Usage of Refactor Branch

This is a cleaner version of Diffsinger, which provides:

fewer code: scripts unused in the DiffSinger are marked *isolated*;
better readability: many important functions are annotated (however, we assume the reader already knows how the neural networks work);
abstract classes: the bass classes are filtered out into the "basics/" folder and are annotated. Other classes inherent from the base classes.
better file structre: tts-related files are filtered out into the "tts/" folder, as they are not used in DiffSinger.
(new) Much condensed version of the preprocessing, training, and inference pipeline. The preprocessing pipeline is at 'preprocessing/opencpop.py', the training pipeline is at 'training/diffsinger.py', the inference pipeline is at 'inference/ds_cascade.py' or 'inference/ds_e2e.py'.

Progress since we forked into this repository

TBD

Getting Started

Installation

Environments and dependencies

# Install PyTorch manually (1.8.2 LTS recommended)
# See instructions at https://pytorch.org/get-started/locally/
# Below is an example for CUDA 11.1
pip3 install torch==1.8.2 torchvision==0.9.2 torchaudio==0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cu111

# Install other requirements
pip install -r requirements.txt

Pretrained models

(Required) Get the pretrained vocoder from the DiffSinger Community Vocoders Project and unzip it into checkpoints/ folder, or train a ultra-lightweight DDSP vocoder first by yourself, then configure it according to the relevant instructions.
Get the acoustic model from releases or elsewhere and unzip into the checkpoints/ folder.

Building your own dataset

This pipeline will guide you from installing dependencies to formatting your recordings and generating the final configuration file.

Preprocessing

The following is only an example for opencpop dataset.

export PYTHONPATH=.
CUDA_VISIBLE_DEVICES=0 python data_gen/binarize.py --config configs/acoustic/nomidi.yaml

Training

The following is only an example for opencpop dataset.

CUDA_VISIBLE_DEVICES=0 python run.py --config configs/acoustic/nomidi.yaml --exp_name $MY_DS_EXP_NAME --reset

Inference

Infer from *.ds file

python main.py path/to/your.ds --exp $MY_DS_EXP_NAME

See more supported arguments with python main.py -h. See examples of *.ds files in the samples/ folder.

Deployment

Export model to ONNX format

Please see this documentation before you run the following command:

python onnx/export/export_acoustic.py --exp $MY_DS_EXP_NAME

See more supported arguments with python onnx/export/export_acoustic.py -h.

Use DiffSinger via OpenUTAU editor

OpenUTAU, an open-sourced SVS editor with modern GUI, has unofficial temporary support for DiffSinger. See OpenUTAU for DiffSinger for more details.

Algorithms, principles and advanced features

See the original paper, the docs/ folder and releases for more details.

Below is the README inherited from the original repository.

DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism

| Interactive🤗 TTS | Interactive🤗 SVS

This repository is the official PyTorch implementation of our AAAI-2022 paper, in which we propose DiffSinger (for Singing-Voice-Synthesis) and DiffSpeech (for Text-to-Speech).

DiffSinger/DiffSpeech at training	DiffSinger/DiffSpeech at inference

🎉 🎉 🎉 Updates:

Sep.11, 2022: 🔌 DiffSinger-PN. Add plug-in PNDM, ICLR 2022 in our laboratory, to accelerate DiffSinger freely.
Jul.27, 2022: Update documents for SVS. Add easy inference A & B; Add Interactive SVS running on HuggingFace🤗 SVS.
Mar.2, 2022: MIDI-B-version.
Mar.1, 2022: NeuralSVB, for singing voice beautifying, has been released.
Feb.13, 2022: NATSpeech, the improved code framework, which contains the implementations of DiffSpeech and our NeurIPS-2021 work PortaSpeech has been released.
Jan.29, 2022: support MIDI-A-version SVS.
Jan.13, 2022: support SVS, release PopCS dataset.
Dec.19, 2021: support TTS. HuggingFace🤗 TTS

🚀 News:

Feb.24, 2022: Our new work, NeuralSVB was accepted by ACL-2022 . Demo Page.
Dec.01, 2021: DiffSinger was accepted by AAAI-2022.
Sep.29, 2021: Our recent work PortaSpeech: Portable and High-Quality Generative Text-to-Speech was accepted by NeurIPS-2021 .
May.06, 2021: We submitted DiffSinger to Arxiv .

Environments

conda create -n your_env_name python=3.8
source activate your_env_name 
pip install -r requirements_2080.txt   (GPU 2080Ti, CUDA 10.2)
or pip install -r requirements_3090.txt   (GPU 3090, CUDA 11.4)

Documents

Tensorboard

tensorboard --logdir_spec exp_name

Audio Demos

Old audio samples can be found in our demo page. Audio samples generated by this repository are listed here:

TTS audio samples

Speech samples (test set of LJSpeech) can be found in demos_1213.

SVS audio samples

Singing samples (test set of PopCS) can be found in demos_0112.

Citation

@article{liu2021diffsinger,
  title={Diffsinger: Singing voice synthesis via shallow diffusion mechanism},
  author={Liu, Jinglin and Li, Chengxi and Ren, Yi and Chen, Feiyang and Liu, Peng and Zhao, Zhou},
  journal={arXiv preprint arXiv:2105.02446},
  volume={2},
  year={2021}}

Acknowledgements

Our codes are based on the following repos:

Also thanks Keon Lee for fast implementation of our work.

6Bpencle/Singer-Backup