README: self-contained inference quickstart

README.md

@@ -25,6 +25,36 @@ NeuCodec, so this model is a drop-in higher-fidelity decoder.
 - **Trainable:** decoder only (GAN: multi-period + spec discriminators; losses: multi-res mel + STFT + feature-matching + adversarial)
 - **Frozen:** w2v-BERT semantic encoder, acoustic `CodecEnc`, `fc_prior`/`fc_post_a`, FSQ codebook
 
+## Inference (self-contained)
+
+Everything needed to run inference is **in this repo** — the `neucodec/` package and
+`infer_widecodec.py`. No other source code required.
+
+```bash
+pip install torch transformers huggingface_hub local-attention einops librosa soundfile
+huggingface-cli login          # if this repo is private (or export HF_TOKEN=hf_...)
+
+huggingface-cli download Scicom-intl/WideCodec --local-dir WideCodec
+cd WideCodec
+python infer_widecodec.py --input my.wav --out-dir out      # one file
+python infer_widecodec.py --input folder/  --out-dir out    # a directory
+```
+
+Or load it directly in Python:
+
+```python
+import sys; sys.path.insert(0, "WideCodec")   # the downloaded repo dir
+import torch, librosa
+from neucodec import NeuCodec
+
+model = NeuCodec._from_pretrained(model_id="Scicom-intl/WideCodec", decoder_depth=20).eval().cuda()
+wav16, _ = librosa.load("my.wav", sr=16000, mono=True)   # encoder ingests 16 kHz mono
+x = torch.from_numpy(wav16).float().view(1, 1, -1).cuda()
+with torch.no_grad():
+    codes  = model.encode_code(x)      # frozen FSQ codes — 0.8 kbps, 50 tok/s
+    wav44  = model.decode_code(codes)  # 44.1 kHz reconstruction
+```
+
 ## 🔗 Source code
 
 Training pipeline, configs and benchmarks: