app.py
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from __future__ import annotations
from pathlib import Path
from tempfile import TemporaryDirectory
from threading import Lock
from typing import Optional
import numpy as np
from fastapi import FastAPI, File, HTTPException, UploadFile
from pydantic import BaseModel
from src.data.voice_chunker import voice_to_chunks
from src.engines.chromaprint_matcher import ChromaprintMatcher
from src.engines.ecapa_embedder import ECAPAEmbedder
from src.engines.hybrid_engine import HybridEngine
from src.service.settings import ServiceSettings
from src.utils.context_exporter import export_match_context, find_best_matching_window
class RecognizeRequest(BaseModel):
query_path: str
data_dir: Optional[str] = None
model_path: Optional[str] = None
index_prefix: Optional[str] = None
top_n: int = 5
device: Optional[str] = None
class BuildIndexRequest(BaseModel):
data_dir: Optional[str] = None
model_path: Optional[str] = None
output_dir: str
device: Optional[str] = None
app = FastAPI(title='ACR Service', version='0.4.0')
settings = ServiceSettings()
_engine_cache: dict[tuple[str, str, str, str], HybridEngine] = {}
_cache_lock = Lock()
def _resolve(req_data_dir=None, req_model_path=None, req_index_prefix=None, req_device=None):
return {
'data_dir': req_data_dir or settings.data_dir,
'model_path': req_model_path or settings.model_path,
'index_prefix': req_index_prefix or settings.index_prefix,
'device': req_device or settings.device,
}
def _readiness_snapshot(data_dir: str, model_path: str, index_prefix: str) -> dict:
chroma_path = str(Path(index_prefix).parent / 'chromaprint.pkl')
embs_path = f'{index_prefix}_embs.npy'
ids_path = f'{index_prefix}_ids.npy'
manifest_candidates = [
str((Path(data_dir) / split).resolve())
for split in ['catalog.json', 'train.json', 'val.json', 'test.json']
if (Path(data_dir) / split).exists()
]
files = {
'data_dir': {'path': str(Path(data_dir).resolve()), 'exists': Path(data_dir).exists()},
'model': {'path': str(Path(model_path).resolve()), 'exists': Path(model_path).exists()},
'chromaprint_index': {'path': str(Path(chroma_path).resolve()), 'exists': Path(chroma_path).exists()},
'embedding_index': {'path': str(Path(embs_path).resolve()), 'exists': Path(embs_path).exists()},
'id_index': {'path': str(Path(ids_path).resolve()), 'exists': Path(ids_path).exists()},
}
return {'ready': all(item['exists'] for item in files.values()), 'files': files, 'manifests': manifest_candidates}
def _load_engine_uncached(data_dir: str, model_path: str, index_prefix: str, device: str) -> HybridEngine:
matcher = ChromaprintMatcher()
chroma_path = str(Path(index_prefix).parent / 'chromaprint.pkl')
if not Path(chroma_path).exists():
raise HTTPException(status_code=400, detail=f'Missing chromaprint index: {chroma_path}')
matcher.load(chroma_path)
if not Path(model_path).exists():
raise HTTPException(status_code=400, detail=f'Missing model: {model_path}')
embedder = ECAPAEmbedder(model_path=model_path, device=device)
embs_path = f'{index_prefix}_embs.npy'
ids_path = f'{index_prefix}_ids.npy'
if not Path(embs_path).exists() or not Path(ids_path).exists():
raise HTTPException(status_code=400, detail='Missing embedding index files')
ref_embs = np.load(embs_path)
ref_ids = np.load(ids_path, allow_pickle=True).tolist()
engine = HybridEngine(matcher, embedder, ref_embs, ref_ids)
for split in ['catalog.json', 'train.json', 'val.json', 'test.json']:
p = Path(data_dir) / split
if p.exists():
engine.load_metadata(str(p))
return engine
def _load_engine(data_dir: str, model_path: str, index_prefix: str, device: str) -> tuple[HybridEngine, bool]:
key = (str(Path(data_dir).resolve()), str(Path(model_path).resolve()), str(Path(index_prefix).resolve()), device)
with _cache_lock:
cached = _engine_cache.get(key)
if cached is not None:
return cached, True
engine = _load_engine_uncached(data_dir, model_path, index_prefix, device)
with _cache_lock:
_engine_cache[key] = engine
return engine, False
def _cache_stats() -> dict:
with _cache_lock:
keys = list(_engine_cache.keys())
return {'engine_cache_size': len(keys), 'cache_keys': keys}
def _aggregate_chunk_results(chunk_results: list[dict], top_n: int) -> list[dict]:
by_song: dict[str, dict] = {}
for chunk in chunk_results:
for cand in chunk.get('candidates', []):
song_id = cand['song_id']
entry = by_song.setdefault(song_id, {
'song_id': song_id,
'best_confidence': -1.0,
'match_count': 0,
'best_chunk': None,
'best_candidate': None,
})
entry['match_count'] += 1
if cand['confidence'] > entry['best_confidence']:
entry['best_confidence'] = cand['confidence']
entry['best_chunk'] = chunk
