Upload 221 files

.dockerignore

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+# Dockerfile.deploy用の.dockerignore
+# 日本語のJP-Extraのエディター稼働のみに必要なファイルを指定する
+
+*
+
+!/style_bert_vits2/
+
+!/bert/deberta-v2-large-japanese-char-wwm/
+!/configs/
+!/dict_data/default.csv
+!/model_assets/
+!/static/
+
+!/config.py
+!/default_config.yml
+!/initialize.py
+!/requirements.txt
+!/server_editor.py

.gitattributes

@@ -1,35 +1,35 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.vscode/
+
+__pycache__/
+venv/
+.ipynb_checkpoints/
+
+/*.yml
+!/default_config.yml
+# /bert/*/*.bin
+/bert/*/*.h5
+/bert/*/*.model
+/bert/*/*.safetensors
+/bert/*/*.msgpack
+
+/pretrained/*.safetensors
+/pretrained/*.pth
+
+/pretrained_jp_extra/*.safetensors
+/pretrained_jp_extra/*.pth
+
+/slm/*/*.bin
+
+/scripts/test/
+*.zip
+*.csv
+*.bak
+/mos_results/
+
+safetensors.ipynb
+*.wav
+
+# pyopenjtalk's dictionary
+*.dic

Dockerfile

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+# Hugging face spaces (CPU) でエディタ (server_editor.py) のデプロイ用
+
+# See https://huggingface.co/docs/hub/spaces-sdks-docker-first-demo
+
+FROM python:3.10
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+ PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+RUN pip install --no-cache-dir --upgrade pip
+
+COPY --chown=user . $HOME/app
+
+RUN pip install --no-cache-dir -r $HOME/app/requirements.txt
+
+# 必要に応じて制限を変更してください
+CMD ["python", "server_editor.py", "--line_length", "50", "--line_count", "3", "--skip_static_files", "--skip_default_models", "--port", "7860"]

README.md

@@ -1,12 +1,10 @@
----
-title: SBV2 Editor Demo
-emoji: ⚡
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 4.44.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Style Bert VITS2 Editor Demo
+emoji: 😊🎙️📖
+colorFrom: gray
+colorTo: yellow
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

bert/deberta-v2-large-japanese-char-wwm/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

bert/deberta-v2-large-japanese-char-wwm/README.md

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+---
+language: ja
+license: cc-by-sa-4.0
+library_name: transformers
+tags:
+ - deberta
+ - deberta-v2
+ - fill-mask
+ - character
+ - wwm
+datasets:
+ - wikipedia
+ - cc100
+ - oscar
+metrics:
+ - accuracy
+mask_token: "[MASK]"
+widget:
+ - text: "京都大学で自然言語処理を[MASK][MASK]する。"
+---
+
+# Model Card for Japanese character-level DeBERTa V2 large
+
+## Model description
+
+This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
+This model is trained with character-level tokenization and whole word masking.
+
+## How to use
+
+You can use this model for masked language modeling as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese-char-wwm')
+model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese-char-wwm')
+
+sentence = '京都大学で自然言語処理を[MASK][MASK]する。'
+encoding = tokenizer(sentence, return_tensors='pt')
+...
+```
+
+You can also fine-tune this model on downstream tasks.
+
+## Tokenization
+
+There is no need to tokenize texts in advance, and you can give raw texts to the tokenizer.
+The texts are tokenized into character-level tokens by [sentencepiece](https://github.com/google/sentencepiece).
+
+## Training data
+
+We used the following corpora for pre-training:
+
+- Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents)
+- Japanese portion of CC-100 (85GB, 619M sentences, 66M documents)
+- Japanese portion of OSCAR (54GB, 326M sentences, 25M documents)
+
+Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR.
+Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB.
+
+## Training procedure
+
+We first segmented texts in the corpora into words using [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) for whole word masking.
+Then, we built a sentencepiece model with 22,012 tokens including all characters that appear in the training corpus.
+
+We tokenized raw corpora into character-level subwords using the sentencepiece model and trained the Japanese DeBERTa model using [transformers](https://github.com/huggingface/transformers) library.
+The training took 26 days using 16 NVIDIA A100-SXM4-40GB GPUs.
+
+The following hyperparameters were used during pre-training:
+
+- learning_rate: 1e-4
+- per_device_train_batch_size: 26
+- distributed_type: multi-GPU
+- num_devices: 16
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 3,328
+- max_seq_length: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
+- lr_scheduler_type: linear schedule with warmup (lr = 0 at 300k steps)
+- training_steps: 260,000
+- warmup_steps: 10,000
+
+The accuracy of the trained model on the masked language modeling task was 0.795.
+The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
+
+## Acknowledgments
+
+This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models".
+For training models, we used the mdx: a platform for the data-driven future.

bert/deberta-v2-large-japanese-char-wwm/config.json

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+{
+ "architectures": [
+ "DebertaV2ForMaskedLM"
+ ],
+ "attention_head_size": 64,
+ "attention_probs_dropout_prob": 0.1,
+ "conv_act": "gelu",
+ "conv_kernel_size": 3,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 1024,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "layer_norm_eps": 1e-07,
+ "max_position_embeddings": 512,
+ "max_relative_positions": -1,
+ "model_type": "deberta-v2",
+ "norm_rel_ebd": "layer_norm",
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "pad_token_id": 0,
+ "pooler_dropout": 0,
+ "pooler_hidden_act": "gelu",
+ "pooler_hidden_size": 1024,
+ "pos_att_type": [
+ "p2c",
+ "c2p"
+ ],
+ "position_biased_input": false,
+ "position_buckets": 256,
+ "relative_attention": true,
+ "share_att_key": true,
+ "torch_dtype": "float16",
+ "transformers_version": "4.25.1",
+ "type_vocab_size": 0,
+ "vocab_size": 22012
+}

bert/deberta-v2-large-japanese-char-wwm/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf0dab8ad87bd7c22e85ec71e04f2240804fda6d33196157d6b5923af6ea1201
+size 1318456639

bert/deberta-v2-large-japanese-char-wwm/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+ "cls_token": "[CLS]",
+ "mask_token": "[MASK]",
+ "pad_token": "[PAD]",
+ "sep_token": "[SEP]",
+ "unk_token": "[UNK]"
+}

bert/deberta-v2-large-japanese-char-wwm/tokenizer_config.json

@@ -0,0 +1,19 @@
+{
+ "cls_token": "[CLS]",
+ "do_lower_case": false,
+ "do_subword_tokenize": true,
+ "do_word_tokenize": true,
+ "jumanpp_kwargs": null,
+ "mask_token": "[MASK]",
+ "mecab_kwargs": null,
+ "model_max_length": 1000000000000000019884624838656,
+ "never_split": null,
+ "pad_token": "[PAD]",
+ "sep_token": "[SEP]",
+ "special_tokens_map_file": null,
+ "subword_tokenizer_type": "character",
+ "sudachi_kwargs": null,
+ "tokenizer_class": "BertJapaneseTokenizer",
+ "unk_token": "[UNK]",
+ "word_tokenizer_type": "basic"
+}

