Update Space for the v1.4-AEC release (GGML engine)

app.py

@@ -267,6 +267,76 @@ def _resolve_v14aec_ckpt() -> str | None:
         return None
 
 
+# v1.4 NS+dereverb backends (full enhancement). Same DAF front-end + slim CCM
+# cascade as v1.4-AEC, but trained toward the anechoic `clean` target so they
+# remove noise + reverb (not echo-only). Local-only; default to the packaged
+# release copies, no HF publish yet.
+def _resolve_v14nsdr200_ckpt() -> str | None:
+    v = os.environ.get("LOCALVQE_V14NSDR200_CKPT")
+    if v:
+        return v
+    d = "/workspace/localvqe/checkpoints/release/localvqe-v1.4-nsdr-200K.pt"
+    return d if os.path.exists(d) else None
+
+
+def _resolve_v14nsdr800_ckpt() -> str | None:
+    v = os.environ.get("LOCALVQE_V14NSDR800_CKPT")
+    if v:
+        return v
+    d = "/workspace/localvqe/checkpoints/release/localvqe-v1.4-nsdr-800K.pt"
+    return d if os.path.exists(d) else None
+
+
+def _resolve_gate_ckpt() -> str | None:
+    # The 1,657-param learned front-end NS/dereverb gate (fegate_proto.pt).
+    # Local-only research entry; default to the trained prototype.
+    v = os.environ.get("LOCALVQE_GATE_CKPT")
+    if v:
+        return v
+    # fegate_preserve.pt = trained with the clean energy-preservation term (the
+    # original fegate_proto.pt collapses to silence — EXPERIMENTS 2026-06-18).
+    for d in ("/workspace/localvqe/checkpoints/fegate_preserve.pt",
+              "/workspace/localvqe/checkpoints/fegate_proto.pt"):
+        if os.path.exists(d):
+            return d
+    return None
+
+
+def _resolve_gtcrn_ckpt() -> str | None:
+    # The 48.9K AEC-aware GTCRN_AEC (gtcrn_ns_aec.pt) — all-scene, yhat far-end
+    # branch. Local-only research entry; default to the trained checkpoint.
+    v = os.environ.get("LOCALVQE_GTCRN_CKPT")
+    if v:
+        return v
+    d = "/workspace/localvqe/checkpoints/gtcrn_ns_aec.pt"
+    return d if os.path.exists(d) else None
+
+
+def _resolve_gtcrn_os400_ckpt() -> str | None:
+    # os-weight 400 + movement bank — preserves quiet near-end (DT level 0.71→0.84)
+    # AND a strong echo guard recovered by the movement clips (FE-ST eval 17.2 dB,
+    # post-movement transient +5.5 dB vs os400). Pareto-dominates os140/os400.
+    v = os.environ.get("LOCALVQE_GTCRN_OS400_CKPT")
+    if v:
+        return v
+    for d in ("/workspace/localvqe/checkpoints/gtcrn_ns_aec_os400_move.pt",
+              "/workspace/localvqe/checkpoints/gtcrn_ns_aec_os400.pt"):
+        if os.path.exists(d):
+            return d
+    return None
+
+
+def _resolve_gtcrn_keepnoise_ckpt() -> str | None:
+    # LocalVQE-Pi-AEC-v1-49k — keep-noise sibling of the full Pi model. Same 49K
+    # GTCRN_AEC arch, trained toward `room` (remove echo; keep noise + reverb)
+    # instead of `clean`. The echo-only counterpart to LocalVQE-Pi-v1-49k.
+    v = os.environ.get("LOCALVQE_GTCRN_KEEPNOISE_CKPT")
+    if v:
+        return v
+    d = "/workspace/localvqe/checkpoints/gtcrn_aec_keepnoise.pt"
+    return d if os.path.exists(d) else None
+
+
 def _build_v14aec():
     ckpt_path = _resolve_v14aec_ckpt()
     if ckpt_path is None:
@@ -301,6 +371,100 @@ def _build_v14aec_fe():
     return model, info
 
