Readme.md update

README.md

@@ -19,7 +19,219 @@ deployspec:
  entry_file: webui.py
 license: Apache License 2.0
 ---
-#### Clone with HTTP
+#### Prerequisite 
+
+For inference:
+
+> - GRAM >= 2 GiB
+> - RAM >= 16 GiB
+> - CUDA or CPU
+
+For training:
+
+> - GRAM >= 6 GiB (1 batch size or so)
+> - RAM >= 24 GiB
+> - CUDA supported (AMD ROCm : linux only)
+
+#### For Quick Start
+
+
+1. Clone the repository
+
 ```bash
  git clone https://www.modelscope.cn/studios/SpicyqSama007/Bert-VITS2-v2.3-clap.git
-```
+```
+
+2. Configure necessary environments
+
+```bash
+# make sure that you've installed anaconda/miniconda, CUDA (tool kit),
+# minimum GRAM >= 2GB
+# (python virtual env is ok too, similiar)
+# here is the example for miniconda
+
+# open 'miniconda' terminal
+# we need python>=3.10 for compatibility
+conda create -n vits python=3.10
+conda activate vits
+cd /d "{path of the project e.g. 'D:/Py/Bert-VITS2-v2.3-clap' }"
+
+# install torch first (CUDA ver >= 12.1)
+pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+
+# then the requirement
+pip install -r requirements.txt
+
+# extra for asr : audio wave -> corresponding transcription
+pip install funasr modelscope openi
+```
+
+3. Configure yaml file
+
+```bash
+# auto-generate a config file
+python config.py
+# then , a file name 'config.yml' generated at the root of the project
+mkdir -p Data/mix/models
+# your model
+mv G_xxx.pth Data/mix/models
+mv config.json Data/mix/
+
+# or manually open 'config.yml'
+vi config.yml
+```
+
+
+```yaml
+# after `vi config.yml` we get into it
+...
+
+# 不填或者填空则路径为相对于项目根目录的路径
+
+# fill the path (relatively to the root)
+dataset_path: "Data/mix"
+# it means the dataset path is in '{...}/Bert-VITS2-v2.3-clap/Data/mix'
+
+...
+
+# webui webui配置
+# 注意， “:” 后需要加空格
+webui:
+# 推理设备 device
+device: "cuda"
+# 模型路径 path to the model
+model: "models/G_xxxx.pth"
+# 配置文件路径
+config_path: "config.json"
+# 端口号
+port: 7860
+# 是否公开部署，对外网开放
+share: false
+# 是否开启debug模式
+debug: false
+# 语种识别库，可选langid, fastlid
+language_identification_library: "langid"
+
+```
+
+​ After configuration, let's start webui.py
+
+```bash
+python webui.py
+```
+
+
+
+#### For Quick Finetuning
+
+> Same as the first 2-steps in 'For Quick Start'
+
+3. if you understand Chinese, you can use my UI, as what the name literally means
+
+```bash
+python all_process.py
+```
+
+ Or :
+
+3. Configure yaml file
+
+```yaml
+# after `vi config.yml` we get into it
+# or manually open yml file
+
+...
+
+# 不填或者填空则路径为相对于项目根目录的路径
+# fill the path (relatively to the root)
+dataset_path: "Data/mix"
+# it means the dataset path is in '{...}/Bert-VITS2-v2.3-clap/Data/mix'
+
+...
+
+
+resample:
+ sampling_rate: 44100
+ in_dir: "audios/raw" # relatively in '/datasetPath/in_dir'
+ out_dir: "audios/wavs"
+
+
+
+preprocess_text:
+ # Format for single line in the list: 
+ # {wav_path}|{speaker_name}|{language}|{text}。
+ transcription_path: "filelists/{custom}.list"
+ cleaned_path: ""
+ train_path: "filelists/train.list"
+ val_path: "filelists/val.list"
+ config_path: "config.json"
+ val_per_lang: 4
+ max_val_total: 12
+ clean: true
+
+# train 训练配置
+# 注意， “:” 后需要加空格
+train_ms:
+
+ model: "models"
+ # 配置文件路径
+ config_path: "config.json"
+ # 训练使用的worker，不建议超过CPU核心数
+ num_workers: 16
+ # 关闭此项可以节约接近50%的磁盘空间，但是可能导致实际训练速度变慢和更高的CPU使用率。
+ spec_cache: True
+ # 保存的检查点数量，多于此数目的权重会被删除来节省空间。
+ keep_ckpts: 8
+
+
+```
+
+After editing , save and quit.
+
+4. Then, we should configure the training 'config.json'
+
+```bash
+# or manually open
+vi Data/mix/config.json
+# if there is no config.json, copy one piece from 'configs/config.json'
+cp configs/config.json Data/mix/
+
+# pay attention to the following part
+```
+
+```json
+ "data": {
+ "training_files": "Data/mix/filelists/train.list",
+ "validation_files": "Data/mix/filelists/val.list",
+ "max_wav_value": 32768.0,
+ ...
+ }
+```
+
+Then, save and quit.
+
+5. Come back to the Terminal ( it's fast and easy), type the following command.
+
+```bash
+# your source audio files (only .wav)
+mv "{your_audios_folder}" Data/mix/audios/raw
+# "{custom}.list" contains your transcriptions of the wav files
+mkdir -p Data/mix/filelists
+mv "{custom}.list" Data/mix/filelists
+python resample.py
+python preprocess_text.py
+python bert_gen.py
+python clap_gen.py
+
+torchrun \
+ --nnodes=1\
+ --nproc_per_node=2\
+ train_ms.py
+```
+
+6. If all normal and successful, we can get the trained models in `Data/mix/model/G_xxxx.pth`
+
+ Tips: 
+
+ > 1. You can terminate the training process at any time by yourself. (after 'Saving models...')
+ > 2. Most of the problems take place with 'incorrect path '