config.py

@@ -0,0 +1,307 @@
+"""
+@Desc: 全局配置文件读取
+"""
+
+import shutil
+from pathlib import Path
+from typing import Any
+
+import torch
+import yaml
+
+from style_bert_vits2.logging import logger
+
+
+class PathConfig:
+ def __init__(self, dataset_root: str, assets_root: str):
+ self.dataset_root = Path(dataset_root)
+ self.assets_root = Path(assets_root)
+
+
+# If not cuda available, set possible devices to cpu
+cuda_available = torch.cuda.is_available()
+
+
+class Resample_config:
+ """重采样配置"""
+
+ def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100):
+ self.sampling_rate = sampling_rate # 目标采样率
+ self.in_dir = Path(in_dir) # 待处理音频目录路径
+ self.out_dir = Path(out_dir) # 重采样输出路径
+
+ @classmethod
+ def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+ """从字典中生成实例"""
+
+ # 不检查路径是否有效，此逻辑在resample.py中处理
+ data["in_dir"] = dataset_path / data["in_dir"]
+ data["out_dir"] = dataset_path / data["out_dir"]
+
+ return cls(**data)
+
+
+class Preprocess_text_config:
+ """数据预处理配置"""
+
+ def __init__(
+ self,
+ transcription_path: str,
+ cleaned_path: str,
+ train_path: str,
+ val_path: str,
+ config_path: str,
+ val_per_lang: int = 5,
+ max_val_total: int = 10000,
+ clean: bool = True,
+ ):
+ self.transcription_path = Path(transcription_path)
+ self.train_path = Path(train_path)
+ if cleaned_path == "" or cleaned_path is None:
+ self.cleaned_path = self.transcription_path.with_name(
+ self.transcription_path.name + ".cleaned"
+ )
+ else:
+ self.cleaned_path = Path(cleaned_path)
+ self.val_path = Path(val_path)
+ self.config_path = Path(config_path)
+ self.val_per_lang = val_per_lang
+ self.max_val_total = max_val_total
+ self.clean = clean
+
+ @classmethod
+ def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+ """从字典中生成实例"""
+
+ data["transcription_path"] = dataset_path / data["transcription_path"]
+ if data["cleaned_path"] == "" or data["cleaned_path"] is None:
+ data["cleaned_path"] = ""
+ else:
+ data["cleaned_path"] = dataset_path / data["cleaned_path"]
+ data["train_path"] = dataset_path / data["train_path"]
+ data["val_path"] = dataset_path / data["val_path"]
+ data["config_path"] = dataset_path / data["config_path"]
+
+ return cls(**data)
+
+
+class Bert_gen_config:
+ """bert_gen 配置"""
+
+ def __init__(
+ self,
+ config_path: str,
+ num_processes: int = 1,
+ device: str = "cuda",
+ use_multi_device: bool = False,
+ ):
+ self.config_path = Path(config_path)
+ self.num_processes = num_processes
+ if not cuda_available:
+ device = "cpu"
+ self.device = device
+ self.use_multi_device = use_multi_device
+
+ @classmethod
+ def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+ data["config_path"] = dataset_path / data["config_path"]
+
+ return cls(**data)
+
+
+class Style_gen_config:
+ """style_gen 配置"""
+
+ def __init__(
+ self,
+ config_path: str,
+ num_processes: int = 4,
+ device: str = "cuda",
+ ):
+ self.config_path = Path(config_path)
+ self.num_processes = num_processes
+ if not cuda_available:
+ device = "cpu"
+ self.device = device
+
+ @classmethod
+ def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+ data["config_path"] = dataset_path / data["config_path"]
+
+ return cls(**data)
+
+
+class Train_ms_config:
+ """训练配置"""
+
+ def __init__(
+ self,
+ config_path: str,
+ env: dict[str, Any],
+ # base: Dict[str, any],
+ model_dir: str,
+ num_workers: int,
+ spec_cache: bool,
+ keep_ckpts: int,
+ ):
+ self.env = env # 需要加载的环境变量
+ # self.base = base # 底模配置
+ self.model_dir = Path(
+ model_dir
+ ) # 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 = Path(config_path) # 配置文件路径
+ self.num_workers = num_workers # worker数量
+ self.spec_cache = spec_cache # 是否启用spec缓存
+ self.keep_ckpts = keep_ckpts # ckpt数量
+
+ @classmethod
+ def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+ # data["model"] = os.path.join(dataset_path, data["model"])
+ data["config_path"] = dataset_path / data["config_path"]
+
+ return cls(**data)
+
+
+class Webui_config:
+ """webui 配置 (for webui.py, not supported now)"""
+
+ def __init__(
+ self,
+ device: str,
+ model: str,
+ config_path: str,
+ language_identification_library: str,
+ port: int = 7860,
+ share: bool = False,
+ debug: bool = False,
+ ):
+ if not cuda_available:
+ device = "cpu"
+ self.device = device
+ self.model = Path(model)
+ self.config_path = Path(config_path)
+ self.port: int = port
+ self.share: bool = share
+ self.debug: bool = debug
+ self.language_identification_library: str = language_identification_library
+
+ @classmethod
+ def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+ data["config_path"] = dataset_path / data["config_path"]
+ data["model"] = dataset_path / data["model"]
+ return cls(**data)
+
+
+class Server_config:
+ def __init__(
+ self,
+ port: int = 5000,
+ device: str = "cuda",
+ limit: int = 100,
+ language: str = "JP",
+ origins: list[str] = ["*"],
+ ):
+ self.port: int = port
+ if not cuda_available:
+ device = "cpu"
+ self.device: str = device
+ self.language: str = language
+ self.limit: int = limit
+ self.origins: list[str] = origins
+
+ @classmethod
+ def from_dict(cls, data: dict[str, Any]):
+ return cls(**data)
+
+
+class Translate_config:
+ """翻译api配置"""
+
+ def __init__(self, app_key: str, secret_key: str):
+ self.app_key = app_key
+ self.secret_key = secret_key
+
+ @classmethod
+ def from_dict(cls, data: dict[str, Any]):
+ return cls(**data)
+
+
+class Config:
+ def __init__(self, config_path: str, path_config: PathConfig):
+ if not Path(config_path).exists():
+ shutil.copy(src="default_config.yml", dst=config_path)
+ logger.info(
+ f"A configuration file {config_path} has been generated based on the default configuration file default_config.yml."
+ )
+ logger.info(
+ "Please do not modify default_config.yml. Instead, modify config.yml."
+ )
+ # sys.exit(0)
+ with open(config_path, encoding="utf-8") as file:
+ yaml_config: dict[str, Any] = yaml.safe_load(file.read())
+ model_name: str = yaml_config["model_name"]
+ self.model_name: str = model_name
+ if "dataset_path" in yaml_config:
+ dataset_path = Path(yaml_config["dataset_path"])
+ else:
+ dataset_path = path_config.dataset_root / model_name
+ self.dataset_path = dataset_path
+ self.dataset_root = path_config.dataset_root
+ self.assets_root = path_config.assets_root
+ self.out_dir = self.assets_root / model_name
+ self.resample_config: Resample_config = Resample_config.from_dict(
+ dataset_path, yaml_config["resample"]
+ )
+ self.preprocess_text_config: Preprocess_text_config = (
+ Preprocess_text_config.from_dict(
+ dataset_path, yaml_config["preprocess_text"]
+ )
+ )
+ self.bert_gen_config: Bert_gen_config = Bert_gen_config.from_dict(
+ dataset_path, yaml_config["bert_gen"]
+ )
+ self.style_gen_config: Style_gen_config = Style_gen_config.from_dict(
+ dataset_path, yaml_config["style_gen"]
+ )
+ self.train_ms_config: Train_ms_config = Train_ms_config.from_dict(
+ dataset_path, yaml_config["train_ms"]
+ )
+ self.webui_config: Webui_config = Webui_config.from_dict(
+ dataset_path, yaml_config["webui"]
+ )
+ self.server_config: Server_config = Server_config.from_dict(
+ yaml_config["server"]
+ )
+ # self.translate_config: Translate_config = Translate_config.from_dict(
+ # yaml_config["translate"]
+ # )
+
+
+# Load and initialize the configuration
+
+
+def get_path_config() -> PathConfig:
+ path_config_path = Path("configs/paths.yml")
+ if not path_config_path.exists():
+ shutil.copy(src="configs/default_paths.yml", dst=path_config_path)
+ logger.info(
+ f"A configuration file {path_config_path} has been generated based on the default configuration file default_paths.yml."
+ )
+ logger.info(
+ "Please do not modify configs/default_paths.yml. Instead, modify configs/paths.yml."
+ )
+ with open(path_config_path, encoding="utf-8") as file:
+ path_config_dict: dict[str, str] = yaml.safe_load(file.read())
+ return PathConfig(**path_config_dict)
+
+
+def get_config() -> Config:
+ path_config = get_path_config()
+ try:
+ config = Config("config.yml", path_config)
+ except (TypeError, KeyError):
+ logger.warning("Old config.yml found. Replace it with default_config.yml.")
+ shutil.copy(src="default_config.yml", dst="config.yml")
+ config = Config("config.yml", path_config)
+
+ return config

