202406210928

.dockerignore

@@ -0,0 +1,18 @@
+# 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

.gitignore

@@ -0,0 +1,33 @@
+.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

@@ -0,0 +1,23 @@
+# 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

@@ -1,8 +1,8 @@
 ---
-title: DolceTTS
-emoji: 📈
+title: Chino TTS
+emoji: ☕🐰🎤
 colorFrom: gray
-colorTo: indigo
+colorTo: yellow
 sdk: docker
 pinned: false
 ---

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

@@ -0,0 +1,34 @@
+*.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

@@ -0,0 +1,89 @@
+---
+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

@@ -0,0 +1,37 @@
+{
+  "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:
+    - "*"

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/タルト/config.json

@@ -0,0 +1,118 @@
+{
+  "model_name": "tart",
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 100,
+    "learning_rate": 0.0001,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "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": "/content/drive/MyDrive/Style-Bert-VITS2/Data/tart/train.list",
+    "validation_files": "/content/drive/MyDrive/Style-Bert-VITS2/Data/tart/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": {
+      "tart": 0
+    },
+    "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": 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": "2.4.1-JP-Extra"
+}

model_assets/タルト/style_vectors.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a99fa9de1781563ffd546451f26b4907acbb8c475a8a4fa4d71e6476e2b0d398
+size 1152

model_assets/タルト/tart_e100_s2100.safetensors

@@ -0,0 +1,3 @@
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+oid sha256:aa4c7963268de235712de1c52a9c953322221ec52ee26bb5d5c259a8982b90f2
+size 251150980

model_assets/タルト/tart_e48_s1000.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9bf14e912a454e7831c69f3acd97fe9dcc350c1d79a48df57e34f6ad5893aa1b
+size 251150980

model_assets/タルト/tart_e96_s2000.safetensors

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

model_assets/ティラミス/config.json

@@ -0,0 +1,122 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 100,
+    "learning_rate": 0.0001,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "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": "/content/drive/MyDrive/Style-Bert-VITS2/Data/tiramisu/train.list",
+    "validation_files": "/content/drive/MyDrive/Style-Bert-VITS2/Data/tiramisu/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": {
+      "tiramisu": 0
+    },
+    "num_styles": 5,
+    "style2id": {
+      "Neutral": 0,
+      "問いかけ": 1,
+      "困惑": 2,
+      "明るい": 3,
+      "落着き": 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": "2.3.1-JP-Extra",
+  "model_name": "tiramisu"
+}

model_assets/ティラミス/style_vectors.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3b2daf20680066ed54888e8d52d8653d053b26d8d8b311976f47611ebed5a18
+size 5248

model_assets/ティラミス/tiramisu_e100_s2700.safetensors

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+oid sha256:9071a122bed4d48e0bb5f1b217d1c8fed596481f8de457a614204ad081b84f5d
+size 251150980

model_assets/ティラミス/tiramisu_e38_s1000.safetensors

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+size 251150980

model_assets/ティラミス/tiramisu_e75_s2000.safetensors

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requirements.txt

@@ -0,0 +1,25 @@
+# cmudict
+# cn2an
+# faster-whisper==0.10.1
+fastapi
+# 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
+numpy==1.26.4

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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