chore: import upstream snapshot with attribution

runtime/llama.cpp/export_vad_gguf.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+"""Export FSMN-VAD (encoder + CMVN) to GGUF for the ggml C++ runtime."""
+import argparse, os, re
+import numpy as np, torch, gguf
+
+def parse_mvn(path):
+    with open(path) as f: txt=f.read()
+    b=[np.array([float(x) for x in m.split()],np.float32) for m in re.findall(r"\[([^\]]*)\]",txt)]
+    v=[x for x in b if x.size>1]; return v[0], v[1]   # shift, scale (both 400-dim)
+
+def main():
+    ap=argparse.ArgumentParser()
+    ap.add_argument("--model_pt",required=True); ap.add_argument("--mvn",required=True); ap.add_argument("--out",required=True)
+    a=ap.parse_args()
+    sd=torch.load(a.model_pt,map_location="cpu"); sd=sd.get("state_dict",sd)
+    w=gguf.GGUFWriter(a.out,"fsmn-vad")
+    w.add_uint32("vad.input_dim",400); w.add_uint32("vad.input_affine_dim",140)
+    w.add_uint32("vad.linear_dim",250); w.add_uint32("vad.proj_dim",128)
+    w.add_uint32("vad.fsmn_layers",4); w.add_uint32("vad.lorder",20)
+    w.add_uint32("vad.output_affine_dim",140); w.add_uint32("vad.output_dim",248)
+    w.add_uint32("vad.n_mels",80); w.add_uint32("vad.lfr_m",5); w.add_uint32("vad.lfr_n",1)
+    shift,scale=parse_mvn(a.mvn); w.add_tensor("cmvn.shift",shift); w.add_tensor("cmvn.scale",scale)
+    n=0
+    for k,v in sd.items():
+        if not k.startswith("encoder."): continue
+        arr=v.detach().to(torch.float32).contiguous().numpy()
+        if k.endswith("conv_left.weight"):   # (C,1,lorder,1) -> (lorder,C) tap-major
+            arr=np.ascontiguousarray(arr[:,0,:,0].T)
+        w.add_tensor(k,arr); n+=1
+    print(f"writing {n} tensors (+cmvn) to {a.out}")
+    w.write_header_to_file(); w.write_kv_data_to_file(); w.write_tensors_to_file(); w.close()
+    print(f"done: {a.out} ({os.path.getsize(a.out)/1e6:.1f} MB)")
+
+if __name__=="__main__": main()