configs/config.json

@@ -0,0 +1,73 @@
+{
+ "model_name": "Dummy",
+ "train": {
+ "log_interval": 200,
+ "eval_interval": 1000,
+ "seed": 42,
+ "epochs": 1000,
+ "learning_rate": 0.0002,
+ "betas": [0.8, 0.99],
+ "eps": 1e-9,
+ "batch_size": 2,
+ "bf16_run": false,
+ "lr_decay": 0.99995,
+ "segment_size": 16384,
+ "init_lr_ratio": 1,
+ "warmup_epochs": 0,
+ "c_mel": 45,
+ "c_kl": 1.0,
+ "skip_optimizer": false,
+ "freeze_ZH_bert": false,
+ "freeze_JP_bert": false,
+ "freeze_EN_bert": false,
+ "freeze_style": false,
+ "freeze_encoder": false
+ },
+ "data": {
+ "use_jp_extra": false,
+ "training_files": "Data/Dummy/train.list",
+ "validation_files": "Data/Dummy/val.list",
+ "max_wav_value": 32768.0,
+ "sampling_rate": 44100,
+ "filter_length": 2048,
+ "hop_length": 512,
+ "win_length": 2048,
+ "n_mel_channels": 128,
+ "mel_fmin": 0.0,
+ "mel_fmax": null,
+ "add_blank": true,
+ "n_speakers": 1,
+ "cleaned_text": true,
+ "num_styles": 1,
+ "style2id": {
+ "Neutral": 0
+ }
+ },
+ "model": {
+ "use_spk_conditioned_encoder": true,
+ "use_noise_scaled_mas": true,
+ "use_mel_posterior_encoder": false,
+ "use_duration_discriminator": true,
+ "inter_channels": 192,
+ "hidden_channels": 192,
+ "filter_channels": 768,
+ "n_heads": 2,
+ "n_layers": 6,
+ "kernel_size": 3,
+ "p_dropout": 0.1,
+ "resblock": "1",
+ "resblock_kernel_sizes": [3, 7, 11],
+ "resblock_dilation_sizes": [
+ [1, 3, 5],
+ [1, 3, 5],
+ [1, 3, 5]
+ ],
+ "upsample_rates": [8, 8, 2, 2, 2],
+ "upsample_initial_channel": 512,
+ "upsample_kernel_sizes": [16, 16, 8, 2, 2],
+ "n_layers_q": 3,
+ "use_spectral_norm": false,
+ "gin_channels": 256
+ },
+ "version": "2.5.0"
+}

configs/config_jp_extra.json

@@ -0,0 +1,80 @@
+{
+ "model_name": "Dummy",
+ "train": {
+ "log_interval": 200,
+ "eval_interval": 1000,
+ "seed": 42,
+ "epochs": 1000,
+ "learning_rate": 0.0001,
+ "betas": [0.8, 0.99],
+ "eps": 1e-9,
+ "batch_size": 2,
+ "bf16_run": false,
+ "fp16_run": false,
+ "lr_decay": 0.99996,
+ "segment_size": 16384,
+ "init_lr_ratio": 1,
+ "warmup_epochs": 0,
+ "c_mel": 45,
+ "c_kl": 1.0,
+ "c_commit": 100,
+ "skip_optimizer": false,
+ "freeze_ZH_bert": false,
+ "freeze_JP_bert": false,
+ "freeze_EN_bert": false,
+ "freeze_emo": false,
+ "freeze_style": false,
+ "freeze_decoder": false
+ },
+ "data": {
+ "use_jp_extra": true,
+ "training_files": "Data/Dummy/train.list",
+ "validation_files": "Data/Dummy/val.list",
+ "max_wav_value": 32768.0,
+ "sampling_rate": 44100,
+ "filter_length": 2048,
+ "hop_length": 512,
+ "win_length": 2048,
+ "n_mel_channels": 128,
+ "mel_fmin": 0.0,
+ "mel_fmax": null,
+ "add_blank": true,
+ "n_speakers": 512,
+ "cleaned_text": true
+ },
+ "model": {
+ "use_spk_conditioned_encoder": true,
+ "use_noise_scaled_mas": true,
+ "use_mel_posterior_encoder": false,
+ "use_duration_discriminator": false,
+ "use_wavlm_discriminator": true,
+ "inter_channels": 192,
+ "hidden_channels": 192,
+ "filter_channels": 768,
+ "n_heads": 2,
+ "n_layers": 6,
+ "kernel_size": 3,
+ "p_dropout": 0.1,
+ "resblock": "1",
+ "resblock_kernel_sizes": [3, 7, 11],
+ "resblock_dilation_sizes": [
+ [1, 3, 5],
+ [1, 3, 5],
+ [1, 3, 5]
+ ],
+ "upsample_rates": [8, 8, 2, 2, 2],
+ "upsample_initial_channel": 512,
+ "upsample_kernel_sizes": [16, 16, 8, 2, 2],
+ "n_layers_q": 3,
+ "use_spectral_norm": false,
+ "gin_channels": 512,
+ "slm": {
+ "model": "./slm/wavlm-base-plus",
+ "sr": 16000,
+ "hidden": 768,
+ "nlayers": 13,
+ "initial_channel": 64
+ }
+ },
+ "version": "2.5.0-JP-Extra"
+}

configs/default_paths.yml

@@ -0,0 +1,8 @@
+# Root directory of the training dataset.
+# The training dataset of {model_name} should be placed in {dataset_root}/{model_name}.
+dataset_root: Data
+
+# Root directory of the model assets (for inference).
+# In training, the model assets will be saved to {assets_root}/{model_name},
+# and in inference, we load all the models from {assets_root}.
+assets_root: model_assets

configs/paths.yml

@@ -0,0 +1,8 @@
+# Root directory of the training dataset.
+# The training dataset of {model_name} should be placed in {dataset_root}/{model_name}.
+dataset_root: Data
+
+# Root directory of the model assets (for inference).
+# In training, the model assets will be saved to {assets_root}/{model_name},
+# and in inference, we load all the models from {assets_root}.
+assets_root: model_assets

default_config.yml

@@ -0,0 +1,70 @@
+model_name: "model_name"
+
+# If you want to use a specific dataset path, uncomment the following line.
+# Otherwise, the dataset path is `{dataset_root}/{model_name}`.
+
+# dataset_path: "your/dataset/path"
+
+resample:
+ sampling_rate: 44100
+ in_dir: "raw"
+ out_dir: "wavs"
+
+preprocess_text:
+ transcription_path: "esd.list"
+ cleaned_path: ""
+ train_path: "train.list"
+ val_path: "val.list"
+ config_path: "config.json"
+ val_per_lang: 0
+ max_val_total: 12
+ clean: true
+
+bert_gen:
+ config_path: "config.json"
+ num_processes: 1
+ device: "cuda"
+ use_multi_device: false
+
+style_gen:
+ config_path: "config.json"
+ num_processes: 4
+ device: "cuda"
+
+train_ms:
+ env:
+ MASTER_ADDR: "localhost"
+ MASTER_PORT: 10086
+ WORLD_SIZE: 1
+ LOCAL_RANK: 0
+ RANK: 0
+ model_dir: "models" # The directory to save the model (for training), relative to `{dataset_root}/{model_name}`.
+ config_path: "config.json"
+ num_workers: 16
+ spec_cache: True
+ keep_ckpts: 1 # Set this to 0 to keep all checkpoints
+
+webui: # For `webui.py`, which is not supported yet in Style-Bert-VITS2.
+ # 推理设备
+ device: "cuda"
+ # 模型路径
+ model: "models/G_8000.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"
+
+# server_fastapi's config
+server:
+ port: 5000
+ device: "cuda"
+ language: "JP"
+ limit: 100
+ origins:
+ - "*"

