diff --git a/genie_tts/Audio/Audio.py b/genie_tts/Audio/Audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c519b4da2c38bb9d88f2dfca96c6000ddb95a8a
--- /dev/null
+++ b/genie_tts/Audio/Audio.py
@@ -0,0 +1,51 @@
+import os
+import soundfile as sf
+import soxr
+import numpy as np
+import logging
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+# 音频时长建议范围 (秒)
+MIN_DURATION_S = 3
+MAX_DURATION_S = 10
+# 在音频末尾追加的静音时长 (秒)
+SILENCE_TO_APPEND_S = 0.3
+# 模型期望的目标采样率
+TARGET_SAMPLING_RATE = 16000
+
+
+def load_audio(
+        audio_path: str,
+        target_sampling_rate: int = TARGET_SAMPLING_RATE
+) -> Optional[np.ndarray]:
+    try:
+        wav, original_sr = sf.read(audio_path, dtype='float32')
+        if wav.ndim > 1:
+            wav = np.mean(wav, axis=1)  # 多声道转单声道。
+        if original_sr != target_sampling_rate:
+            wav = soxr.resample(wav, original_sr, target_sampling_rate, quality='hq')  # 重采样。
+
+    except Exception as e:
+        logger.error(f"Failed to load reference audio: {audio_path}. Error: {e}")
+        return None
+
+    # 检查音频长度是否在建议范围之外
+    min_samples = int(MIN_DURATION_S * target_sampling_rate)
+    max_samples = int(MAX_DURATION_S * target_sampling_rate)
+    if not (min_samples <= wav.shape[0] <= max_samples):
+        duration = len(wav) / target_sampling_rate
+        logger.warning(
+            f"The reference audio '{os.path.basename(audio_path)}' has a duration of {duration:.2f} seconds, "
+            f"which is outside the recommended range of {MIN_DURATION_S} to {MAX_DURATION_S} seconds!"
+        )
+
+    # 创建并拼接静音
+    silence_samples = int(SILENCE_TO_APPEND_S * target_sampling_rate)
+    silence_array = np.zeros(silence_samples, dtype=np.float32)
+    wav_processed = np.concatenate([wav, silence_array])
+
+    # 为模型输入增加批次维度
+    # wav_processed = np.expand_dims(wav_processed, axis=0)
+    return wav_processed
diff --git a/genie_tts/Audio/ReferenceAudio.py b/genie_tts/Audio/ReferenceAudio.py
new file mode 100644
index 0000000000000000000000000000000000000000..a82842f354b92f16f502b8d86bbe5f444bbce44b
--- /dev/null
+++ b/genie_tts/Audio/ReferenceAudio.py
@@ -0,0 +1,82 @@
+from ..Utils.Utils import LRUCacheDict
+from ..GetPhonesAndBert import get_phones_and_bert
+from ..Audio.Audio import load_audio
+from ..ModelManager import model_manager
+
+from onnxruntime import InferenceSession
+import os
+import numpy as np
+import soxr
+from typing import Optional, Dict
+
+
+class ReferenceAudio:
+    _prompt_cache: Dict[str, 'ReferenceAudio'] = LRUCacheDict(
+        capacity=int(os.getenv('Max_Cached_Reference_Audio', '10')))
+
+    def __new__(cls, prompt_wav: str, prompt_text: str, language: str):
+        if prompt_wav in cls._prompt_cache:
+            instance = cls._prompt_cache[prompt_wav]
+            if instance.text != prompt_text:  # 如果文本与缓存内记录的不同，则更新。
+                instance.set_text(prompt_text, language=language)
+            return instance
+
+        instance = super().__new__(cls)
+        cls._prompt_cache[prompt_wav] = instance
+        return instance
+
+    def __init__(self, prompt_wav: str, prompt_text: str, language: str):
+        if hasattr(self, '_initialized'):
+            return
+
+        # 文本相关。
+        self.text: str = prompt_text
+        self.phonemes_seq: Optional[np.ndarray] = None
+        self.text_bert: Optional[np.ndarray] = None
+        self.set_text(prompt_text, language=language)
+
+        # 音频相关。
+        self.audio_32k: Optional[np.ndarray] = load_audio(
+            audio_path=prompt_wav,
+            target_sampling_rate=32000
+        )
+        self.audio_16k: np.ndarray = soxr.resample(self.audio_32k, 32000, 16000, quality='hq')
+
+        # 修复：添加 0.3 秒静音填充，防止参考音频内容泄露到生成结果中
+        # 这是 GPT-SoVITS 的做法，帮助模型区分参考内容和目标内容的边界
+        zero_padding_16k = np.zeros(int(16000 * 0.3), dtype=self.audio_16k.dtype)
+        audio_16k_padded = np.concatenate([self.audio_16k, zero_padding_16k])
+
+        self.audio_32k = np.expand_dims(self.audio_32k, axis=0)
+        self.audio_16k = np.expand_dims(self.audio_16k, axis=0)  # 增加 Batch_Size 维度
+
+        if not model_manager.cn_hubert:
+            model_manager.load_cn_hubert()
+        # 使用添加了静音填充的音频提取 SSL 特征
+        self.ssl_content: Optional[np.ndarray] = model_manager.cn_hubert.run(
+            None, {'input_values': np.expand_dims(audio_16k_padded, axis=0)}
+        )[0]
+
+        self.global_emb: Optional[np.ndarray] = None
+        self.global_emb_advanced: Optional[np.ndarray] = None
+
+        self._initialized = True
+
+    def set_text(self, prompt_text: str, language: str) -> None:
+        self.text = prompt_text
+        self.phonemes_seq, self.text_bert = get_phones_and_bert(prompt_text, language=language)
+
+    @classmethod
+    def clear_cache(cls) -> None:
+        """清空 ReferenceAudio 的缓存"""
+        cls._prompt_cache.clear()
+
+    def update_global_emb(self, prompt_encoder: InferenceSession) -> None:
+        if self.global_emb is not None:
+            return
+        if model_manager.load_sv_model():
+            sv_emb = model_manager.speaker_verification_model.run(None, {'waveform': self.audio_16k})[0]
+            self.global_emb, self.global_emb_advanced = prompt_encoder.run(None, {
+                'ref_audio': self.audio_32k,
+                'sv_emb': sv_emb,
+            })
diff --git a/genie_tts/Audio/__init__.py b/genie_tts/Audio/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/genie_tts/Converter/Converter.py b/genie_tts/Converter/Converter.py
new file mode 100644
index 0000000000000000000000000000000000000000..6951e71aedaf4d08338f12c277c0da3e7cf917bf
--- /dev/null
+++ b/genie_tts/Converter/Converter.py
@@ -0,0 +1,11 @@
+from .v2.Converter import convert as convert_v2
+from .v2ProPlus.Converter import convert as convert_v2pp
+
+import os
+
+
+def convert(torch_ckpt_path: str, torch_pth_path: str, output_dir: str) -> None:
+    if os.path.getsize(torch_pth_path) > 150 * 1024 * 1024:  # 大于 150 MB
+        convert_v2pp(torch_ckpt_path, torch_pth_path, output_dir)
+    else:
+        convert_v2(torch_ckpt_path, torch_pth_path, output_dir)
diff --git a/genie_tts/Converter/__init__.py b/genie_tts/Converter/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/genie_tts/Converter/load_state_dict.py b/genie_tts/Converter/load_state_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..952d3580c094ca9b4c74b7ac94633b7817a3cc81
--- /dev/null
+++ b/genie_tts/Converter/load_state_dict.py
@@ -0,0 +1,26 @@
+import sys
+import os
+
+sys.path.append(os.path.dirname(__file__))
+
+import torch
+from io import BytesIO
+import utils
+
+
+def load_sovits_model(pth_path: str, device: str = 'cpu'):
+    f = open(pth_path, "rb")
+    meta = f.read(2)
+    if meta != b"PK":
+        # noinspection PyTypeChecker
+        data = b"PK" + f.read()
+        bio = BytesIO()
+        # noinspection PyTypeChecker
+        bio.write(data)
+        bio.seek(0)
+        return torch.load(bio, map_location=device, weights_only=False)
+    return torch.load(pth_path, map_location=device, weights_only=False)
+
+
+def load_gpt_model(ckpt_path: str, device: str = 'cpu'):
+    return torch.load(ckpt_path, map_location=device, weights_only=True)
diff --git a/genie_tts/Converter/utils.py b/genie_tts/Converter/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..771e22f0bfc621725884db14c29ef22f3fa5fe42
--- /dev/null
+++ b/genie_tts/Converter/utils.py
@@ -0,0 +1,30 @@
+class HParams:
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            if type(v) == dict:
+                v = HParams(**v)
+            self[k] = v
+
+    def keys(self):
+        return self.__dict__.keys()
+
+    def items(self):
+        return self.__dict__.items()
+
+    def values(self):
+        return self.__dict__.values()
+
+    def __len__(self):
+        return len(self.__dict__)
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        return setattr(self, key, value)
+
+    def __contains__(self, key):
+        return key in self.__dict__
+
+    def __repr__(self):
+        return self.__dict__.__repr__()
diff --git a/genie_tts/Converter/v2/Converter.py b/genie_tts/Converter/v2/Converter.py
new file mode 100644
index 0000000000000000000000000000000000000000..39910b97c9e6cb8aeda2a4d89594dbe1106af0af
--- /dev/null
+++ b/genie_tts/Converter/v2/Converter.py
@@ -0,0 +1,146 @@
+from .VITSConverter import VITSConverter
+from .T2SConverter import T2SModelConverter
+from .EncoderConverter import EncoderConverter
+from ...Utils.Constants import PACKAGE_NAME
+
+import logging
+from typing import Optional, Tuple
+import re
+import os
+import shutil
+import traceback
+import importlib.resources
+import contextlib
+
+logger = logging.getLogger()
+
+CACHE_DIR = os.path.join(os.getcwd(), "Cache")
+ENCODER_RESOURCE_PATH = "Data/v2/Models/t2s_encoder_fp32.onnx"
+STAGE_DECODER_RESOURCE_PATH = "Data/v2/Models/t2s_stage_decoder_fp32.onnx"
+FIRST_STAGE_DECODER_RESOURCE_PATH = "Data/v2/Models/t2s_first_stage_decoder_fp32.onnx"
+VITS_RESOURCE_PATH = "Data/v2/Models/vits_fp32.onnx"
+T2S_KEYS_RESOURCE_PATH = "Data/v2/Keys/t2s_onnx_keys.txt"
+VITS_KEYS_RESOURCE_PATH = "Data/v2/Keys/vits_onnx_keys.txt"
+
+
+def find_ckpt_and_pth(directory: str) -> Tuple[Optional[str], Optional[str]]:
+    """
+    在 directory（不递归子目录）里查找：
+    - .ckpt：从所有 .ckpt 文件名中搜索 'e{正整数}' 作为 epoch（找不到则视为 e0），
+             选择 epoch 最大的那个文件（若无则为 None）
+    - .pth ：从所有 .pth 文件名中搜索 'e{正整数}' 作为 epoch（找不到则视为 e0），
+             选择 epoch 最大的那个文件（若无则为 None）
+    若出现相同 epoch，选修改时间较新的文件以打破平手。
+    """
+    best_ckpt_path: Optional[str] = None
+    best_ckpt_epoch: int = -1
+
+    best_pth_path: Optional[str] = None
+    best_pth_epoch: int = -1
+
+    for filename in os.listdir(directory):
+        full_path = os.path.join(directory, filename)
+
+        if not os.path.isfile(full_path):
+            continue
+
+        # 提取 epoch
+        m = re.search(r"e(\d+)", filename, flags=re.IGNORECASE)
+        epoch = int(m.group(1)) if m else 0
+
+        # .ckpt 文件处理
+        if filename.lower().endswith(".ckpt"):
+            if (
+                    epoch > best_ckpt_epoch
+                    or (
+                    epoch == best_ckpt_epoch
+                    and best_ckpt_path is not None
+                    and os.path.getmtime(full_path) > os.path.getmtime(best_ckpt_path)
+            )
+            ):
+                best_ckpt_epoch = epoch
+                best_ckpt_path = full_path
+
+        # .pth 文件处理
+        elif filename.lower().endswith(".pth"):
+            if (
+                    epoch > best_pth_epoch
+                    or (
+                    epoch == best_pth_epoch
+                    and best_pth_path is not None
+                    and os.path.getmtime(full_path) > os.path.getmtime(best_pth_path)
+            )
+            ):
+                best_pth_epoch = epoch
+                best_pth_path = full_path
+
+    return best_ckpt_path, best_pth_path
+
+
+def remove_folder(folder: str) -> None:
+    try:
+        if os.path.exists(folder):
+            shutil.rmtree(folder)
+            logger.info(f"🧹 Folder cleaned: {folder}")
+    except Exception as e:
+        logger.error(f"❌ Failed to clean folder {folder}: {e}")
+
+
+def convert(torch_ckpt_path: str,
+            torch_pth_path: str,
+            output_dir: str):
+    # 确保缓存和输出目录存在
+    os.makedirs(CACHE_DIR, exist_ok=True)
+    os.makedirs(output_dir, exist_ok=True)
+
+    if len(os.listdir(output_dir)) > 0:
+        logger.warning(f"The output directory {output_dir} is not empty!")
+
+    with contextlib.ExitStack() as stack:
+        files = importlib.resources.files(PACKAGE_NAME)
+
+        def enter(p):
+            return stack.enter_context(importlib.resources.as_file(files.joinpath(p)))
+
+        encoder_onnx_path = enter(ENCODER_RESOURCE_PATH)
+        stage_decoder_path = enter(STAGE_DECODER_RESOURCE_PATH)
+        first_stage_decoder_path = enter(FIRST_STAGE_DECODER_RESOURCE_PATH)
+        vits_onnx_path = enter(VITS_RESOURCE_PATH)
+        t2s_keys_path = enter(T2S_KEYS_RESOURCE_PATH)
+        vits_keys_path = enter(VITS_KEYS_RESOURCE_PATH)
+
+        converter_1 = T2SModelConverter(
+            torch_ckpt_path=torch_ckpt_path,
+            stage_decoder_onnx_path=str(stage_decoder_path),
+            first_stage_decoder_onnx_path=str(first_stage_decoder_path),
+            key_list_file=str(t2s_keys_path),
+            output_dir=output_dir,
+            cache_dir=CACHE_DIR,
+        )
+        converter_2 = VITSConverter(
+            torch_pth_path=torch_pth_path,
+            vits_onnx_path=str(vits_onnx_path),
+            key_list_file=str(vits_keys_path),
+            output_dir=output_dir,
+            cache_dir=CACHE_DIR,
+        )
+        converter_3 = EncoderConverter(
+            ckpt_path=torch_ckpt_path,
+            pth_path=torch_pth_path,
+            onnx_input_path=str(encoder_onnx_path),
+            output_dir=output_dir,
+        )
+
+        try:
+            converter_1.run_full_process()
+            converter_2.run_full_process()
+            converter_3.run_full_process()
+            logger.info(f"🎉 Conversion successful! Saved to: {os.path.abspath(output_dir)}\n"
+                        f"- Model Type: V2")
+        except Exception:
+            logger.error(f"❌ A critical error occurred during the conversion process")
+            logger.error(traceback.format_exc())
+            remove_folder(output_dir)  # 只在失败时清理输出目录
+        finally:
+            # 无论成功还是失败，都尝试清理缓存目录
+            remove_folder(CACHE_DIR)
diff --git a/genie_tts/Converter/v2/EncoderConverter.py b/genie_tts/Converter/v2/EncoderConverter.py
new file mode 100644
index 0000000000000000000000000000000000000000..33ed75ba3277ec535f9c2f88304c7c07cc2752b4
--- /dev/null
+++ b/genie_tts/Converter/v2/EncoderConverter.py
@@ -0,0 +1,106 @@
+import torch
+import onnx
+import os
+
+from ..load_state_dict import load_gpt_model, load_sovits_model
+
+
+class EncoderConverter:
+    """
+    一个转换器，用于为 t2s_encoder 模型创建：
+    1. 一个从 .ckpt 和 .pth 文件中合并而来的全精度 (fp32) .bin 权重文件。
+    2. 一个链接到该 .bin 文件的 ONNX 模型。
+    """
+
+    def __init__(self,
+                 ckpt_path: str,
+                 pth_path: str,
+                 onnx_input_path: str,
+                 output_dir: str,
+                 ):
+        self.ckpt_path: str = ckpt_path
+        self.pth_path: str = pth_path
+        self.onnx_input_path: str = onnx_input_path
+        self.output_dir: str = output_dir
+
+        # 定义最终输出文件的路径
+        self.output_bin_path: str = os.path.join(self.output_dir, "t2s_encoder_fp32.bin")
+        self.output_onnx_path: str = os.path.join(self.output_dir, "t2s_encoder_fp32.onnx")
+
+        # 确保输出目录存在
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        # 检查所有输入文件是否存在
+        for path in [self.ckpt_path, self.pth_path, self.onnx_input_path]:
+            if not os.path.exists(path):
+                raise FileNotFoundError(f"Error: Input file not found! Path: {path}")
+
+    def run_full_process(self):
+        # 1. 定义固定的 ONNX 权重键列表 (此顺序决定了 .bin 文件的布局)
+        onnx_keys = [
+            "encoder.ar_text_embedding.word_embeddings.weight",
+            "encoder.bert_proj.weight",
+            "encoder.bert_proj.bias",
+            "encoder.ar_text_position.alpha",
+            "vits.ssl_proj.weight",
+            "vits.ssl_proj.bias",
+            "vits.quantizer.vq.layers.0._codebook.embed"
+        ]
+
+        # 2. 加载所有必要的模型和权重
+        ckpt_state_dict = load_gpt_model(self.ckpt_path)['weight']
+        pth_state_dict = load_sovits_model(self.pth_path)['weight']
+        model = onnx.load(self.onnx_input_path, load_external_data=False)
+        initializer_map = {init.name: init for init in model.graph.initializer}
+        current_offset = 0
+        bin_filename = os.path.basename(self.output_bin_path)
+
+        # 3. 生成 .bin 文件并同步修改 ONNX 模型
+        with open(self.output_bin_path, 'wb') as f_bin:
+            for onnx_key in onnx_keys:
+                source_key = ""
+                source_dict = None
+
+                if onnx_key.startswith("encoder."):
+                    source_key = "model." + onnx_key[len("encoder."):]
+                    source_dict = ckpt_state_dict
+                elif onnx_key.startswith("vits."):
+                    source_key = onnx_key[len("vits."):]
+                    source_dict = pth_state_dict
+
+                if source_dict is None:
+                    raise ValueError(
+                        f"❌ Critical error: Unable to determine the weight source for ONNX key '{onnx_key}'.")
