Add training mode

mkgui/base/ui/streamlit_ui.py

@@ -51,9 +51,13 @@ def launch_ui(port: int = 8501) -> None:
         python_path = f'PYTHONPATH="$PYTHONPATH:{getcwd()}"'
         if system() == "Windows":
             python_path = f"set PYTHONPATH=%PYTHONPATH%;{getcwd()} &&"
+            subprocess.run(
+                f"""set STREAMLIT_GLOBAL_SHOW_WARNING_ON_DIRECT_EXECUTION=false""",
+                shell=True,
+            )
 
         subprocess.run(
-            f"""{python_path} "{sys.executable}" -m streamlit run --server.port={port} --server.headless=True --runner.magicEnabled=False --server.maxUploadSize=50 --browser.gatherUsageStats=False {f.name}""",
+            f"""{python_path} "{sys.executable}" -m streamlit run --server.port={port} --server.headless=True --runner.magicEnabled=False --server.maxUploadSize=50  --browser.gatherUsageStats=False {f.name}""",
             shell=True,
         )
 
@@ -122,10 +126,11 @@ class InputUI:
                 property["title"] = name_to_title(property_key)
 
             try:
-                self._store_value(
-                    property_key,
-                    self._render_property(streamlit_app_root, property_key, property),
-                )
+                if "input_data" in self._session_state:
+                    self._store_value(
+                        property_key,
+                        self._render_property(streamlit_app_root, property_key, property),
+                    )
             except Exception as e:
                 print("Exception!", e)
                 pass
@@ -807,6 +812,9 @@ def getOpyrator(mode: str) -> Opyrator:
     if mode == None or mode.startswith('预处理'):
         from mkgui.preprocess import preprocess
         return  Opyrator(preprocess)
+    if mode == None or mode.startswith('模型训练'):
+        from mkgui.train import train
+        return  Opyrator(train)
     from mkgui.app import synthesize
     return Opyrator(synthesize)
     
@@ -815,11 +823,13 @@ def render_streamlit_ui() -> None:
     # init
     session_state = st.session_state
     session_state.input_data = {}
+    # Add custom css settings
+    st.markdown(f"<style>{CUSTOM_STREAMLIT_CSS}</style>", unsafe_allow_html=True)
 
     with st.spinner("Loading MockingBird GUI. Please wait..."):
         session_state.mode = st.sidebar.selectbox(
             '模式选择', 
-            ( "AI拟音", "VC拟音", "预处理")
+            ( "AI拟音", "VC拟音", "预处理", "模型训练")
         )
         if "mode" in session_state:
             mode = session_state.mode
@@ -872,6 +882,4 @@ def render_streamlit_ui() -> None:
             # placeholder
             st.caption("请使用左侧控制板进行输入并运行获得结果")
         
-    # Add custom css settings
-    st.markdown(f"<style>{CUSTOM_STREAMLIT_CSS}</style>", unsafe_allow_html=True)
-
+   