dict_data/default.csv

@@ -0,0 +1,5 @@
+Ｂｅｒｔ,,,8609,名詞,固有名詞,一般,*,*,*,Ｂｅｒｔ,バアト,バアト,0/3,*
+ＶＩＴＳ,,,8609,名詞,固有名詞,一般,*,*,*,ＶＩＴＳ,ビッツ,ビッツ,0/3,*
+ＶＩＴＳ二,,,8609,名詞,固有名詞,一般,*,*,*,ＶＩＴＳ二,ビッツツー,ビッツツー,4/5,*
+ＢｅｒｔＶＩＴＳ,,,8609,名詞,固有名詞,一般,*,*,*,ＢｅｒｔＶＩＴＳ,バアトビッツ,バアトビッツ,4/6,*
+担々麺,,,8609,名詞,固有名詞,一般,*,*,*,担々麺,タンタンメン,タンタンメン,3/6,*

initialize.py

@@ -0,0 +1,147 @@
+import argparse
+import json
+import shutil
+from pathlib import Path
+
+import yaml
+from huggingface_hub import hf_hub_download
+
+from style_bert_vits2.logging import logger
+
+
+def download_bert_models():
+ with open("bert/bert_models.json", encoding="utf-8") as fp:
+ models = json.load(fp)
+ for k, v in models.items():
+ local_path = Path("bert").joinpath(k)
+ for file in v["files"]:
+ if not Path(local_path).joinpath(file).exists():
+ logger.info(f"Downloading {k} {file}")
+ hf_hub_download(v["repo_id"], file, local_dir=local_path)
+
+
+def download_slm_model():
+ local_path = Path("slm/wavlm-base-plus/")
+ file = "pytorch_model.bin"
+ if not Path(local_path).joinpath(file).exists():
+ logger.info(f"Downloading wavlm-base-plus {file}")
+ hf_hub_download("microsoft/wavlm-base-plus", file, local_dir=local_path)
+
+
+def download_pretrained_models():
+ files = ["G_0.safetensors", "D_0.safetensors", "DUR_0.safetensors"]
+ local_path = Path("pretrained")
+ for file in files:
+ if not Path(local_path).joinpath(file).exists():
+ logger.info(f"Downloading pretrained {file}")
+ hf_hub_download(
+ "litagin/Style-Bert-VITS2-1.0-base", file, local_dir=local_path
+ )
+
+
+def download_jp_extra_pretrained_models():
+ files = ["G_0.safetensors", "D_0.safetensors", "WD_0.safetensors"]
+ local_path = Path("pretrained_jp_extra")
+ for file in files:
+ if not Path(local_path).joinpath(file).exists():
+ logger.info(f"Downloading JP-Extra pretrained {file}")
+ hf_hub_download(
+ "litagin/Style-Bert-VITS2-2.0-base-JP-Extra", file, local_dir=local_path
+ )
+
+
+def download_default_models():
+ files = [
+ "jvnv-F1-jp/config.json",
+ "jvnv-F1-jp/jvnv-F1-jp_e160_s14000.safetensors",
+ "jvnv-F1-jp/style_vectors.npy",
+ "jvnv-F2-jp/config.json",
+ "jvnv-F2-jp/jvnv-F2_e166_s20000.safetensors",
+ "jvnv-F2-jp/style_vectors.npy",
+ "jvnv-M1-jp/config.json",
+ "jvnv-M1-jp/jvnv-M1-jp_e158_s14000.safetensors",
+ "jvnv-M1-jp/style_vectors.npy",
+ "jvnv-M2-jp/config.json",
+ "jvnv-M2-jp/jvnv-M2-jp_e159_s17000.safetensors",
+ "jvnv-M2-jp/style_vectors.npy",
+ ]
+ for file in files:
+ if not Path(f"model_assets/{file}").exists():
+ logger.info(f"Downloading {file}")
+ hf_hub_download(
+ "litagin/style_bert_vits2_jvnv",
+ file,
+ local_dir="model_assets",
+ )
+ additional_files = {
+ "litagin/sbv2_koharune_ami": [
+ "koharune-ami/config.json",
+ "koharune-ami/style_vectors.npy",
+ "koharune-ami/koharune-ami.safetensors",
+ ],
+ "litagin/sbv2_amitaro": [
+ "amitaro/config.json",
+ "amitaro/style_vectors.npy",
+ "amitaro/amitaro.safetensors",
+ ],
+ }
+ for repo_id, files in additional_files.items():
+ for file in files:
+ if not Path(f"model_assets/{file}").exists():
+ logger.info(f"Downloading {file}")
+ hf_hub_download(
+ repo_id,
+ file,
+ local_dir="model_assets",
+ )
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--skip_default_models", action="store_true")
+ parser.add_argument("--only_infer", action="store_true")
+ parser.add_argument(
+ "--dataset_root",
+ type=str,
+ help="Dataset root path (default: Data)",
+ default=None,
+ )
+ parser.add_argument(
+ "--assets_root",
+ type=str,
+ help="Assets root path (default: model_assets)",
+ default=None,
+ )
+ args = parser.parse_args()
+
+ download_bert_models()
+
+ if not args.skip_default_models:
+ download_default_models()
+ if not args.only_infer:
+ download_slm_model()
+ download_pretrained_models()
+ download_jp_extra_pretrained_models()
+
+ # If configs/paths.yml not exists, create it
+ default_paths_yml = Path("configs/default_paths.yml")
+ paths_yml = Path("configs/paths.yml")
+ if not paths_yml.exists():
+ shutil.copy(default_paths_yml, paths_yml)
+
+ if args.dataset_root is None and args.assets_root is None:
+ return
+
+ # Change default paths if necessary
+ with open(paths_yml, encoding="utf-8") as f:
+ yml_data = yaml.safe_load(f)
+ if args.assets_root is not None:
+ yml_data["assets_root"] = args.assets_root
+ if args.dataset_root is not None:
+ yml_data["dataset_root"] = args.dataset_root
+ with open(paths_yml, "w", encoding="utf-8") as f:
+ yaml.dump(yml_data, f, allow_unicode=True)
+
+
+if __name__ == "__main__":
+ main()

model_assets/.gitignore

@@ -0,0 +1,2 @@
+# *
+!.gitignore

model_assets/amitaro/amitaro.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19725d9bfd9d4b4fb17072ed4d40e01f6cf89c22cd83c15e1cccbf3ddf6b81de
+size 251150980

model_assets/amitaro/config.json

@@ -0,0 +1,91 @@
+{
+ "model_name": "amitaro",
+ "train": {
+ "log_interval": 200,
+ "eval_interval": 1000,
+ "seed": 42,
+ "epochs": 100,
+ "learning_rate": 0.0001,
+ "betas": [0.8, 0.99],
+ "eps": 1e-9,
+ "batch_size": 4,
+ "bf16_run": false,
+ "fp16_run": false,
+ "lr_decay": 0.99996,
+ "segment_size": 16384,
+ "init_lr_ratio": 1,
+ "warmup_epochs": 0,
+ "c_mel": 45,
+ "c_kl": 1.0,
+ "c_commit": 100,
+ "skip_optimizer": false,
+ "freeze_ZH_bert": false,
+ "freeze_JP_bert": false,
+ "freeze_EN_bert": false,
+ "freeze_emo": false,
+ "freeze_style": false,
+ "freeze_decoder": false
+ },
+ "data": {
+ "use_jp_extra": true,
+ "training_files": "Data\\amitaro\\train.list",
+ "validation_files": "Data\\amitaro\\val.list",
+ "max_wav_value": 32768.0,
+ "sampling_rate": 44100,
+ "filter_length": 2048,
+ "hop_length": 512,
+ "win_length": 2048,
+ "n_mel_channels": 128,
+ "mel_fmin": 0.0,
+ "mel_fmax": null,
+ "add_blank": true,
+ "n_speakers": 1,
+ "cleaned_text": true,
+ "spk2id": {
+ "あみたろ": 0
+ },
+ "num_styles": 5,
+ "style2id": {
+ "Neutral": 0,
+ "01": 1,
+ "02": 2,
+ "03": 3,
+ "04": 4
+ }
+ },
+ "model": {
+ "use_spk_conditioned_encoder": true,
+ "use_noise_scaled_mas": true,
+ "use_mel_posterior_encoder": false,
+ "use_duration_discriminator": false,
+ "use_wavlm_discriminator": true,
+ "inter_channels": 192,
+ "hidden_channels": 192,
+ "filter_channels": 768,
+ "n_heads": 2,
+ "n_layers": 6,
+ "kernel_size": 3,
+ "p_dropout": 0.1,
+ "resblock": "1",
+ "resblock_kernel_sizes": [3, 7, 11],
+ "resblock_dilation_sizes": [
+ [1, 3, 5],
+ [1, 3, 5],
+ [1, 3, 5]
+ ],
+ "upsample_rates": [8, 8, 2, 2, 2],
+ "upsample_initial_channel": 512,
+ "upsample_kernel_sizes": [16, 16, 8, 2, 2],
+ "n_layers_q": 3,
+ "use_spectral_norm": false,
+ "gin_channels": 512,
+ "slm": {
+ "model": "./slm/wavlm-base-plus",
+ "sr": 16000,
+ "hidden": 768,
+ "nlayers": 13,
+ "initial_channel": 64
+ }
+ },
+ "version": "2.5.0-JP-Extra"
+}

model_assets/amitaro/style_vectors.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f04b3d679982f11fd29da6ea27f93af86e5876b93aa4fbf910215ce145513cc9
+size 5248

model_assets/jvnv-F1-jp/config.json

@@ -0,0 +1,92 @@
+{
+ "train": {
+ "log_interval": 200,
+ "eval_interval": 1000,
+ "seed": 42,
+ "epochs": 300,
+ "learning_rate": 0.0001,
+ "betas": [0.8, 0.99],
+ "eps": 1e-9,
+ "batch_size": 4,
+ "bf16_run": true,
+ "fp16_run": false,
+ "lr_decay": 0.99996,
+ "segment_size": 16384,
+ "init_lr_ratio": 1,
+ "warmup_epochs": 0,
+ "c_mel": 45,
+ "c_kl": 1.0,
+ "c_commit": 100,
+ "skip_optimizer": true,
+ "freeze_ZH_bert": false,
+ "freeze_JP_bert": false,
+ "freeze_EN_bert": false,
+ "freeze_emo": false,
+ "freeze_style": false
+ },
+ "data": {
+ "use_jp_extra": true,
+ "training_files": "Data/jvnv-F1-jp/train.list",
+ "validation_files": "Data/jvnv-F1-jp/val.list",
+ "max_wav_value": 32768.0,
+ "sampling_rate": 44100,
+ "filter_length": 2048,
+ "hop_length": 512,
+ "win_length": 2048,
+ "n_mel_channels": 128,
+ "mel_fmin": 0.0,
+ "mel_fmax": null,
+ "add_blank": true,
+ "n_speakers": 1,
+ "cleaned_text": true,
+ "spk2id": {
+ "jvnv-F1-jp": 0
+ },
+ "num_styles": 7,
+ "style2id": {
+ "Neutral": 0,
+ "Angry": 1,
+ "Disgust": 2,
+ "Fear": 3,
+ "Happy": 4,
+ "Sad": 5,
+ "Surprise": 6
+ }
+ },
+ "model": {
+ "use_spk_conditioned_encoder": true,
+ "use_noise_scaled_mas": true,
+ "use_mel_posterior_encoder": false,
+ "use_duration_discriminator": false,
+ "use_wavlm_discriminator": true,
+ "inter_channels": 192,
+ "hidden_channels": 192,
+ "filter_channels": 768,
+ "n_heads": 2,
+ "n_layers": 6,
+ "kernel_size": 3,
+ "p_dropout": 0.1,
+ "resblock": "1",
+ "resblock_kernel_sizes": [3, 7, 11],
+ "resblock_dilation_sizes": [
+ [1, 3, 5],
+ [1, 3, 5],
+ [1, 3, 5]
+ ],
+ "upsample_rates": [8, 8, 2, 2, 2],
+ "upsample_initial_channel": 512,
+ "upsample_kernel_sizes": [16, 16, 8, 2, 2],
+ "n_layers_q": 3,
+ "use_spectral_norm": false,
+ "gin_channels": 512,
+ "slm": {
+ "model": "./slm/wavlm-base-plus",
+ "sr": 16000,
+ "hidden": 768,
+ "nlayers": 13,
+ "initial_channel": 64
+ }
+ },
+ "version": "2.0-JP-Extra",
+ "model_name": "jvnv-F1-jp"
+}

model_assets/jvnv-F1-jp/jvnv-F1-jp_e160_s14000.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a90fa6c9444d9235c9ec4db99daf7c5c6a21cc26ca141b4c48455d66a3257d01
+size 251150980

model_assets/jvnv-F1-jp/style_vectors.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1f959bb45ed0922efc31ff24e9147253814f42cb1d2d1e2bb10391a9df368489
+size 7296

model_assets/jvnv-F2-jp/config.json

@@ -0,0 +1,92 @@
+{
+ "train": {
+ "log_interval": 200,
+ "eval_interval": 1000,
+ "seed": 42,
+ "epochs": 300,
+ "learning_rate": 0.0001,
+ "betas": [0.8, 0.99],
+ "eps": 1e-9,
+ "batch_size": 4,
+ "bf16_run": false,
+ "fp16_run": false,
+ "lr_decay": 0.99996,
+ "segment_size": 16384,
+ "init_lr_ratio": 1,
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+ "c_commit": 100,
+ "skip_optimizer": true,
+ "freeze_ZH_bert": false,
+ "freeze_JP_bert": false,
+ "freeze_EN_bert": false,
+ "freeze_emo": false,
+ "freeze_style": false
+ },
+ "data": {
+ "use_jp_extra": true,
+ "training_files": "/content/drive/MyDrive/Style-Bert-VITS2/Data/jvnv-F2/train.list",
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+ "Sad": 5,
+ "Surprise": 6
+ }
+ },
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+ [1, 3, 5],
+ [1, 3, 5],
+ [1, 3, 5]
+ ],
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+ "upsample_kernel_sizes": [16, 16, 8, 2, 2],
+ "n_layers_q": 3,
+ "use_spectral_norm": false,
+ "gin_channels": 512,
+ "slm": {
+ "model": "./slm/wavlm-base-plus",
+ "sr": 16000,
+ "hidden": 768,
+ "nlayers": 13,
+ "initial_channel": 64
+ }
+ },
+ "version": "2.0-JP-Extra",
+ "model_name": "jvnv-F2-jp"
+}

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model_assets/jvnv-F2-jp/style_vectors.npy

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model_assets/jvnv-M1-jp/config.json

