--- 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 ---

Variational Inference with adversarial learning for end-to-end Singing Voice Conversion based on VITS

[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/maxmax20160403/sovits5.0) GitHub Repo stars GitHub forks GitHub issues GitHub [δΈ­ζ–‡ζ–‡ζ‘£](./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
- 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 | βœ… | strong noise immunity | | bigvgan | NVIDA | βœ… | alias and snake | The formant is clearer and the sound quality is obviously improved | | natural speech | Microsoft | βœ… | reduce mispronunciation | | neural source-filter | Xin Wang | βœ… | solve the problem of audio F0 discontinuity | | pitch quantization | Xin Wang | βœ… | quantize the F0 for embedding | | speaker encoder | Google | βœ… | Timbre Encoding and Clustering | | GRL for speaker | Ubisoft |βœ… | Preventing Encoder Leakage Timbre | | SNAC | Samsung | βœ… | One Shot Clone of VITS | | SCLN | Microsoft | βœ… | Improve Clone | | Diffusion | HuaWei | βœ… | Improve sound quality | | PPG perturbation | this project | βœ… | Improved noise immunity and de-timbre | | HuBERT perturbation | this project | βœ… | Improved noise immunity and de-timbre | | VAE perturbation | this project | βœ… | Improve sound quality | | MIX encoder | this project | βœ… | Improve conversion stability | | USP infer | this project | βœ… | Improve conversion stability | | HiFTNet | Columbia University | βœ… | NSF-iSTFTNet for speed up | | RoFormer | Zhuiyi Technology | βœ… | 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 β”œβ”€β”€β”€speaker0 β”‚ β”œβ”€β”€β”€000001.wav β”‚ β”œβ”€β”€β”€... β”‚ └───000xxx.wav └───speaker1 β”œβ”€β”€β”€000001.wav β”œβ”€β”€β”€... └───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 β”‚ └── speaker0 β”‚ β”‚ β”œβ”€β”€ 000001.wav β”‚ β”‚ └── 000xxx.wav β”‚ └── speaker1 β”‚ β”œβ”€β”€ 000001.wav β”‚ └── 000xxx.wav └── waves-32k β”‚ └── speaker0 β”‚ β”‚ β”œβ”€β”€ 000001.wav β”‚ β”‚ └── 000xxx.wav β”‚ └── speaker1 β”‚ β”œβ”€β”€ 000001.wav β”‚ └── 000xxx.wav └── pitch β”‚ └── speaker0 β”‚ β”‚ β”œβ”€β”€ 000001.pit.npy β”‚ β”‚ └── 000xxx.pit.npy β”‚ └── speaker1 β”‚ β”œβ”€β”€ 000001.pit.npy β”‚ └── 000xxx.pit.npy └── hubert β”‚ └── speaker0 β”‚ β”‚ β”œβ”€β”€ 000001.vec.npy β”‚ β”‚ └── 000xxx.vec.npy β”‚ └── speaker1 β”‚ β”œβ”€β”€ 000001.vec.npy β”‚ └── 000xxx.vec.npy └── whisper β”‚ └── speaker0 β”‚ β”‚ β”œβ”€β”€ 000001.ppg.npy β”‚ β”‚ └── 000xxx.ppg.npy β”‚ └── speaker1 β”‚ β”œβ”€β”€ 000001.ppg.npy β”‚ └── 000xxx.ppg.npy └── speaker β”‚ └── speaker0 β”‚ β”‚ β”œβ”€β”€ 000001.spk.npy β”‚ β”‚ └── 000xxx.spk.npy β”‚ └── speaker1 β”‚ β”œβ”€β”€ 000001.spk.npy β”‚ └── 000xxx.spk.npy └── singer β”‚ β”œβ”€β”€ speaker0.spk.npy β”‚ └── speaker1.spk.npy | └── indexes β”œβ”€β”€ speaker0 β”‚ β”œβ”€β”€ some_prefix_hubert.index β”‚ └── some_prefix_whisper.index └── speaker1 β”œβ”€β”€ 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 β”œβ”€β”€ speaker0 β”‚ β”œβ”€β”€ some_prefix_hubert.index β”‚ └── some_prefix_whisper.index └── speaker1 β”œβ”€β”€ 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 ## 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)