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so-vits-svc
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Update README.md

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Miuzarte 2023-04-05 15:24:53 +08:00
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@ -109,20 +109,22 @@ python preprocess_hubert_f0.py
After completing the above steps, the dataset directory will contain the preprocessed data, and the dataset_raw folder can be deleted.
#### You can modify some parameters in the generated config.json
* `keep_ckpts`: Keep the last `keep_ckpts` models during training. Set to `0` will keep them all. Default is `3`.
* `all_in_mem`: Load all dataset to RAM. It can be enabled when the disk IO of some platforms is too low and the system memory is much larger than your dataset.
## 🏋️‍♀️ Training
```shell
python train.py -c configs/config.json -m 44k
```
Note: During training, the old models will be automatically cleared and only the latest three models will be kept. If you want to prevent overfitting, you need to manually backup the model checkpoints, or modify the configuration file `keep_ckpts` to 0 to never clear them.
## 🤖 Inference
Use [inference_main.py](https://github.com/svc-develop-team/so-vits-svc/blob/4.0/inference_main.py)
Up to this point, the usage of version 4.0 (training and inference) is exactly the same as version 3.0, with no changes (inference now has command line support).
```shell
# Example
python inference_main.py -m "logs/44k/G_30400.pth" -c "configs/config.json" -n "君の知らない物語-src.wav" -t 0 -s "nen"

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@ -107,7 +107,13 @@ python preprocess_flist_config.py
python preprocess_hubert_f0.py
```
执行完以上步骤后 dataset 目录便是预处理完成的数据，可以删除dataset_raw文件夹了
执行完以上步骤后 dataset 目录便是预处理完成的数据，可以删除 dataset_raw 文件夹了
#### 此时可以在生成的config.json修改部分参数
* `keep_ckpts`：训练时保留最后几个模型，`0`为保留所有，默认只保留最后`3`个
* `all_in_mem`：加载所有数据集到内存中，某些平台的硬盘IO过于低下、同时内存容量*远大于*数据集体积时可以启用
## 🏋️‍♀️ 训练
@ -115,14 +121,10 @@ python preprocess_hubert_f0.py
python train.py -c configs/config.json -m 44k
```
注：训练时会自动清除老的模型，只保留最新3个模型，如果想防止过拟合需要自己手动备份模型记录点,或修改配置文件keep_ckpts 0为永不清除
## 🤖 推理
使用 [inference_main.py](inference_main.py)
截止此处，4.0使用方法（训练、推理）和3.0完全一致，没有任何变化（推理增加了命令行支持）
```shell
# 例
python inference_main.py -m "logs/44k/G_30400.pth" -c "configs/config.json" -n "君の知らない物語-src.wav" -t 0 -s "nen"