entry['best_candidate'] = cand
ranked = []
for entry in by_song.values():
combined = float(entry['best_confidence']) + 0.05 * float(entry['match_count'])
ranked.append({
'song_id': entry['song_id'],
'combined_confidence': round(combined, 4),
'best_confidence': round(float(entry['best_confidence']), 4),
'match_count': entry['match_count'],
'best_chunk': entry['best_chunk'],
'best_candidate': entry['best_candidate'],
})
ranked.sort(key=lambda x: x['combined_confidence'], reverse=True)
return ranked[:top_n]
def _reference_audio_for_song(engine: HybridEngine, song_id: str) -> str | None:
return engine.song_audio_paths.get(song_id)
@app.get('/health')
def health():
resolved = _resolve()
readiness = _readiness_snapshot(resolved['data_dir'], resolved['model_path'], resolved['index_prefix'])
return {'status': 'ok', 'service': 'acr', 'version': '0.4.0', 'ready': readiness['ready']}
@app.get('/ready')
def ready():
resolved = _resolve()
readiness = _readiness_snapshot(resolved['data_dir'], resolved['model_path'], resolved['index_prefix'])
return {'service': 'acr', 'version': '0.4.0', **readiness, **_cache_stats()}
@app.get('/config')
def config():
return settings.model_dump()
@app.get('/cache')
def cache_status():
return _cache_stats()
@app.post('/recognize')
def recognize(req: RecognizeRequest):
resolved = _resolve(req.data_dir, req.model_path, req.index_prefix, req.device)
if not Path(req.query_path).exists():
raise HTTPException(status_code=400, detail=f'Missing query file: {req.query_path}')
engine, cache_hit = _load_engine(**resolved)
result = engine.recognize(req.query_path, top_n=req.top_n)
return {'cache_hit': cache_hit, 'resolved': resolved, 'result': result}
@app.post('/index/build')
def build_index(req: BuildIndexRequest):
from run_demo import build_chroma_index, build_embedding_index
resolved = _resolve(req.data_dir, req.model_path, None, req.device)
data_dir = Path(resolved['data_dir'])
out_dir = Path(req.output_dir)
out_dir.mkdir(parents=True, exist_ok=True)
build_chroma_index(data_dir, out_dir)
_, ref_embs, ref_ids = build_embedding_index(data_dir, Path(resolved['model_path']), out_dir / 'reference', resolved['device'])
return {
'status': 'ok',
'num_reference_windows': len(ref_ids),
'embedding_dim': int(ref_embs.shape[1]) if len(ref_embs.shape) > 1 else 0,
'output_dir': str(out_dir.resolve()),
}
@app.post('/recognize/voice')
async def recognize_voice(
file: UploadFile = File(...),
top_n: int = 5,
data_dir: Optional[str] = None,
model_path: Optional[str] = None,
index_prefix: Optional[str] = None,
device: Optional[str] = None,
context_sec: float = 10.0,
output_format: str = 'mp3',
):
resolved = _resolve(data_dir, model_path, index_prefix, device)
engine, cache_hit = _load_engine(**resolved)
with TemporaryDirectory(prefix='acr_voice_') as tmpdir:
tmp = Path(tmpdir)
suffix = Path(file.filename or 'upload.wav').suffix or '.wav'
raw_path = tmp / f'input{suffix}'
raw_path.write_bytes(await file.read())
chunk_dir = tmp / 'chunks'
chunks = voice_to_chunks(str(raw_path), str(chunk_dir))
if not chunks:
raise HTTPException(status_code=400, detail='No voiced chunks detected from input audio')
chunk_results = []
for chunk in chunks:
result = engine.recognize(chunk['audio_path'], top_n=top_n)
chunk_results.append({
'chunk': chunk,
'candidates': result['candidates'],
'processing_time_ms': result['processing_time_ms'],
})
ranked = _aggregate_chunk_results(chunk_results, top_n=top_n)
response_candidates = []
for item in ranked:
song_id = item['song_id']
ref_audio = _reference_audio_for_song(engine, song_id)
context_info = None
if ref_audio and item['best_chunk'] is not None:
match = find_best_matching_window(
query_audio_path=item['best_chunk']['chunk']['audio_path'],
reference_audio_path=ref_audio,
)
out_path = tmp / 'contexts' / f'{song_id}.{output_format}'
context_info = export_match_context(
audio_path=ref_audio,
window_start_sec=match['window_start_sec'],
window_end_sec=match['window_end_sec'],
output_path=str(out_path),
context_sec=context_sec,
output_format=output_format,
)
context_info['match'] = match
response_candidates.append({
'song_id': song_id,
'combined_confidence': item['combined_confidence'],
'best_confidence': item['best_confidence'],
'match_count': item['match_count'],
'reference_audio_path': ref_audio,
'best_candidate': item['best_candidate'],
'best_chunk': item['best_chunk']['chunk'] if item['best_chunk'] else None,
'context_clip': context_info,
})
return {
'cache_hit': cache_hit,
'resolved': resolved,
'query_audio_filename': file.filename,
'chunk_count': len(chunks),
'chunk_results': chunk_results,
'candidates': response_candidates,
}