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README.md

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+---
+license: apache-2.0
+tags:
+- audio
+- speech
+- audio-to-audio
+- speech-language-models
+datasets:
+- amphion/Emilia-Dataset
+- facebook/multilingual_librispeech
+- CSTR-Edinburgh/vctk
+- google/fleurs
+- mozilla-foundation/common_voice_13_0
+- mythicinfinity/libritts_r
+---
+
+# Model Details
+
+Distill-NeuCodec is a version of NeuCodec with a compatible, distilled encoder. 
+
+The distilled encoder is 10x smaller in parameter count and uses ~7.5x less MACs at inference time. 
+
+The distilled model makes the following adjustments to the model:
+* Swap the notoriuously slow [BigCodec](https://arxiv.org/abs/2409.05377) acoustic encoder for the [SQCodec](https://arxiv.org/abs/2504.04949) acoustic encoder (70m → 36m)
+* Swap the [w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) semantic encoder for [DistilHuBERT](https://huggingface.co/ntu-spml/distilhubert) (600m → 21m)
+
+Our work is largely based on extending the work of [X-Codec2.0](https://huggingface.co/HKUSTAudio/xcodec2) and [SQCodec](https://arxiv.org/abs/2504.04949).
+
+- **Developed by:** Neuphonic
+- **Model type:** Neural Audio Codec
+- **License:** apache-2.0
+- **Repository:** https://github.com/neuphonic/neucodec
+- **Paper:** [arXiv](https://arxiv.org/abs/2509.09550)
+- **Pre-encoded Datasets:**
+  - [Emilia-YODAS-EN](https://huggingface.co/datasets/neuphonic/emilia-yodas-english-neucodec)
+  - *More coming soon!*
+
+
+## Get Started
+
+Use the code below to get started with the model.
+
+To install from pypi in a dedicated environment, using Python 3.10 or above:
+
+```bash
+conda create -n neucodec python=3.10
+conda activate neucodec
+pip install neucodec
+```
+Then, to use in python:
+
+```python
+import librosa
+import torch
+import torchaudio
+from torchaudio import transforms as T
+from neucodec import DistillNeuCodec
+ 
+model = DistillNeuCodec.from_pretrained("neuphonic/distill-neucodec")
+model.eval().cuda()   
+ 
+y, sr = torchaudio.load(librosa.ex("libri1"))
+if sr != 16_000:
+    y = T.Resample(sr, 16_000)(y)[None, ...] # (B, 1, T_16)
+
+with torch.no_grad():
+    fsq_codes = model.encode_code(y)
+    # fsq_codes = model.encode_code(librosa.ex("libri1")) # or directly pass your filepath!
+    print(f"Codes shape: {fsq_codes.shape}")  
+    recon = model.decode_code(fsq_codes).cpu() # (B, 1, T_24)
+
+torchaudio.save("reconstructed.wav", recon[0, :, :], 24_000)
+```
+
+## Training Details
+
+The model was trained using the same data as the full model, with an additional distillation loss (MSE between distilled and original encoder ouputs).

meta.yaml

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+author: neuphonic
+license: apache-2.0

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