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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) | |
| <br> | |
| <div align="center"> | |
| <img src="../../../imgs/svc/MultipleContentsSVC.png" width="85%"> | |
| </div> | |
| <br> | |
| 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} | |
| } | |
| ``` | |