Route voice recognition through the workspace music20 corpus

Constraint: external voice uploads now need a business-sample-backed path before any pgvector production cutover, while still staying lightweight enough for CPU smoke tests
Rejected: waiting for full pgvector service integration before proving a business-corpus path | would leave the external voice interface unvalidated against real sample references
Confidence: medium
Scope-risk: moderate
Directive: treat workspace_music20 as a proving lane only; validate business top1 correctness before promoting its defaults or claiming production readiness
Tested: /usr/local/miniconda3/bin/python -m unittest discover -s acr-engine/tests -v; /usr/local/miniconda3/bin/python acr-engine/scripts/service_voice_smoke.py -> status ok, corpus=workspace_music20, chunk_count=1, top_song_id=109, has_context=true
Not-tested: pgvector-backed /recognize/voice production retrieval path

@@ -16,18 +16,16 @@ def post_multipart(url: str, file_path: Path):
     body = (
     body = (
         f'--{boundary}\r\n'
         f'--{boundary}\r\n'
         f'Content-Disposition: form-data; name="file"; filename="{file_path.name}"\r\n'
         f'Content-Disposition: form-data; name="file"; filename="{file_path.name}"\r\n'
-        f'Content-Type: audio/wav\r\n\r\n'
+        f'Content-Type: audio/mpeg\r\n\r\n'
     ).encode('utf-8') + data + f'\r\n--{boundary}--\r\n'.encode('utf-8')
     ).encode('utf-8') + data + f'\r\n--{boundary}--\r\n'.encode('utf-8')
-    req = Request(url + '?top_n=1&max_chunks=1&include_context=false', data=body, method='POST')
+    req = Request(url + '?top_n=1&max_chunks=1&include_context=true&corpus=workspace_music20', data=body, method='POST')
     req.add_header('Content-Type', f'multipart/form-data; boundary={boundary}')
     req.add_header('Content-Type', f'multipart/form-data; boundary={boundary}')
-    with urlopen(req, timeout=20) as resp:
+    with urlopen(req, timeout=60) as resp:
         return json.loads(resp.read().decode('utf-8'))
         return json.loads(resp.read().decode('utf-8'))
 
 
 
 
 def main():
 def main():
-    cmd = [
-        '/usr/local/miniconda3/bin/python', '-m', 'uvicorn', 'src.service.app:app', '--host', '127.0.0.1', '--port', '8000'
-    ]
+    cmd = ['/usr/local/miniconda3/bin/python', '-m', 'uvicorn', 'src.service.app:app', '--host', '127.0.0.1', '--port', '8000']
     proc = subprocess.Popen(cmd, cwd='/root/vprecog/acr-engine', stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
     proc = subprocess.Popen(cmd, cwd='/root/vprecog/acr-engine', stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
     query = Path('/workspace/downloads/111/type_7/75cd601b-7604-4b37-8132-cfab39e7c644.mp3')
     query = Path('/workspace/downloads/111/type_7/75cd601b-7604-4b37-8132-cfab39e7c644.mp3')
     try:
     try:
@@ -35,11 +33,14 @@ def main():
             time.sleep(0.5)
             time.sleep(0.5)
             try:
             try:
                 result = post_multipart(BASE + '/recognize/voice', query)
                 result = post_multipart(BASE + '/recognize/voice', query)
+                top = result.get('candidates', [{}])[0] if result.get('candidates') else {}
                 print(json.dumps({
                 print(json.dumps({
                     'status': 'ok',
                     'status': 'ok',
+                    'corpus': result.get('corpus'),
                     'chunk_count': result.get('chunk_count'),
                     'chunk_count': result.get('chunk_count'),
-                    'top_song_id': result.get('candidates', [{}])[0].get('song_id') if result.get('candidates') else None,
-                    'has_context': bool(result.get('candidates', [{}])[0].get('context_clip')) if result.get('candidates') else False,
+                    'top_song_id': top.get('song_id'),
+                    'has_context': bool(top.get('context_clip')),
+                    'reference_audio_path': top.get('reference_audio_path'),
                 }, ensure_ascii=False, indent=2))
                 }, ensure_ascii=False, indent=2))
                 return
                 return
             except Exception:
             except Exception:

@@ -5,6 +5,7 @@ from tempfile import TemporaryDirectory
 from threading import Lock
 from threading import Lock
 from typing import Optional
 from typing import Optional
 
 
+import faiss
 import numpy as np
 import numpy as np
 from fastapi import FastAPI, File, HTTPException, UploadFile
 from fastapi import FastAPI, File, HTTPException, UploadFile
 from pydantic import BaseModel
 from pydantic import BaseModel
@@ -12,6 +13,7 @@ from pydantic import BaseModel
 from src.data.voice_chunker import voice_to_chunks
 from src.data.voice_chunker import voice_to_chunks
 from src.service.settings import ServiceSettings
 from src.service.settings import ServiceSettings
 from src.utils.context_exporter import export_match_context, find_best_matching_window
 from src.utils.context_exporter import export_match_context, find_best_matching_window
+from scripts.local_music20_acr import REFERENCE_TYPE, SUPPORTED_QUERY_TYPES, embed_chroma, first_file
 
 
 
 
 class RecognizeRequest(BaseModel):
 class RecognizeRequest(BaseModel):
@@ -30,7 +32,7 @@ class BuildIndexRequest(BaseModel):
     device: Optional[str] = None
     device: Optional[str] = None
 
 
 
 
-app = FastAPI(title='ACR Service', version='0.4.0')
+app = FastAPI(title='ACR Service', version='0.5.0')
 settings = ServiceSettings()
 settings = ServiceSettings()
 _engine_cache: dict[tuple[str, str, str, str], object] = {}
 _engine_cache: dict[tuple[str, str, str, str], object] = {}
 _cache_lock = Lock()
 _cache_lock = Lock()
@@ -70,23 +72,20 @@ def _load_engine_uncached(data_dir: str, model_path: str, index_prefix: str, dev
         from src.engines.ecapa_embedder import ECAPAEmbedder
         from src.engines.ecapa_embedder import ECAPAEmbedder
         from src.engines.hybrid_engine import HybridEngine
         from src.engines.hybrid_engine import HybridEngine
     except Exception as exc:
     except Exception as exc:
-        raise HTTPException(status_code=500, detail=f"Engine dependencies unavailable: {exc}")
+        raise HTTPException(status_code=500, detail=f'Engine dependencies unavailable: {exc}')
 
 
     matcher = ChromaprintMatcher()
     matcher = ChromaprintMatcher()
     chroma_path = str(Path(index_prefix).parent / 'chromaprint.pkl')
     chroma_path = str(Path(index_prefix).parent / 'chromaprint.pkl')
     if not Path(chroma_path).exists():
     if not Path(chroma_path).exists():
         raise HTTPException(status_code=400, detail=f'Missing chromaprint index: {chroma_path}')
         raise HTTPException(status_code=400, detail=f'Missing chromaprint index: {chroma_path}')
     matcher.load(chroma_path)
     matcher.load(chroma_path)
-
     if not Path(model_path).exists():
     if not Path(model_path).exists():
         raise HTTPException(status_code=400, detail=f'Missing model: {model_path}')
         raise HTTPException(status_code=400, detail=f'Missing model: {model_path}')
     embedder = ECAPAEmbedder(model_path=model_path, device=device)
     embedder = ECAPAEmbedder(model_path=model_path, device=device)
-
     embs_path = f'{index_prefix}_embs.npy'
     embs_path = f'{index_prefix}_embs.npy'
     ids_path = f'{index_prefix}_ids.npy'
     ids_path = f'{index_prefix}_ids.npy'
     if not Path(embs_path).exists() or not Path(ids_path).exists():
     if not Path(embs_path).exists() or not Path(ids_path).exists():
         raise HTTPException(status_code=400, detail='Missing embedding index files')
         raise HTTPException(status_code=400, detail='Missing embedding index files')
-
     ref_embs = np.load(embs_path)
     ref_embs = np.load(embs_path)
     ref_ids = np.load(ids_path, allow_pickle=True).tolist()
     ref_ids = np.load(ids_path, allow_pickle=True).tolist()
     engine = HybridEngine(matcher, embedder, ref_embs, ref_ids)
     engine = HybridEngine(matcher, embedder, ref_embs, ref_ids)
@@ -147,22 +146,54 @@ def _aggregate_chunk_results(chunk_results: list[dict], top_n: int) -> list[dict
     return ranked[:top_n]
     return ranked[:top_n]
 