+                # 从源文件中提取张量
+                tensor = source_dict.get(source_key)
+                if tensor is None:
+                    raise ValueError(
+                        f"❌ Critical error: Key '{source_key}' (corresponding to ONNX key '{onnx_key}') not found in the source file.")
+
+                # 转换为 fp32 numpy 数组并获取字节
+                numpy_array_fp32 = tensor.to(torch.float32).cpu().numpy()
+                tensor_bytes = numpy_array_fp32.tobytes()
+                tensor_length = len(tensor_bytes)
+                f_bin.write(tensor_bytes)
+
+                # 在 ONNX 模型中找到对应的 initializer 并修改它
+                if onnx_key in initializer_map:
+                    tensor_proto = initializer_map[onnx_key]
+
+                    tensor_proto.ClearField('raw_data')
+                    tensor_proto.data_location = onnx.TensorProto.EXTERNAL
+                    del tensor_proto.external_data[:]
+
+                    keys_to_set = ["location", "offset", "length"]
+                    values_to_set = [bin_filename, str(current_offset), str(tensor_length)]
+
+                    for k, v in zip(keys_to_set, values_to_set):
+                        entry = tensor_proto.external_data.add()
+                        entry.key = k
+                        entry.value = v
+
+                # 更新下一个权重的偏移量
+                current_offset += tensor_length
+
+        # 4. 保存修改后的 ONNX 模型
+        onnx.save(model, self.output_onnx_path)
diff --git a/genie_tts/Converter/v2/T2SConverter.py b/genie_tts/Converter/v2/T2SConverter.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a8de6925697ec911f6a22cfa4cdb31f4f73cbac
--- /dev/null
+++ b/genie_tts/Converter/v2/T2SConverter.py
@@ -0,0 +1,125 @@
+import torch
+import onnx
+import numpy as np
+import json
+import os
+from collections import OrderedDict
+
+from ..load_state_dict import load_gpt_model
+
+
+class T2SModelConverter:
+    """
+    一个专门的转换器，用于处理 t2s (Text-to-Speech) 模型。
+    - PyTorch 模型: .ckpt 文件
+    - ONNX 模型: t2s_stage_decoder_fp32.onnx
+    - 遵循特定的键名映射规则。
+    """
+
+    def __init__(self,
+                 torch_ckpt_path: str,
+                 stage_decoder_onnx_path: str,
+                 first_stage_decoder_onnx_path: str,
+                 key_list_file: str,
+                 output_dir: str,
+                 cache_dir: str,
+                 ):
+        self.torch_ckpt_path: str = torch_ckpt_path
+        self.stage_decoder_onnx_path: str = stage_decoder_onnx_path
+        self.first_stage_decoder_onnx_path: str = first_stage_decoder_onnx_path
+        self.key_list_file: str = key_list_file
+        self.output_dir: str = output_dir
+        self.cache_dir: str = cache_dir
+
+        os.makedirs(self.output_dir, exist_ok=True)
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        # 定义输出文件路径
+        self.fp16_bin_path: str = os.path.join(self.output_dir, "t2s_shared_fp16.bin")
+        self.index_table_path: str = os.path.join(self.cache_dir, "t2s_weights_index_fp32.json")
+        self.relinked_encoder_path: str = os.path.join(self.output_dir, "t2s_encoder_fp32.onnx")
+        self.relinked_stage_decoder_path: str = os.path.join(self.output_dir, "t2s_stage_decoder_fp32.onnx")
+        self.relinked_first_stage_decoder_path: str = os.path.join(self.output_dir, "t2s_first_stage_decoder_fp32.onnx")
+        self.reconstructed_fp32_bin_path = os.path.join(self.output_dir, "t2s_shared_fp32.bin")
+
+    def step1_create_fp16_bin_with_key_mapping(self):
+        """
+        (1) 根据特定的键映射规则，从 .ckpt 创建 fp16 .bin 和 fp32 索引。
+            (已根据用户验证脚本的正确逻辑进行最终修正)
+        """
+        if not os.path.exists(self.key_list_file):
+            raise FileNotFoundError(
+                f"Error: Stage 1 requires the key list file, but it was not found: {self.key_list_file}")
+
+        with open(self.key_list_file, 'r') as f:
+            onnx_keys = [line.strip() for line in f.readlines()]
+
+        ckpt_data = load_gpt_model(self.torch_ckpt_path)
+        if 'weight' not in ckpt_data:
+            raise KeyError(
+                f"❌ Error: 'weight' key not found in the .ckpt file. Top-level keys in the file are: {list(ckpt_data.keys())}")
+
+        torch_state_dict = ckpt_data['weight']
+
+        index_table = OrderedDict()
+        current_fp32_offset = 0
+
+        with open(self.fp16_bin_path, 'wb') as f_bin:
+            for onnx_key in onnx_keys:
+                transformed_onnx_key = onnx_key.replace('transformer_encoder', 'h')
+                torch_lookup_key = f"model.{transformed_onnx_key}"
+                torch_tensor = torch_state_dict.get(torch_lookup_key)
+                numpy_array_fp16 = torch_tensor.to(torch.float16).cpu().numpy()
+                f_bin.write(numpy_array_fp16.tobytes())
+                tensor_length_fp32 = numpy_array_fp16.nbytes * 2
+                index_table[onnx_key] = {'offset': current_fp32_offset, 'length': tensor_length_fp32}
+                current_fp32_offset += tensor_length_fp32
+
+        with open(self.index_table_path, 'w') as f_json:
+            json.dump(index_table, f_json, indent=4)  # type: ignore
+
+    def step2_relink_onnx_for_fp32(self, old_model: str, new_model: str):
+        """
+        (2) 根据 fp32 索引表，修改 ONNX 模型，使其链接到未来的全精度 .bin。
+            (使用与第一个脚本相同的、更稳定的底层方法)
+        """
+        if not os.path.exists(self.index_table_path):
+            raise FileNotFoundError(
+                f"Error: Stage 2 requires the index file, but it was not found: {self.index_table_path}")
+
+        # 加载描述 fp32 布局的索引表
+        with open(self.index_table_path, 'r') as f:
+            index_table = json.load(f)
+
+        model = onnx.load_model(old_model, load_external_data=False)
+        reconstructed_bin_filename = os.path.basename(self.reconstructed_fp32_bin_path)
+
+        for tensor in model.graph.initializer:
+            if tensor.name in index_table:
+                tensor.ClearField('raw_data')
+                tensor.data_location = onnx.TensorProto.EXTERNAL
+                info = index_table[tensor.name]
+                del tensor.external_data[:]
+                keys = ["location", "offset", "length"]
+                values = [reconstructed_bin_filename, str(info['offset']), str(info['length'])]
+
+                for k, v in zip(keys, values):
+                    entry = tensor.external_data.add()
+                    entry.key = k
+                    entry.value = v
+
+        onnx.save(model, new_model)
+
+    @staticmethod
+    def step3_reconstruct_fp32_bin_from_fp16(fp16_bin_path: str, output_fp32_bin_path: str):
+        """
+        (3) 静态工具函数：从半精度 .bin 文件还原出全精度 .bin 文件。
+        """
+        fp16_array = np.fromfile(fp16_bin_path, dtype=np.float16)
+        fp32_array = fp16_array.astype(np.float32)
+        fp32_array.tofile(output_fp32_bin_path)
+
+    def run_full_process(self):
+        self.step1_create_fp16_bin_with_key_mapping()
+        self.step2_relink_onnx_for_fp32(self.stage_decoder_onnx_path, self.relinked_stage_decoder_path)
+        self.step2_relink_onnx_for_fp32(self.first_stage_decoder_onnx_path, self.relinked_first_stage_decoder_path)
diff --git a/genie_tts/Converter/v2/VITSConverter.py b/genie_tts/Converter/v2/VITSConverter.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b04086a174d0edd3e0da75b7a76da9fc09491c6
--- /dev/null
+++ b/genie_tts/Converter/v2/VITSConverter.py
@@ -0,0 +1,129 @@
+import torch
+import onnx
+import numpy as np
+import json
+import os
+from collections import OrderedDict
+
+from ..load_state_dict import load_sovits_model
+
+
+class VITSConverter:
+    """
+    一个转换器，用于从 PyTorch 模型创建：
+    1. 一个用于分发的半精度 (fp16) .bin 权重文件。
+    2. 一个与全精度 (fp32) 布局兼容的 ONNX 模型。
+    3. 一个可以将 fp16 .bin 文件还原为 fp32 .bin 的工具函数。
+    """
+
+    def __init__(self,
+                 torch_pth_path: str,
+                 vits_onnx_path: str,
+                 key_list_file: str,
+                 output_dir: str,
+                 cache_dir: str,
+                 ):
+        self.torch_pth_path: str = torch_pth_path
+        self.vits_onnx_path: str = vits_onnx_path
+        self.key_list_file: str = key_list_file
+        self.output_dir: str = output_dir
+        self.cache_dir: str = cache_dir
+        # 定义输出文件路径
+        self.fp16_bin_path: str = os.path.join(self.output_dir, "vits_fp16.bin")
+        self.index_table_path: str = os.path.join(self.cache_dir, "vits_weights_index_fp32.json")
+        self.relinked_fp32_onnx_path: str = os.path.join(self.output_dir, "vits_fp32.onnx")
+        self.reconstructed_fp32_bin_path: str = os.path.join(self.output_dir, "vits_fp32.bin")
+
+        # 确保输出目录存在
+        os.makedirs(self.cache_dir, exist_ok=True)
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        if not os.path.exists(self.key_list_file):
+            raise FileNotFoundError(f"Error: Key list file not found! Path: {self.key_list_file}")
+
+    def step1_create_fp16_bin_and_fp32_index(self):
+        """
+        (1) 创建一个半精度 (fp16) 的 .bin 文件，但生成一个
+            描述全精度 (fp32) 布局的索引表。
+        """
+        # 加载 key 列表
+        with open(self.key_list_file, 'r') as f:
+            onnx_keys = [line.strip() for line in f.readlines()]
+
+        # 加载 PyTorch 模型权重
+        torch_state_dict = load_sovits_model(self.torch_pth_path)['weight']
+
+        index_table = OrderedDict()
+        current_fp32_offset = 0
+
+        with open(self.fp16_bin_path, 'wb') as f_bin:
+            for onnx_key in onnx_keys:
+                torch_key = onnx_key[len("vq_model."):] if onnx_key.startswith("vq_model.") else onnx_key
+
+                torch_tensor = torch_state_dict.get(torch_key)
+                if torch_tensor is None:
+                    raise ValueError(f"❌ Critical error: Key '{torch_key}' not found in the PyTorch weights")
+
+                # 转换为 fp16 并写入文件
+                torch_tensor_fp16 = torch_tensor.to(torch.float16)
+                numpy_array_fp16 = torch_tensor_fp16.cpu().numpy()
+                tensor_bytes_fp16 = numpy_array_fp16.tobytes()
+                f_bin.write(tensor_bytes_fp16)
+                tensor_length_fp32 = len(tensor_bytes_fp16) * 2
+                index_table[onnx_key] = {
+                    'offset': current_fp32_offset,
+                    'length': tensor_length_fp32
+                }
+                current_fp32_offset += tensor_length_fp32
+
+        # 保存描述 fp32 布局的索引表
+        with open(self.index_table_path, 'w') as f_json:
+            json.dump(index_table, f_json, indent=4)  # type: ignore
+
+    def step2_relink_onnx_for_fp32(self):
+        """
+        (2) 根据 fp32 索引表，修改 ONNX 模型，使其链接到一个
+            未来的、全精度的 .bin 文件。
+        """
+        # 加载描述 fp32 布局的索引表
+        with open(self.index_table_path, 'r') as f:
+            index_table = json.load(f)
+
+        model = onnx.load_model(self.vits_onnx_path, load_external_data=False)
+        reconstructed_bin_filename = os.path.basename(self.reconstructed_fp32_bin_path)
+
+        for tensor in model.graph.initializer:
+            if tensor.name in index_table:
+                tensor.ClearField('raw_data')
+                tensor.data_location = onnx.TensorProto.EXTERNAL
+                info = index_table[tensor.name]
+
+                del tensor.external_data[:]
+
+                keys = ["location", "offset", "length"]
+                values = [reconstructed_bin_filename, str(info['offset']), str(info['length'])]
+
+                for k, v in zip(keys, values):
+                    entry = tensor.external_data.add()
+                    entry.key = k
+                    entry.value = v
+
+        # 保存修改后的、链接到 fp32 权重的 ONNX 模型
+        onnx.save(model, self.relinked_fp32_onnx_path)
+
+    @staticmethod
+    def step3_reconstruct_fp32_bin_from_fp16(fp16_bin_path: str, output_fp32_bin_path: str):
+        """
+        (3) 静态工具函数：从半精度 .bin 文件还原出全精度 .bin 文件。
+
+        Args:
+            fp16_bin_path (str): 输入的半精度 .bin 文件路径。
+            output_fp32_bin_path (str): 输出的全精度 .bin 文件路径。
+        """
+        fp16_array = np.fromfile(fp16_bin_path, dtype=np.float16)
+        fp32_array = fp16_array.astype(np.float32)
+        fp32_array.tofile(output_fp32_bin_path)
+
+    def run_full_process(self):
+        self.step1_create_fp16_bin_and_fp32_index()
+        self.step2_relink_onnx_for_fp32()
diff --git a/genie_tts/Converter/v2/__init__.py b/genie_tts/Converter/v2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/genie_tts/Converter/v2ProPlus/Converter.py b/genie_tts/Converter/v2ProPlus/Converter.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fbac43d89704024911e8618a13413bce834ddc2
--- /dev/null
+++ b/genie_tts/Converter/v2ProPlus/Converter.py
@@ -0,0 +1,89 @@
+import logging
+import traceback
+import os
+import contextlib
+import importlib.resources
+
+from ...Utils.Constants import PACKAGE_NAME
+from ..v2.VITSConverter import VITSConverter
+from ..v2.T2SConverter import T2SModelConverter
+from ..v2.EncoderConverter import EncoderConverter
+from ..v2.Converter import (ENCODER_RESOURCE_PATH, STAGE_DECODER_RESOURCE_PATH,
+                            FIRST_STAGE_DECODER_RESOURCE_PATH, T2S_KEYS_RESOURCE_PATH, CACHE_DIR, remove_folder)
+from .PromptEncoderConverter import PromptEncoderConverter
+
+logger = logging.getLogger()
+
+# 使用 V2 ProPlus 的文件。
+VITS_RESOURCE_PATH = "Data/v2ProPlus/Models/vits_fp32.onnx"
+PROMPT_ENCODER_RESOURCE_PATH = "Data/v2ProPlus/Models/prompt_encoder_fp32.onnx"
+VITS_KEYS_RESOURCE_PATH = "Data/v2ProPlus/Keys/vits_weights.txt"
+PROMPT_ENCODER_KEYS_RESOURCE_PATH = "Data/v2ProPlus/Keys/prompt_encoder_weights.txt"
+
+
+def convert(torch_ckpt_path: str, torch_pth_path: str, output_dir: str) -> None:
+    # 确保缓存和输出目录存在
+    os.makedirs(CACHE_DIR, exist_ok=True)
+    os.makedirs(output_dir, exist_ok=True)
+
+    if len(os.listdir(output_dir)) > 0:
+        logger.warning(f"The output directory {output_dir} is not empty!")
