perf: reuse GCC-PHAT TDOA; skip 16 kHz AudioNormalizer

Delay-and-Sum still uses the tutorial STFT/cov/GCC-PHAT/DelaySum/ISTFT
path. TDOA is refreshed every BEAMFORM_TDOA_EVERY hops (default 5).

.env.example

@@ -36,6 +36,8 @@ ENHANCE_HOP_S=0.2
 BEAMFORM_MODE=auto
 # One BLAS/Torch thread per worker (20 workers vs 28 cores)
 TORCH_NUM_THREADS=1
+# Recompute GCC-PHAT TDOA every N hops (5 * 200 ms = 1 s)
+BEAMFORM_TDOA_EVERY=5
 
 # Misc
 PUBLISH_TIMEOUT_S=60

audio_cleanup.py

@@ -64,7 +64,8 @@ class AudioCleaner:
                  chunk_s: float = 1.0,
                  hop_s: float = 0.2,
                  beamform: str = "auto",
-                 torch_num_threads: int = 1):
+                 torch_num_threads: int = 1,
+                 tdoa_every: int = 5):
         self.mode = mode
         self.beamform_mode = beamform
         self.sample_rate = sample_rate
@@ -78,6 +79,14 @@ class AudioCleaner:
         self.backend = "off"
         self.last_dt_ms = -1.0
         self.torch_num_threads = max(1, int(torch_num_threads))
+        self.tdoa_every = max(1, int(tdoa_every))
+        self._stft = None
+        self._cov = None
+        self._gccphat = None
+        self._delaysum = None
+        self._istft = None
+        self._last_tdoas = None
+        self._tdoa_age = 0
 
         self._load_torch()
         if beamform == "force":
@@ -130,12 +139,23 @@ class AudioCleaner:
             return
         try:
             log.info("loading speechbrain DelaySum_Beamformer (GCC-PHAT)")
-            self._normalizer = AudioNormalizer(
-                sample_rate=self.sample_rate, mix="keep")
+            # Native 16 kHz C920s: AudioNormalizer would resample 16k->16k.
+            if self.sample_rate != SAMPLE_RATE:
+                self._normalizer = AudioNormalizer(
+                    sample_rate=self.sample_rate, mix="keep")
+            else:
+                self._normalizer = None
             bf = DelaySum_Beamformer(sampling_rate=self.sample_rate)
             bf.eval()
             self._beamformer = bf
-            log.info("DelaySum beamformer ready (stereo in, mono out)")
+            self._stft = bf.stft
+            self._cov = bf.cov
+            self._gccphat = bf.gccphat
+            self._delaysum = bf.delaysum
+            self._istft = bf.istft
+            log.info(
+                "DelaySum beamformer ready (stereo in, mono out, tdoa_every=%d)",
+                self.tdoa_every)
         except Exception as e:  # noqa: BLE001
             log.warning("failed to load DelaySum_Beamformer: %s", e)
             self._beamformer = None
@@ -216,9 +236,18 @@ class AudioCleaner:
         wav = torch.from_numpy(np.ascontiguousarray(stereo, dtype=np.float32))
         if self._normalizer is not None:
             wav = self._normalizer(wav, self.sample_rate)
-        # DelaySum_Beamformer expects [batch, time, channels]
+        # DelaySum expects [batch, time, channels]
+        xs = wav.unsqueeze(0)
         with torch.no_grad():
-            y = self._beamformer(wav.unsqueeze(0))
+            Xs = self._stft(xs)
+            if (self._last_tdoas is None
+                    or self._tdoa_age >= self.tdoa_every):
+                XXs = self._cov(Xs)
+                self._last_tdoas = self._gccphat(XXs)
+                self._tdoa_age = 0
+            self._tdoa_age += 1
+            Ys = self._delaysum(Xs, self._last_tdoas)
+            y = self._istft(Ys)
         out = y.squeeze().detach().cpu().numpy().astype(np.float32, copy=False)
         if out.ndim > 1:
             out = np.reshape(out, -1)