 
+def _build_v14aec_path(ckpt_path, label, frontend_only=False):
+    """Load a v1.4-AEC cascade from an explicit path (e.g. a controller-grafted
+    blob) so a retrained front-end controller can be A/B'd vs the deployed one.
+    frontend_only=True drops the neural mask -> the adaptive filter alone, so the
+    controller's echo behaviour is audible without the backend masking it."""
+    if not ckpt_path or not os.path.exists(ckpt_path):
+        return None, None
+    from localvqe_v14aec import load_cascade
+    model = load_cascade(ckpt_path, frontend_only=frontend_only)
+    info = {
+        "source": ckpt_path,
+        "sha256": _sha256(ckpt_path),
+        "n_params": 2742 if frontend_only else sum(p.numel() for p in model.parameters()),
+        "label": label,
+    }
+    print(f"{label}: loaded sha={info['sha256'][:16]}… src={ckpt_path}")
+    return model, info
+
+
+def _build_v14nsdr(resolve, label):
+    """v1.4 NS+dereverb cascade. Same loader as v1.4-AEC (load_cascade); wrapped
+    in try/except so an unreachable/incompatible checkpoint hides the entry
+    rather than crashing app startup."""
+    ckpt_path = resolve()
+    if ckpt_path is None:
+        return None, None
+    from localvqe_v14aec import load_cascade
+    try:
+        model = load_cascade(ckpt_path)
+    except Exception as e:
+        print(f"{label}: load failed ({e})")
+        return None, None
+    info = {
+        "source": ckpt_path,
+        "sha256": _sha256(ckpt_path),
+        "n_params": sum(p.numel() for p in model.parameters()),
+        "label": label,
+    }
+    print(f"{label} loaded: {info['n_params']:,} params  "
+          f"sha={info['sha256'][:16]}…  src={ckpt_path}")
+    return model, info
+
+
+def _build_gate():
+    """The 1.7K learned front-end gate. Reuses a v1.4 cascade's DAF front-end (so
+    it sees the same `e` as the CCM backends) and applies the per-bin magnitude
+    gate to STFT(e). Local-only; needs both the gate weights and a front-end ckpt."""
+    gate_ckpt = _resolve_gate_ckpt()
+    fe_ckpt = _resolve_v14nsdr200_ckpt() or _resolve_v14aec_ckpt()
+    if gate_ckpt is None or fe_ckpt is None:
+        return None, None
+    from localvqe_gate import load_gate
+    try:
+        model = load_gate(fe_ckpt, gate_ckpt)
+    except Exception as e:
+        print(f"gate: load failed ({e})")
+        return None, None
+    info = {
+        "source": gate_ckpt,
+        "sha256": _sha256(gate_ckpt),
+        "n_params": sum(p.numel() for p in model.parameters()),
+        "label": "gate (1.7K front-end magnitude gain — ≈passthrough, see EXPERIMENTS 2026-06-18)",
+    }
+    print(f"gate loaded: {info['n_params']:,} params  sha={info['sha256'][:16]}…  "
+          f"front-end={fe_ckpt}")
+    return model, info
+
+
+def _build_gtcrn(resolve, label, fe_ckpt_override=None):
+    """The 48.9K AEC-aware GTCRN_AEC. Reuses a v1.4 cascade's DAF front-end (same `e`
+    as the CCM backends) plus the echo estimate yhat=mic-e, and applies a complex-ratio
+    mask on STFT(e). Local-only; needs both the GTCRN weights and a front-end ckpt.
+    fe_ckpt_override lets a grafted (retrained-controller) cascade supply the front-end."""
+    gtcrn_ckpt = resolve()
+    fe_ckpt = fe_ckpt_override or _resolve_v14nsdr200_ckpt() or _resolve_v14aec_ckpt()
+    if gtcrn_ckpt is None or fe_ckpt is None:
+        return None, None
+    from localvqe_gtcrn import load_gtcrn_aec
+    try:
+        model = load_gtcrn_aec(fe_ckpt, gtcrn_ckpt)
+    except Exception as e:
+        print(f"gtcrn-aec ({label}): load failed ({e})")
+        return None, None
+    info = {
+        "source": gtcrn_ckpt,
+        "sha256": _sha256(gtcrn_ckpt),
+        "n_params": sum(p.numel() for p in model.parameters()),
+        "label": label,
+    }
+    print(f"gtcrn-aec loaded: {info['n_params']:,} params  sha={info['sha256'][:16]}…  "
+          f"front-end={fe_ckpt}  [{label}]")
+    return model, info
+
+
 def _build_v1():
     ckpt_path = _resolve_v1_ckpt()
     if ckpt_path is None:
@@ -519,6 +683,12 @@ GGUF_SPECS = [
     # adaptive filter's `e` directly (no neural mask). Same process_f32 path.
     ("v1.4-AEC front-end only (2.7K, GGUF)",
      "localvqe-v1.4-aec-2.7K-f32.gguf", 2_742, False),
+    # Compact / low-power line (~49K GTCRN-AEC). Self-contained GGUFs (the DAF
+    # front-end is embedded), so they load through the same GGUFModel path with
+    # no extra files. v1 = full enhance; AEC = echo-only keep-noise.
+    ("LocalVQE-Pi-v1-49k (GGUF)", "localvqe-pi-v1-49k-f32.gguf", 48_965, False),
+    ("LocalVQE-Pi-AEC-v1-49k (GGUF)",
+     "localvqe-pi-aec-v1-49k-f32.gguf", 48_965, False),
 ]
 
