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 acoustic encoder for the SQCodec acoustic encoder (70m → 36m)
• Swap the w2v-bert-2.0 semantic encoder for DistilHuBERT (600m → 21m)

Our work is largely based on extending the work of X-Codec2.0 and SQCodec.

• Developed by: Neuphonic
• Model type: Neural Audio Codec
• License: apache-2.0
• Repository: https://github.com/neuphonic/neucodec
• Paper: arXiv
• Pre-encoded Datasets:
  • Emilia-YODAS-EN
  • 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:

conda create -n neucodec python=3.10
conda activate neucodec
pip install neucodec

Then, to use in 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).