point at localvqe-v1-1.3M.pt (renamed on model repo)

app.py

@@ -1,28 +1,35 @@
 """Gradio demo for LocalVQE — real-time AEC + NS + dereverb.
 
-Downloads the shipped `localvqe-v1.pt` checkpoint from
-huggingface.co/LocalAI-io/LocalVQE on first run and caches it. Runs
-inference on CPU in the Space's default hardware tier.
+By default downloads the shipped `localvqe-v1-1.3M.pt` checkpoint from
+huggingface.co/LocalAI-io/LocalVQE. Set the env var
+`LOCALVQE_LOCAL_CKPT=/path/to/checkpoint.pt` to load a local file
+instead — useful for auditioning new training runs.
 """
+import os
 from pathlib import Path
 
 import gradio as gr
 import numpy as np
 import soundfile as sf
 import torch
-from huggingface_hub import hf_hub_download
 from scipy.signal import resample_poly
 
 from localvqe_model import Config, LocalVQE, apply_ckpt_model_config, load_checkpoint
 
 SR = 16000
 REPO_ID = "LocalAI-io/LocalVQE"
-CKPT_FILE = "localvqe-v1.pt"
+CKPT_FILE = "localvqe-v1-1.3M.pt"
 EXAMPLES_DIR = Path(__file__).resolve().parent / "examples"
 
 
 def _build_model() -> LocalVQE:
-    ckpt_path = hf_hub_download(repo_id=REPO_ID, filename=CKPT_FILE)
+    local_override = os.environ.get("LOCALVQE_LOCAL_CKPT")
+    if local_override:
+        ckpt_path = local_override
+        print(f"Loading local checkpoint: {ckpt_path}")
+    else:
+        from huggingface_hub import hf_hub_download
+        ckpt_path = hf_hub_download(repo_id=REPO_ID, filename=CKPT_FILE)
     cfg = Config()
     peek = torch.load(ckpt_path, map_location="cpu", weights_only=False)
     apply_ckpt_model_config(peek, cfg)