+
+    with contextlib.ExitStack() as stack:
+        files = importlib.resources.files(PACKAGE_NAME)
+
+        def enter(p: str) -> str:
+            return str(stack.enter_context(importlib.resources.as_file(files.joinpath(p))))
+
+        encoder_onnx_path = enter(ENCODER_RESOURCE_PATH)
+        stage_decoder_path = enter(STAGE_DECODER_RESOURCE_PATH)
+        first_stage_decoder_path = enter(FIRST_STAGE_DECODER_RESOURCE_PATH)
+        vits_onnx_path = enter(VITS_RESOURCE_PATH)
+        t2s_keys_path = enter(T2S_KEYS_RESOURCE_PATH)
+        vits_keys_path = enter(VITS_KEYS_RESOURCE_PATH)
+        prompt_encoder_path = enter(PROMPT_ENCODER_RESOURCE_PATH)
+        prompt_encoder_keys_path = enter(PROMPT_ENCODER_KEYS_RESOURCE_PATH)
+
+        converter_1 = T2SModelConverter(
+            torch_ckpt_path=torch_ckpt_path,
+            stage_decoder_onnx_path=stage_decoder_path,
+            first_stage_decoder_onnx_path=first_stage_decoder_path,
+            key_list_file=t2s_keys_path,
+            output_dir=output_dir,
+            cache_dir=CACHE_DIR,
+        )
+        converter_2 = VITSConverter(
+            torch_pth_path=torch_pth_path,
+            vits_onnx_path=vits_onnx_path,
+            key_list_file=vits_keys_path,
+            output_dir=output_dir,
+            cache_dir=CACHE_DIR,
+        )
+        converter_3 = EncoderConverter(
+            ckpt_path=torch_ckpt_path,
+            pth_path=torch_pth_path,
+            onnx_input_path=encoder_onnx_path,
+            output_dir=output_dir,
+        )
+        converter_4 = PromptEncoderConverter(
+            torch_pth_path=torch_pth_path,
+            prompt_encoder_onnx_path=prompt_encoder_path,
+            key_list_file=prompt_encoder_keys_path,
+            output_dir=output_dir,
+            cache_dir=CACHE_DIR,
+        )
+
+        try:
+            converter_1.run_full_process()
+            converter_2.run_full_process()
+            converter_3.run_full_process()
+            converter_4.run_full_process()
+            logger.info(f"🎉 Conversion successful! Saved to: {os.path.abspath(output_dir)}\n"
+                        f"- Model Type: V2ProPlus")
+        except Exception:
+            logger.error(f"❌ A critical error occurred during the conversion process")
+            logger.error(traceback.format_exc())
+            remove_folder(output_dir)  # 只在失败时清理输出目录
+        finally:
+            # 无论成功还是失败，都尝试清理缓存目录
+            remove_folder(CACHE_DIR)
diff --git a/genie_tts/Converter/v2ProPlus/PromptEncoderConverter.py b/genie_tts/Converter/v2ProPlus/PromptEncoderConverter.py
new file mode 100644
index 0000000000000000000000000000000000000000..55bac8664c15b2bbaae6fcfc71f13f4feb8b4b1d
--- /dev/null
+++ b/genie_tts/Converter/v2ProPlus/PromptEncoderConverter.py
@@ -0,0 +1,128 @@
+import torch
+import onnx
+import json
+import os
+from collections import OrderedDict
+
+from ..load_state_dict import load_sovits_model
+
+
+class PromptEncoderConverter:
+    """
+    一个转换器，用于从 PyTorch 模型创建：
+    1. 一个用于分发的半精度 (fp16) .bin 权重文件。
+    2. 一个与全精度 (fp32) 布局兼容的 ONNX 模型。
+    3. 一个可以将 fp16 .bin 文件还原为 fp32 .bin 的工具函数。
+    """
+
+    def __init__(self,
+                 torch_pth_path: str,
+                 prompt_encoder_onnx_path: str,
+                 key_list_file: str,
+                 output_dir: str,
+                 cache_dir: str,
+                 ):
+        self.torch_pth_path: str = torch_pth_path
+        self.vits_onnx_path: str = prompt_encoder_onnx_path
+        self.key_list_file: str = key_list_file
+        self.output_dir: str = output_dir
+        self.cache_dir: str = cache_dir
+        # 定义输出文件路径
+        self.fp16_bin_path: str = os.path.join(self.output_dir, "prompt_encoder_fp16.bin")
+        self.index_table_path: str = os.path.join(self.cache_dir, "prompt_encoder_weights_index_fp32.json")
+        self.relinked_fp32_onnx_path: str = os.path.join(self.output_dir, "prompt_encoder_fp32.onnx")
+        self.reconstructed_fp32_bin_path: str = os.path.join(self.output_dir, "prompt_encoder_fp32.bin")
+
+        # 确保输出目录存在
+        os.makedirs(self.cache_dir, exist_ok=True)
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        if not os.path.exists(self.key_list_file):
+            raise FileNotFoundError(f"错误: Key 列表文件未找到! 路径: {self.key_list_file}")
+
+    def step1_create_fp16_bin_and_fp32_index(self):
+        """
+        (1) 创建一个半精度 (fp16) 的 .bin 文件，但生成一个
+            描述全精度 (fp32) 布局的索引表。
+        """
+        # 加载 key 列表
+        with open(self.key_list_file, 'r') as f:
+            onnx_keys = [line.strip() for line in f.readlines()]
+
+        # 加载 PyTorch 模型权重
+        torch_state_dict = load_sovits_model(self.torch_pth_path)['weight']
+
+        index_table = OrderedDict()
+        # 这个偏移量将按照 fp32 的大小进行累加
+        current_fp32_offset = 0
+
+        with open(self.fp16_bin_path, 'wb') as f_bin:
+            for onnx_key in onnx_keys:
+                torch_key = onnx_key[len("vq_model."):] if onnx_key.startswith("vq_model.") else onnx_key
+
+                torch_tensor = torch_state_dict.get(torch_key)
+                if torch_tensor is None:
+                    raise ValueError(f"❌ 严重错误: 在 PyTorch 权重中找不到 Key '{torch_key}'")
+
+                # 转换为 fp16 并写入文件
+                torch_tensor_fp16 = torch_tensor.to(torch.float16)
+                numpy_array_fp16 = torch_tensor_fp16.cpu().numpy()
+                tensor_bytes_fp16 = numpy_array_fp16.tobytes()
+                f_bin.write(tensor_bytes_fp16)
+
+                # 关键步骤：计算并记录 fp32 的长度和偏移量
+                # 一个 fp32 = 4 字节, 一个 fp16 = 2 字节。所以 fp32 长度是 fp16 的两倍。
+                tensor_length_fp32 = len(tensor_bytes_fp16) * 2
+
+                index_table[onnx_key] = {
+                    'offset': current_fp32_offset,
+                    'length': tensor_length_fp32
+                }
+
+                # 偏移量也按照 fp32 的长度进行累加
+                current_fp32_offset += tensor_length_fp32
+
+        # 保存描述 fp32 布局的索引表
+        with open(self.index_table_path, 'w') as f_json:
+            json.dump(index_table, f_json, indent=4)  # type: ignore
+
+    def step2_relink_onnx_for_fp32(self):
+        """
+        (2) 根据 fp32 索引表，修改 ONNX 模型，使其链接到一个
+            未来的、全精度的 .bin 文件。
+        """
+        # 加载描述 fp32 布局的索引表
+        with open(self.index_table_path, 'r') as f:
+            index_table = json.load(f)
+
+        # 加载 ONNX 模型结构
+        model = onnx.load_model(self.vits_onnx_path, load_external_data=False)
+
+        # 这个 ONNX 模型将要链接的 .bin 文件名
+        reconstructed_bin_filename = os.path.basename(self.reconstructed_fp32_bin_path)
+
+        for tensor in model.graph.initializer:
+            if tensor.name in index_table:
+                tensor.ClearField('raw_data')
+                tensor.data_location = onnx.TensorProto.EXTERNAL
+                info = index_table[tensor.name]
+
+                del tensor.external_data[:]
+
+                keys = ["location", "offset", "length"]
+                values = [reconstructed_bin_filename, str(info['offset']), str(info['length'])]
+
+                for k, v in zip(keys, values):
+                    entry = tensor.external_data.add()
+                    entry.key = k
+                    entry.value = v
+
+        # 保存修改后的、链接到 fp32 权重的 ONNX 模型
+        onnx.save(model, self.relinked_fp32_onnx_path)
+
+    def run_full_process(self):
+        """
+        按顺序执行核心的转换步骤 (1 和 2)。
+        """
+        self.step1_create_fp16_bin_and_fp32_index()
+        self.step2_relink_onnx_for_fp32()
diff --git a/genie_tts/Core/Inference.py b/genie_tts/Core/Inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..3486750f262fe8b56eddc322f394f630238a97cb
--- /dev/null
+++ b/genie_tts/Core/Inference.py
@@ -0,0 +1,112 @@
+import onnxruntime as ort
+import numpy as np
+from typing import List, Optional
+import threading
+
+from ..Audio.ReferenceAudio import ReferenceAudio
+from ..GetPhonesAndBert import get_phones_and_bert
+
+MAX_T2S_LEN = 1000
+
+
+class GENIE:
+    def __init__(self):
+        self.stop_event: threading.Event = threading.Event()
+
+    def tts(
+            self,
+            text: str,
+            prompt_audio: ReferenceAudio,
+            encoder: ort.InferenceSession,
+            first_stage_decoder: ort.InferenceSession,
+            stage_decoder: ort.InferenceSession,
+            vocoder: ort.InferenceSession,
+            prompt_encoder: Optional[ort.InferenceSession],
+            language: str = 'japanese',
+    ) -> Optional[np.ndarray]:
+        text = '。' + text  # 防止漏第一句。
+        text_seq, text_bert = get_phones_and_bert(text, language=language)
+
+        semantic_tokens: np.ndarray = self.t2s_cpu(
+            ref_seq=prompt_audio.phonemes_seq,
+            ref_bert=prompt_audio.text_bert,
+            text_seq=text_seq,
+            text_bert=text_bert,
+            ssl_content=prompt_audio.ssl_content,
+            encoder=encoder,
+            first_stage_decoder=first_stage_decoder,
+            stage_decoder=stage_decoder,
+        )
+
+        eos_indices = np.where(semantic_tokens >= 1024)  # 剔除不合法的元素，例如 EOS Token。
+        if len(eos_indices[0]) > 0:
+            first_eos_index = eos_indices[-1][0]
+            semantic_tokens = semantic_tokens[..., :first_eos_index]
+
+        if prompt_encoder is None:
+            return vocoder.run(None, {
+                "text_seq": text_seq,
+                "pred_semantic": semantic_tokens,
+                "ref_audio": prompt_audio.audio_32k
+            })[0]
+        else:
+            # V2ProPlus 新增。
+            prompt_audio.update_global_emb(prompt_encoder=prompt_encoder)
+            audio_chunk = vocoder.run(None, {
+                "text_seq": text_seq,
+                "pred_semantic": semantic_tokens,
+                "ge": prompt_audio.global_emb,
+                "ge_advanced": prompt_audio.global_emb_advanced,
+            })[0]
+            return audio_chunk
+
+    def t2s_cpu(
+            self,
+            ref_seq: np.ndarray,
+            ref_bert: np.ndarray,
+            text_seq: np.ndarray,
+            text_bert: np.ndarray,
+            ssl_content: np.ndarray,
+            encoder: ort.InferenceSession,
+            first_stage_decoder: ort.InferenceSession,
+            stage_decoder: ort.InferenceSession,
+    ) -> Optional[np.ndarray]:
+        """在CPU上运行T2S模型"""
+        # Encoder
+        x, prompts = encoder.run(
+            None,
+            {
+                "ref_seq": ref_seq,
+                "text_seq": text_seq,
+                "ref_bert": ref_bert,
+                "text_bert": text_bert,
+                "ssl_content": ssl_content,
+            },
+        )
+
+        # First Stage Decoder
+        y, y_emb, *present_key_values = first_stage_decoder.run(
+            None, {"x": x, "prompts": prompts}
+        )
+
+        # Stage Decoder
+        input_names: List[str] = [inp.name for inp in stage_decoder.get_inputs()]
+        idx: int = 0
+        for idx in range(0, 500):
+            if self.stop_event.is_set():
+                return None
+            input_feed = {
+                name: data
+                for name, data in zip(input_names, [y, y_emb, *present_key_values])
+            }
+            outputs = stage_decoder.run(None, input_feed)
+            y, y_emb, stop_condition_tensor, *present_key_values = outputs
+
+            if stop_condition_tensor:
+                break
+
+        y[0, -1] = 0
+        return np.expand_dims(y[:, -idx:], axis=0)
+
+
+tts_client: GENIE = GENIE()
diff --git a/genie_tts/Core/Resources.py b/genie_tts/Core/Resources.py
new file mode 100644
index 0000000000000000000000000000000000000000..d049cec7c68450ed5abc59f0188838583fa053ab
--- /dev/null
+++ b/genie_tts/Core/Resources.py
@@ -0,0 +1,76 @@
+import os
+from huggingface_hub import snapshot_download
+
+
+def download_genie_data() -> None:
+    print(f"🚀 Starting download Genie-TTS resources… This may take a few moments. ⏳")
+    snapshot_download(
+        repo_id="High-Logic/Genie",
+        repo_type="model",
+        allow_patterns="GenieData/*",
+        local_dir=".",
+        local_dir_use_symlinks=True,  # 软链接
+    )
+    print("✅ Genie-TTS resources downloaded successfully.")
+
+
+def ensure_exists(path: str, name: str):
+    if not os.path.exists(path):
+        raise FileNotFoundError(
+            f"Required directory or file '{name}' was not found at: {path}\n"
+            f"Please download the pretrained models and place them under './GenieData', "
+            f"or set the environment variable GENIE_DATA_DIR to the correct directory."
+        )
+
+
+"""
+文件结构与项目 Midori 同步。
+"""
+
+GENIE_DATA_DIR: str = os.getenv(
+    "GENIE_DATA_DIR",
+    "./GenieData"
+)
+
+"""
+Japanese_G2P_DIR: str = os.getenv(
+    "Japanese_G2P_DIR",
+    f"{GENIE_DATA_DIR}/G2P/JapaneseG2P"
+)
+"""
+
+English_G2P_DIR: str = os.getenv(
+    "English_G2P_DIR",
+    f"{GENIE_DATA_DIR}/G2P/EnglishG2P"
+)
+
+Chinese_G2P_DIR: str = os.getenv(
+    "Chinese_G2P_DIR",
+    f"{GENIE_DATA_DIR}/G2P/ChineseG2P"
+)
+
+HUBERT_MODEL_DIR: str = os.getenv(
+    "HUBERT_MODEL_DIR",
+    f"{GENIE_DATA_DIR}/chinese-hubert-base"
+)
+
+SV_MODEL: str = os.getenv(
+    "SV_MODEL",
+    f"{GENIE_DATA_DIR}/speaker_encoder.onnx"
+)
+
+ROBERTA_MODEL_DIR: str = os.getenv(
+    "ROBERTA_MODEL_DIR",
+    f"{GENIE_DATA_DIR}/RoBERTa"
+)
+
+if not os.path.exists(GENIE_DATA_DIR):
+    print("⚠️ GenieData folder not found.")
+    choice = input("Would you like to download it automatically from HuggingFace? (y/N): ").strip().lower()
+    if choice == "y":
+        download_genie_data()
+
+# ---- Run directory checks ----
+ensure_exists(HUBERT_MODEL_DIR, "HUBERT_MODEL_DIR")
+ensure_exists(SV_MODEL, "SV_MODEL")
+# ensure_exists(ROBERTA_MODEL_DIR, "ROBERTA_MODEL_DIR")
diff --git a/genie_tts/Core/TTSPlayer.py b/genie_tts/Core/TTSPlayer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c70691e43622cfac3437122e91cef1270477468
--- /dev/null
+++ b/genie_tts/Core/TTSPlayer.py
@@ -0,0 +1,241 @@
+# 文件: .../Core/TTSPlayer.py
+
+import queue
+import os
+import threading
+
+import numpy as np
+import wave
+from typing import Optional, List, Callable
+import logging
+
+from ..Utils.TextSplitter import TextSplitter
+from ..Core.Inference import tts_client
+from ..ModelManager import model_manager
+from ..Utils.Shared import context
+from ..Utils.Utils import clear_queue
+
+logger = logging.getLogger(__name__)
+
+STREAM_END = 'STREAM_END'  # 这是一个特殊的标记，表示文本流结束
+AUDIO_STREAM_END = 'AUDIO_STREAM_END'  # 新增：特殊的标记，表示音频流播放结束
+
+
+class TTSPlayer:
+    def __init__(self, sample_rate: int = 32000):
+        self._text_splitter = TextSplitter()
+
+        self.sample_rate: int = sample_rate
+        self.channels: int = 1
+        self.bytes_per_sample: int = 2  # 16-bit audio
+
+        self._text_queue: queue.Queue = queue.Queue()
+        self._audio_queue: queue.Queue = queue.Queue()
+
+        self._stop_event: threading.Event = threading.Event()
+        self._tts_done_event: threading.Event = threading.Event()
+        self._playback_done_event: threading.Event = threading.Event()  # 新增：用于标记播放完成
+        self._api_lock: threading.Lock = threading.Lock()
+
+        self._tts_worker: Optional[threading.Thread] = None
+        self._playback_worker: Optional[threading.Thread] = None
+
+        self._play: bool = False
+        self._current_save_path: Optional[str] = None
+        self._session_audio_chunks: List[np.ndarray] = []
+        self._split: bool = False
+
+        self._chunk_callback: Optional[Callable[[Optional[bytes]], None]] = None
+
+    @staticmethod
+    def _preprocess_for_playback(audio_float: np.ndarray) -> bytes:
+        audio_int16 = (audio_float.squeeze() * 32767).astype(np.int16)
+        return audio_int16.tobytes()
+
+    def _tts_worker_loop(self):
+        """从文本队列取句子，生成音频，并通过回调函数或音频队列分发。"""
+        while not self._stop_event.is_set():
+            try:
+                sentence = self._text_queue.get(timeout=1)
+                if sentence is None or self._stop_event.is_set():
+                    break
+            except queue.Empty:
+                continue
+
+            try:
+                if sentence is STREAM_END:
+                    if self._current_save_path and self._session_audio_chunks:
+                        self._save_session_audio()
+
+                    # 在TTS工作线程完成时，通过回调发送结束信号
+                    if self._chunk_callback:
+                        self._chunk_callback(None)
+
+                    # 新增：如果开启了播放，通知音频队列流已结束
+                    if self._play:
+                        self._audio_queue.put(AUDIO_STREAM_END)
+
+                    self._tts_done_event.set()
+                    continue
+
+                gsv_model = model_manager.get(context.current_speaker)
+                if not gsv_model or not context.current_prompt_audio:
+                    logger.error("Missing model or reference audio.")
+                    continue
+
+                tts_client.stop_event.clear()
+                audio_chunk = tts_client.tts(
+                    text=sentence,
+                    prompt_audio=context.current_prompt_audio,
+                    encoder=gsv_model.T2S_ENCODER,
+                    first_stage_decoder=gsv_model.T2S_FIRST_STAGE_DECODER,
+                    stage_decoder=gsv_model.T2S_STAGE_DECODER,
+                    vocoder=gsv_model.VITS,
+                    prompt_encoder=gsv_model.PROMPT_ENCODER,
+                    language=gsv_model.LANGUAGE,
+                )
+
+                if audio_chunk is not None:
+                    if self._play:
+                        self._audio_queue.put(audio_chunk)
+                    if self._current_save_path:
+                        self._session_audio_chunks.append(audio_chunk)
+
+                    # 使用回调函数处理流式数据
+                    if self._chunk_callback:
+                        audio_data = self._preprocess_for_playback(audio_chunk)
+                        self._chunk_callback(audio_data)
+
+            except Exception as e:
+                logger.error(f"A critical error occurred while processing the TTS task: {e}", exc_info=True)
+                # 发生错误时，也要确保发送结束信号
+                if self._chunk_callback:
+                    self._chunk_callback(None)
+                self._tts_done_event.set()
+
+    def _playback_worker_loop(self):
+        try:
+            import sounddevice as sd
+            with sd.OutputStream(samplerate=self.sample_rate,
+                                 channels=self.channels,
+                                 dtype='float32') as stream:
+                while not self._stop_event.is_set():
+                    try:
+                        audio_chunk = self._audio_queue.get(timeout=1)
+                        if audio_chunk is None:
+                            break
+                        if audio_chunk is AUDIO_STREAM_END:
+                            self._playback_done_event.set()
+                            continue
+                        stream.write(audio_chunk.squeeze())
+                    except queue.Empty:
+                        continue
+                    except Exception as e:
+                        logger.error(f"Error during audio playback: {e}", exc_info=True)
+
+        except Exception as e:
+            logger.warning(f"Failed to initialize sounddevice: {e}. Audio playback will be skipped.")
