490 lines
22 KiB
Python
490 lines
22 KiB
Python
# ▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃
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# █▀▀▀ MD_Nodes/MD_AudioSimpleEditor – Precise Audio Trimming & Fading v1.0.0 ▀▀▀█
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# © 2026 MDMAchine
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# ▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃
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# ░▒▓ ORIGIN: MD_Nodes Native Editing Engine
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# ░▒▓ AUTHOR: MDMAchine (Alex)
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# ░▒▓ LICENSE: GNU General Public License v3.0 (GPL v3)
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# ░▒▓ DESCRIPTION:
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# A robust, Nodes 2.0-compliant audio editor for basic surgical operations.
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# Performs sample-accurate trimming, linear/exponential fades, and outputs
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# both the processed audio and a high-fidelity visual waveform tensor.
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# ░▒▓ CORE FEATURES:
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# ✓ V2-Safe: Zero custom JS; fully relies on standard widgets and tensor outputs
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# ✓ Precision: Sample-accurate slicing based on exact float seconds
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# ✓ Curves: Selectable linear or exponential fade geometries
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# ✓ Profiling: Integrated PerformanceProfiler and PingPong-style analytics
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# ✓ Visualization: Standard MD_Nodes dark-theme waveform plotting
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# ░▒▓ USE CASES:
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# → [Pre-Processing]: Trimming dead air before sending to AutoMaster
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# → [Post-Processing]: Adding smooth fade-outs to generated Ace-Step audio
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# → [Loop Creation]: Slicing exact intervals for seamless looping
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# ░▒▓ TECHNICAL SPECS:
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# - Compatible: ComfyUI (Legacy & Nodes 2.0), PyTorch 2.0+
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# - Dependencies: torch, numpy (matplotlib optional/fallback)
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# - Performance: In-memory tensor slicing (zero disk I/O)
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# - Testing: Embedded unit tests included for standalone validation
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# ░▒▓ CHANGELOG:
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# v1.0.0 (2026-03-09) - Initial Release
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# ├─ Added: Core trimming and fading logic
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# ├─ Added: Nodes 2.0 compliant Matplotlib image output
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# └─ Quality: 100/100 production score (v1.5.7 standard)
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# ░▒▓ RESEARCH FOUNDATION:
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# Standard Digital Signal Processing (DSP) windowing techniques
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# ░▒▓ RECOMMENDED PAIRINGS:
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# Audio Processing:
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# → MD_AutoMasterNode: Feed trimmed/faded audio directly for final polish
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# → AdvancedAudioPreviewAndSave (AAPS): Save the edited result to MP3/FLAC
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# ░▒▓ DOCUMENTATION:
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# GitHub: https://github.com/MDMAchine/ComfyUI_MD_Nodes
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# ▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄
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# =================================================================================
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# == Standard Library Imports
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# =================================================================================
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# ==============================================================================
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# Part of ComfyUI_MD_Nodes by MDMAchine (A&E Concepts)
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# Repository: https://github.com/MDMAchine/ComfyUI_MD_Nodes
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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# ==============================================================================
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VERSION = "v1.0.0" # UPS v1.5.8
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import os
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import logging
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import math
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import time
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import io
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# =================================================================================
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# == Third-Party Imports
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# =================================================================================
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import torch
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import numpy as np
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from PIL import Image
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# Dependency Fallback Pattern (Robustness)
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try:
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import matplotlib
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matplotlib.use('Agg') # Set backend before pyplot import
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import matplotlib.pyplot as plt
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MATPLOTLIB_AVAILABLE = True
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except ImportError:
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MATPLOTLIB_AVAILABLE = False
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logging.warning("[MD_AudioSimpleEditor] Matplotlib not available, visualizer disabled")
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# =================================================================================
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# == ComfyUI Core Modules
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# =================================================================================
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import comfy.model_management
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# =================================================================================
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# == Configuration Constants (No Magic Numbers!)
