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---
title: Whisper Vits SVC
emoji: 🎵
python_version: 3.10.12
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.7.1
app_file: main.py
pinned: false
license: mit
---

<div align="center">
<h1> Variational Inference with adversarial learning for end-to-end Singing Voice Conversion based on VITS </h1>
    
[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/maxmax20160403/sovits5.0)
<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/PlayVoice/so-vits-svc-5.0">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/PlayVoice/so-vits-svc-5.0">
<img alt="GitHub issues" src="https://img.shields.io/github/issues/PlayVoice/so-vits-svc-5.0">
<img alt="GitHub" src="https://img.shields.io/github/license/PlayVoice/so-vits-svc-5.0">

[中文文档](./README_ZH.md)

The tree [bigvgan-mix-v2](https://github.com/PlayVoice/whisper-vits-svc/tree/bigvgan-mix-v2) has good audio quality

The tree [RoFormer-HiFTNet](https://github.com/PlayVoice/whisper-vits-svc/tree/RoFormer-HiFTNet) has fast infer speed

No More Upgrade

</div>

- This project targets deep learning beginners, basic knowledge of Python and PyTorch are the prerequisites for this project;
- This project aims to help deep learning beginners get rid of boring pure theoretical learning, and master the basic knowledge of deep learning by combining it with practices;
- This project does not support real-time voice converting; (need to replace whisper if real-time voice converting is what you are looking for)
- This project will not develop one-click packages for other purposes;

![vits-5.0-frame](https://github.com/PlayVoice/so-vits-svc-5.0/assets/16432329/3854b281-8f97-4016-875b-6eb663c92466)

- A minimum VRAM requirement of 6GB for training

- Support for multiple speakers

- Create unique speakers through speaker mixing

- It can even convert voices with light accompaniment

- You can edit F0 using Excel

https://github.com/PlayVoice/so-vits-svc-5.0/assets/16432329/6a09805e-ab93-47fe-9a14-9cbc1e0e7c3a

Powered by [@ShadowVap](https://space.bilibili.com/491283091)

## Model properties

| Feature | From | Status | Function |
| :--- | :--- | :--- | :--- |
| whisper | OpenAI | ✅ | strong noise immunity |
| bigvgan  | NVIDA | ✅ | alias and snake | The formant is clearer and the sound quality is obviously improved |
| natural speech | Microsoft | ✅ | reduce mispronunciation |
| neural source-filter | Xin Wang | ✅ | solve the problem of audio F0 discontinuity |
| pitch quantization | Xin Wang | ✅ | quantize the F0 for embedding |
| speaker encoder | Google | ✅ | Timbre Encoding and Clustering |
| GRL for speaker | Ubisoft |✅ | Preventing Encoder Leakage Timbre |
| SNAC |  Samsung | ✅ | One Shot Clone of VITS |
| SCLN |  Microsoft | ✅ | Improve Clone |
| Diffusion |  HuaWei | ✅ | Improve sound quality |
| PPG perturbation | this project | ✅ | Improved noise immunity and de-timbre |
| HuBERT perturbation | this project | ✅ | Improved noise immunity and de-timbre |
| VAE perturbation | this project | ✅ | Improve sound quality |
| MIX encoder | this project | ✅ | Improve conversion stability |
| USP infer | this project | ✅ | Improve conversion stability |
| HiFTNet | Columbia University | ✅ | NSF-iSTFTNet for speed up |
| RoFormer | Zhuiyi Technology | ✅ | Rotary Positional Embeddings |

due to the use of data perturbation, it takes longer to train than other projects.

**USP : Unvoice and Silence with Pitch when infer**
![vits_svc_usp](https://github.com/PlayVoice/so-vits-svc-5.0/assets/16432329/ba733b48-8a89-4612-83e0-a0745587d150)

## Why mix

![mix_frame](https://github.com/PlayVoice/whisper-vits-svc/assets/16432329/3ffa1be0-1a21-4752-96b5-6220f98f2313)

## Plug-In-Diffusion

![plug-in-diffusion](https://github.com/PlayVoice/so-vits-svc-5.0/assets/16432329/54a61c90-a97b-404d-9cc9-a2151b2db28f)

## Setup Environment

1. Install [PyTorch](https://pytorch.org/get-started/locally/).

2. Install project dependencies
    ```shell
    pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt
    ```
    **Note: whisper is already built-in, do not install it again otherwise it will cuase conflict and error**
3. Download the Timbre Encoder: [Speaker-Encoder by @mueller91](https://drive.google.com/drive/folders/15oeBYf6Qn1edONkVLXe82MzdIi3O_9m3), put `best_model.pth.tar`  into `speaker_pretrain/`.

4. Download whisper model [whisper-large-v2](https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt). Make sure to download `large-v2.pt`，put it into `whisper_pretrain/`.

5. Download [hubert_soft model](https://github.com/bshall/hubert/releases/tag/v0.1)，put `hubert-soft-0d54a1f4.pt` into `hubert_pretrain/`.

6. Download pitch extractor [crepe full](https://github.com/maxrmorrison/torchcrepe/tree/master/torchcrepe/assets)，put `full.pth` into `crepe/assets`.

   **Note: crepe full.pth is 84.9 MB, not 6kb**
   
7. Download pretrain model [sovits5.0.pretrain.pth](https://github.com/PlayVoice/so-vits-svc-5.0/releases/tag/5.0/), and put it into `vits_pretrain/`.
    ```shell
    python svc_inference.py --config configs/base.yaml --model ./vits_pretrain/sovits5.0.pretrain.pth --spk ./configs/singers/singer0001.npy --wave test.wav
    ```

## Dataset preparation

Necessary pre-processing:
1. Separate voice and accompaniment with [UVR](https://github.com/Anjok07/ultimatevocalremovergui) (skip if no accompaniment)
2. Cut audio input to shorter length with [slicer](https://github.com/flutydeer/audio-slicer), whisper takes input less than 30 seconds.
3. Manually check generated audio input, remove inputs shorter than 2 seconds or with obivous noise.
4. Adjust loudness if necessary, recommend Adobe Audiiton.
5. Put the dataset into the `dataset_raw` directory following the structure below.
```
dataset_raw
├───speaker0
│   ├───000001.wav
│   ├───...
│   └───000xxx.wav
└───speaker1
    ├───000001.wav
    ├───...
    └───000xxx.wav
```

## Data preprocessing
```shell
python svc_preprocessing.py -t 2
```
`-t`: threading, max number should not exceed CPU core count, usually 2 is enough.
After preprocessing you will get an output with following structure.
```
data_svc/
└── waves-16k
│    └── speaker0
│    │      ├── 000001.wav
│    │      └── 000xxx.wav
│    └── speaker1
│           ├── 000001.wav
│           └── 000xxx.wav
└── waves-32k
│    └── speaker0
│    │      ├── 000001.wav
│    │      └── 000xxx.wav
│    └── speaker1
│           ├── 000001.wav
│           └── 000xxx.wav
└── pitch
│    └── speaker0
│    │      ├── 000001.pit.npy
│    │      └── 000xxx.pit.npy
│    └── speaker1
│           ├── 000001.pit.npy
│           └── 000xxx.pit.npy
└── hubert
│    └── speaker0
│    │      ├── 000001.vec.npy
│    │      └── 000xxx.vec.npy
│    └── speaker1
│           ├── 000001.vec.npy
│           └── 000xxx.vec.npy
└── whisper
│    └── speaker0
│    │      ├── 000001.ppg.npy
│    │      └── 000xxx.ppg.npy
│    └── speaker1
│           ├── 000001.ppg.npy
│           └── 000xxx.ppg.npy
└── speaker
│    └── speaker0
│    │      ├── 000001.spk.npy
│    │      └── 000xxx.spk.npy
│    └── speaker1
│           ├── 000001.spk.npy
│           └── 000xxx.spk.npy
└── singer
│   ├── speaker0.spk.npy
│   └── speaker1.spk.npy
|
└── indexes
    ├── speaker0
    │   ├── some_prefix_hubert.index
    │   └── some_prefix_whisper.index
    └── speaker1
        ├── hubert.index
        └── whisper.index
```

1.  Re-sampling
    - Generate audio with a sampling rate of 16000Hz in `./data_svc/waves-16k` 
    ```
    python prepare/preprocess_a.py -w ./dataset_raw -o ./data_svc/waves-16k -s 16000
    ```
    