+            # 如果音频设备初始化失败，即使不播放，也要消费队列中的结束信号，防止主线程死锁
+            while not self._stop_event.is_set():
+                try:
+                    item = self._audio_queue.get(timeout=0.5)
+                    if item is None:
+                        break
+                    if item is AUDIO_STREAM_END:
+                        self._playback_done_event.set()
+                except queue.Empty:
+                    continue
+
+    def _save_session_audio(self):
+        try:
+            full_audio = np.concatenate(self._session_audio_chunks, axis=0)
+            with wave.open(self._current_save_path, 'wb') as wf:
+                wf.setnchannels(self.channels)
+                wf.setsampwidth(self.bytes_per_sample)
+                wf.setframerate(self.sample_rate)
+                wf.writeframes(self._preprocess_for_playback(full_audio))
+            logger.info(f"Audio successfully saved to {os.path.abspath(self._current_save_path)}")
+        except Exception as e:
+            logger.error(f"Failed to save audio: {e}")
+        finally:
+            self._session_audio_chunks = []
+            self._current_save_path = None
+
+    def start_session(
+            self,
+            play: bool = False,
+            split: bool = False,
+            save_path: Optional[str] = None,
+            chunk_callback: Optional[Callable[[Optional[bytes]], None]] = None
+    ):
+        with self._api_lock:
+            self._tts_done_event.clear()
+            self._playback_done_event.clear()  # 新增：重置播放完成事件
+            self._chunk_callback = chunk_callback
+            self._stop_event.clear()
+
+            if self._tts_worker is None or not self._tts_worker.is_alive():
+                self._tts_worker = threading.Thread(target=self._tts_worker_loop, daemon=True)
+                self._tts_worker.start()
+
+            if self._playback_worker is None or not self._playback_worker.is_alive():
+                self._playback_worker = threading.Thread(target=self._playback_worker_loop, daemon=True)
+                self._playback_worker.start()
+
+            clear_queue(self._text_queue)
+            clear_queue(self._audio_queue)
+
+            self._play = play
+            self._split = split
+            self._current_save_path = save_path
+            self._session_audio_chunks = []
+
+    def feed(self, text_chunk: str):
+        with self._api_lock:
+            if not text_chunk:
+                return
+            if self._split:
+                sentences = self._text_splitter.split(text_chunk.strip())
+                for sentence in sentences:
+                    self._text_queue.put(sentence)
+            else:
+                self._text_queue.put(text_chunk)
+
+    def end_session(self):
+        with self._api_lock:
+            self._text_queue.put(STREAM_END)
+
+    def stop(self):
+        with self._api_lock:
+            if self._tts_worker is None and self._playback_worker is None:
+                return
+            if self._stop_event.is_set():
+                return
+            tts_client.stop_event.set()
+            self._stop_event.set()
+            self._tts_done_event.set()
+            self._text_queue.put(None)
+            self._audio_queue.put(None)
+            if self._tts_worker and self._tts_worker.is_alive():
+                self._tts_worker.join()
+            if self._playback_worker and self._playback_worker.is_alive():
+                self._playback_worker.join()
+            self._tts_worker = None
+            self._playback_worker = None
+
+    def wait_for_tts_completion(self):
+        if self._tts_done_event.is_set():
+            return
+        self._tts_done_event.wait()
+
+    def wait_for_playback_done(self):
+        # 1. 首先等待TTS生成全部完成
+        self.wait_for_tts_completion()
+
+        # 2. 如果开启了播放且没有被强制停止，则等待播放结束
+        if self._play and not self._stop_event.is_set():
+            if not self._playback_done_event.is_set():
+                self._playback_done_event.wait()
+
+
+tts_player: TTSPlayer = TTSPlayer()
diff --git a/genie_tts/Core/__init__.py b/genie_tts/Core/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/genie_tts/Data/v2/Keys/t2s_onnx_keys.txt b/genie_tts/Data/v2/Keys/t2s_onnx_keys.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0a0b35764b2f696e8b72b6c68809ac463d55987a
--- /dev/null
+++ b/genie_tts/Data/v2/Keys/t2s_onnx_keys.txt
@@ -0,0 +1,291 @@
+ar_audio_embedding.word_embeddings.weight
+ar_audio_position.alpha
+transformer_encoder.layers.0.self_attn.in_proj_weight
+transformer_encoder.layers.0.self_attn.in_proj_bias
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diff --git a/genie_tts/Data/v2/Keys/vits_onnx_keys.txt b/genie_tts/Data/v2/Keys/vits_onnx_keys.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d4f0b4bbe0d8fd259d320e2f2c7967e0a92660a1
--- /dev/null
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diff --git a/genie_tts/Data/v2ProPlus/Models/prompt_encoder_fp32.onnx b/genie_tts/Data/v2ProPlus/Models/prompt_encoder_fp32.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..d750f20662c2abfa85ce0664c316b7ad10c4a7f6
--- /dev/null
+++ b/genie_tts/Data/v2ProPlus/Models/prompt_encoder_fp32.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de17d88fdfe9598f9d25710b7299bccac3c0a79851cd8085fc210492260cc0b9
+size 44533
diff --git a/genie_tts/Data/v2ProPlus/Models/vits_fp32.onnx b/genie_tts/Data/v2ProPlus/Models/vits_fp32.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..0d7391be80821f4a5770ace88b37aa6b6338bb7e
--- /dev/null
+++ b/genie_tts/Data/v2ProPlus/Models/vits_fp32.onnx
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+version https://git-lfs.github.com/spec/v1
+oid sha256:9f9cb0882bbd028e73e5b54b94b54f53370a0f65605ca7dee4e4f1ab5edac89c
+size 1613193
diff --git a/genie_tts/G2P/Chinese/ChineseG2P.py b/genie_tts/G2P/Chinese/ChineseG2P.py
new file mode 100644
index 0000000000000000000000000000000000000000..df19ca7d8fed3e6a544afda6a3bd1f85a13bcdac
--- /dev/null
+++ b/genie_tts/G2P/Chinese/ChineseG2P.py
@@ -0,0 +1,186 @@
+import os
+import re
+from typing import List, Tuple, Dict
+import logging
+
+from pypinyin.contrib.tone_convert import to_finals_tone3, to_initials
+import jieba_fast
+import jieba_fast.posseg as psg
+from g2pM import G2pM
+
+from ...Core.Resources import Chinese_G2P_DIR
+from ..SymbolsV2 import symbols_v2, symbol_to_id_v2
+from .ToneSandhi import ToneSandhi
+from .Normalization.text_normlization import TextNormalizer
+from .CorrectPronunciation import correct_pronunciation
+from .Erhua import ErhuaProcessor
+
+jieba_fast.setLogLevel(logging.ERROR)
+
+PUNCTUATION = ["!", "?", "…", ",", ".", "-"]
+PUNCTUATION_REPLACEMENTS = {
+    "：": ",", "；": ",", "，": ",", "。": ".", "！": "!",
+    "？": "?", "\n": ".", "·": ",", "、": ",", "$": ".",
+    "/": ",", "—": "-", "~": "…", "～": "…",
+}
+SPECIAL_REPLACEMENTS = {"...": "…"}  # 特殊的多字符替换
+
+
+class ChineseG2P:
+    def __init__(self):
+        # --- 资源加载 ---
+        self.g2pm: G2pM = G2pM()
+        self.tone_modifier: ToneSandhi = ToneSandhi()
+        self.erhua_processor: ErhuaProcessor = ErhuaProcessor()
+        self.text_normalizer: TextNormalizer = TextNormalizer()
+        self.pinyin_to_symbol_map: Dict[str, str] = {}
+
+        # 预编译正则
+        # 1. 匹配替换表中的字符
+        self.pattern_punct_map = re.compile("|".join(re.escape(p) for p in PUNCTUATION_REPLACEMENTS.keys()))
+        # 2. 过滤非中文字符和允许的标点
+        allowed_chars = "".join(re.escape(p) for p in PUNCTUATION)
+        self.pattern_filter = re.compile(r"[^\u4e00-\u9fa5" + allowed_chars + r"]+")
+        # 3. 句内分割 (Lookbehind)
+        self.pattern_split = re.compile(r"(?<=[{0}])\s*".format(allowed_chars))
+        # 4. 连续标点去重
+        self.pattern_consecutive = re.compile(f"([{allowed_chars}])\\1+")
+        # 5. 英文单词移除
+        self.pattern_eng = re.compile(r"[a-zA-Z]+")
+
+        # --- 拼音映射查找表 (用于 _pinyin_to_opencpop_phones) ---
+        self.v_rep_map = {"uei": "ui", "iou": "iu", "uen": "un"}
+        self.pinyin_rep_map = {"ing": "ying", "i": "yi", "in": "yin", "u": "wu"}
+        self.single_rep_map = {"v": "yu", "e": "e", "i": "y", "u": "w"}
+
+        self.load_opencpop_dict()
+
+    def load_opencpop_dict(self):
+        # 加载 Opencpop 映射表
+        map_path = os.path.join(Chinese_G2P_DIR, "opencpop-strict.txt")
+        with open(map_path, 'r', encoding='utf-8') as f:
+            for line in f:
+                parts = line.strip().split("\t")
+                if len(parts) >= 2:
+                    self.pinyin_to_symbol_map[parts[0]] = parts[1]
+
+    def _replace_punctuation(self, text: str) -> str:
+        """处理特定字符替换和非标准标点清洗"""
+        # text = text.replace("嗯", "恩").replace("呣", "母")
+        for k, v in SPECIAL_REPLACEMENTS.items():
+            text = text.replace(k, v)
+        text = self.pattern_punct_map.sub(lambda x: PUNCTUATION_REPLACEMENTS[x.group()], text)
+        text = self.pattern_filter.sub("", text)
+        return text
+
+    def normalize_text(self, text: str) -> str:
+        """执行完整的文本归一化流程"""
+        # 1. TextNormalizer 转换 (如数字转汉字)
+        sentences = self.text_normalizer.normalize(text)
+        # 2. 标点映射与清洗
+        dest_parts = [self._replace_punctuation(s) for s in sentences]
+        dest_text = "".join(dest_parts)
+        # 3. 避免重复标点
+        dest_text = self.pattern_consecutive.sub(r"\1", dest_text)
+        return dest_text
+
+    def _pinyin_to_opencpop_phones(self, c: str, v: str) -> List[str]:
+        """将声母韵母转换为 Opencpop 格式的音素"""
+        # 提取声调
+        v_without_tone = v[:-1]
+        tone = v[-1]
+        if c:
+            # 多音节逻辑
+            final = self.v_rep_map.get(v_without_tone, v_without_tone)
+            pinyin_key = c + final
+        else:
+            # 零声母/单音节逻辑
+            temp_key = c + v_without_tone  # c is empty string here usually
+            if temp_key in self.pinyin_rep_map:
+                pinyin_key = self.pinyin_rep_map[temp_key]
+            else:
+                # 处理首字母变化
+                if temp_key and temp_key[0] in self.single_rep_map:
+                    pinyin_key = self.single_rep_map[temp_key[0]] + temp_key[1:]
+                else:
+                    pinyin_key = temp_key
+        # 查表获取音素
+        phone_str = self.pinyin_to_symbol_map[pinyin_key]
+        new_c, new_v = phone_str.split(" ")
+        new_v = new_v + tone
+        return [new_c, new_v]
+
+    def g2p(self, text: str) -> Tuple[List[str], List[int]]:
+        """生成音素列表和 Word-to-Phone 映射"""
+        sentences = [i for i in self.pattern_split.split(text) if i.strip() != ""]
+        all_phones = []
+        all_word2ph = []
+        for seg in sentences:
+            # 移除英文
+            seg = self.pattern_eng.sub("", seg)
+            # 分词
+            seg_cut = psg.lcut(seg)
+            seg_cut = self.tone_modifier.pre_merge_for_modify(seg_cut)
+            initials = []
+            finals = []
+            # G2PM 整句推理
+            pinyins = self.g2pm(seg, char_split=True)
+            pinyins = [p.replace("u:", "v") for p in pinyins]
+            pre_word_length = 0
+            for word, pos in seg_cut:
+                now_word_length = pre_word_length + len(word)
+                if pos == "eng":
+                    pre_word_length = now_word_length
+                    continue
+                word_pinyins = pinyins[pre_word_length:now_word_length]
+                # 多音字修正
+                word_pinyins = correct_pronunciation(word, word_pinyins)
+                sub_initials = []
+                sub_finals = []
+                for pinyin in word_pinyins:
+                    if pinyin[0].isalpha():
+                        sub_initials.append(to_initials(pinyin))
+                        sub_finals.append(to_finals_tone3(pinyin, neutral_tone_with_five=True))
+                    else:
+                        # 处理非字母（如标点）
+                        sub_initials.append(pinyin)
+                        sub_finals.append(pinyin)
+                pre_word_length = now_word_length
+                # 变调处理
+                sub_finals = self.tone_modifier.modified_tone(word, pos, sub_finals)
+                # 儿化处理
+                sub_initials, sub_finals = self.erhua_processor.merge_erhua(sub_initials, sub_finals, word, pos)
+                initials.extend(sub_initials)
+                finals.extend(sub_finals)
+
+            for c, v in zip(initials, finals):
+                if c == v:
+                    # 标点符号逻辑
+                    all_phones.append(c)
+                    all_word2ph.append(1)
+                else:
+                    # 正常拼音转换逻辑
+                    try:
+                        phone_pair = self._pinyin_to_opencpop_phones(c, v)
+                        all_phones.extend(phone_pair)
+                        all_word2ph.append(len(phone_pair))
+                    except KeyError:
+                        # 遇到未知的拼音组合，记录错误或跳过
+                        continue
+
+        return all_phones, all_word2ph
+
+    def process(self, text: str) -> Tuple[str, List[str], List[int], List[int]]:
+        normalized_text = self.normalize_text(text)
+        # print(normalized_text)
+        phones, word2ph = self.g2p(normalized_text)
+        phones = [ph for ph in phones if ph in symbols_v2]
+        phones_ids = [symbol_to_id_v2[ph] for ph in phones]
+        return normalized_text, phones, phones_ids, word2ph
+
+
+processor: ChineseG2P = ChineseG2P()
+
+
+def chinese_to_phones(text: str) -> Tuple[str, List[str], List[int], List[int]]:
+    return processor.process(text)
diff --git a/genie_tts/G2P/Chinese/CorrectPronunciation.py b/genie_tts/G2P/Chinese/CorrectPronunciation.py
new file mode 100644
index 0000000000000000000000000000000000000000..33d2024fd9ccea293e44afad15a2819235848f44
--- /dev/null
+++ b/genie_tts/G2P/Chinese/CorrectPronunciation.py
@@ -0,0 +1,50 @@
+import os
+import pickle
+from typing import List, Dict, Any, Union
+
+from ...Core.Resources import Chinese_G2P_DIR
+
+# 常量定义
+DEFAULT_CACHE_PATH = os.path.join(Chinese_G2P_DIR, "polyphonic.pickle")
+
+
+class PolyphonicDictManager:
+    _data: Dict[str, Any] = {}
+
+    @classmethod
+    def get_data(cls, path: str = DEFAULT_CACHE_PATH) -> Dict[str, Any]:
+        if not cls._data:
+            with open(path, "rb") as f:
+                cls._data = pickle.load(f)
+        return cls._data
+
+
+def correct_pronunciation(word: str, word_pinyin: List[str]) -> Union[List[str], str]:
+    """
+        根据加载的字典修正发音，作为供外部程序调用的独立接口。
+        逻辑：优先查找整词修正，如果没有整词匹配，则遍历每个字符进行单字修正。
+
+        Input:
+            word (str): 原始中文字符串，例如 "银行"。
+            word_pinyins (List[str]): 当前预测的拼音列表，例如 ['yin2', 'xing2']。
+
+        Output:
+            Union[List[str], str]: 修正后的拼音列表或字符串。
+
+        Example:
+            # 字典包含整词 {'银行': ['yin2', 'hang2']}
+            result = correct_pronunciation("银行", ["yin2", "xing2"])
+            # Result: ["yin2", "hang2"]
+        """
+    pp_dict = PolyphonicDictManager.get_data()
+    new_word_pinyin = list(word_pinyin)
+    # 1. 尝试整词匹配
+    if new_pinyin := pp_dict.get(word):
+        return new_pinyin
+    # 2. 逐字修正
+    for idx, w in enumerate(word):
+        if idx >= len(new_word_pinyin):
+            break
+        if w_pinyin := pp_dict.get(w):
+            new_word_pinyin[idx] = w_pinyin[0]
+    return new_word_pinyin
diff --git a/genie_tts/G2P/Chinese/Erhua.py b/genie_tts/G2P/Chinese/Erhua.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ccc87b895563bc03e4943427219a2495094208b
--- /dev/null
+++ b/genie_tts/G2P/Chinese/Erhua.py
@@ -0,0 +1,49 @@
+from typing import List, Tuple, Set
+
+
+class ErhuaProcessor:
+    """
+    处理中文G2P中的儿化音逻辑。
+    """
+
+    def __init__(self):
+        self.must_erhua: Set[str] = {
+            "小院儿", "胡同儿", "范儿", "老汉儿", "撒欢儿", "寻老礼儿", "妥妥儿", "媳妇儿"
+        }
+        self.not_erhua: Set[str] = {
+            "虐儿", "为儿", "护儿", "瞒儿", "救儿", "替儿", "有儿", "一儿", "我儿", "俺儿",
+            "妻儿", "拐儿", "聋儿", "乞儿", "患儿", "幼儿", "孤儿", "婴儿", "婴幼儿", "连体儿",
+            "脑瘫儿", "流浪儿", "体弱儿", "混血儿", "蜜雪儿", "舫儿", "祖儿", "美儿", "应采儿", "可儿",
+            "侄儿", "孙儿", "侄孙儿", "女儿", "男儿", "红孩儿", "花儿", "虫儿", "马儿", "鸟儿",
+            "猪儿", "猫儿", "狗儿", "少儿",
+        }
+
+    def merge_erhua(self, initials: List[str], finals: List[str], word: str, pos: str) -> Tuple[List[str], List[str]]:
+        # 1. 修正 er1 发音为 er2 (当'儿'在词尾且发音为er1时)
+        for i, phn in enumerate(finals):
+            if i == len(finals) - 1 and word[i] == "儿" and phn == "er1":
+                finals[i] = "er2"
+        # 2. 检查是否跳过儿化处理
+        if word not in self.must_erhua and (word in self.not_erhua or pos in {"a", "j", "nr"}):
+            return initials, finals
+        # 3. 长度校验 (处理如 "……" 等长度不一致的特殊符号情况)
+        if len(finals) != len(word):
+            return initials, finals
+        # 4. 执行儿化合并逻辑 (与前一个字发同音)
+        new_initials = []
+        new_finals = []
+        for i, phn in enumerate(finals):
+            # 判断是否需要合并儿化音
+            # 条件: 是最后一个字 + 是"儿" + 发音是er2/er5 + 后两字不在非儿化表中 + 前面已有韵母
+            if (
+                    i == len(finals) - 1
+                    and word[i] == "儿"
+                    and phn in {"er2", "er5"}
+                    and word[-2:] not in self.not_erhua
+                    and new_finals
+            ):
+                # 将 'er' 加上前一个字的声调
+                phn = "er" + new_finals[-1][-1]
+            new_initials.append(initials[i])
+            new_finals.append(phn)
+        return new_initials, new_finals
diff --git a/genie_tts/G2P/Chinese/Normalization/__init__.py b/genie_tts/G2P/Chinese/Normalization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/genie_tts/G2P/Chinese/Normalization/char_convert.py b/genie_tts/G2P/Chinese/Normalization/char_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..144dd63a95dbfc1847e2fd04de7fb7d88ac5a6b9
--- /dev/null
+++ b/genie_tts/G2P/Chinese/Normalization/char_convert.py
@@ -0,0 +1,35 @@
+# coding=utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Traditional and simplified Chinese conversion, a simplified character may correspond to multiple traditional characters."""
