from .helper_functions import ( generate_dim_variables, parse_expr, getIndexTensorAlongDim, as_tensor, normalize_to_common_shape, make_zero_like, get_v_variable, get_f_variable ) from .Parser.UnifiedMathVisitor import UnifiedMathVisitor from comfy_api.latest import io from antlr4 import InputStream, CommonTokenStream from .Parser.MathExprLexer import MathExprLexer from .Parser.MathExprParser import MathExprParser import re import torch from .Stack import MrmthStack import copy class AudioMathNode(io.ComfyNode): """ Enables math expressions on Audio. Inputs: I: Autogrow image inputs (I0, I1, ...) F: Autogrow float inputs (F0, F1, ...) Image: Expression """ @classmethod def define_schema(cls) -> io.Schema: return io.Schema( node_id="mrmth_ag_AudioMathNode", category="More math", display_name="Audio math", inputs=[ io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Audio.Input("values"), prefix="V", min=1, max=50)), 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)), io.String.Input(id="Expression", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on input audio"), io.Combo.Input( id="length_mismatch", options=["do nothing","error","tile", "pad"], display_name="on size mismatch", default="error", tooltip="How to handle mismatched image batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero." ), io.Int.Input(id="batching", default=0), MrmthStack.Input(id="stack", tooltip="Access stack between nodes",optional=True) ], outputs=[ io.Audio.Output(is_output_list=True), MrmthStack.Output(), ], ) @classmethod def check_lazy_status(cls, Expression, V, F, length_mismatch="tile",batching=0,stack={}): input_stream = InputStream(Expression) lexer = MathExprLexer(input_stream) stream = CommonTokenStream(lexer) stream.fill() # Support aliases aliases_img = {"a": "V0", "b": "V1", "c": "V2", "d": "V3"} aliases_flt = {"w": "F0", "x": "F1", "y": "F2", "z": "F3"} needed = [] needed1 = [] for token in filter(lambda t: t.type == MathExprParser.VARIABLE, stream.tokens): var_name = token.text if re.match(r"[VF][0-9]+", var_name): needed.append(var_name) elif var_name in aliases_img: needed.append(aliases_img[var_name]) elif var_name in aliases_flt: needed.append(aliases_flt[var_name]) for v in needed: if v.startswith("V"): if v not in V or V[v] is None: needed1.append(v) elif v.startswith("F"): if v not in F or F[v] is None: needed1.append(v) return needed1 @classmethod def execute(cls, V, F, Expression, length_mismatch="tile",batching=0,stack={}): # Identify all present audio inputs and their keys tensor_keys = [k for k, v in V.items() if v is not None and isinstance(v, dict) and "waveform" in v] if not tensor_keys: raise ValueError("At least one audio input is required.") stack = copy.deepcopy(stack) if stack is not None else {} waveforms = {k: V[k]["waveform"] for k in tensor_keys} sample_rates = {k + "sr": V[k].get("sample_rate", 44100) for k in tensor_keys} # Normalize all waveforms together normalized_waveforms = normalize_to_common_shape(*waveforms.values(), mode=length_mismatch) V_norm_waveforms = dict(zip(tensor_keys, normalized_waveforms)) ref_waveform = normalized_waveforms[0] common_shape = ref_waveform.shape sample_rate = V[tensor_keys[0]].get("sample_rate", 44100) if(length_mismatch == "error"): for name in tensor_keys: if waveforms[name].shape != common_shape: raise ValueError(f"Input '{name}' has shape ({waveforms[name].shape[0]}, {waveforms[name].shape[2]}), expected ({common_shape[0]}, {common_shape[2]}) to match input.") # Setup legacy variables a, b, c, d a_w = V_norm_waveforms.get("V0", make_zero_like(ref_waveform)) b_w = V_norm_waveforms.get("V1", make_zero_like(a_w)) c_w = V_norm_waveforms.get("V2", make_zero_like(a_w)) d_w = V_norm_waveforms.get("V3", make_zero_like(a_w)) # Ensure legacy are normalized a_w, b_w, c_w, d_w = normalize_to_common_shape(a_w, b_w, c_w, d_w, mode=length_mismatch) variables = { "a": a_w, "b": b_w, "c": c_w, "d": d_w, "w": F.get("F0", 0.0) if F.get("F0") is not None else 0.0, "x": F.get("F1", 0.0) if F.get("F1") is not None else 0.0, "y": F.get("F2", 0.0) if F.get("F2") is not None else 0.0, "z": F.get("F3", 0.0) if F.get("F3") is not None else 0.0, "B": getIndexTensorAlongDim(a_w, 0), "C": getIndexTensorAlongDim(a_w, 1), "channel": getIndexTensorAlongDim(a_w, 1), "S": getIndexTensorAlongDim(a_w, 2), "sample": getIndexTensorAlongDim(a_w, 2), "R": sample_rate, "sample_rate": sample_rate, "batch": getIndexTensorAlongDim(a_w, 0), "T": a_w.shape[0], "batch_count": a_w.shape[0], } | generate_dim_variables(a_w) | V_norm_waveforms | sample_rates v_stacked, v_cnt = get_v_variable(V_norm_waveforms, length_mismatch=length_mismatch) if v_stacked is not None: variables["V"] = v_stacked variables["Vcnt"] = float(v_cnt) variables["V_count"] = float(v_cnt) f_stacked, f_cnt = get_f_variable(F) if f_stacked is not None: variables["F"] = f_stacked variables["Fcnt"] = float(f_cnt) variables["F_count"] = float(f_cnt) for k, val in F.items(): variables[k] = val if val is not None else 0.0 tree = parse_expr(Expression); visitor = UnifiedMathVisitor(variables, a_w.shape,a_w.device,state_storage=stack) result = visitor.visit(tree) result = as_tensor(result, a_w.shape) if batching and batching > 0: res = torch.split(result, batching, dim=0) res_list = [] for result_chunk in res: res_list.append({"waveform": result_chunk, "sample_rate": sample_rate}) return (res_list, stack) else: return ([{"waveform": result, "sample_rate": sample_rate}], stack)