    - Generate audio with a sampling rate of 32000Hz in `./data_svc/waves-32k`
    ```
    python prepare/preprocess_a.py -w ./dataset_raw -o ./data_svc/waves-32k -s 32000
    ```
2. Use 16K audio to extract pitch
    ```
    python prepare/preprocess_crepe.py -w data_svc/waves-16k/ -p data_svc/pitch
    ```
3. Use 16K audio to extract ppg
    ```
    python prepare/preprocess_ppg.py -w data_svc/waves-16k/ -p data_svc/whisper
    ```
4. Use 16K audio to extract hubert
    ```
    python prepare/preprocess_hubert.py -w data_svc/waves-16k/ -v data_svc/hubert
    ```
5. Use 16k audio to extract timbre code
    ```
    python prepare/preprocess_speaker.py data_svc/waves-16k/ data_svc/speaker
    ```
6. Extract the average value of the timbre code for inference; it can also replace a single audio timbre in generating the training index, and use it as the unified timbre of the speaker for training 
    ```
    python prepare/preprocess_speaker_ave.py data_svc/speaker/ data_svc/singer
    ``` 
7. Use 32k audio to extract the linear spectrum
    ```
    python prepare/preprocess_spec.py -w data_svc/waves-32k/ -s data_svc/specs
    ``` 
8. Use 32k audio to generate training index
    ```
    python prepare/preprocess_train.py
    ```
11. Training file debugging
    ```
    python prepare/preprocess_zzz.py
    ```

## Train
1. If fine-tuning is based on the pre-trained model, you need to download the pre-trained model: [sovits5.0.pretrain.pth](https://github.com/PlayVoice/so-vits-svc-5.0/releases/tag/5.0). Put pretrained model under project root, change this line
    ```
    pretrain: "./vits_pretrain/sovits5.0.pretrain.pth"
    ```
    in `configs/base.yaml`，and adjust the learning rate appropriately, eg 5e-5.
   
   `batch_size`: for GPU with 6G VRAM, 6 is the recommended value, 8 will work but step speed will be much slower.
2. Start training
   ```
   python svc_trainer.py -c configs/base.yaml -n sovits5.0
   ``` 
3. Resume training
   ```
   python svc_trainer.py -c configs/base.yaml -n sovits5.0 -p chkpt/sovits5.0/sovits5.0_***.pt
   ```
4. Log visualization
   ```
   tensorboard --logdir logs/
   ```

![sovits5 0_base](https://github.com/PlayVoice/so-vits-svc-5.0/assets/16432329/1628e775-5888-4eac-b173-a28dca978faa)

![sovits_spec](https://github.com/PlayVoice/so-vits-svc-5.0/assets/16432329/c4223cf3-b4a0-4325-bec0-6d46d195a1fc)

## Inference

1. Export inference model: text encoder, Flow network, Decoder network
   ```
   python svc_export.py --config configs/base.yaml --checkpoint_path chkpt/sovits5.0/***.pt
   ```
2. Inference
   - if there is no need to adjust `f0`, just run the following command.
   ```
   python svc_inference.py --config configs/base.yaml --model sovits5.0.pth --spk ./data_svc/singer/your_singer.spk.npy --wave test.wav --shift 0
   ```
   - if `f0` will be adjusted manually, follow the steps:
     1. use whisper to extract content encoding, generate `test.vec.npy`.
       ```
       python whisper/inference.py -w test.wav -p test.ppg.npy
       ```
     2. use hubert to extract content vector, without using one-click reasoning, in order to reduce GPU memory usage
       ```
       python hubert/inference.py -w test.wav -v test.vec.npy
       ```
     3. extract the F0 parameter to the csv text format, open the csv file in Excel, and manually modify the wrong F0 according to Audition or SonicVisualiser
       ```
       python pitch/inference.py -w test.wav -p test.csv
       ```
     4. final inference
       ```
       python svc_inference.py --config configs/base.yaml --model sovits5.0.pth --spk ./data_svc/singer/your_singer.spk.npy --wave test.wav --ppg test.ppg.npy --vec test.vec.npy --pit test.csv --shift 0
       ```
3. Notes

    - when `--ppg` is specified, when the same audio is reasoned multiple times, it can avoid repeated extraction of audio content codes; if it is not specified, it will be automatically extracted;

    - when `--vec` is specified, when the same audio is reasoned multiple times, it can avoid repeated extraction of audio content codes; if it is not specified, it will be automatically extracted;

    - when `--pit` is specified, the manually tuned F0 parameter can be loaded; if not specified, it will be automatically extracted;

    - generate files in the current directory:svc_out.wav

4. Arguments ref

    | args |--config | --model | --spk | --wave | --ppg | --vec | --pit | --shift |
    | :---:  | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
    | name | config path | model path | speaker | wave input | wave ppg | wave hubert | wave pitch | pitch shift |

