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# Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion
[](https://arxiv.org/abs/2310.11160)
[](https://www.zhangxueyao.com/data/MultipleContentsSVC/index.html)
[](https://huggingface.co/amphion/singing_voice_conversion)
[](https://huggingface.co/spaces/amphion/singing_voice_conversion)
[](https://openxlab.org.cn/apps/detail/Amphion/singing_voice_conversion)
<br>
<div align="center">
<img src="../../../imgs/svc/MultipleContentsSVC.png" width="85%">
</div>
<br>
This is the official implementation of the paper "[Leveraging Diverse Semantic-based Audio Pretrained Models for Singing Voice Conversion](https://arxiv.org/abs/2310.11160)" (2024 IEEE Spoken Language Technology Workshop). Specially,
- The muptile content features are from [Whipser](https://github.com/wenet-e2e/wenet) and [ContentVec](https://github.com/auspicious3000/contentvec).
- The acoustic model is based on Bidirectional Non-Causal Dilated CNN (called `DiffWaveNetSVC` in Amphion), which is similar to [WaveNet](https://arxiv.org/pdf/1609.03499.pdf), [DiffWave](https://openreview.net/forum?id=a-xFK8Ymz5J), and [DiffSVC](https://ieeexplore.ieee.org/document/9688219).
- The vocoder is [BigVGAN](https://github.com/NVIDIA/BigVGAN) architecture and we fine-tuned it in over 120 hours singing voice data.
## A Little Taste Before Getting Started
Before you delve into the code, we suggest exploring the interactive DEMO we've provided for a comprehensive overview. There are several ways you can engage with it:
1. **Online DEMO**
| HuggingFace | OpenXLab |
| :----------------------------------------------------------: | :----------------------------------------------------------: |
| [](https://huggingface.co/spaces/amphion/singing_voice_conversion)<br />(Worldwide) | [](https://openxlab.org.cn/apps/detail/Amphion/singing_voice_conversion)<br />(Suitable for Mainland China Users) |
2. **Run Local Gradio DEMO**
| Run with Docker | Duplicate Space with Private GPU |
| :----------------------------------------------------------: | :----------------------------------------------------------: |
| [](https://huggingface.co/spaces/amphion/singing_voice_conversion?docker=true) | [](https://huggingface.co/spaces/amphion/singing_voice_conversion?duplicate=true) |
3. **Run with the Extended Colab**
You can check out [this repo](https://github.com/camenduru/singing-voice-conversion-colab) to run it with Colab. Thanks to [@camenduru](https://x.com/camenduru?s=20) and the community for their support!
## Usage Overview
To train a `DiffWaveNetSVC` model, 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]"
},
```
### Custom Dataset
We support custom dataset, see [here](../../datasets/README.md#customsvcdataset) for the file structure to follow.
After constructing proper file structure, specify your dataset name in `dataset` and its path in `dataset_path`, also add its name in `use_custom_dataset`:
```json
"dataset": [
"[Exisiting Dataset Name]",
//...
"[Your Custom Dataset Name]"
],
"dataset_path": {
"[Exisiting Dataset Name]": "[Exisiting Dataset Path]",
//...
"[Your Custom Dataset Name]": "[Your Custom Dataset Path]"
},
"use_custom_dataset": [
"[Your Custom Dataset Name]"
],
```
> **NOTE:** Custom dataset name does not have to be the same as the folder name. But it needs to satisfy these rules:
> 1. It can not be the same as the exisiting dataset name.
> 2. It can not contain any space or underline(`_`).
> 3. It must be a valid folder name for operating system.
>
> Some examples of valid custom dataset names are `mydataset`, `myDataset`, `my-dataset`, `mydataset1`, `my-dataset-1`, etc.
## 2. Features Extraction
### Content-based Pretrained Models Download
By default, we utilize the Whisper and ContentVec 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/MultipleContentsSVC/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
},
...