mkgui/train.py

@@ -0,0 +1,148 @@
+from pydantic import BaseModel, Field
+import os
+from pathlib import Path
+from enum import Enum
+from typing import Any
+import numpy as np
+from utils.load_yaml import HpsYaml
+from utils.util import AttrDict
+import torch
+
+# TODO: seperator for *unix systems
+# Constants
+EXT_MODELS_DIRT = "ppg_extractor\\saved_models"
+CONV_MODELS_DIRT = "ppg2mel\\saved_models"
+ENC_MODELS_DIRT = "encoder\\saved_models"
+
+
+if os.path.isdir(EXT_MODELS_DIRT):    
+    extractors =  Enum('extractors', list((file.name, file) for file in Path(EXT_MODELS_DIRT).glob("**/*.pt")))
+    print("Loaded extractor models: " + str(len(extractors)))
+if os.path.isdir(CONV_MODELS_DIRT):    
+    convertors =  Enum('convertors', list((file.name, file) for file in Path(CONV_MODELS_DIRT).glob("**/*.pth")))
+    print("Loaded convertor models: " + str(len(convertors)))
+if os.path.isdir(ENC_MODELS_DIRT):    
+    encoders = Enum('encoders', list((file.name, file) for file in Path(ENC_MODELS_DIRT).glob("**/*.pt")))
+    print("Loaded encoders models: " + str(len(encoders)))
+
+class Model(str, Enum):
+    VC_PPG2MEL = "ppg2mel"
+
+class Dataset(str, Enum):
+    AIDATATANG_200ZH = "aidatatang_200zh"
+    AIDATATANG_200ZH_S = "aidatatang_200zh_s"
+
+class Input(BaseModel):
+    # def render_input_ui(st, input) -> Dict: 
+    #     input["selected_dataset"] = st.selectbox(
+    #         '选择数据集', 
+    #         ("aidatatang_200zh", "aidatatang_200zh_s")
+    #     )
+    # return input
+    model: Model = Field(
+        Model.VC_PPG2MEL, title="模型类型",
+    )
+    # datasets_root: str = Field(
+    #     ..., alias="预处理数据根目录", description="输入目录（相对/绝对）,不适用于ppg2mel模型",
+    #     format=True,
+    #     example="..\\trainning_data\\"
+    # )
+    output_root: str = Field(
+        ..., alias="输出目录(可选)", description="建议不填，保持默认",
+        format=True,
+        example=""
+    )
+    continue_mode: bool = Field(
+        True, alias="继续训练模式", description="选择“是”，则从下面选择的模型中继续训练",
+    )
+    gpu: bool = Field(
+        True, alias="GPU训练", description="选择“是”，则使用GPU训练",
+    )
+    verbose: bool = Field(
+        True, alias="打印详情", description="选择“是”，输出更多详情",
+    )
+    # TODO: Move to hiden fields by default
+    convertor: convertors = Field(
+        ..., alias="转换模型", 
+        description="选择语音转换模型文件."
+    )
+    extractor: extractors = Field(
+        ..., alias="特征提取模型", 
+        description="选择PPG特征提取模型文件."
+    )
+    encoder: encoders = Field(
+        ..., alias="语音编码模型", 
+        description="选择语音编码模型文件."
+    )
+    njobs: int = Field(
+        8, alias="进程数", description="适用于ppg2mel",
+    )
+    seed: int = Field(
+        default=0, alias="初始随机数", description="适用于ppg2mel",
+    )
+    model_name: str = Field(
+        ..., alias="新模型名", description="仅在重新训练时生效,选中继续训练时无效",
+        example="test"
+    )
+    model_config: str = Field(
+        ..., alias="新模型配置", description="仅在重新训练时生效,选中继续训练时无效",
+        example=".\\ppg2mel\\saved_models\\seq2seq_mol_ppg2mel_vctk_libri_oneshotvc_r4_normMel_v2"
+    )
+
+class AudioEntity(BaseModel):
+    content: bytes
+    mel: Any
+
+class Output(BaseModel):
+    __root__: tuple[str, int]
+
+    def render_output_ui(self, streamlit_app, input) -> None:  # type: ignore
+        """Custom output UI.
+        If this method is implmeneted, it will be used instead of the default Output UI renderer.
+        """
+        sr, count = self.__root__
+        streamlit_app.subheader(f"Dataset {sr} done processed total of {count}")
+
+def train(input: Input) -> Output:
+    """Train(训练)"""
+
+    print(">>> OneShot VC training ...")
+    params = AttrDict()
+    params.update({
+        "gpu": input.gpu,
+        "cpu": not input.gpu,
+        "njobs": input.njobs,
+        "seed": input.seed,
+        "verbose": input.verbose,
+        "load": input.convertor.value,
+        "warm_start": False,
+    })
+    if input.continue_mode: 
+        # trace old model and config
+        p = Path(input.convertor.value)
+        params.name = p.parent.name
+        # search a config file
+        model_config_fpaths = list(p.parent.rglob("*.yaml"))
+        if len(model_config_fpaths) == 0:
+            raise "No model yaml config found for convertor"
+        config = HpsYaml(model_config_fpaths[0])
+        params.ckpdir = p.parent.parent
+        params.config = model_config_fpaths[0]
+        params.logdir = os.path.join(p.parent, "log")
+    else:
+        # Make the config dict dot visitable
+        config = HpsYaml(input.config)    
+    np.random.seed(input.seed)
+    torch.manual_seed(input.seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(input.seed)
+    mode = "train"
+    from ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
+    solver = Solver(config, params, mode)
+    solver.load_data()
+    solver.set_model()
+    solver.exec()
+    print(">>> Oneshot VC train finished!")
+
+    # TODO: pass useful return code
+    return Output(__root__=(input.dataset, 0))

