# Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion [![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2310.11160) [![demo](https://img.shields.io/badge/SVC-Demo-red)](https://www.zhangxueyao.com/data/MultipleContentsSVC/index.html) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Models-pink)](https://huggingface.co/amphion/singing_voice_conversion) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Spaces-yellow)](https://huggingface.co/spaces/amphion/singing_voice_conversion) [![openxlab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/Amphion/singing_voice_conversion)

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 | | :----------------------------------------------------------: | :----------------------------------------------------------: | | [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Spaces-yellow)](https://huggingface.co/spaces/amphion/singing_voice_conversion)
(Worldwide) | [![openxlab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/Amphion/singing_voice_conversion)
(Suitable for Mainland China Users) | 2. **Run Local Gradio DEMO** | Run with Docker | Duplicate Space with Private GPU | | :----------------------------------------------------------: | :----------------------------------------------------------: | | [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Spaces-yellow)](https://huggingface.co/spaces/amphion/singing_voice_conversion?docker=true) | [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Spaces-yellow)](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} } ```