123 lines
5.1 KiB
Python
123 lines
5.1 KiB
Python
import torch
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from .helper_functions import generate_dim_variables, parse_expr, getIndexTensorAlongDim, as_tensor, prepare_inputs, normalize_to_common_shape
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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from comfy_api.latest import io
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from antlr4 import InputStream, CommonTokenStream
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from .Parser.MathExprLexer import MathExprLexer
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from .Parser.MathExprParser import MathExprParser
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import re
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class AudioMathNode(io.ComfyNode):
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"""
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Enables math expressions on Audio.
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Inputs:
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I: Autogrow image inputs (I0, I1, ...)
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F: Autogrow float inputs (F0, F1, ...)
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Image: Expression
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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_ag_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.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Audio.Input("values"), prefix="V", min=1, max=50)),
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io.Autogrow.Input(id="F", template=io.Autogrow.TemplatePrefix(io.Float.Input("float", default=0.0, optional=True, lazy=True, force_input=True), prefix="F", min=1, max=50)),
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io.String.Input(id="Expression", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on input audio"),
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io.Combo.Input(
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id="length_mismatch",
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options=["error", "error", "pad"],
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default="error",
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tooltip="How to handle mismatched image batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
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)
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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 check_lazy_status(cls, Expression, V, F, length_mismatch="tile"):
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input_stream = InputStream(Expression)
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lexer = MathExprLexer(input_stream)
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stream = CommonTokenStream(lexer)
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stream.fill()
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# Support aliases
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aliases_img = {"a": "V0", "b": "V1", "c": "V2", "d": "V3"}
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aliases_flt = {"w": "F0", "x": "F1", "y": "F2", "z": "F3"}
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needed = []
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needed1 = []
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for token in filter(lambda t: t.type == MathExprParser.VARIABLE, stream.tokens):
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var_name = token.text
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if re.match(r"[VF][0-9]+", var_name):
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needed.append(var_name)
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elif var_name in aliases_img:
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needed.append(aliases_img[var_name])
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elif var_name in aliases_flt:
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needed.append(aliases_flt[var_name])
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for v in needed:
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if v.startswith("V"):
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if v not in V or V[v] is None:
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needed1.append(v)
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elif v.startswith("F"):
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if v not in F or F[v] is None:
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needed1.append(v)
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return needed1
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@classmethod
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def execute(cls, V, F, Expression, length_mismatch="tile"):
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if(length_mismatch == "error"):
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max_lengths = V.get("V0")["waveform"].shape
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for name, tensor in V.items():
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if tensor["waveform"] is not None and max_lengths!=tensor["waveform"].shape:
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raise ValueError(f"Input '{name}' has shape {tensor['waveform'].shape}, expected {max_lengths} to match input.")
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waveforms={}
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sample_rates={}
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for key, audio in V.items():
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if audio is not None and isinstance(audio, dict) and "waveform" in audio:
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waveforms[key] = audio["waveform"]
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sample_rates[key+"sr"] = audio.get("sample_rate", 44100)
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else:
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waveforms[key] = torch.zeros(V["V0"]["waveform"].shape)
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sample_rates[key] = 44100
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sample_rate = sample_rates["V0sr"] if len(sample_rates) > 0 else 44100
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new_values = normalize_to_common_shape(*waveforms.values(), mode=length_mismatch)
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waveforms.update(zip(waveforms.keys(), new_values))
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a,b,c,d = prepare_inputs(V.get("V0"),V.get("V1"),V.get("V2"),V.get("V3"))
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a = a["waveform"]
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b = b["waveform"]
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c = c["waveform"]
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d = d["waveform"]
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variables = {
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"a": a, "b": b, "c": c, "d": d,
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"w": F.get("F0", 0.0) if F.get("F0") is not None else 0.0,
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"x": F.get("F1", 0.0) if F.get("F1") is not None else 0.0,
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"y": F.get("F2", 0.0) if F.get("F2") is not None else 0.0,
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"z": F.get("F3", 0.0) if F.get("F3") is not None else 0.0,
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"B": getIndexTensorAlongDim(a, 0),
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"C": getIndexTensorAlongDim(a, 1),
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"channel": getIndexTensorAlongDim(a, 1),
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"S": getIndexTensorAlongDim(a, 2),
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"sample": getIndexTensorAlongDim(a, 2),
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"R": sample_rate,
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"sample_rate": sample_rate,
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"batch": getIndexTensorAlongDim(a, 0),
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"T": a.shape[0],
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"batch_count": a.shape[0],
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} | generate_dim_variables(a) | waveforms | sample_rates
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tree = parse_expr(Expression);
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visitor = UnifiedMathVisitor(variables, a.shape)
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result = visitor.visit(tree)
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result = as_tensor(result, a.shape)
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return ({"waveform":result,"sample_rate":sample_rate},)
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