- 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;
- 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**
## Why mix
## Plug-In-Diffusion
## 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/
```
## 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
[中文文档](https://github.com/PlayVoice/whisper-vits-svc/blob/HEAD/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