wehub-resource-sync
39 lines
2.0 KiB
Markdown
39 lines
2.0 KiB
Markdown
# DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
|
|
[](https://arxiv.org/abs/2105.02446)
|
|
[](https://github.com/MoonInTheRiver/DiffSinger)
|
|
[](https://github.com/MoonInTheRiver/DiffSinger/releases)
|
|
[](https://huggingface.co/spaces/NATSpeech/DiffSpeech)
|
|
|
|
## DiffSpeech (TTS)
|
|
### 1. Preparation
|
|
|
|
#### Data Preparation
|
|
a) Download and extract the [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/), then create a link to the dataset folder: `ln -s /xxx/LJSpeech-1.1/ data/raw/`
|
|
|
|
b) Download and Unzip the [ground-truth duration](https://github.com/MoonInTheRiver/DiffSinger/releases/download/pretrain-model/mfa_outputs.tar) extracted by [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner/releases/download/v1.0.1/montreal-forced-aligner_linux.tar.gz): `tar -xvf mfa_outputs.tar; mv mfa_outputs data/processed/ljspeech/`
|
|
|
|
c) Run the following scripts to pack the dataset for training/inference.
|
|
|
|
```sh
|
|
export PYTHONPATH=.
|
|
CUDA_VISIBLE_DEVICES=0 python data_gen/tts/bin/binarize.py --config configs/tts/lj/fs2.yaml
|
|
|
|
# `data/binary/ljspeech` will be generated.
|
|
```
|
|
|
|
#### Vocoder Preparation
|
|
We provide the pre-trained model of [HifiGAN](https://github.com/MoonInTheRiver/DiffSinger/releases/download/pretrain-model/0414_hifi_lj_1.zip) vocoder.
|
|
Please unzip this file into `checkpoints` before training your acoustic model.
|
|
|
|
### 2. Training Example
|
|
|
|
```sh
|
|
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/lj_ds_pndm.yaml --exp_name ds_pndm_lj_1 --reset
|
|
```
|
|
|
|
### 3. Inference Example
|
|
|
|
```sh
|
|
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/lj_ds_pndm.yaml --exp_name ds_pndm_lj_1 --reset --infer
|
|
```
|