NeuCodec 🎧

HuggingFace 🤗: Model, Distilled Model

NeuCodec Demo

Created by Neuphonic - building faster, smaller, on-device voice AI

A lightweight neural codec that encodes audio at just 0.8 kbps - perfect for researchers and builders who need something that just works for training high quality text-to-speech models.

Key Features

🔊 Low bit-rate compression - a speech codec that compresses and reconstructs audio with near-inaudible reconstruction loss
🎼 Upsamples from 16kHz → 24kHz
🌍 Ready for real-world use - train your own SpeechLMs without needing to build your own codec
🏢 Commercial use permitted - use it in your own tools or products
📊 Released with large pre-encoded datasets - we’ve compressed Emilia-YODAS from 1.7TB to 41GB using NeuCodec, significantly reducing the compute requirements needed for training

Model Details

NeuCodec is a Finite Scalar Quantisation (FSQ) based 0.8kbps audio codec for speech tokenization. It takes advantage of the following features:

• It uses both audio (BigCodec) and semantic (Wav2Vec2-BERT) encoders.
• We make use of Finite Scalar Quantisation (FSQ) resulting in a single vector for the quantised output, which makes it ideal for downstream modeling with Speech Language Models.
• At 50 tokens/sec and 16 bits per token, the overall bit-rate is 0.8kbps.
• The codec takes in 16kHz input and outputs 24kHz using an upsampling decoder.

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

• 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

Get Started

Use the code below to get started with the model.

To install from pypi in a dedicated environment:

Using conda + pip:

conda create -n neucodec python>3.9
conda activate neucodec
pip install neucodec

Using uv:

uv venv neucodec --python 3.10
source neucodec/bin/activate  # On Windows: neucodec\Scripts\activate
uv pip install neucodec

If you would like to use the onnx decoder, also install onnxruntime:

pip install onnxruntime

Then, to use the regular codec in python:

import librosa
import torch
import torchaudio
from torchaudio import transforms as T
from neucodec import NeuCodec
 
model = NeuCodec.from_pretrained("neuphonic/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 following data:

• Emilia-YODAS
• MLS
• LibriTTS
• Fleurs
• CommonVoice
• HUI
• Additional proprietary set

All publically available data was covered by either the CC-BY-4.0 or CC0 license.

Citation

To cite this project, use the following bibtex entry:

@article{julian2025fsq,
  title={Finite Scalar Quantization Enables Redundant and Transmission-Robust Neural Audio Compression at Low Bit-rates},
  author={Julian, Harry and Beeson, Rachel and Konathala, Lohith and Ulin, Johanna and Gao, Jiameng},
  journal={arXiv preprint arXiv:2509.09550},
  year={2025},
  url={https://arxiv.org/abs/2509.09550}
}