+
+simplified_charcters = 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镡鐗馗镫镢镨鐡锎镄鐩镌鐬鐱镭鐶鐻鐽镱鑀鑅镔鑐鑕鑚鑛鑢鑤镥鑪镧鑯鑱鑴鑵镊镢钃镻闫闬闶闳閒闵閗閟阂関合閤哄阆閲阉閺阎阏阍阌暗闉阕阗闑闒闿闘闚阚闟闠闤闼阞阢阤阨阬阯阹阼阽陁陑陔陛陜陡陥陬骘陴険陼陾阴隃隈隒隗隞隠隣隤隩隮隰颧隳隷隹雂雈雉雊雎雑雒雗雘雚雝雟雩雰雱驿霂霅霈霊沾霒霓霙霝霢霣霤霨霩霪霫霮靁叇叆靑靓靣腼靪靮靰靳靷靸靺靼靿鞀鞃鞄鞍鞗鞙鞚鞝鞞鞡鞣鞨鞫鞬鞮鞶鞹鞾鞑韅鞯驮韍韎韔韖韘韝韫韡韣韭韭韱韹韺頀刮頄顸顼頍颀颃颁頖頞頠頫頬颅頯頲颕頼悴顋顑颙颛颜顕顚顜颟顣颥颞飐飑台飓颸飏飖颽颾颿飀飂飚飌翻飡飣饲飥饨饫飮飧飶餀餂饸饹餇餈饽哺馂餖餗餚馄馃餟餠餤餧餩餪餫糊餮糇餲饧馎糕饩馈馊馌馒饇馑馓膳饎饐饘饟馕馘馥馝馡馣骝骡馵馹駃駄駅駆駉駋驽駓驵駗骀驸駜骂骈駪駬骃駴骎駹駽駾騂騄骓騆騉騋骒骐麟騑騒験騕骛騠騢騣騤騧骧騵驺骟騺蓦骖骠骢驆驈骅驌骁驎骣驒驔驖驙驦驩驫骺鲠骫骭肮骱骴骶骷髅骾髁髂髄髆膀髇髑髌髋髙髝髞髟髡髣髧髪髫髭髯髲髳髹髺髽髾鬁鬃鬅鬈鬋鬎鬏鬐鬑鬒鬖鬗鬘鬙鬠鬣斗鬫鬬阄鬯鬰鬲鬵鬷魆魈魊魋魍魉魑魖鳔魛魟魣魦魨魬鲂魵魸鮀鲅鮆鲧鲇鲍鲋鮓鲒鲕鮟鱇鮠鮦鮨鲔鲑鮶鮸鮿鲧鯄鯆鲩鯈鲻鯕鲭鲞鯙鯠鲲鯥鲰鲶鳀鯸鳊鲗䲠鹣鳇鰋鳄鳆鰕鰛鰜鲥鰤鳏鰦鳎鳐鳁鳓鰶鲦鲡鰼鰽鱀鱄鳙鱆鳕鱎鱐鳝鳝鳜鲟鲎鱠鳣鱨鲚鱮鱲鱵鱻鲅鳦凫鳯鳲鳷鳻鴂鴃鴄鸩鴈鴎鸰鴔鴗鸳鸯鸲鹆鸱鴠鴢鸪鴥鸸鹋鴳鸻鴷鴽鵀鵁鸺鹁鵖鵙鹈鹕鹅鵟鵩鹌鵫鵵鵷鵻鹍鶂鶊鶏鶒鹙鶗鶡鶤鶦鶬鶱鹟鶵鶸鶹鹡鶿鹚鷁鷃鷄鷇䴘䴘鷊鷏鹧鷕鹥鸷鷞鷟鸶鹪鹩鷩鷫鷭鹇鹇鸴鷾䴙鸂鸇䴙鸏鸑鸒鸓鸬鹳鸜鹂鹸咸鹾麀麂麃麄麇麋麌麐麑麒麚麛麝麤麸面麫麮麯麰麺麾黁黈黉黢黒黓黕黙黝黟黥黦黧黮黰黱黪黶黹黻黼黾鼋鼂鼃鼅鼈鼍鼏鼐鼒冬鼖鼙鼚鼛鼡鼩鼱鼪鼫鼯鼷鼽齁齆齇齈齉齌赍齑龀齕齗龅齚龇齞龃龉龆齢出齧齩齮齯齰齱齵齾厐龑龒龚龖龘龝龡龢龤"
+
+traditional_characters = 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鐔鐗馗鐙鐝鐠鐡鐦鐨鐩鐫鐬鐱鐳鐶鐻鐽鐿鑀鑅鑌鑐鑕鑚鑛鑢鑤鑥鑪鑭鑯鑱鑴鑵鑷钁钃镻閆閈閌閎閒閔閗閟閡関閤閤閧閬閲閹閺閻閼閽閿闇闉闋闐闑闒闓闘闚闞闟闠闤闥阞阢阤阨阬阯阹阼阽陁陑陔陛陜陡陥陬騭陴険陼陾隂隃隈隒隗隞隠隣隤隩隮隰顴隳隷隹雂雈雉雊雎雑雒雗雘雚雝雟雩雰雱驛霂霅霈霊霑霒霓霙霝霢霣霤霨霩霪霫霮靁靆靉靑靚靣靦靪靮靰靳靷靸靺靼靿鞀鞃鞄鞌鞗鞙鞚鞝鞞鞡鞣鞨鞫鞬鞮鞶鞹鞾韃韅韉馱韍韎韔韖韘韝韞韡韣韭韮韱韹韺頀颳頄頇頊頍頎頏頒頖頞頠頫頬顱頯頲頴頼顇顋顑顒顓顔顕顚顜顢顣顬顳颭颮颱颶颸颺颻颽颾颿飀飂飈飌飜飡飣飤飥飩飫飮飱飶餀餂餄餎餇餈餑餔餕餖餗餚餛餜餟餠餤餧餩餪餫餬餮餱餲餳餺餻餼餽餿饁饅饇饉饊饍饎饐饘饟饢馘馥馝馡馣騮騾馵馹駃駄駅駆駉駋駑駓駔駗駘駙駜駡駢駪駬駰駴駸駹駽駾騂騄騅騆騉騋騍騏驎騑騒験騕騖騠騢騣騤騧驤騵騶騸騺驀驂驃驄驆驈驊驌驍驎驏驒驔驖驙驦驩驫骺鯁骫骭骯骱骴骶骷髏骾髁髂髄髆髈髐髑髕髖髙髝髞髟髡髣髧髪髫髭髯髲髳髹髺髽髾鬁鬃鬅鬈鬋鬎鬏鬐鬑鬒鬖鬗鬘鬙鬠鬣鬪鬫鬬鬮鬯鬰鬲鬵鬷魆魈魊魋魍魎魑魖鰾魛魟魣魦魨魬魴魵魸鮀鮁鮆鮌鮎鮑鮒鮓鮚鮞鮟鱇鮠鮦鮨鮪鮭鮶鮸鮿鯀鯄鯆鯇鯈鯔鯕鯖鯗鯙鯠鯤鯥鯫鯰鯷鯸鯿鰂鰆鶼鰉鰋鰐鰒鰕鰛鰜鰣鰤鰥鰦鰨鰩鰮鰳鰶鰷鱺鰼鰽鱀鱄鱅鱆鱈鱎鱐鱓鱔鱖鱘鱟鱠鱣鱨鱭鱮鱲鱵鱻鲅鳦鳧鳯鳲鳷鳻鴂鴃鴄鴆鴈鴎鴒鴔鴗鴛鴦鴝鵒鴟鴠鴢鴣鴥鴯鶓鴳鴴鴷鴽鵀鵁鵂鵓鵖鵙鵜鶘鵞鵟鵩鵪鵫鵵鵷鵻鵾鶂鶊鶏鶒鶖鶗鶡鶤鶦鶬鶱鶲鶵鶸鶹鶺鶿鷀鷁鷃鷄鷇鷈鷉鷊鷏鷓鷕鷖鷙鷞鷟鷥鷦鷯鷩鷫鷭鷳鷴鷽鷾鷿鸂鸇鸊鸏鸑鸒鸓鸕鸛鸜鸝鹸鹹鹺麀麂麃麄麇麋麌麐麑麒麚麛麝麤麩麪麫麮麯麰麺麾黁黈黌黢黒黓黕黙黝黟黥黦黧黮黰黱黲黶黹黻黼黽黿鼂鼃鼅鼈鼉鼏鼐鼒鼕鼖鼙鼚鼛鼡鼩鼱鼪鼫鼯鼷鼽齁齆齇齈齉齌齎齏齔齕齗齙齚齜齞齟齬齠齢齣齧齩齮齯齰齱齵齾龎龑龒龔龖龘龝龡龢龤"
+
+assert len(simplified_charcters) == len(simplified_charcters)
+
+s2t_dict = {}
+t2s_dict = {}
+for i, item in enumerate(simplified_charcters):
+    s2t_dict[item] = traditional_characters[i]
+    t2s_dict[traditional_characters[i]] = item
+
+
+def tranditional_to_simplified(text: str) -> str:
+    return "".join([t2s_dict[item] if item in t2s_dict else item for item in text])
+
+
+def simplified_to_traditional(text: str) -> str:
+    return "".join([s2t_dict[item] if item in s2t_dict else item for item in text])
diff --git a/genie_tts/G2P/Chinese/Normalization/chronology.py b/genie_tts/G2P/Chinese/Normalization/chronology.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbf6495aebec4fa59e6e436c26b1a2914812643c
--- /dev/null
+++ b/genie_tts/G2P/Chinese/Normalization/chronology.py
@@ -0,0 +1,144 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+
+from .num import (
+    DIGITS,
+    num2str,
+    verbalize_cardinal,
+    verbalize_digit,
+)
+
+
+def _time_num2str(num_string: str) -> str:
+    """A special case for verbalizing number in time."""
+    result = num2str(num_string.lstrip("0"))
+    if num_string.startswith("0"):
+        result = DIGITS["0"] + result
+    return result
+
+
+# 时刻表达式
+RE_TIME = re.compile(
+    r"([0-1]?[0-9]|2[0-3])"
+    r":([0-5][0-9])"
+    r"(:([0-5][0-9]))?"
+)
+
+# 时间范围，如8:30-12:30
+RE_TIME_RANGE = re.compile(
+    r"([0-1]?[0-9]|2[0-3])"
+    r":([0-5][0-9])"
+    r"(:([0-5][0-9]))?"
+    r"(~|-)"
+    r"([0-1]?[0-9]|2[0-3])"
+    r":([0-5][0-9])"
+    r"(:([0-5][0-9]))?"
+)
+
+
+def replace_time(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+
+    is_range = len(match.groups()) > 5
+
+    hour = match.group(1)
+    minute = match.group(2)
+    second = match.group(4)
+    hour_2 = ""
+    minute_2 = ""
+    second_2 = ""
+
+    if is_range:
+        hour_2 = match.group(6)
+        minute_2 = match.group(7)
+        second_2 = match.group(9)
+
+    result = f"{num2str(hour)}点"
+    if minute.lstrip("0"):
+        if int(minute) == 30:
+            result += "半"
+        else:
+            result += f"{_time_num2str(minute)}分"
+    if second and second.lstrip("0"):
+        result += f"{_time_num2str(second)}秒"
+
+    if is_range:
+        result += "至"
+        result += f"{num2str(hour_2)}点"
+        if minute_2.lstrip("0"):
+            if int(minute) == 30:
+                result += "半"
+            else:
+                result += f"{_time_num2str(minute_2)}分"
+        if second_2 and second_2.lstrip("0"):
+            result += f"{_time_num2str(second_2)}秒"
+
+    return result
+
+
+RE_DATE = re.compile(
+    r"(\d{4}|\d{2})年"
+    r"((0?[1-9]|1[0-2])月)?"
+    r"(((0?[1-9])|((1|2)[0-9])|30|31)([日号]))?"
+)
+
+
+def replace_date(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    year = match.group(1)
+    month = match.group(3)
+    day = match.group(5)
+    result = ""
+    if year:
+        result += f"{verbalize_digit(year)}年"
+    if month:
+        result += f"{verbalize_cardinal(month)}月"
+    if day:
+        result += f"{verbalize_cardinal(day)}{match.group(9)}"
+    return result
+
+
+# 用 / 或者 - 分隔的 YY/MM/DD 或者 YY-MM-DD 日期
+RE_DATE2 = re.compile(r"(\d{4})([- /.])(0[1-9]|1[012])\2(0[1-9]|[12][0-9]|3[01])")
+
+
+def replace_date2(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    year = match.group(1)
+    month = match.group(3)
+    day = match.group(4)
+    result = ""
+    if year:
+        result += f"{verbalize_digit(year)}年"
+    if month:
+        result += f"{verbalize_cardinal(month)}月"
+    if day:
+        result += f"{verbalize_cardinal(day)}日"
+    return result
diff --git a/genie_tts/G2P/Chinese/Normalization/constants.py b/genie_tts/G2P/Chinese/Normalization/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..b31adf329f9c2b2db30a0f2d6589c070b596d52f
--- /dev/null
+++ b/genie_tts/G2P/Chinese/Normalization/constants.py
@@ -0,0 +1,61 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+import string
+from pypinyin.compat import SUPPORT_UCS4
+
+# 全角半角转换
+# 英文字符全角 -> 半角映射表 (num: 52)
+F2H_ASCII_LETTERS = {ord(char) + 65248: ord(char) for char in string.ascii_letters}
+
+# 英文字符半角 -> 全角映射表
+H2F_ASCII_LETTERS = {value: key for key, value in F2H_ASCII_LETTERS.items()}
+
+# 数字字符全角 -> 半角映射表 (num: 10)
+F2H_DIGITS = {ord(char) + 65248: ord(char) for char in string.digits}
+# 数字字符半角 -> 全角映射表
+H2F_DIGITS = {value: key for key, value in F2H_DIGITS.items()}
+
+# 标点符号全角 -> 半角映射表 (num: 32)
+F2H_PUNCTUATIONS = {ord(char) + 65248: ord(char) for char in string.punctuation}
+# 标点符号半角 -> 全角映射表
+H2F_PUNCTUATIONS = {value: key for key, value in F2H_PUNCTUATIONS.items()}
+
+# 空格 (num: 1)
+F2H_SPACE = {"\u3000": " "}
+H2F_SPACE = {" ": "\u3000"}
+
+# 非"有拼音的汉字"的字符串，可用于NSW提取
+if SUPPORT_UCS4:
+    RE_NSW = re.compile(
+        r"(?:[^"
+        r"\u3007"  # 〇
+        r"\u3400-\u4dbf"  # CJK扩展A:[3400-4DBF]
+        r"\u4e00-\u9fff"  # CJK基本:[4E00-9FFF]
+        r"\uf900-\ufaff"  # CJK兼容:[F900-FAFF]
+        r"\U00020000-\U0002A6DF"  # CJK扩展B:[20000-2A6DF]
+        r"\U0002A703-\U0002B73F"  # CJK扩展C:[2A700-2B73F]
+        r"\U0002B740-\U0002B81D"  # CJK扩展D:[2B740-2B81D]
+        r"\U0002F80A-\U0002FA1F"  # CJK兼容扩展:[2F800-2FA1F]
+        r"])+"
+    )
+else:
+    RE_NSW = re.compile(  # pragma: no cover
+        r"(?:[^"
+        r"\u3007"  # 〇
+        r"\u3400-\u4dbf"  # CJK扩展A:[3400-4DBF]
+        r"\u4e00-\u9fff"  # CJK基本:[4E00-9FFF]
+        r"\uf900-\ufaff"  # CJK兼容:[F900-FAFF]
+        r"])+"
+    )
diff --git a/genie_tts/G2P/Chinese/Normalization/num.py b/genie_tts/G2P/Chinese/Normalization/num.py
new file mode 100644
index 0000000000000000000000000000000000000000..be23c1d29247b1d6f5fefc8e4781677d6ed9cf72
--- /dev/null
+++ b/genie_tts/G2P/Chinese/Normalization/num.py
@@ -0,0 +1,340 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Rules to verbalize numbers into Chinese characters.