}
```
### Train From Scratch
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/MultipleContentsSVC/run.sh --stage 2 --name [YourExptName]
```
### Train From Existing Source
We support training from existing source for various purposes. You can resume training the model from a checkpoint or fine-tune a model from another checkpoint.
Setting `--resume true`, the training will resume from the **latest checkpoint** by default. For example, if you want to resume training from the latest checkpoint in `Amphion/ckpts/svc/[YourExptName]/checkpoint`, run:
```bash
sh egs/svc/MultipleContentsSVC/run.sh --stage 2 --name [YourExptName] \
--resume true
```
You can choose a **specific checkpoint** for retraining by `--resume_from_ckpt_path` argument. For example, if you want to fine-tune from the checkpoint `Amphion/ckpts/svc/[YourExptName]/checkpoint/[SpecificCheckpoint]`, run:
```bash
sh egs/svc/MultipleContentsSVC/run.sh --stage 2 --name [YourExptName] \
--resume true
--resume_from_ckpt_path "Amphion/ckpts/svc/[YourExptName]/checkpoint/[SpecificCheckpoint]" \
```
If you want to **fine-tune from another checkpoint**, just use `--resume_type` and set it to `"finetune"`. For example, If you want to fine-tune from the checkpoint `Amphion/ckpts/svc/[AnotherExperiment]/checkpoint/[SpecificCheckpoint]`, run:
```bash
sh egs/svc/MultipleContentsSVC/run.sh --stage 2 --name [YourExptName] \
--resume true
--resume_from_ckpt_path "Amphion/ckpts/svc/[AnotherExperiment]/checkpoint/[SpecificCheckpoint]" \
--resume_type "finetune"
```
> **NOTE:** The `--resume_type` is set as `"resume"` in default. It's not necessary to specify it when resuming training.
>
> The difference between `"resume"` and `"finetune"` is that the `"finetune"` will **only** load the pretrained model weights from the checkpoint, while the `"resume"` will load all the training states (including optimizer, scheduler, etc.) from the checkpoint.
Here are some example scenarios to better understand how to use these arguments:
| Scenario | `--resume` | `--resume_from_ckpt_path` | `--resume_type` |
| ------ | -------- | ----------------------- | ------------- |
| You want to train from scratch | no | no | no |
| The machine breaks down during training and you want to resume training from the latest checkpoint | `true` | no | no |
| You find the latest model is overfitting and you want to re-train from the checkpoint before | `true` | `SpecificCheckpoint Path` | no |
| You want to fine-tune a model from another checkpoint | `true` | `SpecificCheckpoint Path` | `"finetune"` |
> **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
### Pretrained Vocoder Download
We fine-tune the official BigVGAN pretrained model with over 120 hours singing voice data. The benifits of fine-tuning has been investigated in our paper (see this [demo page](https://www.zhangxueyao.com/data/MultipleContentsSVC/vocoder.html)). The final pretrained singing voice vocoder is released [here](../../../pretrained/README.md#amphion-singing-bigvgan) (called `Amphion Singing BigVGAN`).
### 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` | `Amphion/ckpts/svc/[YourExptName]` |
| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/svc/[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 `Amphion/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 `Amphion/ckpts/svc/[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/MultipleContentsSVC/run.sh --stage 3 --gpu "0" \
--infer_expt_dir ckpts/svc/[YourExptName] \
--infer_output_dir ckpts/svc/[YourExptName]/result \
--infer_source_audio_dir [Your Audios Folder] \
--infer_target_speaker "opencpop_female1" \
--infer_key_shift "autoshift"
```
## Citations
```bibtex
@inproceedings{zhang2024leveraging,
author={Zhang, Xueyao and Fang, Zihao and Gu, Yicheng and Chen, Haopeng and Zou, Lexiao and Zhang, Junan and Xue, Liumeng and Wu, Zhizheng},
title={Leveraging Diverse Semantic-based Audio Pretrained Models for Singing Voice Conversion},
booktitle={{IEEE} Spoken Language Technology Workshop, {SLT} 2024},
year={2024}
}
```
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