zhenye234/xcodec
AAAI 2025: Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model
This project offers a way to enhance existing audio codecs by integrating both acoustic and semantic information. It takes raw audio inputs and processes them to produce higher-quality audio suitable for large audio language models. It would be used by researchers and developers working on advanced audio processing, speech synthesis, and general audio understanding applications.
294 stars. No commits in the last 6 months.
Use this if you are building or improving audio language models and need to ensure your audio encoding effectively captures both the sound quality and the meaning of the audio.
Not ideal if you are looking for a simple audio compression tool for general media consumption or basic audio file conversion.
Stars
294
Forks
23
Language
Python
License
MIT
Category
Last pushed
Oct 12, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/llm-tools/zhenye234/xcodec"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
zhuhanqing/APOLLO
APOLLO: SGD-like Memory, AdamW-level Performance; MLSys'25 Oustanding Paper Honorable Mention
HITESHLPATEL/Mamba-Papers
Awesome Mamba Papers: A Curated Collection of Research Papers , Tutorials & Blogs
Y-Research-SBU/CSRv2
Official Repository for CSRv2 - ICLR 2026
psychofict/llm-effective-context-length
Investigating Why the Effective Context Length of LLMs Falls Short (Based on STRING, ICLR 2025)
rishikksh20/mamba3-pytorch
Readable implementation of Mamba 3 SSM model