# 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)

This is the official implementation of the paper "[Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion](https://arxiv.org/abs/2310.11160)" (NeurIPS 2023 Workshop on Machine Learning for Audio). 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. 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 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 }, ... } ``` ### 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/MultipleContentsSVC/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 ### 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 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" ``` ## Citations ```bibtex @article{zhang2023leveraging, title={Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion}, author={Zhang, Xueyao and Gu, Yicheng and Chen, Haopeng and Fang, Zihao and Zou, Lexiao and Xue, Liumeng and Wu, Zhizheng}, journal={Machine Learning for Audio Worshop, NeurIPS 2023}, year={2023} } ```