5. post by vad
```
python svc_inference_post.py --ref test.wav --svc svc_out.wav --out svc_out_post.wav
```

## Train Feature Retrieval Index (Optional)

To increase the stability of the generated timbre, you can use the method described in the 
[Retrieval-based-Voice-Conversion](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/docs/en/README.en.md) 
repository. This method consists of 2 steps: 

1. Training the retrieval index on hubert and whisper features
    Run training with default settings:
    ```
    python svc_train_retrieval.py
    ```
   
    If the number of vectors is more than 200_000 they will be compressed to 10_000 using the MiniBatchKMeans algorithm.
    You can change these settings using command line options:
    ```
    usage: crate faiss indexes for feature retrieval [-h] [--debug] [--prefix PREFIX] [--speakers SPEAKERS [SPEAKERS ...]] [--compress-features-after COMPRESS_FEATURES_AFTER]
                                                     [--n-clusters N_CLUSTERS] [--n-parallel N_PARALLEL]

    options:
      -h, --help            show this help message and exit
      --debug
      --prefix PREFIX       add prefix to index filename
      --speakers SPEAKERS [SPEAKERS ...]
                            speaker names to create an index. By default all speakers are from data_svc
      --compress-features-after COMPRESS_FEATURES_AFTER
                            If the number of features is greater than the value compress feature vectors using MiniBatchKMeans.
      --n-clusters N_CLUSTERS
                            Number of centroids to which features will be compressed
      --n-parallel N_PARALLEL
                            Nuber of parallel job of MinibatchKmeans. Default is cpus-1
    ``` 
    Compression of training vectors can speed up index inference, but reduces the quality of the retrieve.
    Use vector count compression if you really have a lot of them.
 
    The resulting indexes will be stored in the "indexes" folder as:
    ``` 
    data_svc
    ...
    └── indexes
        ├── speaker0
        │   ├── some_prefix_hubert.index
        │   └── some_prefix_whisper.index
        └── speaker1
            ├── hubert.index
            └── whisper.index
    ```
2. At the inference stage adding the n closest features in a certain proportion of the vits model
    Enable Feature Retrieval with settings:
    ```
    python svc_inference.py --config configs/base.yaml --model sovits5.0.pth --spk ./data_svc/singer/your_singer.spk.npy --wave test.wav --shift 0 \
    --enable-retrieval \
    --retrieval-ratio 0.5 \
    --n-retrieval-vectors 3
    ``` 
    For a better retrieval effect, you can try to cycle through different parameters: `--retrieval-ratio` and `--n-retrieval-vectors`
 
    If you have multiple sets of indexes, you can specify a specific set via the parameter: `--retrieval-index-prefix`
 
    You can explicitly specify the paths to the hubert and whisper indexes using the parameters: `--hubert-index-path` and `--whisper-index-path`
    

## Create singer
named by pure coincidence：average -> ave -> eva，eve(eva) represents conception and reproduction

```
python svc_eva.py
```

```python
eva_conf = {
    './configs/singers/singer0022.npy': 0,
    './configs/singers/singer0030.npy': 0,
    './configs/singers/singer0047.npy': 0.5,
    './configs/singers/singer0051.npy': 0.5,
}
```

the generated singer file will be `eva.spk.npy`.

## Data set

| Name | URL |
| :--- | :--- |
|KiSing         |http://shijt.site/index.php/2021/05/16/kising-the-first-open-source-mandarin-singing-voice-synthesis-corpus/|
|PopCS          |https://github.com/MoonInTheRiver/DiffSinger/blob/master/resources/apply_form.md|
|opencpop       |https://wenet.org.cn/opencpop/download/|
|Multi-Singer   |https://github.com/Multi-Singer/Multi-Singer.github.io|
|M4Singer       |https://github.com/M4Singer/M4Singer/blob/master/apply_form.md|
|CSD            |https://zenodo.org/record/4785016#.YxqrTbaOMU4|
|KSS            |https://www.kaggle.com/datasets/bryanpark/korean-single-speaker-speech-dataset|
|JVS MuSic      |https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_music|
|PJS            |https://sites.google.com/site/shinnosuketakamichi/research-topics/pjs_corpus|
|JUST Song      |https://sites.google.com/site/shinnosuketakamichi/publication/jsut-song|
|MUSDB18        |https://sigsep.github.io/datasets/musdb.html#musdb18-compressed-stems|
|DSD100         |https://sigsep.github.io/datasets/dsd100.html|
|Aishell-3      |http://www.aishelltech.com/aishell_3|
|VCTK           |https://datashare.ed.ac.uk/handle/10283/2651|
|Korean Songs   |http://urisori.co.kr/urisori-en/doku.php/|

