Update README.md

README.md

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 ---
 license: cc-by-nc-4.0
+language:
+- en
+pipeline_tag: audio-to-audio
 ---
+# AgnesTachyon So-vits-svc 4.1 Model
+
+A so-vits-svc 4.1 model of AgnesTachyon in Uma Musume: Pretty Derby.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+This is a so-vits-svc 4.1 model of AgnesTachyon in Uma Musume: Pretty Derby. 
+
+
+- **Developed by:** [svc-develop-team](https://github.com/svc-develop-team)
+- **Shared by:** [70295](https://space.bilibili.com/700776013)
+- **Model type:** Audio to Audio
+- **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0)
+
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+- Clone the [so-vits-svc repository](https://github.com/svc-develop-team/so-vits-svc) and install all dependencies.  
+- Create a new folder named "models" and place the "AgnesTachyon" folder inside it.  
+- Navigate to the directory of "so-vits-svc" and execute the following command by replacing "xxx.wav" with the name of your source audio file and "x" with the desired key to raise/lower.
+```
+python inference_main.py -m "models/AgnesTachyon/AgnesTachyon.pth" -c "models/AgnesTachyon/config.json" -n "xxx.wav" -t x -s "AgnesTachyon"
+```
+Shallow diffusion model, cluster model and feature index model is also provided. Check [the README.md file of the *so-vits-svc project*](https://github.com/svc-develop-team/so-vits-svc/blob/4.1-Stable/README.md)
+for more information.
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+All of the training data is extracted from the Windows client of Uma Musume: Pretty Derby using the [umamusume-voice-text-extractor](https://github.com/chinosk6/umamusume-voice-text-extractor)
+The copyright of the training dataset belongs to Cygames.
+Only the voice is used, the live music soundtrack is not included in the training dataset.
+
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing
+
+Navigate to the directory of "so-vits-svc" and execute `python resample.py --skip_loudnorm` .
+
+#### Training Hyperparameters
+
+*Please check config.json and diffusion.yaml for training hyperparameters*
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** RTX 3090
+- **Hours used:** 41.6
+- **Provider:** Myself
+- **Compute Region:** Mainland China
+- **Carbon Emitted:** ~16.02kg CO2
+
+