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+{
+ "train": {
+ "log_interval": 200,
+ "eval_interval": 1000,
+ "seed": 42,
+ "epochs": 300,
+ "learning_rate": 0.0001,
+ "betas": [0.8, 0.99],
+ "eps": 1e-9,
+ "batch_size": 4,
+ "bf16_run": true,
+ "fp16_run": false,
+ "lr_decay": 0.99996,
+ "segment_size": 16384,
+ "init_lr_ratio": 1,
+ "warmup_epochs": 0,
+ "c_mel": 45,
+ "c_kl": 1.0,
+ "c_commit": 100,
+ "skip_optimizer": true,
+ "freeze_ZH_bert": false,
+ "freeze_JP_bert": false,
+ "freeze_EN_bert": false,
+ "freeze_emo": false,
+ "freeze_style": false
+ },
+ "data": {
+ "use_jp_extra": true,
+ "training_files": "Data/jvnv-M1-jp/train.list",
+ "validation_files": "Data/jvnv-M1-jp/val.list",
+ "max_wav_value": 32768.0,
+ "sampling_rate": 44100,
+ "filter_length": 2048,
+ "hop_length": 512,
+ "win_length": 2048,
+ "n_mel_channels": 128,
+ "mel_fmin": 0.0,
+ "mel_fmax": null,
+ "add_blank": true,
+ "n_speakers": 1,
+ "cleaned_text": true,
+ "spk2id": {
+ "jvnv-M1-jp": 0
+ },
+ "num_styles": 7,
+ "style2id": {
+ "Neutral": 0,
+ "Angry": 1,
+ "Disgust": 2,
+ "Fear": 3,
+ "Happy": 4,
+ "Sad": 5,
+ "Surprise": 6
+ }
+ },
+ "model": {
+ "use_spk_conditioned_encoder": true,
+ "use_noise_scaled_mas": true,
+ "use_mel_posterior_encoder": false,
+ "use_duration_discriminator": false,
+ "use_wavlm_discriminator": true,
+ "inter_channels": 192,
+ "hidden_channels": 192,
+ "filter_channels": 768,
+ "n_heads": 2,
+ "n_layers": 6,
+ "kernel_size": 3,
+ "p_dropout": 0.1,
+ "resblock": "1",
+ "resblock_kernel_sizes": [3, 7, 11],
+ "resblock_dilation_sizes": [
+ [1, 3, 5],
+ [1, 3, 5],
+ [1, 3, 5]
+ ],
+ "upsample_rates": [8, 8, 2, 2, 2],
+ "upsample_initial_channel": 512,
+ "upsample_kernel_sizes": [16, 16, 8, 2, 2],
+ "n_layers_q": 3,
+ "use_spectral_norm": false,
+ "gin_channels": 512,
+ "slm": {
+ "model": "./slm/wavlm-base-plus",
+ "sr": 16000,
+ "hidden": 768,
+ "nlayers": 13,
+ "initial_channel": 64
+ }
+ },
+ "version": "2.0-JP-Extra",
+ "model_name": "jvnv-M1-jp"
+}

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model_assets/jvnv-M2-jp/config.json

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+{
+ "train": {
+ "log_interval": 200,
+ "eval_interval": 1000,
+ "seed": 42,
+ "epochs": 300,
+ "learning_rate": 0.0001,
+ "betas": [0.8, 0.99],
+ "eps": 1e-9,
+ "batch_size": 4,
+ "bf16_run": true,
+ "fp16_run": false,
+ "lr_decay": 0.99996,
+ "segment_size": 16384,
+ "init_lr_ratio": 1,
+ "warmup_epochs": 0,
+ "c_mel": 45,
+ "c_kl": 1.0,
+ "c_commit": 100,
+ "skip_optimizer": true,
+ "freeze_ZH_bert": false,
+ "freeze_JP_bert": false,
+ "freeze_EN_bert": false,
+ "freeze_emo": false,
+ "freeze_style": false
+ },
+ "data": {
+ "use_jp_extra": true,
+ "training_files": "Data/jvnv-M2-jp/train.list",
+ "validation_files": "Data/jvnv-M2-jp/val.list",
+ "max_wav_value": 32768.0,
+ "sampling_rate": 44100,
+ "filter_length": 2048,
+ "hop_length": 512,
+ "win_length": 2048,
+ "n_mel_channels": 128,
+ "mel_fmin": 0.0,
+ "mel_fmax": null,
+ "add_blank": true,
+ "n_speakers": 1,
+ "cleaned_text": true,
+ "spk2id": {
+ "jvnv-M2-jp": 0
+ },
+ "num_styles": 7,
+ "style2id": {
+ "Neutral": 0,
+ "Angry": 1,
+ "Disgust": 2,
+ "Fear": 3,
+ "Happy": 4,
+ "Sad": 5,
+ "Surprise": 6
+ }
+ },
+ "model": {
+ "use_spk_conditioned_encoder": true,
+ "use_noise_scaled_mas": true,
+ "use_mel_posterior_encoder": false,
+ "use_duration_discriminator": false,
+ "use_wavlm_discriminator": true,
+ "inter_channels": 192,
+ "hidden_channels": 192,
+ "filter_channels": 768,
+ "n_heads": 2,
+ "n_layers": 6,
+ "kernel_size": 3,
+ "p_dropout": 0.1,
+ "resblock": "1",
+ "resblock_kernel_sizes": [3, 7, 11],
+ "resblock_dilation_sizes": [
+ [1, 3, 5],
+ [1, 3, 5],
+ [1, 3, 5]
+ ],
+ "upsample_rates": [8, 8, 2, 2, 2],
+ "upsample_initial_channel": 512,
+ "upsample_kernel_sizes": [16, 16, 8, 2, 2],
+ "n_layers_q": 3,
+ "use_spectral_norm": false,
+ "gin_channels": 512,
+ "slm": {
+ "model": "./slm/wavlm-base-plus",
+ "sr": 16000,
+ "hidden": 768,
+ "nlayers": 13,
+ "initial_channel": 64
+ }
+ },
+ "version": "2.0-JP-Extra",
+ "model_name": "jvnv-M2-jp"
+}

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model_assets/koharune-ami/config.json

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+{
+ "model_name": "小春音アミ",
+ "train": {
+ "log_interval": 200,
+ "eval_interval": 1000,
+ "seed": 42,
+ "epochs": 300,
+ "learning_rate": 0.0001,
+ "betas": [0.8, 0.99],
+ "eps": 1e-9,
+ "batch_size": 4,
+ "bf16_run": false,
+ "fp16_run": false,
+ "lr_decay": 0.99996,
+ "segment_size": 16384,
+ "init_lr_ratio": 1,
+ "warmup_epochs": 0,
+ "c_mel": 45,
+ "c_kl": 1.0,
+ "c_commit": 100,
+ "skip_optimizer": false,
+ "freeze_ZH_bert": false,
+ "freeze_JP_bert": false,
+ "freeze_EN_bert": false,
+ "freeze_emo": false,
+ "freeze_style": false,
+ "freeze_decoder": false
+ },
+ "data": {
+ "use_jp_extra": true,
+ "training_files": "Data\\amitaro_combined\\train.list",
+ "validation_files": "Data\\amitaro_combined\\val.list",
+ "max_wav_value": 32768.0,
+ "sampling_rate": 44100,
+ "filter_length": 2048,
+ "hop_length": 512,
+ "win_length": 2048,
+ "n_mel_channels": 128,
+ "mel_fmin": 0.0,
+ "mel_fmax": null,
+ "add_blank": true,
+ "n_speakers": 1,
+ "cleaned_text": true,
+ "spk2id": {
+ "小春音アミ": 0
+ },
+ "num_styles": 6,
+ "style2id": {
+ "Neutral": 0,
+ "るんるん": 1,
+ "ささやきA（無声）": 2,
+ "ささやきB（有声）": 3,
+ "ノーマル": 4,
+ "よふかし": 5
+ }
+ },
+ "model": {
+ "use_spk_conditioned_encoder": true,
+ "use_noise_scaled_mas": true,
+ "use_mel_posterior_encoder": false,
+ "use_duration_discriminator": false,
+ "use_wavlm_discriminator": true,
+ "inter_channels": 192,
+ "hidden_channels": 192,
+ "filter_channels": 768,
+ "n_heads": 2,
+ "n_layers": 6,
+ "kernel_size": 3,
+ "p_dropout": 0.1,
+ "resblock": "1",
+ "resblock_kernel_sizes": [3, 7, 11],
+ "resblock_dilation_sizes": [
+ [1, 3, 5],
+ [1, 3, 5],
+ [1, 3, 5]
+ ],
+ "upsample_rates": [8, 8, 2, 2, 2],
+ "upsample_initial_channel": 512,
+ "upsample_kernel_sizes": [16, 16, 8, 2, 2],
+ "n_layers_q": 3,
+ "use_spectral_norm": false,
+ "gin_channels": 512,
+ "slm": {
+ "model": "./slm/wavlm-base-plus",
+ "sr": 16000,
+ "hidden": 768,
+ "nlayers": 13,
+ "initial_channel": 64
+ }
+ },
+ "version": "2.5.0-JP-Extra"
+}