## Code sources and references

https://github.com/facebookresearch/speech-resynthesis [paper](https://arxiv.org/abs/2104.00355)

https://github.com/jaywalnut310/vits [paper](https://arxiv.org/abs/2106.06103)

https://github.com/openai/whisper/ [paper](https://arxiv.org/abs/2212.04356)

https://github.com/NVIDIA/BigVGAN [paper](https://arxiv.org/abs/2206.04658)

https://github.com/mindslab-ai/univnet [paper](https://arxiv.org/abs/2106.07889)

https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts/tree/master/project/01-nsf

https://github.com/huawei-noah/Speech-Backbones/tree/main/Grad-TTS

https://github.com/brentspell/hifi-gan-bwe

https://github.com/mozilla/TTS

https://github.com/bshall/soft-vc

https://github.com/maxrmorrison/torchcrepe

https://github.com/MoonInTheRiver/DiffSinger

https://github.com/OlaWod/FreeVC [paper](https://arxiv.org/abs/2210.15418)

https://github.com/yl4579/HiFTNet [paper](https://arxiv.org/abs/2309.09493)

[Autoregressive neural f0 model for statistical parametric speech synthesis](https://web.archive.org/web/20210718024752id_/https://ieeexplore.ieee.org/ielx7/6570655/8356719/08341752.pdf)

[One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization](https://arxiv.org/abs/1904.05742)

[SNAC : Speaker-normalized Affine Coupling Layer in Flow-based Architecture for Zero-Shot Multi-Speaker Text-to-Speech](https://github.com/hcy71o/SNAC)

[Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers](https://arxiv.org/abs/2211.00585)

[AdaSpeech: Adaptive Text to Speech for Custom Voice](https://arxiv.org/pdf/2103.00993.pdf)

[AdaVITS: Tiny VITS for Low Computing Resource Speaker Adaptation](https://arxiv.org/pdf/2206.00208.pdf)

[Cross-Speaker Prosody Transfer on Any Text for Expressive Speech Synthesis](https://github.com/ubisoft/ubisoft-laforge-daft-exprt)

[Learn to Sing by Listening: Building Controllable Virtual Singer by Unsupervised Learning from Voice Recordings](https://arxiv.org/abs/2305.05401)

[Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion](https://arxiv.org/pdf/2305.09167.pdf)

[Multilingual Speech Synthesis and Cross-Language Voice Cloning: GRL](https://arxiv.org/abs/1907.04448)

[RoFormer: Enhanced Transformer with rotary position embedding](https://arxiv.org/abs/2104.09864)

## Method of Preventing Timbre Leakage Based on Data Perturbation

https://github.com/auspicious3000/contentvec/blob/main/contentvec/data/audio/audio_utils_1.py

https://github.com/revsic/torch-nansy/blob/main/utils/augment/praat.py

https://github.com/revsic/torch-nansy/blob/main/utils/augment/peq.py

https://github.com/biggytruck/SpeechSplit2/blob/main/utils.py

https://github.com/OlaWod/FreeVC/blob/main/preprocess_sr.py

## Contributors

<a href="https://github.com/PlayVoice/so-vits-svc/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=PlayVoice/so-vits-svc" />
</a>

## Thanks to

https://github.com/Francis-Komizu/Sovits

## Relevant Projects
- [LoRA-SVC](https://github.com/PlayVoice/lora-svc): decoder only svc
- [Grad-SVC](https://github.com/PlayVoice/Grad-SVC): diffusion based svc

## Original evidence
2022.04.12 https://mp.weixin.qq.com/s/autNBYCsG4_SvWt2-Ll_zA

2022.04.22 https://github.com/PlayVoice/VI-SVS

2022.07.26 https://mp.weixin.qq.com/s/qC4TJy-4EVdbpvK2cQb1TA

2022.09.08 https://github.com/PlayVoice/VI-SVC

## Be copied by svc-develop-team/so-vits-svc
![coarse_f0_1](https://github.com/PlayVoice/so-vits-svc-5.0/assets/16432329/e2f5e5d3-d169-42c1-953f-4e1648b6da37)