+https://zh.wikipedia.org/wiki/中文数字#現代中文
+"""
+
+import re
+from collections import OrderedDict
+from typing import List
+
+DIGITS = {str(i): tran for i, tran in enumerate("零一二三四五六七八九")}
+UNITS = OrderedDict(
+    {
+        1: "十",
+        2: "百",
+        3: "千",
+        4: "万",
+        8: "亿",
+    }
+)
+
+COM_QUANTIFIERS = "(处|台|架|枚|趟|幅|平|方|堵|间|床|株|批|项|例|列|篇|栋|注|亩|封|艘|把|目|套|段|人|所|朵|匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|毫|厘|(公)分|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|小时|旬|纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块|元|(亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|美|)元|(亿|千万|百万|万|千|百|十|)吨|(亿|千万|百万|万|千|百|)块|角|毛|分)"
+
+# 分数表达式
+RE_FRAC = re.compile(r"(-?)(\d+)/(\d+)")
+
+
+def replace_frac(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    nominator = match.group(2)
+    denominator = match.group(3)
+    sign: str = "负" if sign else ""
+    nominator: str = num2str(nominator)
+    denominator: str = num2str(denominator)
+    result = f"{sign}{denominator}分之{nominator}"
+    return result
+
+
+# 百分数表达式
+RE_PERCENTAGE = re.compile(r"(-?)(\d+(\.\d+)?)%")
+
+
+def replace_percentage(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    percent = match.group(2)
+    sign: str = "负" if sign else ""
+    percent: str = num2str(percent)
+    result = f"{sign}百分之{percent}"
+    return result
+
+
+# 整数表达式
+# 带负号的整数 -10
+RE_INTEGER = re.compile(r"(-)" r"(\d+)")
+
+
+def replace_negative_num(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    number = match.group(2)
+    sign: str = "负" if sign else ""
+    number: str = num2str(number)
+    result = f"{sign}{number}"
+    return result
+
+
+# 编号-无符号整形
+# 00078
+RE_DEFAULT_NUM = re.compile(r"\d{3}\d*")
+
+
+def replace_default_num(match):
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    number = match.group(0)
+    return verbalize_digit(number, alt_one=True)
+
+
+# 加减乘除
+# RE_ASMD = re.compile(
+#     r'((-?)((\d+)(\.\d+)?)|(\.(\d+)))([\+\-\×÷=])((-?)((\d+)(\.\d+)?)|(\.(\d+)))')
+RE_ASMD = re.compile(
+    r"((-?)((\d+)(\.\d+)?[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|(\.\d+[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|([A-Za-z][⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*))([+\-×÷=])((-?)((\d+)(\.\d+)?[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|(\.\d+[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|([A-Za-z][⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*))"
+)
+
+asmd_map = {"+": "加", "-": "减", "×": "乘", "÷": "除", "=": "等于"}
+
+
+def replace_asmd(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    result = match.group(1) + asmd_map[match.group(8)] + match.group(9)
+    return result
+
+
+# 次方专项
+RE_POWER = re.compile(r"[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]+")
+
+power_map = {
+    "⁰": "0",
+    "¹": "1",
+    "²": "2",
+    "³": "3",
+    "⁴": "4",
+    "⁵": "5",
+    "⁶": "6",
+    "⁷": "7",
+    "⁸": "8",
+    "⁹": "9",
+    "ˣ": "x",
+    "ʸ": "y",
+    "ⁿ": "n",
+}
+
+
+def replace_power(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    power_num = ""
+    for m in match.group(0):
+        power_num += power_map[m]
+    result = "的" + power_num + "次方"
+    return result
+
+
+# 数字表达式
+# 纯小数
+RE_DECIMAL_NUM = re.compile(r"(-?)((\d+)(\.\d+))" r"|(\.(\d+))")
+# 正整数 + 量词
+RE_POSITIVE_QUANTIFIERS = re.compile(r"(\d+)([多余几+])?" + COM_QUANTIFIERS)
+RE_NUMBER = re.compile(r"(-?)((\d+)(\.\d+)?)" r"|(\.(\d+))")
+
+
+def replace_positive_quantifier(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    number = match.group(1)
+    match_2 = match.group(2)
+    if match_2 == "+":
+        match_2 = "多"
+    match_2: str = match_2 if match_2 else ""
+    quantifiers: str = match.group(3)
+    number: str = num2str(number)
+    number = "两" if number == "二" else number
+    result = f"{number}{match_2}{quantifiers}"
+    return result
+
+
+def replace_number(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    number = match.group(2)
+    pure_decimal = match.group(5)
+    if pure_decimal:
+        result = num2str(pure_decimal)
+    else:
+        sign: str = "负" if sign else ""
+        number: str = num2str(number)
+        result = f"{sign}{number}"
+    return result
+
+
+# 范围表达式
+# match.group(1) and match.group(8) are copy from RE_NUMBER
+
+RE_RANGE = re.compile(
+    r"""
+    (?<![\d+\-×÷=])      # 使用反向前瞻以确保数字范围之前没有其他数字和操作符
+    ((-?)((\d+)(\.\d+)?))  # 匹配范围起始的负数或正数（整数或小数）
+    [-~]                   # 匹配范围分隔符
+    ((-?)((\d+)(\.\d+)?))  # 匹配范围结束的负数或正数（整数或小数）
+    (?![\d+\-×÷=])       # 使用正向前瞻以确保数字范围之后没有其他数字和操作符
+    """,
+    re.VERBOSE,
+)
+
+
+def replace_range(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    first, second = match.group(1), match.group(6)
+    first = RE_NUMBER.sub(replace_number, first)
+    second = RE_NUMBER.sub(replace_number, second)
+    result = f"{first}到{second}"
+    return result
+
+
+# ~至表达式
+RE_TO_RANGE = re.compile(
+    r"((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)[~]((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)"
+)
+
+
+def replace_to_range(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    result = match.group(0).replace("~", "至")
+    return result
+
+
+RE_VERSION_NUM = re.compile(r"((\d+)(\.\d+)(\.\d+)?(\.\d+)+)")
+
+
+def replace_vrsion_num(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    result = ""
+    for c in match.group(1):
+        if c == ".":
+            result += "点"
+        else:
+            result += num2str(c)
+    return result
+
+
+def _get_value(value_string: str, use_zero: bool = True) -> List[str]:
+    stripped = value_string.lstrip("0")
+    if len(stripped) == 0:
+        return []
+    elif len(stripped) == 1:
+        if use_zero and len(stripped) < len(value_string):
+            return [DIGITS["0"], DIGITS[stripped]]
+        else:
+            return [DIGITS[stripped]]
+    else:
+        largest_unit = next(power for power in reversed(UNITS.keys()) if power < len(stripped))
+        first_part = value_string[:-largest_unit]
+        second_part = value_string[-largest_unit:]
+        return _get_value(first_part) + [UNITS[largest_unit]] + _get_value(second_part)
+
+
+def verbalize_cardinal(value_string: str) -> str:
+    if not value_string:
+        return ""
+
+    # 000 -> '零' , 0 -> '零'
+    value_string = value_string.lstrip("0")
+    if len(value_string) == 0:
+        return DIGITS["0"]
+
+    result_symbols = _get_value(value_string)
+    # verbalized number starting with '一十*' is abbreviated as `十*`
+    if len(result_symbols) >= 2 and result_symbols[0] == DIGITS["1"] and result_symbols[1] == UNITS[1]:
+        result_symbols = result_symbols[1:]
+    return "".join(result_symbols)
+
+
+def verbalize_digit(value_string: str, alt_one=False) -> str:
+    result_symbols = [DIGITS[digit] for digit in value_string]
+    result = "".join(result_symbols)
+    if alt_one:
+        result = result.replace("一", "幺")
+    return result
+
+
+def num2str(value_string: str) -> str:
+    integer_decimal = value_string.split(".")
+    if len(integer_decimal) == 1:
+        integer = integer_decimal[0]
+        decimal = ""
+    elif len(integer_decimal) == 2:
+        integer, decimal = integer_decimal
+    else:
+        raise ValueError(f"The value string: '${value_string}' has more than one point in it.")
+
+    result = verbalize_cardinal(integer)
+
+    if decimal.endswith("0"):
+        decimal = decimal.rstrip("0") + "0"
+    else:
+        decimal = decimal.rstrip("0")
+
+    if decimal:
+        # '.22' is verbalized as '零点二二'
+        # '3.20' is verbalized as '三点二'
+        result = result if result else "零"
+        result += "点" + verbalize_digit(decimal)
+    return result
diff --git a/genie_tts/G2P/Chinese/Normalization/phonecode.py b/genie_tts/G2P/Chinese/Normalization/phonecode.py
new file mode 100644
index 0000000000000000000000000000000000000000..3560ac2ed265580e5f150da191c21acea7390087
--- /dev/null
+++ b/genie_tts/G2P/Chinese/Normalization/phonecode.py
@@ -0,0 +1,59 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+
+from .num import verbalize_digit
+
+# 规范化固话/手机号码
+# 手机
+# http://www.jihaoba.com/news/show/13680
+# 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
+# 联通：130、131、132、156、155、186、185、176
+# 电信：133、153、189、180、181、177
+RE_MOBILE_PHONE = re.compile(r"(?<!\d)((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})(?!\d)")
+RE_TELEPHONE = re.compile(r"(?<!\d)((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})(?!\d)")
+
+# 全国统一的号码400开头
+RE_NATIONAL_UNIFORM_NUMBER = re.compile(r"(400)(-)?\d{3}(-)?\d{4}")
+
+
+def phone2str(phone_string: str, mobile=True) -> str:
+    if mobile:
+        sp_parts = phone_string.strip("+").split()
+        result = "，".join([verbalize_digit(part, alt_one=True) for part in sp_parts])
+        return result
+    else:
+        sil_parts = phone_string.split("-")
+        result = "，".join([verbalize_digit(part, alt_one=True) for part in sil_parts])
+        return result
+
+
+def replace_phone(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    return phone2str(match.group(0), mobile=False)
+
+
+def replace_mobile(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    return phone2str(match.group(0))
diff --git a/genie_tts/G2P/Chinese/Normalization/quantifier.py b/genie_tts/G2P/Chinese/Normalization/quantifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8fb1309dc7838c874d33b969bbc59b282273fc5
--- /dev/null
+++ b/genie_tts/G2P/Chinese/Normalization/quantifier.py
@@ -0,0 +1,62 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+from .num import num2str
+
+# 温度表达式，温度会影响负号的读法
+# -3°C 零下三度
+RE_TEMPERATURE = re.compile(r"(-?)(\d+(\.\d+)?)(°C|℃|度|摄氏度)")
+measure_dict = {
+    "cm2": "平方厘米",
+    "cm²": "平方厘米",
+    "cm3": "立方厘米",
+    "cm³": "立方厘米",
+    "cm": "厘米",
+    "db": "分贝",
+    "ds": "毫秒",
+    "kg": "千克",
+    "km": "千米",
+    "m2": "平方米",
+    "m²": "平方米",
+    "m³": "立方米",
+    "m3": "立方米",
+    "ml": "毫升",
+    "m": "米",
+    "mm": "毫米",
+    "s": "秒",
+}
+
+
+def replace_temperature(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    temperature = match.group(2)
+    unit = match.group(3)
+    sign: str = "零下" if sign else ""
+    temperature: str = num2str(temperature)
+    unit: str = "摄氏度" if unit == "摄氏度" else "度"
+    result = f"{sign}{temperature}{unit}"
+    return result
+
+
+def replace_measure(sentence) -> str:
+    for q_notation in measure_dict:
+        if q_notation in sentence:
+            sentence = sentence.replace(q_notation, measure_dict[q_notation])
+    return sentence
diff --git a/genie_tts/G2P/Chinese/Normalization/text_normlization.py b/genie_tts/G2P/Chinese/Normalization/text_normlization.py
new file mode 100644
index 0000000000000000000000000000000000000000..be29aa03265691e12ad029c5146ca5da2a3ea94b
--- /dev/null
+++ b/genie_tts/G2P/Chinese/Normalization/text_normlization.py
@@ -0,0 +1,169 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+from typing import List
+
+from .char_convert import tranditional_to_simplified
+from .chronology import (
+    RE_DATE,
+    RE_DATE2,
+    RE_TIME,
+    RE_TIME_RANGE,
+    replace_date,
+    replace_date2,
+    replace_time,
+)
+from .constants import (
+    F2H_ASCII_LETTERS,
+    F2H_DIGITS,
+    F2H_SPACE,
+)
+from .num import (
+    RE_VERSION_NUM, RE_DECIMAL_NUM, RE_DEFAULT_NUM, RE_FRAC,
+    RE_INTEGER, RE_NUMBER, RE_PERCENTAGE, RE_POSITIVE_QUANTIFIERS,
+    RE_RANGE, RE_TO_RANGE, RE_ASMD, RE_POWER,
+    replace_vrsion_num, replace_default_num, replace_frac,
+    replace_negative_num, replace_number, replace_percentage,
+    replace_positive_quantifier, replace_range, replace_to_range,
+    replace_asmd, replace_power
+)
+from .phonecode import (
+    RE_MOBILE_PHONE,
+    RE_NATIONAL_UNIFORM_NUMBER,
+    RE_TELEPHONE,
+    replace_mobile,
+    replace_phone,
+)
+from .quantifier import (
+    RE_TEMPERATURE,
+    replace_measure,
+    replace_temperature,
+)
+
+
+class TextNormalizer:
+    def __init__(self):
+        self.SENTENCE_SPLITOR = re.compile(r"([：、，；。？！,;?!][”’]?)")
+
+    def _split(self, text: str, lang="zh") -> List[str]:
+        """Split long text into sentences with sentence-splitting punctuations.
+        Args:
+            text (str): The input text.
+        Returns:
+            List[str]: Sentences.
+        """
+        # Only for pure Chinese here
+        if lang == "zh":
+            text = text.replace(" ", "")
+            # 过滤掉特殊字符
+            text = re.sub(r"[——《》【】<>{}()（）#&@“”^_|\\]", "", text)
+        text = self.SENTENCE_SPLITOR.sub(r"\1\n", text)
+        text = text.strip()
+        sentences = [sentence.strip() for sentence in re.split(r"\n+", text)]
+        return sentences
+
+    def _post_replace(self, sentence: str) -> str:
+        sentence = sentence.replace("/", "每")
+        # sentence = sentence.replace('~', '至')
+        # sentence = sentence.replace('～', '至')
+        sentence = sentence.replace("①", "一")
+        sentence = sentence.replace("②", "二")
+        sentence = sentence.replace("③", "三")
+        sentence = sentence.replace("④", "四")
+        sentence = sentence.replace("⑤", "五")
+        sentence = sentence.replace("⑥", "六")
+        sentence = sentence.replace("⑦", "七")
+        sentence = sentence.replace("⑧", "八")
+        sentence = sentence.replace("⑨", "九")
+        sentence = sentence.replace("⑩", "十")
+        sentence = sentence.replace("α", "阿尔法")
+        sentence = sentence.replace("β", "贝塔")
+        sentence = sentence.replace("γ", "伽玛").replace("Γ", "伽玛")
+        sentence = sentence.replace("δ", "德尔塔").replace("Δ", "德尔塔")
+        sentence = sentence.replace("ε", "艾普西龙")
+        sentence = sentence.replace("ζ", "捷塔")
+        sentence = sentence.replace("η", "依塔")
+        sentence = sentence.replace("θ", "西塔").replace("Θ", "西塔")
+        sentence = sentence.replace("ι", "艾欧塔")
+        sentence = sentence.replace("κ", "喀帕")
+        sentence = sentence.replace("λ", "拉姆达").replace("Λ", "拉姆达")
+        sentence = sentence.replace("μ", "缪")
+        sentence = sentence.replace("ν", "拗")
+        sentence = sentence.replace("ξ", "克西").replace("Ξ", "克西")
+        sentence = sentence.replace("ο", "欧米克伦")
+        sentence = sentence.replace("π", "派").replace("Π", "派")
+        sentence = sentence.replace("ρ", "肉")
+        sentence = sentence.replace("ς", "西格玛").replace("Σ", "西格玛").replace("σ", "西格玛")
+        sentence = sentence.replace("τ", "套")
+        sentence = sentence.replace("υ", "宇普西龙")
+        sentence = sentence.replace("φ", "服艾").replace("Φ", "服艾")
+        sentence = sentence.replace("χ", "器")
+        sentence = sentence.replace("ψ", "普赛").replace("Ψ", "普赛")
+        sentence = sentence.replace("ω", "欧米伽").replace("Ω", "欧米伽")
+        # 兜底数学运算，顺便兼容懒人用语
+        sentence = sentence.replace("+", "加")
+        sentence = sentence.replace("-", "减")
+        sentence = sentence.replace("×", "乘")
+        sentence = sentence.replace("÷", "除")
+        sentence = sentence.replace("=", "等")
+        # re filter special characters, have one more character "-" than line 68
+        sentence = re.sub(r"[-——《》【】<=>{}()（）#&@“”^_|\\]", "", sentence)
+        return sentence
+
+    def normalize_sentence(self, sentence: str) -> str:
+        # basic character conversions
+        sentence = tranditional_to_simplified(sentence)
+        sentence = sentence.translate(F2H_ASCII_LETTERS).translate(F2H_DIGITS).translate(F2H_SPACE)
+
+        # number related NSW verbalization
+        sentence = RE_DATE.sub(replace_date, sentence)
+        sentence = RE_DATE2.sub(replace_date2, sentence)
+
+        # range first
+        sentence = RE_TIME_RANGE.sub(replace_time, sentence)
+        sentence = RE_TIME.sub(replace_time, sentence)
+
+        # 处理~波浪号作为至的替换
+        sentence = RE_TO_RANGE.sub(replace_to_range, sentence)
+        sentence = RE_TEMPERATURE.sub(replace_temperature, sentence)
+        sentence = replace_measure(sentence)
+
+        # 处理数学运算
+        while RE_ASMD.search(sentence):
+            sentence = RE_ASMD.sub(replace_asmd, sentence)
+        sentence = RE_POWER.sub(replace_power, sentence)
+
+        sentence = RE_FRAC.sub(replace_frac, sentence)
+        sentence = RE_PERCENTAGE.sub(replace_percentage, sentence)
+        sentence = RE_MOBILE_PHONE.sub(replace_mobile, sentence)
+
+        sentence = RE_TELEPHONE.sub(replace_phone, sentence)
+        sentence = RE_NATIONAL_UNIFORM_NUMBER.sub(replace_phone, sentence)
+
+        sentence = RE_RANGE.sub(replace_range, sentence)
+
+        sentence = RE_INTEGER.sub(replace_negative_num, sentence)
+        sentence = RE_VERSION_NUM.sub(replace_vrsion_num, sentence)
+        sentence = RE_DECIMAL_NUM.sub(replace_number, sentence)
+        sentence = RE_POSITIVE_QUANTIFIERS.sub(replace_positive_quantifier, sentence)
+        sentence = RE_DEFAULT_NUM.sub(replace_default_num, sentence)
+        sentence = RE_NUMBER.sub(replace_number, sentence)
+        sentence = self._post_replace(sentence)
+
+        return sentence
+
+    def normalize(self, text: str) -> List[str]:
+        sentences = self._split(text)
+        sentences = [self.normalize_sentence(sent) for sent in sentences]
+        return sentences
diff --git a/genie_tts/G2P/Chinese/ToneSandhi.py b/genie_tts/G2P/Chinese/ToneSandhi.py
new file mode 100644
index 0000000000000000000000000000000000000000..e57622b21f3abbd13a3ada62c67c96e9aa67e57e
--- /dev/null
+++ b/genie_tts/G2P/Chinese/ToneSandhi.py
@@ -0,0 +1,354 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+中文拼音变调（Tone Sandhi）自动处理器
+"""
+
+from typing import List
+from typing import Tuple
+import jieba_fast as jieba
+from pypinyin import lazy_pinyin
+from pypinyin import Style
+
+
+class ToneSandhi:
+    def __init__(self):
+        self.must_neural_tone_words = {
+            "麻烦", "麻利", "鸳鸯", "高粱", "骨头", "骆驼", "马虎", "首饰", "馒头", "馄饨",
+            "风筝", "难为", "队伍", "阔气", "闺女", "门道", "锄头", "铺盖", "铃铛", "铁匠",
+            "钥匙", "里脊", "里头", "部分", "那么", "道士", "造化", "迷糊", "连累", "这么",
+            "这个", "运气", "过去", "软和", "转悠", "踏实", "跳蚤", "跟头", "趔趄", "财主",
+            "豆腐", "讲究", "记性", "记号", "认识", "规矩", "见识", "裁缝", "补丁", "衣裳",
+            "衣服", "衙门", "街坊", "行李", "行当", "蛤蟆", "蘑菇", "薄荷", "葫芦", "葡萄",
+            "萝卜", "荸荠", "苗条", "苗头", "苍蝇", "芝麻", "舒服", "舒坦", "舌头", "自在",
+            "膏药", "脾气", "脑袋", "脊梁", "能耐", "胳膊", "胭脂", "胡萝", "胡琴", "胡同",
+            "聪明", "耽误", "耽搁", "耷拉", "耳朵", "老爷", "老实", "老婆", "老头", "老太",
+            "翻腾", "罗嗦", "罐头", "编辑", "结实", "红火", "累赘", "糨糊", "糊涂", "精神",
+            "粮食", "簸箕", "篱笆", "算计", "算盘", "答应", "笤帚", "笑语", "笑话", "窟窿",
+            "窝囊", "窗户", "稳当", "稀罕", "称呼", "秧歌", "秀气", "秀才", "福气", "祖宗",
+            "砚台", "码头", "石榴", "石头", "石匠", "知识", "眼睛", "眯缝", "眨巴", "眉毛",
+            "相声", "盘算", "白净", "痢疾", "痛快", "疟疾", "疙瘩", "疏忽", "畜生", "生意",
+            "甘蔗", "琵琶", "琢磨", "琉璃", "玻璃", "玫瑰", "玄乎", "狐狸", "状元", "特务",
+            "牲口", "牙碜", "牌楼", "爽快", "爱人", "热闹", "烧饼", "烟筒", "烂糊", "点心",
+            "炊帚", "灯笼", "火候", "漂亮", "滑溜", "溜达", "温和", "清楚", "消息", "浪头",
+            "活泼", "比方", "正经", "欺负", "模糊", "槟榔", "棺材", "棒槌", "棉花", "核桃",
+            "栅栏", "柴火", "架势", "枕头", "枇杷", "机灵", "本事", "木头", "木匠", "朋友",
+            "月饼", "月亮", "暖和", "明白", "时候", "新鲜", "故事", "收拾", "收成", "提防",
+            "挖苦", "挑剔", "指甲", "指头", "拾掇", "拳头", "拨弄", "招牌", "招呼", "抬举",
+            "护士", "折腾", "扫帚", "打量", "打算", "打点", "打扮", "打听", "打发", "扎实",
+            "扁担", "戒指", "懒得", "意识", "意思", "情形", "悟性", "怪物", "思量", "怎么",
+            "念头", "念叨", "快活", "忙活", "志气", "心思", "得罪", "张罗", "弟兄", "开通",
+            "应酬", "庄稼", "干事", "帮手", "帐篷", "希罕", "师父", "师傅", "巴结", "巴掌",
+            "差事", "工夫", "岁数", "屁股", "尾巴", "少爷", "小气", "小伙", "将就", "对头",
+            "对付", "寡妇", "家伙", "客气", "实在", "官司", "学问", "学生", "字号", "嫁妆",
+            "媳妇", "媒人", "婆家", "娘家", "委屈", "姑娘", "姐夫", "妯娌", "妥当", "妖精",
+            "奴才", "女婿", "头发", "太阳", "大爷", "大方", "大意", "大夫", "多少", "多么",
+            "外甥", "壮实", "地道", "地方", "在乎", "困难", "嘴巴", "嘱咐", "嘟囔", "嘀咕",
+            "喜欢", "喇嘛", "喇叭", "商量", "唾沫", "哑巴", "哈欠", "哆嗦", "咳嗽", "和尚",
+            "告诉", "告示", "含糊", "吓唬", "后头", "名字", "名堂", "合同", "吆喝", "叫唤",
+            "口袋", "厚道", "厉害", "千斤", "包袱", "包涵", "匀称", "勤快", "动静", "动弹",
+            "功夫", "力气", "前头", "刺猬", "刺激", "别扭", "利落", "利索", "利害", "分析",
+            "出息", "凑合", "凉快", "冷战", "冤枉", "冒失", "养活", "关系", "先生", "兄弟",
+            "便宜", "使唤", "佩服", "作坊", "体面", "位置", "似的", "伙计", "休息", "什么",
+            "人家", "亲戚", "亲家", "交情", "云彩", "事情", "买卖", "主意", "丫头", "丧气",
+            "两口", "东西", "东家", "世故", "不由", "不在", "下水", "下巴", "上头", "上司",
+            "丈夫", "丈人", "一辈", "那个", "菩萨", "父亲", "母亲", "咕噜", "邋遢", "费用",
+            "冤家", "甜头", "介绍", "荒唐", "大人", "泥鳅", "幸福", "熟悉", "计划", "扑腾",
+            "蜡烛", "姥爷", "照顾", "喉咙", "吉他", "弄堂", "蚂蚱", "凤凰", "拖沓", "寒碜",
+            "糟蹋", "倒腾", "报复", "逻辑", "盘缠", "喽啰", "牢骚", "咖喱", "扫把", "惦记",
+        }
+        self.must_not_neural_tone_words = {
+            "男子", "女子", "分子", "原子", "量子", "莲子", "石子", "瓜子", "电子", "人人",
+            "虎虎", "幺幺", "干嘛", "学子", "哈哈", "数数", "袅袅", "局地", "以下", "娃哈哈",
+            "花花草草", "留得", "耕地", "想想", "熙熙", "攘攘", "卵子", "死死", "冉冉", "恳恳",
+            "佼佼", "吵吵", "打打", "考考", "整整", "莘莘", "落地", "算子", "家家户户", "青青",
+        }
+        self.punc = "：，；。？！“”‘’':,;.?!"