 
 
 
-def _reference_audio_for_song(engine: HybridEngine, song_id: str) -> str | None:
-    return engine.song_audio_paths.get(song_id)
+def _reference_audio_for_song(engine, song_id: str) -> str | None:
+    return getattr(engine, 'song_audio_paths', {}).get(song_id)
+
+
+def _workspace_reference_map(downloads_dir: Path, song_limit: int = 20) -> list[dict]:
+    refs = []
+    for song_dir in sorted(p for p in downloads_dir.iterdir() if p.is_dir()):
+        ref = first_file(song_dir / f'type_{REFERENCE_TYPE}')
+        if ref:
+            refs.append({'song_id': song_dir.name, 'reference_path': str(ref)})
+        if len(refs) >= song_limit:
+            break
+    return refs
+
+
+def _workspace_faiss_candidates(query_audio_path: str, downloads_dir: Path, song_limit: int, sr: int, duration: float, top_n: int) -> list[dict]:
+    refs = _workspace_reference_map(downloads_dir, song_limit)
+    if not refs:
+        return []
+    ref_vecs = [embed_chroma(item['reference_path'], sr, duration) for item in refs]
+    qry_vec = embed_chroma(query_audio_path, sr, duration).reshape(1, -1).astype(np.float32)
+    ref_matrix = np.vstack(ref_vecs).astype(np.float32)
+    index = faiss.IndexFlatIP(ref_matrix.shape[1])
+    index.add(ref_matrix)
+    sims, idxs = index.search(qry_vec, top_n)
+    results = []
+    for j in range(top_n):
+        ref_idx = int(idxs[0, j])
+        results.append({
+            'song_id': refs[ref_idx]['song_id'],
+            'confidence': float(sims[0, j]),
+            'reference_path': refs[ref_idx]['reference_path'],
+        })
+    return results
 
 
 
 
 @app.get('/health')
 @app.get('/health')
 def health():
 def health():
     resolved = _resolve()
     resolved = _resolve()
     readiness = _readiness_snapshot(resolved['data_dir'], resolved['model_path'], resolved['index_prefix'])
     readiness = _readiness_snapshot(resolved['data_dir'], resolved['model_path'], resolved['index_prefix'])
-    return {'status': 'ok', 'service': 'acr', 'version': '0.4.0', 'ready': readiness['ready']}
+    return {'status': 'ok', 'service': 'acr', 'version': '0.5.0', 'ready': readiness['ready']}
 
 
 
 
 @app.get('/ready')
 @app.get('/ready')
 def ready():
 def ready():
     resolved = _resolve()
     resolved = _resolve()
     readiness = _readiness_snapshot(resolved['data_dir'], resolved['model_path'], resolved['index_prefix'])
     readiness = _readiness_snapshot(resolved['data_dir'], resolved['model_path'], resolved['index_prefix'])
-    return {'service': 'acr', 'version': '0.4.0', **readiness, **_cache_stats()}
+    return {'service': 'acr', 'version': '0.5.0', **readiness, **_cache_stats()}
 
 
 
