#!/usr/bin/env python3 # pyright: reportDeprecated=false, reportUnknownParameterType=false, reportMissingTypeArgument=false, reportUnknownVariableType=false, reportUnknownMemberType=false, reportUnknownArgumentType=false, reportUnusedCallResult=false, reportUnusedImport=false import argparse import json import random import subprocess import time from pathlib import Path from typing import List, Optional, Sequence, Tuple from utils import * AUDIO_EXTS = {".mp3", ".wav", ".flac", ".ogg", ".m4a", ".aac"} DEFAULT_HUMAN_SOURCES = [ Path("/ssd_data/dataset/ai_music_dataset/real/MTG"), ] DEFAULT_AI_SOURCES = [FAKE_DIR / "acestep"] C_CATEGORY_CONFIG = { "C1": { "folder": "C1_mixset_concat", "processing_method": "concatenation", "ai_component": "partial_segment", "human_component": "partial_segment", }, "C2": { "folder": "C2_mixset_crossfade", "processing_method": "crossfade", "ai_component": "partial_segment", "human_component": "partial_segment", }, } def run_cmd(cmd: Sequence[str], timeout: int = 300) -> bool: try: result = subprocess.run( list(cmd), capture_output=True, text=True, timeout=timeout ) if result.returncode != 0: stderr = (result.stderr or "").strip() if stderr: logger.warning(stderr[-500:]) return False return True except Exception as exc: logger.warning(f"Command failed: {exc}") return False def collect_audio_files(paths: Sequence[Path]) -> List[Path]: files: List[Path] = [] for base in paths: if not base.exists(): continue for p in base.rglob("*"): if p.is_file() and p.suffix.lower() in AUDIO_EXTS: files.append(p) return files def resolve_output_dir(category: str, output_dir: Optional[str]) -> Path: if output_dir: return Path(output_dir) return FAKE_DIR / "C_mixing" / C_CATEGORY_CONFIG[category]["folder"] def ffmpeg_mix( human_path: Path, ai_path: Path, output_path: Path, filter_complex: str, timeout: int = 600, ) -> bool: cmd = [ "ffmpeg", "-y", "-hide_banner", "-loglevel", "error", "-i", str(human_path), "-i", str(ai_path), "-filter_complex", filter_complex, "-map", "[out]", "-ac", "2", "-ar", "44100", "-c:a", "libmp3lame", "-b:a", "192k", str(output_path), ] return run_cmd(cmd, timeout=timeout) def choose_segment( info: dict, min_len: float = 15.0, max_len: float = 60.0 ) -> Optional[Tuple[float, float]]: duration = float(info.get("duration_sec") or 0) if duration < min_len + 1: return None seg_len = random.uniform(min_len, min(max_len, duration - 0.2)) start_max = max(0.0, duration - seg_len) start = random.uniform(0.0, start_max) return start, seg_len def build_metadata( category: str, track_id: str, filename: str, output_path: Path, human_src: Path, ai_src: Path, prompt: Optional[str] = None, ) -> TrackMetadata: info = get_audio_info(output_path) cfg = C_CATEGORY_CONFIG[category] return TrackMetadata( track_id=track_id, filename=filename, category="C_mixing", subcategory=cfg["folder"], source_platform="hybrid_pipeline", source_type="hybrid", model_name="temporal_mixing", model_version="ffmpeg_v1", duration_sec=info.get("duration_sec"), sample_rate=info.get("sample_rate"), channels=info.get("channels"), bitrate_kbps=info.get("bitrate_kbps"), file_size_bytes=info.get("file_size_bytes") or output_path.stat().st_size, audio_format="mp3", prompt=prompt, original_source=f"human={human_src}|ai={ai_src}", processing_method=cfg["processing_method"], ai_component=cfg["ai_component"], human_component=cfg["human_component"], collection_method="process", md5_hash=compute_md5(output_path), ) def add_metadata_if_new(manager: MetadataManager, meta: TrackMetadata) -> bool: if manager.has_track(meta.track_id): return False manager.add_track(meta) return True def process_c1( manager: MetadataManager, output_dir: Path, target: int, human_files: Sequence[Path], ai_files: Sequence[Path], ) -> int: if not human_files or not ai_files: logger.warning("C1 needs both human and AI source files.") return 0 added = 0 attempts = 0 while manager.get_count() < target and attempts < target * 8: attempts += 1 if not ensure_disk_space(): break human = random.choice(human_files) ai = random.choice(ai_files) human_seg = choose_segment(get_audio_info(human)) ai_seg = choose_segment(get_audio_info(ai)) if not human_seg or not ai_seg: continue h_start, h_len = human_seg a_start, a_len = ai_seg gap = random.uniform(0.1, 0.5) ai_first = random.choice([True, False]) track_id = f"C1_{compute_md5(human)[:8]}_{compute_md5(ai)[:8]}_{attempts}" if manager.has_track(track_id): continue if ai_first: filt = ( f"[1:a]atrim=start={a_start:.3f}:end={a_start + a_len:.3f},asetpts=PTS-STARTPTS[a];" f"[0:a]atrim=start={h_start:.3f}:end={h_start + h_len:.3f},asetpts=PTS-STARTPTS[h];" f"aevalsrc=0:d={gap:.3f}[g];" f"[a][g][h]concat=n=3:v=0:a=1[out]" ) else: filt = ( f"[0:a]atrim=start={h_start:.3f}:end={h_start + h_len:.3f},asetpts=PTS-STARTPTS[h];" f"[1:a]atrim=start={a_start:.3f}:end={a_start + a_len:.3f},asetpts=PTS-STARTPTS[a];" f"aevalsrc=0:d={gap:.3f}[g];" f"[h][g][a]concat=n=3:v=0:a=1[out]" ) out_name = f"{track_id}.mp3" out_path = output_dir / out_name if not ffmpeg_mix(human, ai, out_path, filt): continue if