added more nodes

SignalProcessingVolumeControl.py

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+import torch
+import os, sys
+from typing import Tuple, List, Dict, Union
+
+class SignalProcessingVolumeControl:
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict:
+        return {
+            "required": {
+                "audio_input": ("AUDIO",),  # Single audio input
+                "gain": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),
+            }
+        }
+
+    RETURN_TYPES = ("AUDIO","INT")
+    RETURN_NAMES = ("adjusted_audio","sample_rate")
+    CATEGORY = "Audio Processing"
+    FUNCTION = "process"
+
+    def process(
+        self,
+        audio_input: Dict[str, Union[torch.Tensor, int]],
+        gain: float,
+    ) -> Tuple[Dict[str, torch.Tensor]]:
+        """
+        Adjust the volume of the input audio by applying a gain factor.
+
+        Parameters:
+            audio_input (Dict): Dictionary containing 'waveform' and 'sample_rate'.
+            gain (float): Gain factor to adjust the volume. >1.0 amplifies, <1.0 attenuates.
+
+        Returns:
+            Tuple[Dict[str, torch.Tensor]]: Dictionary with adjusted 'waveform' and 'sample_rate'.
+        """
+
+        # Extract waveform and sample rate
+        waveform = audio_input.get('waveform')
+        sample_rate = audio_input.get('sample_rate')
+
+        if waveform is None or sample_rate is None:
+            raise ValueError("Input audio must contain 'waveform' and 'sample_rate'.")
+
+        # Validate waveform tensor
+        if not isinstance(waveform, torch.Tensor):
+            raise TypeError("Waveform must be a torch.Tensor.")
+        if waveform.ndim != 3:
+            raise ValueError("Waveform must be a 3D tensor with shape (batch, channels, samples).")
+        if waveform.dtype != torch.float32:
+            waveform = waveform.float()
+        if not waveform.is_contiguous():
+            waveform = waveform.contiguous()
+
+        # Apply gain as a multiplier
+        adjusted_waveform = waveform * gain
+        print(f"AudioVolumeControl: Applied gain factor {gain}")
+
+        # Clamp to [-1.0, 1.0] to prevent clipping
+        adjusted_waveform = torch.clamp(adjusted_waveform, -1.0, 1.0)
+        print("AudioVolumeControl: Clamped adjusted waveform to [-1.0, 1.0]")
+
+        # Ensure the tensor is on CPU
+        if adjusted_waveform.device != torch.device('cpu'):
+            adjusted_waveform = adjusted_waveform.to('cpu')
+
+        # Prepare the output dictionary
+        adjusted_audio = {
+            'waveform': adjusted_waveform,
+            'sample_rate': sample_rate
+        }
+
+        return adjusted_audio, sample_rate