diff --git a/README.md b/README.md
index c321d6e..a47a445 100644
--- a/README.md
+++ b/README.md
@@ -1,97 +1,11 @@
-![Release](https://img.shields.io/github/v/release/ahmetoner/whisper-asr-webservice.svg)
-![Docker Pulls](https://img.shields.io/docker/pulls/onerahmet/openai-whisper-asr-webservice.svg)
-![Build](https://img.shields.io/github/actions/workflow/status/ahmetoner/whisper-asr-webservice/docker-publish.yml.svg)
-![Licence](https://img.shields.io/github/license/ahmetoner/whisper-asr-webservice.svg)
+The latest version of asr-webservice has added support for the RTX 5090 GPU. By recompiling the source code, we have added support for torch2.7+cuda128. Now, it can efficiently provide transcription services for applications like Speaker using the GPU!
 
-> 🎉 **Join our Discord Community!** Connect with other users, get help, and stay updated on the latest features: [https://discord.gg/4Q5YVrePzZ](https://discord.gg/4Q5YVrePzZ)
+You can directly pull the pre-built image I have packaged using the following command:
 
-# Whisper ASR Box
+docker pull crpi-n9jif4z5nex2rnkd.cn-hangzhou.personal.cr.aliyuncs.com/docker_2025-images/whisper-asr-webservice_for_5090:latest
 
-Whisper ASR Box is a general-purpose speech recognition toolkit. Whisper Models are trained on a large dataset of diverse audio and is also a multitask model that can perform multilingual speech recognition as well as speech translation and language identification.
+为asr-webservice的最新版本添加RTX 5090显卡支持，通过对源代码重新编译，添加了torch2.7+cuda128，现在可以使用GPU来高效地为Speaker等应用提供转写服务啦！
 
-## Features
+直接通过以下命令拉取我打包好的镜像即可：
 
-Current release (v1.9.1) supports following whisper models:
-
-- [openai/whisper](https://github.com/openai/whisper)@[v20250625](https://github.com/openai/whisper/releases/tag/v20250625)
-- [SYSTRAN/faster-whisper](https://github.com/SYSTRAN/faster-whisper)@[v1.1.1](https://github.com/SYSTRAN/faster-whisper/releases/tag/v1.1.1)
-- [whisperX](https://github.com/m-bain/whisperX)@[v3.4.2](https://github.com/m-bain/whisperX/releases/tag/v3.4.2)
-
-## Quick Usage
-
-### CPU
-
-```shell
-docker run -d -p 9000:9000 \
-  -e ASR_MODEL=base \
-  -e ASR_ENGINE=openai_whisper \
-  onerahmet/openai-whisper-asr-webservice:latest
-```
-
-### GPU
-
-```shell
-docker run -d --gpus all -p 9000:9000 \
-  -e ASR_MODEL=base \
-  -e ASR_ENGINE=openai_whisper \
-  onerahmet/openai-whisper-asr-webservice:latest-gpu
-```
-
-#### Cache
-
-To reduce container startup time by avoiding repeated downloads, you can persist the cache directory:
-
-```shell
-docker run -d -p 9000:9000 \
-  -v $PWD/cache:/root/.cache/ \
-  onerahmet/openai-whisper-asr-webservice:latest
-```
-
-## Key Features
-
-- Multiple ASR engines support (OpenAI Whisper, Faster Whisper, WhisperX)
-- Multiple output formats (text, JSON, VTT, SRT, TSV)
-- Word-level timestamps support
-- Voice activity detection (VAD) filtering
-- Speaker diarization (with WhisperX)
-- FFmpeg integration for broad audio/video format support
-- GPU acceleration support
-- Configurable model loading/unloading
-- REST API with Swagger documentation
-
-## Environment Variables
-
-Key configuration options:
-
-- `ASR_ENGINE`: Engine selection (openai_whisper, faster_whisper, whisperx)
-- `ASR_MODEL`: Model selection (tiny, base, small, medium, large-v3, etc.)
-- `ASR_MODEL_PATH`: Custom path to store/load models
-- `ASR_DEVICE`: Device selection (cuda, cpu)
-- `MODEL_IDLE_TIMEOUT`: Timeout for model unloading
-
-## Documentation
-
-For complete documentation, visit:
-[https://ahmetoner.github.io/whisper-asr-webservice](https://ahmetoner.github.io/whisper-asr-webservice)
-
-## Development
-
-```shell
-# Install poetry v2.X
-pip3 install poetry
-
-# Install dependencies for cpu
-poetry install --extras cpu
-
-# Install dependencies for cuda
-poetry install --extras cuda
-
-# Run service
-poetry run whisper-asr-webservice --host 0.0.0.0 --port 9000
-```
-
-After starting the service, visit `http://localhost:9000` or `http://0.0.0.0:9000` in your browser to access the Swagger UI documentation and try out the API endpoints.
-
-## Credits
-
-- This software uses libraries from the [FFmpeg](http://ffmpeg.org) project under the [LGPLv2.1](http://www.gnu.org/licenses/old-licenses/lgpl-2.1.html)
+docker pull crpi-n9jif4z5nex2rnkd.cn-hangzhou.personal.cr.aliyuncs.com/docker_2025-images/whisper-asr-webservice_for_5090:latest