added more nodes

SignalProcessingLoadAudio.py

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+import sys,os 
+import torch, torchaudio
+import hashlib
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
+
+import folder_paths
+
+class SignalProcessingLoadAudio():
+    supported_formats = ['wav','mp3','ogg','m4a','flac','mp4']
+    @classmethod
+    def INPUT_TYPES(s):
+        supported_extensions = tuple(f".{fmt.lower()}" for fmt in SignalProcessingLoadAudio.supported_formats)
+        
+        input_dir = folder_paths.get_input_directory()
+        all_items = os.listdir(input_dir)
+        filtered_files = [
+            x for x in all_items
+            if x.lower().endswith(supported_extensions)
+        ]
+        files = [os.path.join(input_dir,x) for x in filtered_files]
+
+        return {
+            "required":  {"audio_file": (sorted(files), {"image_upload": True})},
+            "optional" : {"seek_seconds": ("FLOAT", {"default": 0, "min": 0})}
+        }
+
+    RETURN_TYPES = ("AUDIO","INT")
+    RETURN_NAMES = ("audio","sample_rate")
+    CATEGORY = "Signal Processing"
+    FUNCTION = "process"
+
+    def process(self, audio_file, seek_seconds):
+
+        audio_file_path = folder_paths.get_annotated_filepath(audio_file)
+
+        waveform, sample_rate = torchaudio.load(audio_file_path)
+        waveform = waveform.unsqueeze(0).contiguous()
+
+        print('SignalProcessingLoadAudio.waveform',waveform.shape)
+        return {'waveform': waveform, 'sample_rate': sample_rate} , sample_rate
+
+    @classmethod
+    def IS_CHANGED(s, audio_file, seek_seconds):
+        audio_file_path = folder_paths.get_annotated_filepath(audio_file)
+        m = hashlib.sha256()
+        with open(audio_file_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, audio_file, seek_seconds):
+        if not folder_paths.exists_annotated_filepath(audio_file):
+            return "Invalid image file: {}".format(audio_file)
+
+        return True