+
+    # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
+    # e.g.
+    # word: "家里"
+    # pos: "s"
+    # finals: ['ia1', 'i3']
+    def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
+        # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
+        for j, item in enumerate(word):
+            if (
+                    j - 1 >= 0
+                    and item == word[j - 1]
+                    and pos[0] in {"n", "v", "a"}
+                    and word not in self.must_not_neural_tone_words
+            ):
+                finals[j] = finals[j][:-1] + "5"
+        ge_idx = word.find("个")
+        if len(word) >= 1 and word[-1] in "吧呢哈啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
+            finals[-1] = finals[-1][:-1] + "5"
+        elif len(word) >= 1 and word[-1] in "的地得":
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 走了, 看着, 去过
+        elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
+            finals[-1] = finals[-1][:-1] + "5"
+        elif len(word) > 1 and word[-1] in "们子" and pos in {"r", "n"} and word not in self.must_not_neural_tone_words:
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 桌上, 地下, 家里
+        elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 上来, 下去
+        elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
+            finals[-1] = finals[-1][:-1] + "5"
+        # 个做量词
+        elif (
+                ge_idx >= 1 and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
+        ) or word == "个":
+            finals[ge_idx] = finals[ge_idx][:-1] + "5"
+        else:
+            if word in self.must_neural_tone_words or word[-2:] in self.must_neural_tone_words:
+                finals[-1] = finals[-1][:-1] + "5"
+
+        word_list = self._split_word(word)
+        finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]):]]
+        for i, word in enumerate(word_list):
+            # conventional neural in Chinese
+            if word in self.must_neural_tone_words or word[-2:] in self.must_neural_tone_words:
+                finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
+        finals = sum(finals_list, [])
+        return finals
+
+    @staticmethod
+    def _bu_sandhi(word: str, finals: List[str]) -> List[str]:
+        # e.g. 看不懂
+        if len(word) == 3 and word[1] == "不":
+            finals[1] = finals[1][:-1] + "5"
+        else:
+            for i, char in enumerate(word):
+                # "不" before tone4 should be bu2, e.g. 不怕
+                if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
+                    finals[i] = finals[i][:-1] + "2"
+        return finals
+
+    def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        # "一" in number sequences, e.g. 一零零, 二一零
+        if word.find("一") != -1 and all([item.isnumeric() for item in word if item != "一"]):
+            return finals
+        # "一" between reduplication words should be yi5, e.g. 看一看
+        elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
+            finals[1] = finals[1][:-1] + "5"
+        # when "一" is ordinal word, it should be yi1
+        elif word.startswith("第一"):
+            finals[1] = finals[1][:-1] + "1"
+        else:
+            for i, char in enumerate(word):
+                if char == "一" and i + 1 < len(word):
+                    # "一" before tone4 should be yi2, e.g. 一段
+                    if finals[i + 1][-1] == "4":
+                        finals[i] = finals[i][:-1] + "2"
+                    # "一" before non-tone4 should be yi4, e.g. 一天
+                    else:
+                        # "一" 后面如果是标点，还读一声
+                        if word[i + 1] not in self.punc:
+                            finals[i] = finals[i][:-1] + "4"
+        return finals
+
+    @staticmethod
+    def _split_word(word: str) -> List[str]:
+        word_list = jieba.cut_for_search(word)
+        word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
+        first_subword = word_list[0]
+        first_begin_idx = word.find(first_subword)
+        if first_begin_idx == 0:
+            second_subword = word[len(first_subword):]
+            new_word_list = [first_subword, second_subword]
+        else:
+            second_subword = word[: -len(first_subword)]
+            new_word_list = [second_subword, first_subword]
+        return new_word_list
+
+    def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        if len(word) == 2 and self._all_tone_three(finals):
+            finals[0] = finals[0][:-1] + "2"
+        elif len(word) == 3:
+            word_list = self._split_word(word)
+            if self._all_tone_three(finals):
+                #  disyllabic + monosyllabic, e.g. 蒙古/包
+                if len(word_list[0]) == 2:
+                    finals[0] = finals[0][:-1] + "2"
+                    finals[1] = finals[1][:-1] + "2"
+                #  monosyllabic + disyllabic, e.g. 纸/老虎
+                elif len(word_list[0]) == 1:
+                    finals[1] = finals[1][:-1] + "2"
+            else:
+                finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]):]]
+                if len(finals_list) == 2:
+                    for i, sub in enumerate(finals_list):
+                        # e.g. 所有/人
+                        if self._all_tone_three(sub) and len(sub) == 2:
+                            finals_list[i][0] = finals_list[i][0][:-1] + "2"
+                        # e.g. 好/喜欢
+                        elif (
+                                i == 1
+                                and not self._all_tone_three(sub)
+                                and finals_list[i][0][-1] == "3"
+                                and finals_list[0][-1][-1] == "3"
+                        ):
+                            finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
+                        finals = sum(finals_list, [])
+        # split idiom into two words whose length is 2
+        elif len(word) == 4:
+            finals_list = [finals[:2], finals[2:]]
+            finals = []
+            for sub in finals_list:
+                if self._all_tone_three(sub):
+                    sub[0] = sub[0][:-1] + "2"
+                finals += sub
+
+        return finals
+
+    @staticmethod
+    def _all_tone_three(finals: List[str]) -> bool:
+        # 增加 len(x) > 0 的判断，防止空字符串导致崩溃
+        return all(len(x) > 0 and x[-1] == "3" for x in finals)
+
+    @staticmethod
+    def _merge_bu(seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        last_word = ""
+        for word, pos in seg:
+            if last_word == "不":
+                word = last_word + word
+            if word != "不":
+                new_seg.append((word, pos))
+            last_word = word[:]
+        if last_word == "不":
+            new_seg.append((last_word, "d"))
+        return new_seg
+
+    @staticmethod
+    def _merge_yi(seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        i = 0
+        # function 1
+        while i < len(seg):
+            word, pos = seg[i]
+            merged = False
+            if i - 1 >= 0 and word == "一" and i + 1 < len(seg):
+                last = new_seg[-1] if new_seg else seg[i - 1]
+                if last[0] == seg[i + 1][0] and last[1] == "v" and seg[i + 1][1] == "v":
+                    combined = last[0] + "一" + seg[i + 1][0]
+                    new_seg[-1] = [combined, last[1]]
+                    i += 2
+                    merged = True
+            if not merged:
+                new_seg.append([word, pos])
+                i += 1
+        seg = new_seg
+        new_seg = []
+        # function 2
+        for word, pos in seg:
+            if new_seg and new_seg[-1][0] == "一":
+                new_seg[-1][0] = new_seg[-1][0] + word
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    # the first and the second words are all_tone_three
+    def _merge_continuous_three_tones(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        sub_finals_list = [
+            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) for (word, pos) in seg
+        ]
+        assert len(sub_finals_list) == len(seg)
+        merge_last = [False] * len(seg)
+        for i, (word, pos) in enumerate(seg):
+            if (
+                    i - 1 >= 0
+                    and self._all_tone_three(sub_finals_list[i - 1])
+                    and self._all_tone_three(sub_finals_list[i])
+                    and not merge_last[i - 1]
+            ):
+                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
+                if not self._is_reduplication(seg[i - 1][0]) and len(seg[i - 1][0]) + len(seg[i][0]) <= 3:
+                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+                    merge_last[i] = True
+                else:
+                    new_seg.append([word, pos])
+            else:
+                new_seg.append([word, pos])
+
+        return new_seg
+
+    @staticmethod
+    def _is_reduplication(word: str) -> bool:
+        return len(word) == 2 and word[0] == word[1]
+
+    # the last char of first word and the first char of second word is tone_three
+    def _merge_continuous_three_tones_2(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        sub_finals_list = [
+            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) for (word, pos) in seg
+        ]
+        assert len(sub_finals_list) == len(seg)
+        merge_last = [False] * len(seg)
+        for i, (word, pos) in enumerate(seg):
+            if (
+                    i - 1 >= 0
+                    and sub_finals_list[i - 1][-1][-1] == "3"
+                    and sub_finals_list[i][0][-1] == "3"
+                    and not merge_last[i - 1]
+            ):
+                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
+                if not self._is_reduplication(seg[i - 1][0]) and len(seg[i - 1][0]) + len(seg[i][0]) <= 3:
+                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+                    merge_last[i] = True
+                else:
+                    new_seg.append([word, pos])
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    @staticmethod
+    def _merge_er(seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        for i, (word, pos) in enumerate(seg):
+            if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
+                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    @staticmethod
+    def _merge_reduplication(seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        for i, (word, pos) in enumerate(seg):
+            if new_seg and word == new_seg[-1][0]:
+                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        seg = self._merge_bu(seg)
+        seg = self._merge_yi(seg)
+        seg = self._merge_reduplication(seg)
+        seg = self._merge_continuous_three_tones(seg)
+        seg = self._merge_continuous_three_tones_2(seg)
+        seg = self._merge_er(seg)
+        return seg
+
+    def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
+        finals = self._bu_sandhi(word, finals)
+        finals = self._yi_sandhi(word, finals)
+        finals = self._neural_sandhi(word, pos, finals)
+        finals = self._three_sandhi(word, finals)
+        return finals
diff --git a/genie_tts/G2P/Chinese/__init__.py b/genie_tts/G2P/Chinese/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/genie_tts/G2P/English/EnglishG2P.py b/genie_tts/G2P/English/EnglishG2P.py
new file mode 100644
index 0000000000000000000000000000000000000000..5325385ce889018d128f9fec298de7836a0c5aeb
--- /dev/null
+++ b/genie_tts/G2P/English/EnglishG2P.py
@@ -0,0 +1,296 @@
+import pickle
+import os
+import re
+from typing import List, Dict, Tuple
+
+import numpy as np
+import nltk
+from nltk.tokenize import TweetTokenizer
+from nltk import pos_tag
+
+from .Normalization import normalize
+from .WordSegment import segment_text
+from ..SymbolsV2 import symbols_v2, symbol_to_id_v2
+from ..SymbolsV2 import PUNCTUATION
+from ...Core.Resources import English_G2P_DIR
+
+# nltk 路径和分词器初始化
+nltk.data.path.append(English_G2P_DIR)
+word_tokenize = TweetTokenizer().tokenize
+
+# 路径定义
+CMU_DICT_PATH = os.path.join(English_G2P_DIR, "cmudict.rep")
+CMU_DICT_FAST_PATH = os.path.join(English_G2P_DIR, "cmudict-fast.rep")
+CMU_DICT_HOT_PATH = os.path.join(English_G2P_DIR, "engdict-hot.rep")
+CACHE_PATH = os.path.join(English_G2P_DIR, "engdict_cache.pickle")
+NAMECACHE_PATH = os.path.join(English_G2P_DIR, "namedict_cache.pickle")
+MODEL_PATH = os.path.join(English_G2P_DIR, "checkpoint20.npz")
+
+# 正则表达式和映射
+REP_MAP = {
+    "[;:：，；]": ",",
+    '["’]': "'",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+}
+REP_MAP_PATTERN = re.compile("|".join(re.escape(p) for p in REP_MAP.keys()))
+PUNCTUATIONS_FOR_REGEX = "".join(re.escape(p) for p in PUNCTUATION)
+CONSECUTIVE_PUNCTUATION_PATTERN = re.compile(rf"([{PUNCTUATIONS_FOR_REGEX}\s])([{PUNCTUATIONS_FOR_REGEX}])+")
+
+
+# 辅助函数
+def _read_cmu_dict(file_path: str) -> Dict[str, List[str]]:
+    g2p_dict = {}
+    with open(file_path, 'r', encoding='utf-8') as f:
+        for line in f:
+            line = line.strip()
+            if not line or line.startswith(';;;'): continue
+            parts = re.split(r'\s+', line, maxsplit=1)
+            if len(parts) < 2: continue
+            word, pron_str = parts[0].lower(), parts[1]
+            pron = pron_str.split(" ")
+            word = re.sub(r'\(\d+\)$', '', word)
+            if word not in g2p_dict: g2p_dict[word] = [pron]
+    return g2p_dict
+
+
+def _load_and_cache_dict() -> Dict[str, List[List[str]]]:
+    with open(CACHE_PATH, "rb") as f:
+        g2p_dict = pickle.load(f)
+    hot_dict = _read_cmu_dict(CMU_DICT_HOT_PATH)
+    if hot_dict: g2p_dict.update(hot_dict)
+    return g2p_dict
+
+
+def replace_phs(phs: List[str]) -> List[str]:
+    rep_map = {"'": "-"}
+    phs_new = []
+    for ph in phs:
+        if ph in symbols_v2:
+            phs_new.append(ph)
+        elif ph in rep_map:
+            phs_new.append(rep_map[ph])
+    return phs_new
+
+
+def replace_consecutive_punctuation(text: str) -> str:
+    return CONSECUTIVE_PUNCTUATION_PATTERN.sub(r"\1", text)
+
+
+def text_normalize(text: str) -> str:
+    text = REP_MAP_PATTERN.sub(lambda x: REP_MAP[x.group()], text)
+    text = normalize(text)
+    text = replace_consecutive_punctuation(text)
+    return text
+
+
+class CleanG2p:
+    """
+    一个集成了神经网络预测功能的、独立的英文G2P转换器。
+    - 不再依赖 g2p_en 库，将模型推理逻辑直接内置。
+    - 依赖 numpy 库进行计算。
+    """
+
+    def __init__(self):
+        # 1. 初始化标准组件
+        self.cmu = _load_and_cache_dict()
+        self.namedict = self._load_name_dict()
+        for word in ["AE", "AI", "AR", "IOS", "HUD", "OS"]:
+            self.cmu.pop(word.lower(), None)
+        self._setup_homographs()
+
+        # 2. 初始化神经网络模型组件
+        self._setup_nn_components()
+        self._load_nn_model()
+
+    def _setup_nn_components(self):
+        """设置 G2P 神经网络所需的字母和音素表。"""
+        self.graphemes = ["<pad>", "<unk>", "</s>"] + list("abcdefghijklmnopqrstuvwxyz")
+        self.phonemes = ["<pad>", "<unk>", "<s>", "</s>"] + ['AA0', 'AA1', 'AA2', 'AE0', 'AE1', 'AE2', 'AH0', 'AH1',
+                                                             'AH2', 'AO0',
+                                                             'AO1', 'AO2', 'AW0', 'AW1', 'AW2', 'AY0', 'AY1', 'AY2',
+                                                             'B', 'CH', 'D', 'DH',
+                                                             'EH0', 'EH1', 'EH2', 'ER0', 'ER1', 'ER2', 'EY0', 'EY1',
+                                                             'EY2', 'F', 'G', 'HH',
+                                                             'IH0', 'IH1', 'IH2', 'IY0', 'IY1', 'IY2', 'JH', 'K', 'L',
+                                                             'M', 'N', 'NG', 'OW0', 'OW1',
+                                                             'OW2', 'OY0', 'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH',
+                                                             'UH0', 'UH1', 'UH2', 'UW',
+                                                             'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH']
+        self.g2idx = {g: idx for idx, g in enumerate(self.graphemes)}
+        self.idx2g = {idx: g for idx, g in enumerate(self.graphemes)}
+        self.p2idx = {p: idx for idx, p in enumerate(self.phonemes)}
+        self.idx2p = {idx: p for idx, p in enumerate(self.phonemes)}
+
+    def _load_nn_model(self):
+        """从 .npz 文件加载预训练的神经网络权重。"""
+        if not os.path.exists(MODEL_PATH):
+            raise FileNotFoundError(f"G2P model file not found at: {MODEL_PATH}. "
+                                    f"Please ensure 'checkpoint20.npz' is in the correct directory.")
