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| # Transformer for Singing Voice Conversion | |
| This is an implementation of **vanilla transformer encoder**/**conformer** as acoustic model for singing voice conversion. | |
| 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/TransformerSVC/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 | |
| Specify the detailed configuration for transformer block in `exp_config.json`. For key `type`, `conformer` and `transformer` are supported: | |
| ```json | |
| "model": { | |
| ... | |
| "transformer":{ | |
| // 'conformer' or 'transformer' | |
| "type": "conformer", | |
| "input_dim": 384, | |
| "output_dim": 100, | |
| "n_heads": 2, | |
| "n_layers": 6, | |
| "filter_channels":512, | |
| "dropout":0.1, | |
| } | |
| } | |
| ``` | |
| 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/TransformerSVC/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 | |
| cd Amphion | |
| sh egs/svc/TransformerSVC/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 | |
| @inproceedings{transformer, | |
| author = {Ashish Vaswani and | |
| Noam Shazeer and | |
| Niki Parmar and | |
| Jakob Uszkoreit and | |
| Llion Jones and | |
| Aidan N. Gomez and | |
| Lukasz Kaiser and | |
| Illia Polosukhin}, | |
| title = {Attention is All you Need}, | |
| booktitle = {{NIPS}}, | |
| pages = {5998--6008}, | |
| year = {2017} | |
| } | |
| ``` |