Update README.md

README.md

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-<div align="center">
-
-<h1>GPT-SoVITS-WebUI</h1>
-A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.<br><br>
-
-[![madewithlove](https://img.shields.io/badge/made_with-%E2%9D%A4-red?style=for-the-badge&labelColor=orange)](https://github.com/RVC-Boss/GPT-SoVITS)
-
-<img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br>
-
-[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
-[![Licence](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
-[![Huggingface](https://img.shields.io/badge/🤗%20-Models%20Repo-yellow.svg?style=for-the-badge)](https://huggingface.co/lj1995/GPT-SoVITS/tree/main)
-
-[**English**](./README.md) | [**中文简体**](./docs/cn/README.md) | [**日本語**](./docs/ja/README.md) | [**한국어**](./docs/ko/README.md)
-
-</div>
-
+---
+title: GPT SoVITS Emo
+emoji: 🐠
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 4.23.0
+app_file: app.py
+pinned: false
+license: mit
 ---
 
-## Features:
-
-1. **Zero-shot TTS:** Input a 5-second vocal sample and experience instant text-to-speech conversion.
-
-2. **Few-shot TTS:** Fine-tune the model with just 1 minute of training data for improved voice similarity and realism.
-
-3. **Cross-lingual Support:** Inference in languages different from the training dataset, currently supporting English, Japanese, and Chinese.
-
-4. **WebUI Tools:** Integrated tools include voice accompaniment separation, automatic training set segmentation, Chinese ASR, and text labeling, assisting beginners in creating training datasets and GPT/SoVITS models.
-
-**Check out our [demo video](https://www.bilibili.com/video/BV12g4y1m7Uw) here!**
-
-Unseen speakers few-shot fine-tuning demo:
-
-https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-80c060ab47fb
-
-**User guide: [简体中文](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e) | [English](https://rentry.co/GPT-SoVITS-guide#/)**
-
-## Installation
-
-For users in China region, you can [click here](https://www.codewithgpu.com/i/RVC-Boss/GPT-SoVITS/GPT-SoVITS-Official) to use AutoDL Cloud Docker to experience the full functionality online.
-
-### Tested Environments
-
-- Python 3.9, PyTorch 2.0.1, CUDA 11
-- Python 3.10.13, PyTorch 2.1.2, CUDA 12.3
-- Python 3.9, PyTorch 2.3.0.dev20240122, macOS 14.3 (Apple silicon)
-
-_Note: numba==0.56.4 requires py<3.11_
-
-### Windows
-
-If you are a Windows user (tested with win>=10), you can directly download the [pre-packaged distribution](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true) and double-click on _go-webui.bat_ to start GPT-SoVITS-WebUI.
-
-Users in China region can download the file by clicking [here](https://www.icloud.com.cn/iclouddrive/061bfkcVJcBfsMfLF5R2XKdTQ#GPT-SoVITS-beta0217) and then selecting "Download a copy."
-
-### Linux
-
-```bash
-conda create -n GPTSoVits python=3.9
-conda activate GPTSoVits
-bash install.sh
-```
-
-### macOS
-
-**Note: The models trained with GPUs on Macs result in significantly lower quality compared to those trained on other devices, so we are temporarily using CPUs instead.**
-
-First make sure you have installed FFmpeg by running `brew install ffmpeg` or `conda install ffmpeg`, then install by using the following commands:
-
-```bash
-conda create -n GPTSoVits python=3.9
-conda activate GPTSoVits
-
-pip install -r requirements.txt
-```
-
-### Install Manually
-
-#### Install Dependences
-
-```bash
-pip install -r requirements.txt
-```
-
-#### Install FFmpeg
-
-##### Conda Users
-
-```bash
-conda install ffmpeg
-```
-
-##### Ubuntu/Debian Users
-
-```bash
-sudo apt install ffmpeg
-sudo apt install libsox-dev
-conda install -c conda-forge 'ffmpeg<7'
-```
-
-##### Windows Users
-
-Download and place [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) and [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) in the GPT-SoVITS root.
-
-### Using Docker
-
-#### docker-compose.yaml configuration
-
-0. Regarding image tags: Due to rapid updates in the codebase and the slow process of packaging and testing images, please check [Docker Hub](https://hub.docker.com/r/breakstring/gpt-sovits) for the currently packaged latest images and select as per your situation, or alternatively, build locally using a Dockerfile according to your own needs.
-1. Environment Variables：
-
-- is_half: Controls half-precision/double-precision. This is typically the cause if the content under the directories 4-cnhubert/5-wav32k is not generated correctly during the "SSL extracting" step. Adjust to True or False based on your actual situation.
-
-2. Volumes Configuration，The application's root directory inside the container is set to /workspace. The default docker-compose.yaml lists some practical examples for uploading/downloading content.
-3. shm_size： The default available memory for Docker Desktop on Windows is too small, which can cause abnormal operations. Adjust according to your own situation.
-4. Under the deploy section, GPU-related settings should be adjusted cautiously according to your system and actual circumstances.
-
-#### Running with docker compose
-
-```
-docker compose -f "docker-compose.yaml" up -d
-```
-
-#### Running with docker command
-
-As above, modify the corresponding parameters based on your actual situation, then run the following command:
-
-```
-docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-DockerTest\output:/workspace/output --volume=G:\GPT-SoVITS-DockerTest\logs:/workspace/logs --volume=G:\GPT-SoVITS-DockerTest\SoVITS_weights:/workspace/SoVITS_weights --workdir=/workspace -p 9880:9880 -p 9871:9871 -p 9872:9872 -p 9873:9873 -p 9874:9874 --shm-size="16G" -d breakstring/gpt-sovits:xxxxx
-```
-
-## Pretrained Models
-
-Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS/pretrained_models`.
-
-For UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally), download models from [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) and place them in `tools/uvr5/uvr5_weights`.
-
-Users in China region can download these two models by entering the links below and clicking "Download a copy"
-
-- [GPT-SoVITS Models](https://www.icloud.com.cn/iclouddrive/056y_Xog_HXpALuVUjscIwTtg#GPT-SoVITS_Models)
-
-- [UVR5 Weights](https://www.icloud.com.cn/iclouddrive/0bekRKDiJXboFhbfm3lM2fVbA#UVR5_Weights)
-
-For Chinese ASR (additionally), download models from [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), and [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) and place them in `tools/asr/models`.
-
-For English or Japanese ASR (additionally), download models from [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) and place them in `tools/asr/models`. Also, [other models](https://huggingface.co/Systran) may have the similar effect with smaller disk footprint. 
-
-Users in China region can download this model by entering the links below
-
-- [Faster Whisper Large V3](https://www.icloud.com/iclouddrive/0c4pQxFs7oWyVU1iMTq2DbmLA#faster-whisper-large-v3) (clicking "Download a copy")
-
-- [Faster Whisper Large V3](https://hf-mirror.com/Systran/faster-whisper-large-v3) (HuggingFace mirror site)
-
-## Dataset Format
-
-The TTS annotation .list file format:
-
-```
-vocal_path|speaker_name|language|text
-```
-
-Language dictionary:
-
-- 'zh': Chinese
-- 'ja': Japanese
-- 'en': English
-
-Example:
-
-```
-D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
-```
-
-## Todo List
-
-- [ ] **High Priority:**
-
-  - [x] Localization in Japanese and English.
-  - [x] User guide.
-  - [x] Japanese and English dataset fine tune training.
-
-- [ ] **Features:**
-  - [ ] Zero-shot voice conversion (5s) / few-shot voice conversion (1min).
-  - [ ] TTS speaking speed control.
-  - [ ] Enhanced TTS emotion control.
-  - [ ] Experiment with changing SoVITS token inputs to probability distribution of vocabs.
-  - [ ] Improve English and Japanese text frontend.
-  - [ ] Develop tiny and larger-sized TTS models.
-  - [x] Colab scripts.
-  - [ ] Try expand training dataset (2k hours -> 10k hours).
-  - [ ] better sovits base model (enhanced audio quality)
-  - [ ] model mix
-
-## (Optional) If you need, here will provide the command line operation mode
-Use the command line to open the WebUI for UVR5
-```
-python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
-```
-If you can't open a browser, follow the format below for UVR processing,This is using mdxnet for audio processing
-```
-python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision 
-```
-This is how the audio segmentation of the dataset is done using the command line
-```
-python audio_slicer.py \
-    --input_path "<path_to_original_audio_file_or_directory>" \
-    --output_root "<directory_where_subdivided_audio_clips_will_be_saved>" \
-    --threshold <volume_threshold> \
-    --min_length <minimum_duration_of_each_subclip> \
-    --min_interval <shortest_time_gap_between_adjacent_subclips> 
-    --hop_size <step_size_for_computing_volume_curve>
-```
-This is how dataset ASR processing is done using the command line(Only Chinese)
-```
-python tools/asr/funasr_asr.py -i <input> -o <output>
-```
-ASR processing is performed through Faster_Whisper(ASR marking except Chinese)
-
-(No progress bars, GPU performance may cause time delays)
-```
-python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language>
-```
-A custom list save path is enabled
-
-## Credits
-
-Special thanks to the following projects and contributors:
-
-### Theoretical
-- [ar-vits](https://github.com/innnky/ar-vits)
-- [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
-- [vits](https://github.com/jaywalnut310/vits)
-- [TransferTTS](https://github.com/hcy71o/TransferTTS/blob/master/models.py#L556)
-- [contentvec](https://github.com/auspicious3000/contentvec/)
-- [hifi-gan](https://github.com/jik876/hifi-gan)
-- [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
-### Pretrained Models
-- [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
-- [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
-### Text Frontend for Inference
-- [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization)
-- [LangSegment](https://github.com/juntaosun/LangSegment)
-### WebUI Tools
-- [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
-- [audio-slicer](https://github.com/openvpi/audio-slicer)
-- [SubFix](https://github.com/cronrpc/SubFix)
-- [FFmpeg](https://github.com/FFmpeg/FFmpeg)
-- [gradio](https://github.com/gradio-app/gradio)
-- [faster-whisper](https://github.com/SYSTRAN/faster-whisper)
-- [FunASR](https://github.com/alibaba-damo-academy/FunASR)
-  
-## Thanks to all contributors for their efforts
-
-<a href="https://github.com/RVC-Boss/GPT-SoVITS/graphs/contributors" target="_blank">
-  <img src="https://contrib.rocks/image?repo=RVC-Boss/GPT-SoVITS" />
-</a>
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference