74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
import torch
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from .helper_functions import generate_dim_variables, getIndexTensorAlongDim, comonLazy, eval_tensor_expr, make_zero_like,as_tensor
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from comfy_api.latest import io
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from .MathNodeBase import MathNodeBase
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class AudioMathNode(MathNodeBase):
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"""
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Enables math expressions on Audio tensors.
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Inputs:
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a, b, c, d: Audio inputs (b, c, d default to zero if not provided)
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w, x, y, z: Float variables for expressions
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AudioExpr: Expression to apply on audio tensors
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Outputs:
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AUDIO: Result of applying expression to input audio
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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_AudioMathNode",
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category="More math",
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display_name="Audio math",
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inputs=[
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io.Audio.Input(id="a", tooltip="Input audio tensor"),
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io.Audio.Input(id="b", optional=True, lazy=True, tooltip="Second input audio tensor"),
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io.Audio.Input(id="c", optional=True, lazy=True, tooltip="Third input audio tensor"),
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io.Audio.Input(id="d", optional=True, lazy=True, tooltip="Fourth input audio tensor"),
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io.Float.Input(id="w", default=0.0, optional=True, lazy=True, force_input=True),
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io.Float.Input(id="x", default=0.0, optional=True, lazy=True, force_input=True),
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io.Float.Input(id="y", default=0.0, optional=True, lazy=True, force_input=True),
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io.Float.Input(id="z", default=0.0, optional=True, lazy=True, force_input=True),
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io.String.Input(id="AudioExpr", default="a*(1-w)+b*w", tooltip="Expression to apply on input audio tensors"),
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],
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outputs=[
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io.Audio.Output(),
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],
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)
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@classmethod
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def execute(cls, AudioExpr, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0):
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av = a['waveform']
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sample_rate = a['sample_rate']
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a, b, c, d = cls.prepare_inputs(a, b, c, d)
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bv, cv, dv = b['waveform'], c['waveform'], d['waveform']
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variables = {
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'a': av, 'b': bv, 'c': cv, 'd': dv,
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'w': w, 'x': x, 'y': y, 'z': z,
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'B': getIndexTensorAlongDim(av, 0),
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'C': getIndexTensorAlongDim(av, 1),
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'S': getIndexTensorAlongDim(av, 2),
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'R': torch.full_like(av, sample_rate, dtype=torch.float32),
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'T': torch.full_like(av, av.shape[2], dtype=torch.float32),
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'N': av.shape[1],
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'batch': getIndexTensorAlongDim(av, 0),
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'channel': getIndexTensorAlongDim(av, 1),
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'sample': getIndexTensorAlongDim(av, 2),
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'sample_rate': torch.full_like(av, sample_rate, dtype=torch.float32),
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'sample_count': torch.full_like(av, av.shape[2], dtype=torch.float32),
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'channel_count': av.shape[1],
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} | generate_dim_variables(av)
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result_tensor = eval_tensor_expr(AudioExpr, variables, av.shape)
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return ({'waveform': as_tensor(result_tensor,a.shape), 'sample_rate': sample_rate},)
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