run_demo.py
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#!/usr/bin/env python3
import argparse
import json
import sys
from pathlib import Path
import numpy as np
ROOT = Path(__file__).parent
sys.path.insert(0, str(ROOT))
from src.data.synthetic import generate_dataset
from src.engines.chromaprint_matcher import ChromaprintMatcher
from src.engines.ecapa_embedder import ECAPAEmbedder
from src.engines.hybrid_engine import HybridEngine
def cmd_generate_data(args):
generate_dataset(
output_dir=args.output,
num_songs=args.num_songs,
song_duration=args.song_duration,
num_segments_per_song=args.num_segments,
segment_duration=args.segment_duration,
seed=args.seed,
)
print(f"[done] dataset generated at {args.output}")
def build_chroma_index(data_dir: Path, output_dir: Path):
matcher = ChromaprintMatcher()
matcher.index_songs_from_dir(
songs_dir=str(data_dir / 'songs'),
metadata_path=str(data_dir / 'catalog.json' if (data_dir / 'catalog.json').exists() else data_dir / 'train.json'),
cache_path=str(output_dir / 'chromaprint.pkl'),
)
print(f"[done] chromaprint index built: hashes={matcher.num_hashes}, postings={matcher.index_size}")
return matcher
def build_embedding_index(data_dir: Path, model_path: Path, output_prefix: Path, device: str):
embedder = ECAPAEmbedder(model_path=str(model_path), device=device)
ref_embs, ref_ids = embedder.build_reference_index(
songs_dir=str(data_dir / 'songs'),
metadata_path=str(data_dir / 'catalog.json' if (data_dir / 'catalog.json').exists() else data_dir / 'train.json'),
output_path=str(output_prefix),
)
print(f"[done] embedding index built: {len(ref_ids)} refs")
return embedder, ref_embs, ref_ids
def cmd_build_index(args):
data_dir = Path(args.data)
out_dir = Path(args.output)
out_dir.mkdir(parents=True, exist_ok=True)
build_chroma_index(data_dir, out_dir)
build_embedding_index(data_dir, Path(args.model), out_dir / 'reference', args.device)
def load_index(prefix: Path):
ref_embs = np.load(f"{prefix}_embs.npy")
ref_ids = np.load(f"{prefix}_ids.npy", allow_pickle=True).tolist()
return ref_embs, ref_ids
def cmd_recognize(args):
data_dir = Path(args.data)
matcher = ChromaprintMatcher()
matcher.load(str(Path(args.index_prefix).parent / 'chromaprint.pkl'))
embedder = ECAPAEmbedder(model_path=args.model, device=args.device)
ref_embs, ref_ids = load_index(Path(args.index_prefix))
engine = HybridEngine(
chroma_matcher=matcher,
ecapa_embedder=embedder,
ref_embs=ref_embs,
ref_ids=ref_ids,
)
for split in ['train.json', 'val.json', 'test.json']:
p = data_dir / split
if p.exists():
engine.load_metadata(str(p))
result = engine.recognize(args.query, top_n=args.top_n)
print(json.dumps(result, ensure_ascii=False, indent=2))
def cmd_full_demo(args):
data_dir = Path(args.data)
model_dir = Path(args.model_dir)
index_dir = Path(args.index_dir)
if not data_dir.exists() or not (data_dir / 'train.json').exists():
generate_dataset(
output_dir=str(data_dir),
num_songs=args.num_songs,
song_duration=args.song_duration,
num_segments_per_song=args.num_segments,
segment_duration=args.segment_duration,
seed=args.seed,
)
print(f"[done] dataset generated at {data_dir}")
model_path = model_dir / 'best_model.pt'
if not model_path.exists():
import subprocess
model_dir.mkdir(parents=True, exist_ok=True)
cmd = [
'/usr/local/miniconda3/bin/python', 'train.py',
'--data', str(data_dir), '--output', str(model_dir),
'--device', args.device, '--epochs', '3', '--batch-size', '8'
]
print('[full-demo] training model:', ' '.join(cmd))
subprocess.run(cmd, check=True)
index_dir.mkdir(parents=True, exist_ok=True)
matcher = build_chroma_index(data_dir, index_dir)
embedder, ref_embs, ref_ids = build_embedding_index(data_dir, model_path, index_dir / 'reference', args.device)
with open(data_dir / 'test.json') as f:
test_meta = json.load(f)
query_item = next((x for x in test_meta if 'segments/' in x['audio_path']), test_meta[0])
query_path = data_dir / query_item['audio_path']
engine = HybridEngine(matcher, embedder, ref_embs, ref_ids)
for split in ['train.json', 'val.json', 'test.json']:
engine.load_metadata(str(data_dir / split))
result = engine.recognize(str(query_path), top_n=5)
print('[demo-query]', query_item['song_id'], query_item['audio_path'])
print(json.dumps(result, ensure_ascii=False, indent=2))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='ACR demo utilities')
sub = parser.add_subparsers(dest='cmd', required=True)
p = sub.add_parser('generate-data')
p.add_argument('--output', default='data/synthetic')
p.add_argument('--num-songs', type=int, default=24)
p.add_argument('--song-duration', type=float, default=20.0)
p.add_argument('--num-segments', type=int, default=4)
p.add_argument('--segment-duration', type=float, default=5.0)
p.add_argument('--seed', type=int, default=42)
p.set_defaults(func=cmd_generate_data)
p = sub.add_parser('build-index')
p.add_argument('--data', default='data/synthetic')
p.add_argument('--model', required=True)
p.add_argument('--output', default='data/index')
p.add_argument('--device', default='cpu')
p.set_defaults(func=cmd_build_index)
p = sub.add_parser('recognize')
p.add_argument('--query', required=True)
p.add_argument('--data', default='data/synthetic')
p.add_argument('--model', required=True)
p.add_argument('--index-prefix', default='data/index/reference')
p.add_argument('--top-n', type=int, default=5)
p.add_argument('--device', default='cpu')
p.set_defaults(func=cmd_recognize)
p = sub.add_parser('full-demo')
p.add_argument('--data', default='data/synthetic')
p.add_argument('--model-dir', default='data/models')
p.add_argument('--index-dir', default='data/index')
p.add_argument('--num-songs', type=int, default=24)
p.add_argument('--song-duration', type=float, default=20.0)
p.add_argument('--num-segments', type=int, default=4)
p.add_argument('--segment-duration', type=float, default=5.0)
p.add_argument('--seed', type=int, default=42)
p.add_argument('--device', default='cpu')
p.set_defaults(func=cmd_full_demo)
args = parser.parse_args()
args.func(args)