egs/svc/VitsSVC/README.md

# VITS for Singing Voice Conversion

This is an implementation of VITS as acoustic model for end-to-end singing voice conversion. Adapted from [so-vits-svc](https://github.com/svc-develop-team/so-vits-svc), SoftVC content encoder is used to extract content features from the source audio. These feature vectors are directly fed into VITS without the need for conversion to a text-based intermediate representation.

There are four stages in total:

1. Data preparation
2. Features extraction
3. Training
4. Inference/conversion

> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
> ```bash
> cd Amphion
> ```

## 1. Data Preparation

### Dataset Download

By default, we utilize the five datasets for training: M4Singer, Opencpop, OpenSinger, SVCC, and VCTK. How to download them is detailed [here](../../datasets/README.md).

### Configuration

Specify the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets.

```json
 "dataset": [
 "m4singer",
 "opencpop",
 "opensinger",
 "svcc",
 "vctk"
 ],
 "dataset_path": {
 // TODO: Fill in your dataset path
 "m4singer": "[M4Singer dataset path]",
 "opencpop": "[Opencpop dataset path]",
 "opensinger": "[OpenSinger dataset path]",
 "svcc": "[SVCC dataset path]",
 "vctk": "[VCTK dataset path]"
 },
```

## 2. Features Extraction

### Content-based Pretrained Models Download

By default, we utilize ContentVec and Whisper to extract content features. How to download them is detailed [here](../../../pretrained/README.md).

### Configuration

Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`:

```json
 // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc"
 "log_dir": "ckpts/svc",
 "preprocess": {
 // TODO: Fill in the output data path. The default value is "Amphion/data"
 "processed_dir": "data",
 ...
 },
```

### Run

Run the `run.sh` as the preproces stage (set `--stage 1`).

```bash
sh egs/svc/VitsSVC/run.sh --stage 1
```

> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`.

## 3. Training

### Configuration

We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines.

```json
"train": {
 "batch_size": 32,
 ...
 "adamw": {
 "lr": 2.0e-4
 },
 ...
 }
```

### Run

Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/svc/[YourExptName]`.

```bash
sh egs/svc/VitsSVC/run.sh --stage 2 --name [YourExptName]
```

> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`.

## 4. Inference/Conversion

### Run

For inference/conversion, you need to specify the following configurations when running `run.sh`:

| Parameters | Description | Example |
| --------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `[Your path to save logs and checkpoints]/[YourExptName]` |
| `--infer_output_dir` | The output directory to save inferred audios. | `[Your path to save logs and checkpoints]/[YourExptName]/result` |
| `--infer_source_file` or `--infer_source_audio_dir` | The inference source (can be a json file or a dir). | The `infer_source_file` could be `[Your path to save processed data]/[YourDataset]/test.json`, and the `infer_source_audio_dir` is a folder which includes several audio files (*.wav, *.mp3 or *.flac). |
| `--infer_target_speaker` | The target speaker you want to convert into. You can refer to `[Your path to save logs and checkpoints]/[YourExptName]/singers.json` to choose a trained speaker. | For opencpop dataset, the speaker name would be `opencpop_female1`. |
| `--infer_key_shift` | How many semitones you want to transpose. | `"autoshfit"` (by default), `3`, `-3`, etc. |

For example, if you want to make `opencpop_female1` sing the songs in the `[Your Audios Folder]`, just run:

```bash
sh egs/svc/VitsSVC/run.sh --stage 3 --gpu "0" \
 --infer_expt_dir Amphion/ckpts/svc/[YourExptName] \
 --infer_output_dir Amphion/ckpts/svc/[YourExptName]/result \
 --infer_source_audio_dir [Your Audios Folder] \
 --infer_target_speaker "opencpop_female1" \
 --infer_key_shift "autoshift"
```