Added a node to convert audio to spectrogram and spectrigram to audio

2025-12-29 20:04:58 +01:00
parent 1ca839e0d9
commit 744931c6e3
5 changed files with 112 additions and 4 deletions
@@ -0,0 +1,47 @@
+import torch
+from comfy_api.latest import io
+
+windows = {'bartlet':torch.bartlett_window, 'blackman':torch.blackman_window, 'hamming':torch.hamming_window,'hann':torch.hann_window}
+
+class SpectrogramToAudio(io.ComfyNode):
+    """
+    Converts an Image spectrogram back to Audio.
+    Red is real part and blue is imaginary. Green is ignored.
+    """
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="mrmth_ImageSpectrogramToAudio",
+            category="More math",
+            display_name="Image Spectrogram to Audio",
+            inputs=[
+                io.Image.Input(id="image", tooltip="Input spectrogram image (R=Real, G=Magnitude, B=Imaginary)"),
+                io.Int.Input(id="channel_count", default=1, min=1, tooltip="Number of audio channels"),
+                io.Int.Input(id="sample_rate", default=44100, min=1, tooltip="Sample rate of the output audio"),
+                io.Int.Input(id="window_length", default=1024, min=16, tooltip="Window length in samples"),
+                io.Int.Input(id="hop_length", default=256, min=1, tooltip="Stride of the window (hop length) in samples"),
+                io.Combo.Input(id="window_type", default="hann", options=list(windows.keys()), tooltip="Type of window function to apply"),
+
+            ],
+            outputs=[
+                io.Audio.Output(id="audio", tooltip="Output audio"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image, channel_count, sample_rate, window_length, hop_length,window_type):
+        B, H, W, _ = image.shape
+        bucket_count = H // channel_count
+        n_fft = (bucket_count - 1) * 2
+        real = image[..., 0].reshape(B * channel_count, bucket_count, W)
+        imag = image[..., 2].reshape(B * channel_count, bucket_count, W)
+        stft_complex = torch.complex(real, imag)
+        window = windows[window_type](window_length,device = image.device)
+        waveform = torch.istft(
+            stft_complex, n_fft=n_fft, hop_length=hop_length, win_length=window_length,
+            window=window, center=True, normalized=False, onesided=True
+        )
+
+        waveform = waveform.reshape(B, channel_count, -1)
+        return ({"waveform": waveform, "sample_rate": sample_rate},)
+