 
@@ -556,23 +726,31 @@ def _build_gguf(label: str, fname: str, n_params: int):
 # which keeps torch entirely unimported there.
 if ON_SPACE:
     MODEL_V1 = MODEL_V11 = MODEL_V12 = MODEL_V13 = None
-    MODEL_V13R2 = MODEL_V14AEC = MODEL_V14AECFE = None
+    MODEL_V14AEC = MODEL_V14AECFE = None
+    MODEL_GTCRN2 = MODEL_GTCRN_KN = None
     INFO_V1 = INFO_V11 = INFO_V12 = INFO_V13 = None
-    INFO_V13R2 = INFO_V14AEC = INFO_V14AECFE = None
+    INFO_V14AEC = INFO_V14AECFE = None
+    INFO_GTCRN2 = INFO_GTCRN_KN = None
 else:
     MODEL_V1, INFO_V1 = _build_v1()
     MODEL_V11, INFO_V11 = _build_v11()
     MODEL_V12, INFO_V12 = _build_v12()
     MODEL_V13, INFO_V13 = _build_v13()
-    MODEL_V13R2, INFO_V13R2 = _build_v13r2()
     MODEL_V14AEC, INFO_V14AEC = _build_v14aec()
     MODEL_V14AECFE, INFO_V14AECFE = _build_v14aec_fe()
-# Intermediate variants (v1.2.1, v1.2a-d, v1.3-NR/ME/MEP/MEP2) pruned from
-# the lineup — the builders are still defined above for easy revival.
-# v1.3.r2 (1.55M) is kept as a sibling smaller-capacity variant — the
-# 1.55M / 4.8M pair from the same r002/r003 from-scratch experiment lets
-# users feel the size/quality tradeoff locally. It's hidden on HF Spaces
-# because LOCALVQE_V13R2_CKPT is only set by docker-compose.yml.
+    # LocalVQE-Pi 49K GTCRN_AEC family (Raspberry-Pi deployment target). Same
+    # architecture, different training target:
+    #   LocalVQE-Pi-v1-49k      full enhancer  (echo + NS + dereverb; `clean`)
+    #   LocalVQE-Pi-AEC-v1-49k  echo-only      (keep noise + reverb; `room`)
+    MODEL_GTCRN2, INFO_GTCRN2 = _build_gtcrn(
+        _resolve_gtcrn_os400_ckpt, "LocalVQE-Pi-v1-49k")
+    MODEL_GTCRN_KN, INFO_GTCRN_KN = _build_gtcrn(
+        _resolve_gtcrn_keepnoise_ckpt, "LocalVQE-Pi-AEC-v1-49k")
+# Lineup trimmed to the released artifacts + the two LocalVQE-Pi 49K models.
+# Unreleased research entries pruned (builders kept above for revival):
+# v1.2.1/v1.2a-d, v1.3-NR/ME/MEP/MEP2, v1.3.r2 (1.55M), v1.4 NS+dereverb
+# 200K/800K, the retrained-controller v1.4-AEC variants, the 1.7K gate, and
+# the earlier GTCRN echo-priority / +retrained-FE experiments.
 MODEL_V121 = MODEL_V12A = MODEL_V12B = MODEL_V12C = MODEL_V12D = None
 MODEL_V13NR = MODEL_V13ME = MODEL_V13MEP = MODEL_V13MEP2 = None
 INFO_V121 = INFO_V12A = INFO_V12B = INFO_V12C = INFO_V12D = None
@@ -600,9 +778,6 @@ if MODEL_V11 is not None:
 if MODEL_V12 is not None:
     MODELS["v1.2 (1.3M)"] = MODEL_V12
     INFOS["v1.2 (1.3M)"] = INFO_V12
-if MODEL_V13R2 is not None:
-    MODELS["v1.3.r2 (1.55M)"] = MODEL_V13R2
-    INFOS["v1.3.r2 (1.55M)"] = INFO_V13R2
 if MODEL_V13 is not None:
     MODELS["v1.3 (4.8M)"] = MODEL_V13
     INFOS["v1.3 (4.8M)"] = INFO_V13
@@ -612,6 +787,12 @@ if MODEL_V14AEC is not None:
 if MODEL_V14AECFE is not None:
     MODELS["v1.4-AEC front-end only (2.7K)"] = MODEL_V14AECFE
     INFOS["v1.4-AEC front-end only (2.7K)"] = INFO_V14AECFE
+if MODEL_GTCRN2 is not None:
+    MODELS["LocalVQE-Pi-v1-49k"] = MODEL_GTCRN2
+    INFOS["LocalVQE-Pi-v1-49k"] = INFO_GTCRN2
+if MODEL_GTCRN_KN is not None:
+    MODELS["LocalVQE-Pi-AEC-v1-49k"] = MODEL_GTCRN_KN
+    INFOS["LocalVQE-Pi-AEC-v1-49k"] = INFO_GTCRN_KN
 if not MODELS:
     raise RuntimeError(
         "No model could be loaded. Check that space/lib contains the "
@@ -719,6 +900,14 @@ def enhance(mic_path: str, ref_path: str,
         # Released C++ engine: waveform in, waveform out. The debug
         # accordions poke PyTorch internals and don't apply here.
         out = model.process(mic, ref)
+    elif getattr(model, "IS_GATE", False):
+        # Front-end gate: DAF → e → per-bin magnitude gate → iSTFT. Time-domain
+        # in/out (no enc/decoder split); the debug accordions don't apply.
+        out = model.process(mic, ref)
+    elif getattr(model, "IS_GTCRN", False):
+        # AEC-aware GTCRN: DAF → (e, yhat=mic−e) → complex mask on e → iSTFT.
+        # Time-domain in/out; the debug accordions don't apply.
+        out = model.process(mic, ref)
     else:
         import torch
         mic_t = torch.from_numpy(mic).unsqueeze(0)

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