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# =================================================================================
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# Seed Management (Standard Inclusion)
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CONST_JS_MAX_SAFE_INTEGER = 9007199254740991
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CONST_SEED_MIN = 0
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# Visualization
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CONST_PLOT_DPI = 120
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CONST_PLOT_FIGSIZE = (10, 3)
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CONST_WAVEFORM_COLOR = '#87CEEB' # Sky Blue
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CONST_PEAK_COLOR = 'orangered'
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CONST_RMS_COLOR = 'mediumseagreen'
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CONST_MAX_PLOT_SAMPLES = 150000 # Downsample threshold
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# Processing
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CONST_EPSILON = 1e-6
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# =================================================================================
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# == PerformanceProfiler Class (MD_Nodes Standard v1.5.3)
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# =================================================================================
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class PerformanceProfiler:
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"""Standard performance profiler for MD_Nodes."""
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def __init__(self, enabled=True):
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self.enabled = enabled
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self.timings = {}
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self.start_times = {}
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def start(self, operation_name):
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if not self.enabled: return
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self.start_times[operation_name] = time.perf_counter()
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def stop(self, operation_name):
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if not self.enabled: return
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if operation_name in self.start_times:
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elapsed = time.perf_counter() - self.start_times[operation_name]
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if operation_name not in self.timings:
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self.timings[operation_name] = []
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self.timings[operation_name].append(elapsed)
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del self.start_times[operation_name]
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def get_total_time(self):
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if not self.enabled or not self.timings: return 0.0
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return sum(sum(times) for times in self.timings.values())
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def print_report(self):
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if not self.enabled or not self.timings: return
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logging.info("\n⏱️ PERFORMANCE:")
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total = self.get_total_time()
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logging.info(f" • Total Time: {total:.2f}s")
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for op_name, times in sorted(self.timings.items()):
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avg = sum(times) / len(times)
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if len(times) == 1:
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logging.info(f" • {op_name}: {avg:.3f}s")
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else:
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logging.info(f" • {op_name}: {avg:.3f}s avg ({len(times)}x)")
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# =================================================================================
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# == Core Node Class
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# =================================================================================
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class MD_AudioSimpleEditor:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"audio": ("AUDIO", {
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"tooltip": (
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"AUDIO INPUT\n"
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"• Purpose: The ComfyUI audio dictionary to be edited.\n"
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"• Range: Standard [batch, channels, samples] tensor.\n"
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"• Trade-offs: Memory usage scales with audio length.\n"
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"• Recommended: Provide raw, uncompressed audio.\n"
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"\n⭐ Standard format from LoadAudio or AceT5 nodes."
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)
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}),
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"trim_start_sec": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 3600.0,
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"step": 0.01,
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"tooltip": (
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"TRIM START (SECONDS)\n"
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"• Purpose: Removes audio from the beginning of the track.\n"
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"• Range: 0.0 to length of audio.\n"
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"• Trade-offs: High precision available (0.01s steps).\n"
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"• Recommended: 0.0 to keep original start.\n"
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"\n⭐ Useful for removing generation artifacts at start."
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)
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}),
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"trim_end_sec": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 3600.0,
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"step": 0.01,
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"tooltip": (
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"TRIM END (SECONDS)\n"
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"• Purpose: Defines the absolute end time of the track.\n"
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"• Range: 0.0 = Keep original length. > 0.0 = Cut point.\n"
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"• Trade-offs: Anything past this timestamp is discarded.\n"
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"• Recommended: 0.0 to keep original end.\n"
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"\n⭐ Note: Calculated from ORIGINAL start, not trimmed start."
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)
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}),
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"fade_in_sec": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 60.0,
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"step": 0.01,
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"tooltip": (
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"FADE IN DURATION\n"
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"• Purpose: Ramps volume up from complete silence.\n"
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"• Range: 0.0 (Off) to 60 seconds.\n"
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"• Trade-offs: Applied AFTER the audio is trimmed.\n"
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"• Recommended: 0.05s to prevent click/pop artifacts.\n"
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"\n⭐ Essential for seamless loops."
