from __future__ import annotations import torchaudio import folder_paths import os import io import hashlib from comfy_api.v3 import io, ui class PreviewAudio_V3(io.ComfyNodeV3): @classmethod def DEFINE_SCHEMA(cls): return io.SchemaV3( node_id="PreviewAudio_V3", display_name="Preview Audio _V3", category="audio", inputs=[ io.Audio.Input("audio"), ], hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo], is_output_node=True, ) @classmethod def execute(cls, audio): return io.NodeOutput(ui=ui.PreviewAudio(audio, cls=cls)) class LoadAudio_V3(io.ComfyNodeV3): @classmethod def DEFINE_SCHEMA(cls): return io.SchemaV3( node_id="LoadAudio_V3", display_name="Load Audio _V3", category="audio", inputs=[ io.Combo.Input("audio", upload=io.UploadType.audio, options=cls.get_files_options()), ], outputs=[io.Audio.Output()], ) @classmethod def get_files_options(cls) -> list[str]: input_dir = folder_paths.get_input_directory() return sorted(folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"])) @classmethod def execute(cls, audio) -> io.NodeOutput: waveform, sample_rate = torchaudio.load(folder_paths.get_annotated_filepath(audio)) return io.NodeOutput({"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}) @classmethod def fingerprint_inputs(s, audio): image_path = folder_paths.get_annotated_filepath(audio) m = hashlib.sha256() with open(image_path, "rb") as f: m.update(f.read()) return m.digest().hex() @classmethod def validate_inputs(s, audio): if not folder_paths.exists_annotated_filepath(audio): return "Invalid audio file: {}".format(audio) return True NODES_LIST: list[type[io.ComfyNodeV3]] = [ # EmptyLatentAudio_V3, # VAEEncodeAudio_V3, # VAEDecodeAudio_V3, # SaveAudio_V3, # SaveAudioMP3_V3, # SaveAudioOpus_V3, LoadAudio_V3, PreviewAudio_V3, # ConditioningStableAudio_V3, ]