ai_first: first_label, first_dur = "ai", a_len second_label, second_dur = "human", h_len else: first_label, first_dur = "human", h_len second_label, second_dur = "ai", a_len prompt = json.dumps( { "order": "ai_first" if ai_first else "human_first", "human_source": str(human), "ai_source": str(ai), f"{first_label}_start_sec": 0.0, f"{first_label}_end_sec": round(first_dur, 3), "gap_sec": round(gap, 3), f"{second_label}_start_sec": round(first_dur + gap, 3), f"{second_label}_end_sec": round(first_dur + gap + second_dur, 3), } ) meta = build_metadata( "C1", track_id, out_name, out_path, human, ai, prompt=prompt ) if add_metadata_if_new(manager, meta): added += 1 manager.update_summary() return added def process_c2( manager: MetadataManager, output_dir: Path, target: int, human_files: Sequence[Path], ai_files: Sequence[Path], ) -> int: if not human_files or not ai_files: logger.warning("C2 needs both human and AI source files.") return 0 added = 0 attempts = 0 while manager.get_count() < target and attempts < target * 8: attempts += 1 if not ensure_disk_space(): break human = random.choice(human_files) ai = random.choice(ai_files) human_info = get_audio_info(human) ai_info = get_audio_info(ai) human_seg = choose_segment(human_info, min_len=20.0, max_len=70.0) ai_seg = choose_segment(ai_info, min_len=20.0, max_len=70.0) if not human_seg or not ai_seg: continue h_start, h_len = human_seg a_start, a_len = ai_seg crossfade = random.uniform(1.0, 5.0) if h_len <= crossfade + 0.5 or a_len <= crossfade + 0.5: continue ai_first = random.choice([True, False]) track_id = f"C2_{compute_md5(human)[:8]}_{compute_md5(ai)[:8]}_{attempts}" if manager.has_track(track_id): continue if ai_first: filt = ( f"[1:a]atrim=start={a_start:.3f}:end={a_start + a_len:.3f},asetpts=PTS-STARTPTS[a];" f"[0:a]atrim=start={h_start:.3f}:end={h_start + h_len:.3f},asetpts=PTS-STARTPTS[h];" f"[a][h]acrossfade=d={crossfade:.3f}:c1=tri:c2=tri[out]" ) else: filt = ( f"[0:a]atrim=start={h_start:.3f}:end={h_start + h_len:.3f},asetpts=PTS-STARTPTS[h];" f"[1:a]atrim=start={a_start:.3f}:end={a_start + a_len:.3f},asetpts=PTS-STARTPTS[a];" f"[h][a]acrossfade=d={crossfade:.3f}:c1=tri:c2=tri[out]" ) out_name = f"{track_id}.mp3" out_path = output_dir / out_name if not ffmpeg_mix(human, ai, out_path, filt): continue if ai_first: first_label, first_dur = "ai", a_len second_label, second_dur = "human", h_len else: first_label, first_dur = "human", h_len second_label, second_dur = "ai", a_len prompt = json.dumps( { "order": "ai_first" if ai_first else "human_first", "human_source": str(human), "ai_source": str(ai), f"{first_label}_only_start_sec": 0.0, f"{first_label}_only_end_sec": round(first_dur - crossfade, 3), "crossfade_start_sec": round(first_dur - crossfade, 3), "crossfade_end_sec": round(first_dur, 3), "crossfade_duration_sec": round(crossfade, 3), f"{second_label}_only_start_sec": round(first_dur, 3), f"{second_label}_only_end_sec": round( first_dur + second_dur - crossfade, 3 ), } ) meta = build_metadata( "C2", track_id, out_name, out_path, human, ai, prompt=prompt ) if add_metadata_if_new(manager, meta): added += 1 manager.update_summary() return added def process_category(category: str, args: argparse.Namespace) -> int: output_dir = resolve_output_dir(category, args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) manager = MetadataManager(output_dir) if manager.get_count() >= args.target: logger.info( f"{category} already at target: {manager.get_count()}/{args.target}" ) manager.update_summary() return 0 human_sources = ( [Path(args.human_source_dir)] if args.human_source_dir else DEFAULT_HUMAN_SOURCES ) ai_sources = ( [Path(args.ai_source_dir)] if args.ai_source_dir else DEFAULT_AI_SOURCES ) human_files = collect_audio_files(human_sources) ai_files = collect_audio_files(ai_sources) if len(human_files) < args.target: logger.warning(f"Human source shortage: found {len(human_files)} files.") if len(ai_files) < args.target: logger.warning(f"AI source shortage: found {len(ai_files)} files.") if category == "C1": return process_c1(manager, output_dir, args.target, human_files, ai_files) if category == "C2": return process_c2(manager, output_dir, args.target, human_files, ai_files) return 0 def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument( "--category", type=str, default="all", choices=["C1", "C2", "all"] ) parser.add_argument("--target", type=int, default=2000) parser.add_argument("--human-source-dir", type=str, default=None) parser.add_argument("--ai-source-dir", type=str, default=None) parser.add_argument("--output-dir", type=str, default=None) return parser.parse_args() def main() -> None: args = parse_args() categories = [args.category] if args.category != "all" else ["C1", "C2"] total_added = 0 for category in categories: start = time.time() added = process_category(category, args) elapsed = time.time() - start total_added += added logger.info(f"{category} done: +{added} tracks ({elapsed:.1f}s)") logger.info(f"Mixing collection complete. Added {total_added} track(s).") if __name__ == "__main__": main()