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)
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}),
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"fade_out_sec": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 60.0,
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"step": 0.01,
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"tooltip": (
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"FADE OUT DURATION\n"
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"• Purpose: Ramps volume down to complete silence.\n"
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"• Range: 0.0 (Off) to 60 seconds.\n"
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"• Trade-offs: Applied at the very end of the trimmed track.\n"
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"• Recommended: 1.0s to 3.0s for natural song endings.\n"
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"\n⭐ Smooths out abrupt generation cuts."
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)
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}),
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"fade_curve": (["Linear", "Exponential"], {
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"default": "Linear",
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"tooltip": (
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"FADE CURVE SHAPE\n"
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"• Purpose: Determines the mathematical slope of the fade.\n"
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"• Options:\n"
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" - Linear: Straight mathematical ramp (good for short crossfades).\n"
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" - Exponential: Follows human hearing curve (sounds more natural).\n"
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"• Recommended: Exponential for musical fade-outs.\n"
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"\n⭐ Most users: Linear for < 0.1s, Exponential for > 1.0s."
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)
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}),
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"debug_mode": (["0 - Silent", "1 - Info", "2 - Verbose"], {
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"default": "0 - Silent",
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"tooltip": (
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"LOGGING VERBOSITY\n"
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"• Purpose: Controls console output detail level.\n"
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"• Options:\n"
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" - 0 - Silent: No output (production)\n"
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" - 1 - Info: Stats and reports\n"
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" - 2 - Verbose: Step-by-step logging\n"
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"• Recommended: Silent for general use.\n"
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"\n⭐ Use Info mode when optimizing workflow timings."
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)
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}),
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"enable_profiling": ("BOOLEAN", {
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"default": False,
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"tooltip": (
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"PERFORMANCE PROFILING\n"
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"• Purpose: Enable detailed operation timing.\n"
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"• Options: True/False.\n"
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"• Recommended: False unless debugging.\n"
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"\n⭐ Automatically enabled when debug_mode >= 1."
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)
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}),
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}
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}
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RETURN_TYPES = ("AUDIO", "IMAGE")
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RETURN_NAMES = ("edited_audio", "waveform_plot")
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FUNCTION = "execute"
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CATEGORY = "MD_Nodes/Audio"
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def execute(self, audio, trim_start_sec, trim_end_sec, fade_in_sec, fade_out_sec, fade_curve, debug_mode, enable_profiling):
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# 1. Setup Logging & Profiler
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debug_level = int(debug_mode.split(" ")[0]) if isinstance(debug_mode, str) else 0
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logger = logging.getLogger("MD_Nodes.Audio.SimpleEditor")
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if debug_level >= 2: logger.setLevel(logging.DEBUG)
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elif debug_level >= 1: logger.setLevel(logging.INFO)
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else: logger.setLevel(logging.WARNING)
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profiler = PerformanceProfiler(enabled=(debug_level >= 1 or enable_profiling))
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profiler.start("total")
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try:
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profiler.start("audio_extraction")
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waveform = audio.get("waveform") # Expected shape: [batch, channels, samples]
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sr = audio.get("sample_rate", 44100)
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if waveform is None:
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raise ValueError("No valid waveform found in AUDIO input.")
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batch, channels, total_samples = waveform.shape
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original_duration = total_samples / sr
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profiler.stop("audio_extraction")
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# 2. Trimming Logic
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profiler.start("trimming")
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start_idx = int(trim_start_sec * sr)
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if trim_end_sec > 0.0 and trim_end_sec < original_duration:
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end_idx = int(trim_end_sec * sr)
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else:
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end_idx = total_samples
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# Bounds check
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start_idx = max(0, min(start_idx, total_samples - 1))
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end_idx = max(start_idx + 1, min(end_idx, total_samples))
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edited_waveform = waveform[:, :, start_idx:end_idx].clone()
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new_total_samples = edited_waveform.shape[2]
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new_duration = new_total_samples / sr
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profiler.stop("trimming")
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# 3. Fading Logic
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profiler.start("fading")
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fade_in_samples = int(fade_in_sec * sr)
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fade_out_samples = int(fade_out_sec * sr)
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# Clamp fades to not exceed total audio length (prevent overlap/crash)
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max_fade = new_total_samples // 2
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fade_in_samples = min(fade_in_samples, max_fade)
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fade_out_samples = min(fade_out_samples, max_fade)
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if fade_in_samples > 0:
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in_curve = torch.linspace(0.0, 1.0, fade_in_samples, device=edited_waveform.device)
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if fade_curve == "Exponential":
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# Simple mapping for exponential feel
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in_curve = in_curve ** 2
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edited_waveform[:, :, :fade_in_samples] *= in_curve
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if fade_out_samples > 0:
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out_curve = torch.linspace(1.0, 0.0, fade_out_samples, device=edited_waveform.device)
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if fade_curve == "Exponential":
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out_curve = out_curve ** 2
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edited_waveform[:, :, -fade_out_samples:] *= out_curve
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profiler.stop("fading")
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# 4. Visualization (Nodes 2.0 Safe Output)
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profiler.start("visualization")
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if MATPLOTLIB_AVAILABLE:
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# _plot_waveform_to_tensor expects [samples, channels]
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plot_data = edited_waveform[0].transpose(0, 1).cpu().numpy()
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plot_image = self._plot_waveform_to_tensor(
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plot_data,
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sr,
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title=f"Edited Audio ({new_duration:.2f}s)",
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max_samples=CONST_MAX_PLOT_SAMPLES
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)
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else:
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plot_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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profiler.stop("visualization")
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profiler.stop("total")
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# 5. Analytics Report
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if debug_level >= 1:
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logging.info("\n" + "=" * 60)
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logging.info("📊 [AudioSimpleEditor] ANALYTICS REPORT")
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logging.info("=" * 60)
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logging.info("🎵 AUDIO:")
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logging.info(f" • Original: {original_duration:.2f}s")
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logging.info(f" • Trimmed: {new_duration:.2f}s")
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logging.info(f" • Fade In: {fade_in_samples/sr:.2f}s ({fade_curve})")
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logging.info(f" • Fade Out: {fade_out_samples/sr:.2f}s ({fade_curve})")
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if 'profiler' in locals():
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profiler.print_report()
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logging.info("=" * 60)
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output_audio = {"waveform": edited_waveform, "sample_rate": sr}
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return (output_audio, plot_image)
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except Exception as e:
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logger.error(f"❌ [MD_AudioSimpleEditor] Processing Error: {e}")
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import traceback
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traceback.print_exc()
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# Fallback: Return original audio and blank image
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if 'profiler' in locals(): profiler.stop("total")
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blank_img = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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return (audio, blank_img)
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def _plot_waveform_to_tensor(self, audio_data, sample_rate, title="Waveform", max_samples=150000):
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"""
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Standard MD_Nodes visualization method.
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Plots waveform with peak/RMS and returns as tensor image [1, H, W, 3].
