From 9c1e57c621cc873ccf39785b61d01ea8cba6de75 Mon Sep 17 00:00:00 2001 From: Hayeong Date: Mon, 6 May 2024 16:38:47 +0900 Subject: [PATCH] Update README.md --- README.md | 42 +++++++++++++++++++++++++++++++++++++----- 1 file changed, 37 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 515e875..b77aef3 100644 --- a/README.md +++ b/README.md @@ -17,22 +17,53 @@ Our model checkpoints can be downloaded [here](https://drive.google.com/drive/fo - model_base.pth - voc_ckpt.pth +- voc_bigvgan.pth - f0_vqvae.pth -## 🔨 Usage -🐣 Still working on it! ! ! ! +## ⚙️ Setup 1. Clone this rep && Install python requirement ``` git clone https://github.com/hayeong0/DDDM-VC.git pip install -r req* +``` +2. Download the pre-trained model checkpoint from drive. + +## 🔨 Usage +### Preprocess +1. Data +- Training requires both wav files and F0 features, which we extract using YAAPT through the script `./preprocess/extract_f0.py`. +- After extracting F0, create a list of files with the path to each data item, as shown in the following example: ``` +train_wav.txt +/workspace/raid/dataset/LibriTTS_16k/train-clean-360/100/121669/100_121669_000001_000000.wav +/workspace/raid/dataset/LibriTTS_16k/train-clean-360/100/121669/100_121669_000003_000000.wav -2. Download the pre-trained model checkpoint from drive. - -3. Run `infer.sh` +train_f0.txt +/workspace/raid/dataset/LibriTTS_f0_norm/train-clean-360/100/121669/100_121669_000001_000000.pt +/workspace/raid/dataset/LibriTTS_f0_norm/train-clean-360/100/121669/100_121669_000003_000000.pt +``` + +2. F0_VQVAE +- We trained the f0_vqvae model using [SpeechResynthesis repository](https://github.com/facebookresearch/speech-resynthesis). + + +### 🔍 Training +- For training, prepare a file list with the following structure: +``` +|-- filelist +| |-- train_f0.txt +| |-- train_wav.txt +| |-- test_f0.txt +| `-- test_wav.txt +``` +- Run `train_dddmvc.py` + + +### 🔑 Inference +- Run `infer.sh` ``` bash infer.sh @@ -46,6 +77,7 @@ python3 inference.py \ --output_dir './converted' \ -t 6 ``` + 🎧 Train and test it on your own dataset and share your interesting results! 🤗