55 lines
2.5 KiB
Python
55 lines
2.5 KiB
Python
from comfy_api.latest import io
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import torch
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windows = {'bartlet':torch.bartlett_window, 'blackman':torch.blackman_window, 'hamming':torch.hamming_window,'hann':torch.hann_window}
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class AudioToSpectrogram(io.ComfyNode):
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"""
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Converts Audio to an Image spectrogram.
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Red = Real, Green = logarithm of value (just so it looks good), Blue = Imaginary.
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Each audio channel is stacked vertically.
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"""
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="mrmth_AudioToImageSpectrogram",
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category="More math",
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display_name="Audio to Image Spectrogram",
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inputs=[
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io.Audio.Input(id="audio", tooltip="Input audio"),
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io.Int.Input(id="window_length", default=1024, min=16, max=4096, tooltip="Window length in samples"),
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io.Int.Input(id="hop_length", default=256, min=1, max=4096, tooltip="Stride of the window (hop length) in samples"),
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io.Int.Input(id="bucket_count", default=513, min=2, max=4096, tooltip="Number of frequency buckets (determines resolution)"),
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io.Combo.Input(id="window_type", default="hann", options=list(windows.keys()), tooltip="Type of window function to apply"),
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],
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outputs=[
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io.Image.Output(id="image"),
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io.Int.Output(id="channel_count", display_name="Channel count", tooltip="Number of channels in the output image"),
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io.Int.Output(id="sample_rate", display_name="Sample rate", tooltip="Sample rate of the input audio"),
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],
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)
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@classmethod
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def execute(cls, audio, window_length, hop_length, bucket_count,window_type):
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waveform = audio['waveform']
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sample_rate = audio['sample_rate']
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B, C, S = waveform.shape
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n_fft = (bucket_count - 1) * 2
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flat_waveform = waveform.reshape(B * C, S)
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window = windows[window_type](window_length, device=waveform.device)
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stft_out = torch.stft(
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flat_waveform, n_fft=n_fft, hop_length=hop_length, win_length=window_length,
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window=window, center=True, pad_mode='reflect', normalized=False,
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onesided=True, return_complex=True
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)
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F, T = stft_out.shape[1], stft_out.shape[2]
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stft_out = stft_out.reshape(B, C, F, T).reshape(B, C * F, T)
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image = torch.stack([
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stft_out.real,
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torch.log1p(torch.abs(stft_out)),
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stft_out.imag
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], dim=-1)
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print(image.type())
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return (image, C, sample_rate) |