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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} | |
| } | |
| ``` | |