descript-audio-codec
A high-quality general neural audio codec.
Decision gist · record as of 2026-08-14
Yes, if you need audio compression for ML applications. The codec is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers strong compression with high fidelity. Install friction is low for developers already using PyTorch. Not suitable if you need real-time or streaming inference without GPU, or if you require lossless compression.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires CUDA-capable GPU or CPU; torch and torchaudio must be installed.
- Model weights are automatically downloaded on first use.
- Low install friction with a pure Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use, modification, and distribution with minimal restrictions—suitable for most projects.
last release 2023-07-20 (1121 days) · last repo commit 2026-07-16 · 1,842 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 487,316 downloads/mo, #6,387 on PyPI
Alternatives
Verify before relying
pip install descript-audio-codec
import descript_audio_codec
from descript_audiotools import AudioSignal
model = descript_audio_codec.DAC.load(model_path)
model.to('cuda')
signal = AudioSignal('input.wav')
signal.to(model.device)
x = model.preprocess(signal.audio_data, signal.sample_rate)
z, codes, latents, _, _ = model.encode(x)
y = model.decode(z)
y.write('output.wav')- Whether the package supports real-time or streaming encoding/decoding, or only batch processing
- Memory requirements for typical audio file sizes and whether chunking strategies are built-in for long files
- Compatibility with non-PyTorch inference frameworks or ONNX export
What it is and what it does
Descript Audio Codec is a neural audio codec that compresses audio into discrete codes at 8 kbps bitrate while preserving high fidelity. It uses an improved RVQGAN architecture and works universally across speech, music, and environmental audio. The package provides both command-line tools and a Python API for programmatic use via its runtime dependencies including torch, torchaudio, einops, numpy, argbind, descript-audiotools, and tqdm.
The codec is designed as a drop-in replacement for audio language modeling applications. Pre-trained model weights for 16 kHz, 24 kHz, and 44.1 kHz are automatically downloaded and cached on first use. The package is actively maintained and has no known vulnerabilities.
Use it for
- Compress audio for storage or transmission while maintaining perceptual quality in generative audio models
- Use as a backend codec for audio language models or music generation systems
- Encode audio files into discrete codes for downstream machine learning tasks
- Reconstruct high-fidelity audio from compressed .dac files for playback or further processing
- Replace existing codecs in audio ML pipelines that require a universal codec across domains
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need audio compression for ML applications.
The codec is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers strong compression with high fidelity. Install friction is low for developers already using PyTorch. Not suitable if you need real-time or streaming inference without GPU, or if you require lossless compression.
Install
descript-audio-codec on PyPI
Before you install
Low install friction with a pure Python wheel. Requires torch and torchaudio as runtime dependencies, which are substantial downloads but standard for audio ML work. Repository is actively maintained with recent commits.
Requires CUDA-capable GPU or CPU; torch and torchaudio must be installed. Model weights are automatically downloaded on first use.
License in practice
MIT license permits commercial and private use, modification, and distribution with minimal restrictions—suitable for most projects.
Quickstart
pip install descript-audio-codec
import descript_audio_codec
from descript_audiotools import AudioSignal
model = descript_audio_codec.DAC.load(model_path)
model.to('cuda')
signal = AudioSignal('input.wav')
signal.to(model.device)
x = model.preprocess(signal.audio_data, signal.sample_rate)
z, codes, latents, _, _ = model.encode(x)
y = model.decode(z)
y.write('output.wav')Verify before relying
- Whether the package supports real-time or streaming encoding/decoding, or only batch processing
- Memory requirements for typical audio file sizes and whether chunking strategies are built-in for long files
- Compatibility with non-PyTorch inference frameworks or ONNX export
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesargbinddescript-audiotoolseinopsnumpytorchtorchaudiotqdm |
| Maintenance | Actively maintained 1,121 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 487,316 / month, #6,387 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersNatural Language :: EnglishProgramming Language :: Python :: 3.7Topic :: Artistic SoftwareTopic :: MultimediaTopic :: Multimedia :: Sound/AudioTopic :: Multimedia :: Sound/Audio :: EditorsTopic :: Software Development :: Libraries |
Evidence: descript_audio_codec-1.0.0-py3-none-any.whl
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See also descript-audiotools · encodec · snac · silk-python · pcodec · resemble-perth · opuslib-next · opuslib · vocos · numcodecs