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model_assets/koharune-ami/style_vectors.npy

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requirements.txt

@@ -0,0 +1,24 @@
+# cmudict
+# cn2an
+# faster-whisper==0.10.1
+fastapi[standard]
+# g2p_en
+# GPUtil
+# gradio
+# jieba
+# librosa==0.9.2
+loguru
+num2words
+# protobuf==4.25
+# psutil
+# punctuators
+pyannote.audio>=3.1.0
+# pyloudnorm
+pyopenjtalk-dict
+# pypinyin
+pyworld-prebuilt
+# stable_ts
+# tensorboard
+torch
+transformers
+# umap-learn

server_editor.py

@@ -0,0 +1,450 @@
+"""
+Style-Bert-VITS2-Editor用のサーバー。
+次のリポジトリ
+https://github.com/litagin02/Style-Bert-VITS2-Editor
+をビルドしてできあがったファイルをWebフォルダに入れて実行する。
+
+TODO: リファクタリングやドキュメンテーションやAPI整理、辞書周りの改善などが必要。
+"""
+
+import argparse
+import io
+import shutil
+import sys
+import webbrowser
+import zipfile
+from datetime import datetime
+from io import BytesIO
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import requests
+import torch
+import uvicorn
+from fastapi import APIRouter, FastAPI, HTTPException, status
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, Response
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+from scipy.io import wavfile
+
+from config import get_path_config
+from initialize import download_default_models
+from style_bert_vits2.constants import (
+ DEFAULT_ASSIST_TEXT_WEIGHT,
+ DEFAULT_NOISE,
+ DEFAULT_NOISEW,
+ DEFAULT_SDP_RATIO,
+ DEFAULT_STYLE,
+ DEFAULT_STYLE_WEIGHT,
+ VERSION,
+ Languages,
+)
+from style_bert_vits2.logging import logger
+from style_bert_vits2.nlp import bert_models
+from style_bert_vits2.nlp.japanese import pyopenjtalk_worker as pyopenjtalk
+from style_bert_vits2.nlp.japanese.g2p_utils import g2kata_tone, kata_tone2phone_tone
+from style_bert_vits2.nlp.japanese.normalizer import normalize_text
+from style_bert_vits2.nlp.japanese.user_dict import (
+ apply_word,
+ delete_word,
+ read_dict,
+ rewrite_word,
+ update_dict,
+)
+from style_bert_vits2.tts_model import TTSModelHolder, TTSModelInfo
+
+
+# ---フロントエンド部分に関する処理---
+
+# エディターのビルドファイルを配置するディレクトリ
+STATIC_DIR = Path("static")
+# エディターの最新のビルドファイルのダウンロード日時を記録するファイル
+LAST_DOWNLOAD_FILE = STATIC_DIR / "last_download.txt"
+
+
+def download_static_files(user, repo, asset_name):
+ """Style-Bert-VITS2エディターの最新のビルドzipをダウンロードして展開する。"""
+
+ logger.info("Checking for new release...")
+ latest_release = get_latest_release(user, repo)
+ if latest_release is None:
+ logger.warning(
+ "Failed to fetch the latest release. Proceeding without static files."
+ )
+ return
+
+ if not new_release_available(latest_release):
+ logger.info("No new release available. Proceeding with existing static files.")
+ return
+
+ logger.info("New release available. Downloading static files...")
+ asset_url = get_asset_url(latest_release, asset_name)
+ if asset_url:
+ if STATIC_DIR.exists():
+ shutil.rmtree(STATIC_DIR)
+ STATIC_DIR.mkdir(parents=True, exist_ok=True)
+ download_and_extract(asset_url, STATIC_DIR)
+ save_last_download(latest_release)
+ else:
+ logger.warning("Asset not found. Proceeding without static files.")
+
+
+def get_latest_release(user, repo):
+ url = f"https://api.github.com/repos/{user}/{repo}/releases/latest"
+ try:
+ response = requests.get(url)
+ response.raise_for_status()
+ return response.json()
+ except requests.RequestException:
+ return None
+
+
+def get_asset_url(release, asset_name):
+ for asset in release["assets"]:
+ if asset["name"] == asset_name:
+ return asset["browser_download_url"]
+ return None
+
+
+def download_and_extract(url, extract_to: Path):
+ response = requests.get(url)
+ response.raise_for_status()
+ with zipfile.ZipFile(io.BytesIO(response.content)) as zip_ref:
+ zip_ref.extractall(extract_to)
+
+ # 展開先が1つのディレクトリだけの場合、その中身を直下に移動する
+ extracted_dirs = list(extract_to.iterdir())
+ if len(extracted_dirs) == 1 and extracted_dirs[0].is_dir():
+ for file in extracted_dirs[0].iterdir():
+ file.rename(extract_to / file.name)
+ extracted_dirs[0].rmdir()
+
+ # index.htmlが存在するかチェック
+ if not (extract_to / "index.html").exists():
+ logger.warning("index.html not found in the extracted files.")
+
+
+def new_release_available(latest_release):
+ if LAST_DOWNLOAD_FILE.exists():
+ with open(LAST_DOWNLOAD_FILE) as file:
+ last_download_str = file.read().strip()
+ # 'Z'を除去して日時オブジェクトに変換
+ last_download_str = last_download_str.replace("Z", "+00:00")
+ last_download = datetime.fromisoformat(last_download_str)
+ return (
+ datetime.fromisoformat(
+ latest_release["published_at"].replace("Z", "+00:00")
+ )
+ > last_download
+ )
+ return True
+
+
+def save_last_download(latest_release):
+ with open(LAST_DOWNLOAD_FILE, "w") as file:
+ file.write(latest_release["published_at"])
+
+
+# ---フロントエンド部分に関する処理ここまで---
+# 以降はAPIの設定
+
+# pyopenjtalk_worker を起動
+## pyopenjtalk_worker は TCP ソケットサーバーのため、ここで起動する
+pyopenjtalk.initialize_worker()
+
+# pyopenjtalk の辞書を更新
+update_dict()
+
+# 事前に BERT モデル/トークナイザーをロードしておく
+## ここでロードしなくても必要になった際に自動ロードされるが、時間がかかるため事前にロードしておいた方が体験が良い
+## server_editor.py は日本語にしか対応していないため、日本語の BERT モデル/トークナイザーのみロードする
+bert_models.load_model(Languages.JP)
+bert_models.load_tokenizer(Languages.JP)
+
+
+class AudioResponse(Response):
+ media_type = "audio/wav"
+
+
+origins = [
+ "http://localhost:3000",
+ "http://localhost:8000",
+ "http://127.0.0.1:3000",
+ "http://127.0.0.1:8000",
+]
+
+path_config = get_path_config()
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_dir", type=str, default=path_config.assets_root)
+parser.add_argument("--device", type=str, default="cuda")
+parser.add_argument("--port", type=int, default=8000)
+parser.add_argument("--inbrowser", action="store_true")
+parser.add_argument("--line_length", type=int, default=None)
+parser.add_argument("--line_count", type=int, default=None)
+parser.add_argument("--skip_default_models", action="store_true")
+parser.add_argument("--skip_static_files", action="store_true")
+args = parser.parse_args()
+device = args.device
+if device == "cuda" and not torch.cuda.is_available():
+ device = "cpu"
+model_dir = Path(args.model_dir)
+port = int(args.port)
+if not args.skip_default_models:
+ download_default_models()
+skip_static_files = bool(args.skip_static_files)
+
+model_holder = TTSModelHolder(model_dir, device)
+if len(model_holder.model_names) == 0:
+ logger.error(f"Models not found in {model_dir}.")
+ sys.exit(1)
+
+
+app = FastAPI()
+
+
+app.add_middleware(
+ CORSMiddleware,
+ allow_origins=origins,
+ allow_credentials=True,
+ allow_methods=["*"],
+ allow_headers=["*"],
+)
+
