Add model download to train_ms.py (#150)

* download base model

* fix download

* fix download repo

* set HF default

config.py

@@ -118,9 +118,11 @@ class Train_ms_config:
         self,
         config_path: str,
         env: Dict[str, any],
+        base: Dict[str, any],
         model: str,
     ):
         self.env = env  # 需要加载的环境变量
+        self.base = base  # 底模配置
         self.model = model  # 训练模型存储目录，该路径为相对于dataset_path的路径，而非项目根目录
         self.config_path = config_path  # 配置文件路径
 

default_config.yml

@@ -5,8 +5,10 @@
 # 每个数据集与其对应的模型存放至统一路径下，后续所有的路径配置均为相对于datasetPath的路径
 # 不填或者填空则路径为相对于项目根目录的路径
 dataset_path: "Data/你的数据集"
-mirror: "openi" # 模型镜像源
+
-openi_token: "1145141919810"  # openi token
+# 模型镜像源，默认huggingface，使用openi镜像源需指定openi_token
+mirror: ""
+openi_token: ""  # openi token
 
 # resample 音频重采样配置
 # 注意， “:” 后需要加空格
@@ -67,6 +69,11 @@ train_ms:
     RANK: 0
     # 可以填写任意名的环境变量
     THE_ENV_VAR_YOU_NEED_TO_USE: "1234567"
+  # 底模设置
+  base:
+    use_base_model: false
+    repo_id: "Stardust_minus/Bert-VITS2"
+    model_image: "Bert-VITS2中日底模" # openi网页的模型名
   # 训练模型存储目录：与旧版本的区别，原先数据集是存放在logs/model_name下的，现在改为统一存放在Data/你的数据集/models下
   model: "models"
   # 配置文件路径

train_ms.py

@@ -211,6 +211,16 @@ def run():
     dur_resume_lr = None
     if net_dur_disc is not None:
         net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True)
+
+    # 下载底模
+    if config.train_ms_config.base["use_base_model"]:
+        utils.download_checkpoint(
+            hps.model_dir,
+            config.train_ms_config.base,
+            token=config.openi_token,
+            mirror=config.mirror,
+        )
+
     try:
         if net_dur_disc is not None:
             _, _, dur_resume_lr, epoch_str = utils.load_checkpoint(

utils.py

@@ -3,8 +3,10 @@ import glob
 import argparse
 import logging
 import json
+import shutil
 import subprocess
 import numpy as np
+from huggingface_hub import hf_hub_download
 from scipy.io.wavfile import read
 import torch
 
@@ -13,6 +15,34 @@ MATPLOTLIB_FLAG = False
 logger = logging.getLogger(__name__)
 
 
+def download_checkpoint(
+    dir_path, repo_config, token=None, regex="G_*.pth", mirror="openi"
+):
+    repo_id = repo_config["repo_id"]
+    f_list = glob.glob(os.path.join(dir_path, regex))
+    if f_list:
+        print("Use existed model, skip downloading.")
+        return
+    if mirror.lower() == "openi":
+        import openi
+
+        kwargs = {"token": token} if token else {}
+        openi.login(**kwargs)
+
+        model_image = repo_config["model_image"]
+        openi.model.download_model(repo_id, model_image, dir_path)
+
+        fs = glob.glob(os.path.join(dir_path, model_image, "*.pth"))
+        for file in fs:
+            shutil.move(file, dir_path)
+        shutil.rmtree(os.path.join(dir_path, model_image))
+    else:
+        for file in ["DUR_0.pth", "D_0.pth", "G_0.pth"]:
+            hf_hub_download(
+                repo_id, file, local_dir=dir_path, local_dir_use_symlinks=False
+            )
+
+
 def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
     assert os.path.isfile(checkpoint_path)
     checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")