):
         if duration == 0.0:
             duration = None
 
-        audio_data_source = None
         try:
             if path.startswith(('http://', 'https://')):
-                # For network paths, download and load from memory
-                response = requests.get(path, timeout=10) # Added timeout
-                response.raise_for_status() # Raises an exception for bad status codes
-                audio_data_source = io.BytesIO(response.content)
+                response = requests.get(path)
+                response.raise_for_status()
+                audio_data = io.BytesIO(response.content)
+
+                import warnings
+                with warnings.catch_warnings():
+                    warnings.simplefilter("ignore")
+                    audio, _ = core.load(audio_data, sr=sample_rate, offset=offset, duration=duration)
             else:
-                # For local paths (absolute or relative)
-                audio_data_source = path
+                audio, _ = core.load(path, sr=sample_rate, offset=offset, duration=duration)
 
-            # Use librosa to load audio.
-            # mono=False ensures that the output numpy array is always 2D (channels, samples).
-            # For mono audio, this will be (1, samples).
-            # librosa.load will resample to the target 'sample_rate' if it's provided.
-            with warnings.catch_warnings():
-                warnings.simplefilter("ignore") # Suppress librosa warnings if any
-                audio_np, loaded_sr = librosa.load(
-                    audio_data_source,
-                    sr=sample_rate,
-                    offset=offset,
-                    duration=duration,
-                    mono=False # Ensures audio_np is 2D: (channels, samples)
-                )
+                # 将音频数据转换为正确的格式
+            audio_tensor = torch.from_numpy(audio).float()
 
-            # Convert numpy array to PyTorch tensor
-            audio_tensor = torch.from_numpy(audio_np) # Shape: (channels, samples)
-
-            # Add a batch dimension to conform to (batch_size, channels, samples)
-            # Here, batch_size is 1 as we are loading a single audio file.
-            audio_tensor = audio_tensor.unsqueeze(0) # Shape: (1, channels, samples)
-
-            # Prepare the output dictionary for the "AUDIO" type
-            output_audio_dict = {
-                "waveform": audio_tensor,
-                "sample_rate": loaded_sr # Use the actual loaded sample rate (should match input 'sample_rate')
+            # 创建符合 SaveAudio 节点期望的字典格式
+            audio_dict = {
+                "waveform": audio_tensor.unsqueeze(0),  # 添加批次维度 [batch, samples]
+                "sample_rate": sample_rate
             }
 
-            # Return as a tuple, as ComfyUI expects
-            return (output_audio_dict,)
+            return (audio_dict,)
 
-        except requests.exceptions.RequestException as e:
-            # Handle network-specific errors
-            raise Exception(f"Failed to load audio from URL: {str(e)}")
-        except FileNotFoundError as e:
-            # Handle local file not found errors
-            raise Exception(f"Audio file not found: {path} - {str(e)}")
         except Exception as e:
-            # Handle other potential errors (e.g., librosa failing to decode, invalid path)
-            raise Exception(f"Failed to load audio: {str(e)}")
+            raise Exception(f"加载音频失败: {str(e)}")
 
-# Node mappings for ComfyUI
 NODE_CLASS_MAPPINGS = {
-    "AudioLoadPath": AudioLoadPath
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "AudioLoadPath": "Load Audio (Path/URL)"
+    "AudioLoadPath": AudioLoadPath,
 }

requirements.txt

@@ -1 +1,2 @@
-numpy~=2.2.5
+numpy
+librosa