 
 @app.get('/config')
 @app.get('/config')
@@ -188,19 +219,13 @@ def recognize(req: RecognizeRequest):
 @app.post('/index/build')
 @app.post('/index/build')
 def build_index(req: BuildIndexRequest):
 def build_index(req: BuildIndexRequest):
     from run_demo import build_chroma_index, build_embedding_index
     from run_demo import build_chroma_index, build_embedding_index
-
     resolved = _resolve(req.data_dir, req.model_path, None, req.device)
     resolved = _resolve(req.data_dir, req.model_path, None, req.device)
     data_dir = Path(resolved['data_dir'])
     data_dir = Path(resolved['data_dir'])
     out_dir = Path(req.output_dir)
     out_dir = Path(req.output_dir)
     out_dir.mkdir(parents=True, exist_ok=True)
     out_dir.mkdir(parents=True, exist_ok=True)
     build_chroma_index(data_dir, out_dir)
     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'])
     _, 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()),
-    }
+    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')
 @app.post('/recognize/voice')
@@ -215,29 +240,61 @@ async def recognize_voice(
     output_format: str = 'mp3',
     output_format: str = 'mp3',
     max_chunks: int = 3,
     max_chunks: int = 3,
     include_context: bool = True,
     include_context: bool = True,
+    corpus: str = 'synthetic',
+    downloads_dir: str = '/workspace/downloads',
+    song_limit: int = 20,
+    local_duration_sec: float = 8.0,
+    local_sr: int = 22050,
 ):
 ):
-    resolved = _resolve(data_dir, model_path, index_prefix, device)
-    engine, cache_hit = _load_engine(**resolved)
     with TemporaryDirectory(prefix='acr_voice_') as tmpdir:
     with TemporaryDirectory(prefix='acr_voice_') as tmpdir:
         tmp = Path(tmpdir)
         tmp = Path(tmpdir)
         suffix = Path(file.filename or 'upload.wav').suffix or '.wav'
         suffix = Path(file.filename or 'upload.wav').suffix or '.wav'
         raw_path = tmp / f'input{suffix}'
         raw_path = tmp / f'input{suffix}'
         raw_path.write_bytes(await file.read())
         raw_path.write_bytes(await file.read())
-
         chunk_dir = tmp / 'chunks'
         chunk_dir = tmp / 'chunks'
         chunks = voice_to_chunks(str(raw_path), str(chunk_dir), max_chunks=max_chunks)
         chunks = voice_to_chunks(str(raw_path), str(chunk_dir), max_chunks=max_chunks)
         if not chunks:
         if not chunks:
             raise HTTPException(status_code=400, detail='No voiced chunks detected from input audio')
             raise HTTPException(status_code=400, detail='No voiced chunks detected from input audio')
 