ppg2mel/train.py

@@ -31,15 +31,10 @@ def main():
     parser.add_argument('--njobs', default=8, type=int,
                         help='Number of threads for dataloader/decoding.', required=False)
     parser.add_argument('--cpu', action='store_true', help='Disable GPU training.')
-    parser.add_argument('--no-pin', action='store_true',
-                        help='Disable pin-memory for dataloader')
-    parser.add_argument('--test', action='store_true', help='Test the model.')
+    # parser.add_argument('--no-pin', action='store_true',
+    #                     help='Disable pin-memory for dataloader')
     parser.add_argument('--no-msg', action='store_true', help='Hide all messages.')
-    parser.add_argument('--finetune', action='store_true', help='Finetune model')
-    parser.add_argument('--oneshotvc', action='store_true', help='Oneshot VC model')
-    parser.add_argument('--bilstm', action='store_true', help='BiLSTM VC model')
-    parser.add_argument('--lsa', action='store_true', help='Use location-sensitive attention (LSA)')
-
+    
     ###
 
     paras = parser.parse_args()

ppg2mel/train/solver.py

@@ -93,6 +93,7 @@ class BaseSolver():
 
     def load_ckpt(self):
         ''' Load ckpt if --load option is specified '''
+        print(self.paras)
         if self.paras.load is not None:
             if self.paras.warm_start:
                 self.verbose(f"Warm starting model from checkpoint {self.paras.load}.")
@@ -100,7 +101,7 @@ class BaseSolver():
                     self.paras.load, map_location=self.device if self.mode == 'train'
                                                         else 'cpu')
                 model_dict = ckpt['model']
-                if len(self.config.model.ignore_layers) > 0:
+                if "ignore_layers" in self.config.model and len(self.config.model.ignore_layers) > 0:
                     model_dict = {k:v for k, v in model_dict.items()
                                   if k not in self.config.model.ignore_layers}
                     dummy_dict = self.model.state_dict()

utils/util.py

@@ -42,3 +42,9 @@ def human_format(num):
     # add more suffixes if you need them
     return '{:3.1f}{}'.format(num, [' ', 'K', 'M', 'G', 'T', 'P'][magnitude])
 
+
+# provide easy access of attribute from dict, such abc.key 
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self

vocoder/hifigan/env.py

@@ -1,13 +1,6 @@
 import os
 import shutil
 
-
-class AttrDict(dict):
-    def __init__(self, *args, **kwargs):
-        super(AttrDict, self).__init__(*args, **kwargs)
-        self.__dict__ = self
-
-
 def build_env(config, config_name, path):
     t_path = os.path.join(path, config_name)
     if config != t_path:

vocoder/hifigan/inference.py

@@ -3,7 +3,7 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 import os
 import json
 import torch
-from vocoder.hifigan.env import AttrDict
+from utils.util import AttrDict
 from vocoder.hifigan.models import Generator
 
 generator = None       # type: Generator

vocoder/hifigan/train.py

@@ -12,7 +12,6 @@ from torch.utils.data import DistributedSampler, DataLoader
 import torch.multiprocessing as mp
 from torch.distributed import init_process_group
 from torch.nn.parallel import DistributedDataParallel
-from vocoder.hifigan.env import AttrDict, build_env
 from vocoder.hifigan.meldataset import MelDataset, mel_spectrogram, get_dataset_filelist
 from vocoder.hifigan.models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss,\
     discriminator_loss

vocoder_train.py

@@ -1,7 +1,7 @@
 from utils.argutils import print_args
 from vocoder.wavernn.train import train
 from vocoder.hifigan.train import train as train_hifigan
-from vocoder.hifigan.env import AttrDict
+from utils.util import AttrDict
 from pathlib import Path
 import argparse
 import json