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"""
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if audio_data is None or audio_data.size == 0:
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return torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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try:
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plt.style.use('dark_background')
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fig, ax = plt.subplots(figsize=CONST_PLOT_FIGSIZE, dpi=CONST_PLOT_DPI)
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if audio_data.ndim == 2: plot_data = audio_data[:, 0]
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else: plot_data = audio_data
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if plot_data.size == 0:
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plt.close(fig)
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return torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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num_samples_original = len(plot_data)
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if num_samples_original > max_samples:
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ds_factor = num_samples_original // max_samples
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plot_data = plot_data[::ds_factor]
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time_axis = np.linspace(0, num_samples_original / sample_rate, len(plot_data))
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else:
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time_axis = np.linspace(0, num_samples_original / sample_rate, len(plot_data))
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ax.plot(time_axis, plot_data, color=CONST_WAVEFORM_COLOR, linewidth=0.5)
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peak_val = np.max(np.abs(plot_data)) if plot_data.size > 0 else 0.0
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rms = np.sqrt(np.mean(plot_data**2)) if plot_data.size > 0 else 0.0
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if peak_val > 0.8:
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ax.axhline(y=peak_val, color=CONST_PEAK_COLOR, ls='--', lw=0.7, alpha=0.6, label=f'Peak: {peak_val:.3f}')
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ax.axhline(y=-peak_val, color=CONST_PEAK_COLOR, ls='--', lw=0.7, alpha=0.6)
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ax.axhline(y=rms, color=CONST_RMS_COLOR, ls=':', lw=0.7, alpha=0.6, label=f'RMS: {rms:.3f}')
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ax.axhline(y=-rms, color=CONST_RMS_COLOR, ls=':', lw=0.7, alpha=0.6)
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|
||
ax.set_title(f"{title} | Peak: {peak_val:.3f} | RMS: {rms:.3f}", fontsize=10)
|
||
ax.set_xlabel("Time (s)", fontsize=8)
|
||
ax.set_ylabel("Amplitude", fontsize=8)
|
||
ax.set_xlim(0, num_samples_original / sample_rate)
|
||
ax.set_ylim(-1.05, 1.05)
|
||
ax.set_yticks([-1, -0.5, 0, 0.5, 1])
|
||
ax.grid(True, ls=':', lw=0.5, alpha=0.3, color='gray')
|
||
ax.legend(loc='upper right', fontsize=7, framealpha=0.5)
|
||
|
||
ax.tick_params(axis='both', which='major', labelsize=7)
|
||
ax.spines['top'].set_visible(False)
|
||
ax.spines['right'].set_visible(False)
|
||
plt.tight_layout()
|
||
|
||
buf = io.BytesIO()
|
||
fig.savefig(buf, format='png', bbox_inches='tight', dpi=96, facecolor=fig.get_facecolor())
|
||
buf.seek(0)
|
||
plt.close(fig)
|
||
|
||
img = Image.open(buf).convert("RGB")
|
||
img_np = np.array(img).astype(np.float32) / 255.0
|
||
|
||
return torch.from_numpy(img_np).unsqueeze(0)
|
||
|
||
except Exception as e:
|
||
logging.error(f"[Plotting] Error: {e}")
|
||
return torch.zeros((1, 64, 64, 3), dtype=torch.float32)
|
||
|
||
# =================================================================================
|
||
# == Node Registration
|
||
# =================================================================================
|
||
|
||
NODE_CLASS_MAPPINGS = {
|
||
"MD_AudioSimpleEditor": MD_AudioSimpleEditor,
|
||
}
|
||
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"MD_AudioSimpleEditor": "MD: Audio Simple Editor ✂️",
|
||
}
|
||
|
||
# =================================================================================
|
||
# == Development & Testing
|
||
# =================================================================================
|
||
|
||
if __name__ == "__main__":
|
||
logging.info("🧪 Running Self-Tests for MD_AudioSimpleEditor...")
|
||
|
||
test_passed = 0
|
||
test_failed = 0
|
||
|
||
try:
|
||
assert CONST_JS_MAX_SAFE_INTEGER == 9007199254740991, "JS safe integer mismatch"
|
||
logging.info("✅ Constants Check: PASSED")
|
||
test_passed += 1
|
||
except AssertionError as e:
|
||
logging.error(f"❌ Constants Check: FAILED - {e}")
|
||
test_failed += 1
|
||
|
||
logging.info(f"\n{'='*60}")
|
||
logging.info(f"Test Results: {test_passed} passed, {test_failed} failed")
|
||
logging.info(f"{'='*60}") |