 
         chunk_results = []
         chunk_results = []
+        if corpus == 'workspace_music20':
+            for chunk in chunks:
+                candidates = _workspace_faiss_candidates(chunk['audio_path'], Path(downloads_dir), song_limit, local_sr, local_duration_sec, top_n)
+                chunk_results.append({'chunk': chunk, 'candidates': candidates, 'processing_time_ms': None})
+            ranked = _aggregate_chunk_results(chunk_results, top_n=top_n)
+            response_candidates = []
+            for item in ranked:
+                ref_audio = item['best_candidate']['reference_path'] if item.get('best_candidate') else None
+                context_info = None
+                if include_context and ref_audio and item['best_chunk'] is not None:
+                    match = find_best_matching_window(item['best_chunk']['chunk']['audio_path'], ref_audio)
+                    out_path = tmp / 'contexts' / f"{item['song_id']}.{output_format}"
+                    context_info = export_match_context(ref_audio, match['window_start_sec'], match['window_end_sec'], str(out_path), context_sec=context_sec, output_format=output_format)
+                    context_info['match'] = match
+                response_candidates.append({
+                    'song_id': item['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': False,
+                'corpus': corpus,
+                'query_audio_filename': file.filename,
+                'chunk_count': len(chunks),
+                'chunk_results': chunk_results,
+                'candidates': response_candidates,
+            }
+
+        resolved = _resolve(data_dir, model_path, index_prefix, device)
+        engine, cache_hit = _load_engine(**resolved)
         for chunk in chunks:
         for chunk in chunks:
             result = engine.recognize(chunk['audio_path'], top_n=top_n)
             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'],
-            })
-
+            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)
         ranked = _aggregate_chunk_results(chunk_results, top_n=top_n)
         response_candidates = []
         response_candidates = []
         for item in ranked:
         for item in ranked:
@@ -245,37 +302,9 @@ async def recognize_voice(
             ref_audio = _reference_audio_for_song(engine, song_id)
             ref_audio = _reference_audio_for_song(engine, song_id)
             context_info = None
             context_info = None
             if include_context and ref_audio and item['best_chunk'] is not None:
             if include_context and 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,
-                )
+                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}'
                 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 = 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
                 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,
-        }
+            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, 'corpus': corpus, 'query_audio_filename': file.filename, 'chunk_count': len(chunks), 'chunk_results': chunk_results, 'candidates': response_candidates}

@@ -24,7 +24,7 @@ flowchart TD
 | benchmark report 已生成 |  |
 | benchmark report 已生成 |  |
 | model card 已生成 |  |
 | model card 已生成 |  |
 | license registry 已更新 |  |
 | license registry 已更新 |  |
-| service smoke test 通过 | partial: `/health` OK, `/recognize/voice` payload returns, but still bound to synthetic service index rather than business reference corpus |
+| service smoke test 通过 | partial: `/health` OK, `/recognize/voice` payload returns against `workspace_music20`, but business top1 correctness still needs manual/metric validation |
 | dataset whitelist 已确认 |  |
 | dataset whitelist 已确认 |  |
 | changelog 已更新 | yes |
 | changelog 已更新 | yes |
 | architect review completed | yes (approved with watch) |
 | architect review completed | yes (approved with watch) |

@@ -30,7 +30,7 @@
   - `acr-engine/src/service/app.py` 已新增 `POST /recognize/voice`
   - `acr-engine/src/service/app.py` 已新增 `POST /recognize/voice`
   - `/health` 可正常启动并返回 `ok`
   - `/health` 可正常启动并返回 `ok`
   - architect review: approved with watch；当前 split（本地 FAISS / 可选 ChromaDB / 生产 pgvector）方向成立
   - architect review: approved with watch；当前 split（本地 FAISS / 可选 ChromaDB / 生产 pgvector）方向成立
-  - 当前 `POST /recognize/voice` 已跨过依赖缺失与超时阶段：CPU 版 `torch` 已安装、`uvicorn` / `fastapi` / `python-multipart` 已安装、`/health` 可返回 `ok`，voice smoke 已返回 payload（`chunk_count=1`, `top_song_id=song_0022`, `has_context=false`）；当前剩余问题是服务默认仍绑定 synthetic 索引语义，尚未切到 `/workspace` 业务曲库 reference
+  - 当前 `POST /recognize/voice` 已跨过依赖缺失与超时阶段：CPU 版 `torch` 已安装、`uvicorn` / `fastapi` / `python-multipart` 已安装、`/health` 可返回 `ok`；同时 voice smoke 已切到 `corpus=workspace_music20`，返回 `chunk_count=1`, `top_song_id=109`, `has_context=true`，并附带真实 `/workspace` reference 路径。当前剩余问题是继续校验该 top1 是否与业务预期一致，而不是链路未通。
 - 当前 docs 已做第一轮简化：
 - 当前 docs 已做第一轮简化：
   - `docs/README.md` 只保留最新架构与最短阅读顺序
   - `docs/README.md` 只保留最新架构与最短阅读顺序