config.py

@@ -41,6 +41,7 @@ class Config:
     # SpeechBrain Delay-and-Sum (GCC-PHAT) on C920 stereo before enhance
     beamform_mode: str = "auto"  # auto | force | off
     torch_num_threads: int = 1
+    beamform_tdoa_every: int = 5
     # misc
     publish_timeout_s: float = 60.0
     log_level: str = "INFO"
@@ -88,6 +89,7 @@ def load_config(env_path: Optional[Path] = None) -> Config:
         enhance_hop_s=float(os.environ.get("ENHANCE_HOP_S", "0.2")),
         beamform_mode=os.environ.get("BEAMFORM_MODE", "auto").lower(),
         torch_num_threads=int(os.environ.get("TORCH_NUM_THREADS", "1")),
+        beamform_tdoa_every=int(os.environ.get("BEAMFORM_TDOA_EVERY", "5")),
         publish_timeout_s=float(os.environ.get("PUBLISH_TIMEOUT_S", "60")),
         log_level=os.environ.get("LOG_LEVEL", "INFO").upper(),
         cameras=_parse_cameras(os.environ.get("CAMERAS", "all")),
@@ -100,4 +102,6 @@ def load_config(env_path: Optional[Path] = None) -> Config:
             f"BEAMFORM_MODE must be auto|force|off, got {cfg.beamform_mode}")
     if cfg.torch_num_threads < 1:
         raise ValueError("TORCH_NUM_THREADS must be >= 1")
+    if cfg.beamform_tdoa_every < 1:
+        raise ValueError("BEAMFORM_TDOA_EVERY must be >= 1")
     return cfg

run.py

@@ -44,6 +44,7 @@ def worker_args_for(cfg, cam) -> list[str]:
         "--enhance-hop-s", str(cfg.enhance_hop_s),
         "--beamform-mode", cfg.beamform_mode,
         "--torch-num-threads", str(cfg.torch_num_threads),
+        "--tdoa-every", str(cfg.beamform_tdoa_every),
     ]
 
 

run_worker.py

@@ -39,6 +39,7 @@ def main() -> int:
     ap.add_argument("--enhance-hop-s", type=float, default=0.2)
     ap.add_argument("--beamform-mode", default="auto")
     ap.add_argument("--torch-num-threads", type=int, default=1)
+    ap.add_argument("--tdoa-every", type=int, default=5)
     args = ap.parse_args()
 
     cam = json.loads(args.camera_json)
@@ -65,6 +66,7 @@ def main() -> int:
             hop_s=args.enhance_hop_s,
             beamform=args.beamform_mode,
             torch_num_threads=args.torch_num_threads,
+            tdoa_every=args.tdoa_every,
         )
         log.info("audio backend: %s", cleaner.backend)
 

tests/test_audio_cleanup.py

@@ -57,6 +57,32 @@ def test_beamform_loads_delaysum_module():
     assert "beamform" in c.backend
 
 
+def test_beamform_skips_normalizer_at_native_rate():
+    c = AudioCleaner(mode="off", beamform="force", sample_rate=16000)
+    assert c._normalizer is None
+
+
+def test_tdoa_reused_across_hops():
+    c = AudioCleaner(mode="off", beamform="force", tdoa_every=5)
+    assert c._gccphat is not None
+    calls = {"n": 0}
+    orig = c._gccphat
+
+    def wrapped(*a, **k):
+        calls["n"] += 1
+        return orig(*a, **k)
+
+    c._gccphat = wrapped
+    hop = 3200
+    t = np.arange(hop)
+    ch0 = (np.sin(2 * np.pi * 440 * t / 16000) * 8000).astype(np.int16)
+    stereo = np.stack([ch0, np.roll(ch0, 2)], axis=1)
+    for _ in range(3):
+        out = c.process(stereo)
+        assert out.shape == (hop,)
+    assert calls["n"] == 1
+
+
 def test_process_records_last_dt_ms():
     c = AudioCleaner(mode="off", beamform="off")
     hop = 3200