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