Add self-contained inference CLI

infer_widecodec.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+"""WideCodec — self-contained inference (44.1 kHz neural codec, depth-20 decoder).
+
+Encode -> decode any audio to a 44.1 kHz reconstruction. The encoder ingests mono
+16 kHz; the frozen FSQ codebook (single codebook, 50 tokens/s, ~0.8 kbps) yields the
+code stream; the depth-20 decoder renders it back to 44.1 kHz.
+
+This script + the bundled `neucodec/` package are ALL you need — no other source
+repo. Weights (`pytorch_model.bin`) are pulled from the HF model repo on first run.
+
+  pip install torch transformers huggingface_hub local-attention einops librosa soundfile
+  huggingface-cli login          # if the repo is private (or export HF_TOKEN=hf_...)
+  python infer_widecodec.py --input my.wav --out-dir out
+  python infer_widecodec.py --input folder/ --out-dir out    # batch a directory
+"""
+import argparse
+import glob
+import os
+import sys
+
+# make the bundled `neucodec/` package importable regardless of CWD
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+os.environ.setdefault("HF_HUB_DISABLE_XET", "1")  # plain HTTPS: Xet can hang on some networks
+
+import librosa
+import soundfile as sf
+import torch
+
+from neucodec import NeuCodec
+
+AUDIO_EXTS = (".wav", ".mp3", ".flac", ".m4a", ".ogg", ".opus")
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--input", required=True, help="audio file OR a directory of audio")
+    ap.add_argument("--out-dir", default="out")
+    ap.add_argument("--repo", default="Scicom-intl/WideCodec")
+    ap.add_argument("--depth", type=int, default=20, help="decoder depth (WideCodec=20)")
+    ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
+    a = ap.parse_args()
+
+    os.makedirs(a.out_dir, exist_ok=True)
+    print(f"[infer] loading {a.repo} (decoder_depth={a.depth}) on {a.device} …")
+    model = NeuCodec._from_pretrained(
+        model_id=a.repo, decoder_depth=a.depth, token=os.environ.get("HF_TOKEN")
+    ).eval().to(a.device)
+    sr_out = model.sample_rate
+    print(f"[infer] output sample_rate = {sr_out}")
+
+    if os.path.isdir(a.input):
+        files = sorted(f for f in glob.glob(os.path.join(a.input, "*"))
+                       if f.lower().endswith(AUDIO_EXTS))
+    else:
+        files = [a.input]
+    print(f"[infer] {len(files)} file(s)")
+
+    for f in files:
+        wav16, _ = librosa.load(f, sr=16000, mono=True)  # frozen encoder wants 16 kHz mono
+        x = torch.from_numpy(wav16).float().view(1, 1, -1).to(a.device)
+        with torch.no_grad():
+            codes = model.encode_code(x)
+            wav = model.decode_code(codes).squeeze().detach().cpu().float().numpy()
+        base = os.path.splitext(os.path.basename(f))[0]
+        out = os.path.join(a.out_dir, f"{base}_44k.wav")
+        sf.write(out, wav, sr_out)
+        print(f"[infer] {f} -> {out}  ({len(wav) / sr_out:.2f}s @ {sr_out} Hz)")
+
+
+if __name__ == "__main__":
+    main()