distill neucodec

Providerneuphonic
Categoryaudio-to-audio
Licenseapache-2.0
Downloads90
Stars0

Overview

Distill NeuCodec is an efficient audio-to-audio model designed for high-fidelity signal processing and compression. Unlike traditional codecs that rely on rigid mathematical transforms, this model leverages a distilled neural architecture to maintain acoustic transparency while significantly reducing bitrate overhead. For developers, this means the ability to integrate low-latency, high-quality audio streaming or storage into applications without the heavy computational cost of larger neural codecs. It is particularly suited for real-time communication tools, voice-over-IP (VoIP) systems, and edge-device deployment where memory and CPU cycles are constrained. Operating under the Apache-2.0 license, it offers the flexibility needed for both commercial scaling and open-source customization.

Highlights

  • Low-latency audio-to-audio neural compression
  • High-fidelity reconstruction with minimal bitrate
  • Optimized for edge device deployment
  • Permissive Apache-2.0 license for commercial use
  • Reduced computational overhead via model distillation

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("neuphonic/distill-neucodec")
tokenizer = AutoTokenizer.from_pretrained("neuphonic/distill-neucodec")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance:Before downloading, install huggingface_hub with:

Guidance
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository
huggingface-cli download neuphonic/distill-neucodec

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir)
huggingface-cli download neuphonic/distill-neucodec config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('neuphonic/distill-neucodec')

Git Download

Make sure git-lfs is installed first

Git Download
git lfs install
git clone https://huggingface.co/neuphonic/distill-neucodec

To skip LFS large-file downloads, use:

Skip LFS
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/neuphonic/distill-neucodec

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers
pip install -U transformers torch

Load the model and run inference

Load the model and run inference
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('neuphonic/distill-neucodec')
tokenizer = AutoTokenizer.from_pretrained('neuphonic/distill-neucodec')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance:Before downloading, install ModelScope with:

Guidance
pip install modelscope

CLI Download

Download the full repository

Download the full repository
modelscope download --model neuphonic/distill-neucodec

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir)
modelscope download --model neuphonic/distill-neucodec README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('neuphonic/distill-neucodec')

Git Download

Make sure git-lfs is installed first

Git Download
git lfs install
git clone https://www.modelscope.cn/neuphonic/distill-neucodec.git

To skip LFS large-file downloads, use:

Skip LFS
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/neuphonic/distill-neucodec.git

ModelScope 模型页直接下载模型文件;无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'neuphonic/distill-neucodec')
Join our Telegram