+router = APIRouter()
+
+
+@router.get("/version")
+def version() -> str:
+ return VERSION
+
+
+class MoraTone(BaseModel):
+ mora: str
+ tone: int
+
+
+class TextRequest(BaseModel):
+ text: str
+
+
+@router.post("/g2p")
+async def read_item(item: TextRequest):
+ try:
+ # 最初に正規化しないと整合性がとれない
+ text = normalize_text(item.text)
+ kata_tone_list = g2kata_tone(text)
+ except Exception as e:
+ raise HTTPException(
+ status_code=400,
+ detail=f"Failed to convert {item.text} to katakana and tone, {e}",
+ )
+ return [MoraTone(mora=kata, tone=tone) for kata, tone in kata_tone_list]
+
+
+@router.post("/normalize")
+async def normalize(item: TextRequest):
+ return normalize_text(item.text)
+
+
+@router.get("/models_info", response_model=list[TTSModelInfo])
+def models_info():
+ return model_holder.models_info
+
+
+class SynthesisRequest(BaseModel):
+ model: str
+ modelFile: str
+ text: str
+ moraToneList: list[MoraTone]
+ style: str = DEFAULT_STYLE
+ styleWeight: float = DEFAULT_STYLE_WEIGHT
+ assistText: str = ""
+ assistTextWeight: float = DEFAULT_ASSIST_TEXT_WEIGHT
+ speed: float = 1.0
+ noise: float = DEFAULT_NOISE
+ noisew: float = DEFAULT_NOISEW
+ sdpRatio: float = DEFAULT_SDP_RATIO
+ language: Languages = Languages.JP
+ silenceAfter: float = 0.5
+ pitchScale: float = 1.0
+ intonationScale: float = 1.0
+ speaker: Optional[str] = None
+
+
+@router.post("/synthesis", response_class=AudioResponse)
+def synthesis(request: SynthesisRequest):
+ if args.line_length is not None and len(request.text) > args.line_length:
+ raise HTTPException(
+ status_code=400,
+ detail=f"1行の文字数は{args.line_length}文字以下にしてください。",
+ )
+ try:
+ model = model_holder.get_model(
+ model_name=request.model, model_path_str=request.modelFile
+ )
+ except Exception as e:
+ logger.error(e)
+ raise HTTPException(
+ status_code=500,
+ detail=f"Failed to load model {request.model} from {request.modelFile}, {e}",
+ )
+ text = request.text
+ kata_tone_list = [
+ (mora_tone.mora, mora_tone.tone) for mora_tone in request.moraToneList
+ ]
+ phone_tone = kata_tone2phone_tone(kata_tone_list)
+ tone = [t for _, t in phone_tone]
+ try:
+ sid = 0 if request.speaker is None else model.spk2id[request.speaker]
+ except KeyError:
+ raise HTTPException(
+ status_code=400,
+ detail=f"Speaker {request.speaker} not found in {model.spk2id}",
+ )
+ sr, audio = model.infer(
+ text=text,
+ language=request.language,
+ sdp_ratio=request.sdpRatio,
+ noise=request.noise,
+ noise_w=request.noisew,
+ length=1 / request.speed,
+ given_tone=tone,
+ style=request.style,
+ style_weight=request.styleWeight,
+ assist_text=request.assistText,
+ assist_text_weight=request.assistTextWeight,
+ use_assist_text=bool(request.assistText),
+ line_split=False,
+ pitch_scale=request.pitchScale,
+ intonation_scale=request.intonationScale,
+ speaker_id=sid,
+ )
+
+ with BytesIO() as wavContent:
+ wavfile.write(wavContent, sr, audio)
+ return Response(content=wavContent.getvalue(), media_type="audio/wav")
+
+
+class MultiSynthesisRequest(BaseModel):
+ lines: list[SynthesisRequest]
+
+
+@router.post("/multi_synthesis", response_class=AudioResponse)
+def multi_synthesis(request: MultiSynthesisRequest):
+ lines = request.lines
+ if args.line_count is not None and len(lines) > args.line_count:
+ raise HTTPException(
+ status_code=400,
+ detail=f"行数は{args.line_count}行以下にしてください。",
+ )
+ audios = []
+ sr = None
+ for i, req in enumerate(lines):
+ if args.line_length is not None and len(req.text) > args.line_length:
+ raise HTTPException(
+ status_code=400,
+ detail=f"1行の文字数は{args.line_length}文字以下にしてください。",
+ )
+ try:
+ model = model_holder.get_model(
+ model_name=req.model, model_path_str=req.modelFile
+ )
+ except Exception as e:
+ logger.error(e)
+ raise HTTPException(
+ status_code=500,
+ detail=f"Failed to load model {req.model} from {req.modelFile}, {e}",
+ )
+ text = req.text
+ kata_tone_list = [
+ (mora_tone.mora, mora_tone.tone) for mora_tone in req.moraToneList
+ ]
+ phone_tone = kata_tone2phone_tone(kata_tone_list)
+ tone = [t for _, t in phone_tone]
+ sr, audio = model.infer(
+ text=text,
+ language=req.language,
+ sdp_ratio=req.sdpRatio,
+ noise=req.noise,
+ noise_w=req.noisew,
+ length=1 / req.speed,
+ given_tone=tone,
+ style=req.style,
+ style_weight=req.styleWeight,
+ assist_text=req.assistText,
+ assist_text_weight=req.assistTextWeight,
+ use_assist_text=bool(req.assistText),
+ line_split=False,
+ pitch_scale=req.pitchScale,
+ intonation_scale=req.intonationScale,
+ )
+ audios.append(audio)
+ if i < len(lines) - 1:
+ silence = int(sr * req.silenceAfter)
+ audios.append(np.zeros(silence, dtype=np.int16))
+ audio = np.concatenate(audios)
+
+ with BytesIO() as wavContent:
+ wavfile.write(wavContent, sr, audio)
+ return Response(content=wavContent.getvalue(), media_type="audio/wav")
+
+
+class UserDictWordRequest(BaseModel):
+ surface: str
+ pronunciation: str
+ accent_type: int # アクセント核位置（存在しない場合は0、1文字目は1）
+ priority: int = 5
+
+
+@router.get("/user_dict")
+def get_user_dict():
+ return read_dict()
+
+
+@router.post("/user_dict_word")
+def add_user_dict_word(request: UserDictWordRequest):
+ uuid = apply_word(
+ surface=request.surface,
+ pronunciation=request.pronunciation,
+ accent_type=request.accent_type,
+ priority=request.priority,
+ )
+ update_dict()
+
+ return JSONResponse(
+ status_code=status.HTTP_201_CREATED,
+ content={"uuid": uuid},
+ )
+
+
+@router.put("/user_dict_word/{uuid}")
+def update_user_dict_word(uuid: str, request: UserDictWordRequest):
+ rewrite_word(
+ word_uuid=uuid,
+ surface=request.surface,
+ pronunciation=request.pronunciation,
+ accent_type=request.accent_type,
+ priority=request.priority,
+ )
+ update_dict()
+ return JSONResponse(status_code=status.HTTP_200_OK, content={"uuid": uuid})
+
+
+@router.delete("/user_dict_word/{uuid}")
+def delete_user_dict_word(uuid: str):
+ delete_word(uuid)
+ update_dict()
+ return JSONResponse(status_code=status.HTTP_200_OK, content={"uuid": uuid})
+
+
+app.include_router(router, prefix="/api")
+
+if __name__ == "__main__":
+ if not skip_static_files:
+ download_static_files("litagin02", "Style-Bert-VITS2-Editor", "out.zip")
+ app.mount("/", StaticFiles(directory=STATIC_DIR, html=True), name="static")
+ if args.inbrowser:
+ webbrowser.open(f"http://localhost:{port}")
+ uvicorn.run(app, host="0.0.0.0", port=port)

static/404.html

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static/_next/static/7Ebwtq-c5RXrtRkbNZ_HI/_buildManifest.js

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