+
+        variables = np.load(MODEL_PATH)
+        self.enc_emb = variables["enc_emb"]
+        self.enc_w_ih = variables["enc_w_ih"]
+        self.enc_w_hh = variables["enc_w_hh"]
+        self.enc_b_ih = variables["enc_b_ih"]
+        self.enc_b_hh = variables["enc_b_hh"]
+        self.dec_emb = variables["dec_emb"]
+        self.dec_w_ih = variables["dec_w_ih"]
+        self.dec_w_hh = variables["dec_w_hh"]
+        self.dec_b_ih = variables["dec_b_ih"]
+        self.dec_b_hh = variables["dec_b_hh"]
+        self.fc_w = variables["fc_w"]
+        self.fc_b = variables["fc_b"]
+        # logger.info("G2P neural network model loaded successfully.")
+
+    @staticmethod
+    def _sigmoid(x):
+        return 1 / (1 + np.exp(-x))
+
+    def _grucell(self, x, h, w_ih, w_hh, b_ih, b_hh):
+        rzn_ih = np.matmul(x, w_ih.T) + b_ih
+        rzn_hh = np.matmul(h, w_hh.T) + b_hh
+        rz_ih, n_ih = rzn_ih[:, :rzn_ih.shape[-1] * 2 // 3], rzn_ih[:, rzn_ih.shape[-1] * 2 // 3:]
+        rz_hh, n_hh = rzn_hh[:, :rzn_hh.shape[-1] * 2 // 3], rzn_hh[:, rzn_hh.shape[-1] * 2 // 3:]
+        rz = self._sigmoid(rz_ih + rz_hh)
+        r, z = np.split(rz, 2, -1)
+        n = np.tanh(n_ih + r * n_hh)
+        h = (1 - z) * n + z * h
+        return h
+
+    def _gru(self, x, steps, w_ih, w_hh, b_ih, b_hh, h0=None):
+        if h0 is None:
+            h0 = np.zeros((x.shape[0], w_hh.shape[1]), np.float32)
+        h = h0
+        outputs = np.zeros((x.shape[0], steps, w_hh.shape[1]), np.float32)
+        for t in range(steps):
+            h = self._grucell(x[:, t, :], h, w_ih, w_hh, b_ih, b_hh)
+            outputs[:, t, ::] = h
+        return outputs
+
+    def _encode(self, word: str) -> np.ndarray:
+        chars = list(word.lower()) + ["</s>"]
+        x = [self.g2idx.get(char, self.g2idx["<unk>"]) for char in chars]
+        x = np.take(self.enc_emb, np.expand_dims(x, 0), axis=0)
+        return x
+
+    def predict(self, word: str) -> List[str]:
+        """使用内置的神经网络模型预测单词的发音。"""
+        # Encoder
+        enc = self._encode(word)
+        enc = self._gru(enc, len(word) + 1, self.enc_w_ih, self.enc_w_hh,
+                        self.enc_b_ih, self.enc_b_hh, h0=np.zeros((1, self.enc_w_hh.shape[-1]), np.float32))
+        last_hidden = enc[:, -1, :]
+
+        # Decoder
+        dec = np.take(self.dec_emb, [self.p2idx["<s>"]], axis=0)  # Start with <s>
+        h = last_hidden
+        preds = []
+        for _ in range(20):  # Max steps
+            h = self._grucell(dec, h, self.dec_w_ih, self.dec_w_hh, self.dec_b_ih, self.dec_b_hh)
+            logits = np.matmul(h, self.fc_w.T) + self.fc_b
+            pred_idx = logits.argmax()
+            if pred_idx == self.p2idx["</s>"]: break
+            preds.append(pred_idx)
+            dec = np.take(self.dec_emb, [pred_idx], axis=0)
+
+        return [self.idx2p.get(idx, "<unk>") for idx in preds]
+
+    # --- 标准 G2P 逻辑 ---
+
+    @staticmethod
+    def _load_name_dict() -> Dict[str, List[List[str]]]:
+        if os.path.exists(NAMECACHE_PATH):
+            with open(NAMECACHE_PATH, "rb") as f: return pickle.load(f)
+        return {}
+
+    def _setup_homographs(self):
+        self.homograph2features: Dict[str, Tuple[List[str], List[str], str]] = {
+            "read": (["R", "EH1", "D"], ["R", "IY1", "D"], "VBD"),
+            "complex": (["K", "AH0", "M", "P", "L", "EH1", "K", "S"], ["K", "AA1", "M", "P", "L", "EH0", "K", "S"],
+                        "JJ"),
+            "lead": (["L", "IY1", "D"], ["L", "EH1", "D"], "NN"),
+            "presents": (["P", "R", "IY0", "Z", "EH1", "N", "T", "S"], ["P", "R", "EH1", "Z", "AH0", "N", "T", "S"],
+                         "VBZ"),
+        }
+
+    def __call__(self, text: str) -> List[str]:
+        original_words = word_tokenize(text)
+        normalized_text = text_normalize(text)
+        normalized_words = word_tokenize(normalized_text)
+
+        corrected_words = []
+        original_idx, normalized_idx = 0, 0
+        while original_idx < len(original_words) and normalized_idx < len(normalized_words):
+            if original_words[original_idx] == "I" and \
+                    " ".join(normalized_words[normalized_idx:normalized_idx + 2]) == "the first":
+                corrected_words.append("I")
+                original_idx += 1
+                normalized_idx += 2
+            else:
+                corrected_words.append(normalized_words[normalized_idx])
+                original_idx += 1
+                normalized_idx += 1
+        if normalized_idx < len(normalized_words):
+            corrected_words.extend(normalized_words[normalized_idx:])
+
+        if not corrected_words: return []
+
+        tokens = pos_tag(corrected_words)
+        prons = []
+        for o_word, pos in tokens:
+            word = o_word.lower()
+            if re.search("[a-z]", word) is None:
+                pron = [word]
+            elif word in self.homograph2features:
+                pron1, pron2, pos1 = self.homograph2features[word]
+                pron = pron1 if pos.startswith(pos1) else pron2
+            else:
+                pron = self._query_word(o_word)
+            prons.extend(pron)
+            prons.extend([" "])
+        return prons[:-1] if prons else []
+
+    def _query_word(self, o_word: str) -> List[str]:
+        word = o_word.lower()
+        if word in self.cmu:
+            if o_word == "A": return ["AH0"]
+            return self.cmu[word][0]
+        if o_word.istitle() and word in self.namedict:
+            return self.namedict[word][0]
+        if word.endswith("'s") and len(word) > 2:
+            base_pron = self._query_word(word[:-2])
+            if base_pron:
+                last_ph = base_pron[-1]
+                if last_ph in {"S", "Z", "SH", "ZH", "CH", "JH"}: return base_pron + ["AH0", "Z"]
+                if last_ph in {"P", "T", "K", "F", "TH"}: return base_pron + ["S"]
+                return base_pron + ["Z"]
+        if "-" in word and len(word) > 1:
+            parts = [p for p in word.split("-") if p]
+            if len(parts) > 1:
+                result = [ph for part in parts for ph in self._query_word(part)]
+                if result: return result
+        segments = segment_text(word)
+        if len(segments) > 1 and "".join(segments) == word:
+            result = [ph for segment in segments for ph in self._query_word(segment)]
+            if result: return result
+
+        return self.predict(o_word)
+
+
+_g2p_instance: CleanG2p = CleanG2p()
+
+
+def g2p(text: str) -> List[str]:
+    if _g2p_instance is None: raise RuntimeError("G2P model is not available.")
+    raw_phonemes = _g2p_instance(text)
+    undesired = {" ", "<pad>", "UW", "</s>", "<s>"}
+    phones = ["UNK" if ph == "<unk>" else ph for ph in raw_phonemes if ph not in undesired]
+    return replace_phs(phones)
+
+
+def english_to_phones(text: str) -> List[int]:
+    phones = g2p(text)
+    phones = [symbol_to_id_v2[ph] for ph in phones]
+    return phones
diff --git a/genie_tts/G2P/English/Normalization.py b/genie_tts/G2P/English/Normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce5046d73caeaf8750e67a6088012d13d777737a
--- /dev/null
+++ b/genie_tts/G2P/English/Normalization.py
@@ -0,0 +1,286 @@
+import re
+import unicodedata
+from calendar import month_name
+
+
+# ------------------- 核心：自实现数字转单词 (替代 inflect) -------------------
+
+def _number_to_words_custom(num_str):
+    """一个不依赖inflect的、简化的数字到单词转换器。"""
+    num_str = str(num_str).strip()
+    if not num_str.isdigit(): return num_str
+
+    num = int(num_str)
+    if num == 0: return 'zero'
+
+    units = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
+    teens = ["ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen",
+             "nineteen"]
+    tens = ["", "", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"]
+    thousands = ["", "thousand", "million", "billion", "trillion"]
+
+    def convert_less_than_thousand(n):
+        if n == 0: return ""
+        if n < 10: return units[n]
+        if n < 20: return teens[n - 10]
+        if n < 100: return tens[n // 10] + (" " + units[n % 10] if n % 10 != 0 else "")
+        return units[n // 100] + " hundred" + (" " + convert_less_than_thousand(n % 100) if n % 100 != 0 else "")
+
+    words = []
+    i = 0
+    if num == 0: return "zero"
+    while num > 0:
+        if num % 1000 != 0:
+            words.insert(0, convert_less_than_thousand(num % 1000) + " " + thousands[i])
+        num //= 1000
+        i += 1
+    return " ".join(words).strip()
+
+
+def _ordinal_custom(num_str):
+    """一个不依赖inflect的、简化的序数词转换器。"""
+    num = int(num_str)
+    if 10 <= num % 100 <= 20:
+        suffix = 'th'
+    else:
+        suffix = {1: 'st', 2: 'nd', 3: 'rd'}.get(num % 10, 'th')
+    return _number_to_words_custom(str(num)) + suffix
+
+
+# ------------------- 初始化和常量定义 (无 inflect) -------------------
+
+_measurement_map = {
+    "km/h": ["kilometer per hour", "kilometers per hour"], "mph": ["mile per hour", "miles per hour"],
+    "°C": ["degree celsius", "degrees celsius"], "°F": ["degree fahrenheit", "degrees fahrenheit"],
+    "tbsp": ["tablespoon", "tablespoons"], "tsp": ["teaspoon", "teaspoons"],
+    "km": ["kilometer", "kilometers"], "kg": ["kilogram", "kilograms"], "min": ["minute", "minutes"],
+    "ft": ["foot", "feet"], "cm": ["centimeter", "centimeters"], "m": ["meter", "meters"],
+    "L": ["liter", "liters"], "h": ["hour", "hours"], "s": ["second", "seconds"],
+}
+
+_abbreviations = [
+    (re.compile(r"\bMr\.(?=[\s,.]|\Z)", re.IGNORECASE), "Mister"),
+    (re.compile(r"\bMrs\.(?=[\s,.]|\Z)", re.IGNORECASE), "Missus"),
+    (re.compile(r"\bDr\.(?=[\s,.]|\Z)", re.IGNORECASE), "Doctor"),
+    (re.compile(r"\bProf\.(?=[\s,.]|\Z)", re.IGNORECASE), "Professor"),
+    (re.compile(r"\bSt\.(?=[\s,.]|\Z)", re.IGNORECASE), "Street"),
+    (re.compile(r"\bCo\.(?=[\s,.]|\Z)", re.IGNORECASE), "Company"),
+    (re.compile(r"\bLtd\.(?=[\s,.]|\Z)", re.IGNORECASE), "Limited"),
+    (re.compile(r"\be\.g\.(?=[\s,.]|\Z)", re.IGNORECASE), "for example"),
+    (re.compile(r"\bi\.e\.(?=[\s,.]|\Z)", re.IGNORECASE), "that is"),
+]
+
+# ------------------- 正则表达式定义 (与原来保持一致) -------------------
+_currency_suffix_re = re.compile(r"([£$€])([\d,.]*\d)\s*(million|billion|thousand)\b", re.IGNORECASE)
+_phone_re = re.compile(r"(\+?\d{1,3}-)?\b(\d{3})-(?:(\d{3})-)?(\d{4})\b")
+_roman_re = re.compile(r"\b(XIX|XVIII|XVII|XVI|XV|XIV|XIII|XII|XI|X|IX|VIII|VII|VI|V|IV|III|II)\b", re.IGNORECASE)
+_decade_re = re.compile(r"\b((?:1[89]|20)\d0)s\b")
+_score_re = re.compile(r"\b(\d{1,2})-(\d{1,2})\b")
+_dimension_re = re.compile(r"\b(\d+(?:\.\d+)?)\s*x\s*(\d+(?:\.\d+)?)(?:\s*x\s*(\d+(?:\.\d+)?))?\b")
+_alphanumeric_re = re.compile(r"\b([a-zA-Z]+[0-9]+|[0-9]+[a-zA-Z]+)\b")
+_date_re = re.compile(r"\b(0?[1-9]|1[0-2])/([0-2]?\d|3[01])/(\d{2,4})\b")
+_ordinal_number_re = re.compile(r"\b(\d+)\. ")
+_comma_number_re = re.compile(r"(\d[\d,]+\d)")
+_currency_re = re.compile(r"([£$€])(\d*\.?\d+)|(\d*\.?\d+)\s*([£$€])")
+_time_re = re.compile(r"\b([01]?\d|2[0-3]):([0-5]\d)(?::([0-5]\d))?(\s*(?:a\.?m\.?|p\.?m\.?))?\b", re.IGNORECASE)
+units = "|".join(re.escape(key) for key in sorted(_measurement_map.keys(), key=len, reverse=True))
+_measurement_re = re.compile(rf"(?<!\w)(-?(?:\d+/\d+|\d+(?:\.\d+)?))\s*({units})\b")
+_fraction_re = re.compile(r"\b(\d+)/(\d+)\b")
+_decimal_number_re = re.compile(r"(\d+\.\d+)")
+_ordinal_re = re.compile(r"\b\d+(st|nd|rd|th)\b")
+_acronym_re = re.compile(r"\b[A-Z]{2,}\b")
+_number_re = re.compile(r"(?<!\w)-?\d+(?!\w)")
+
+
+# ------------------- 替换与扩展函数 (全部使用 _number_to_words_custom) -------------------
+def _expand_currency_suffix(m):
+    symbol, amount_str, suffix = m.groups()
+    major_map = {"$": "dollars", "£": "pounds", "€": "euros"}
+    amount_word = _number_to_words_custom(amount_str.replace(",", ""))
+    currency_word = major_map.get(symbol, "")
+    return f"{amount_word} {suffix} {currency_word}"
+
+
+def _expand_phone_number(m):
+    country, area, exch, line = m.groups()
+    parts = []
+    if country:
+        country_words = []
+        if country.startswith('+'): country_words.append('plus')
+        digits_only = re.sub(r'\D', '', country)
+        if digits_only: country_words.append(' '.join(_number_to_words_custom(d) for d in digits_only))
+        parts.append(' '.join(country_words))
+    parts.append(' '.join(_number_to_words_custom(c) for c in area))
+    if exch: parts.append(' '.join(_number_to_words_custom(c) for c in exch))
+    parts.append(' '.join(_number_to_words_custom(c) for c in line))
+    return ", ".join(parts)
+
+
+def _expand_roman(m):
+    roman_map = {
+        "ii": "two", "iii": "three", "iv": "four", "v": "five", "vi": "six", "vii": "seven",
+        "viii": "eight", "ix": "nine", "x": "ten", "xi": "eleven", "xii": "twelve",
+        "xiii": "thirteen", "xiv": "fourteen", "xv": "fifteen", "xvi": "sixteen",
+        "xvii": "seventeen", "xviii": "eighteen", "xix": "nineteen"
+    }
+    return roman_map.get(m.group(1).lower(), m.group(1))
+
+
+def _expand_decade(m):
+    year_str = m.group(1)
+    year_words = _expand_number_positive(year_str)
+    if year_words.endswith('ty'): return f"{year_words[:-1]}ies"
+    return f"{year_words}s"
+
+
+def _expand_dimension(m):
+    parts = [p for p in m.groups() if p is not None]
+    expanded_parts = [_number_to_words_custom(p) for p in parts]
+    return " by ".join(expanded_parts)
+
+
+def _expand_score(m):
+    return f"{_number_to_words_custom(m.group(1))} to {_number_to_words_custom(m.group(2))}"
+
+
+def _expand_alphanumeric(m):
+    text = m.group(0)
+    parts = re.findall(r'[a-zA-Z]+|[0-9]+', text)
+    expanded_parts = []
+    for part in parts:
+        if part.isalpha():
+            expanded_parts.append(' '.join(list(part)))
+        elif part.isdigit():
+            expanded_parts.append(' '.join(_number_to_words_custom(c) for c in part))
+    return ' '.join(expanded_parts)
+
+
+def _convert_ordinal(m):
+    return _ordinal_custom(m.group(1)) + ", "
+
+
+def _remove_commas(m): return m.group(1).replace(",", "")
+
+
+def _expand_time(m):
+    h_str, m_str, s_str, am_pm = m.groups()
+    h, m = int(h_str), int(m_str)
+    h_word = _number_to_words_custom(h if h <= 12 or not am_pm else h - 12)
+    if h == 0 and am_pm: h_word = "twelve"
+    m_word = ""
+    if m > 0: m_word = f" oh {_number_to_words_custom(m)}" if m < 10 else f" {_number_to_words_custom(m)}"
+    result = f"{h_word}{m_word}".lstrip()
+    if s_str: result += f" and {_number_to_words_custom(int(s_str))} seconds"
+    if am_pm: result += ' pm' if 'p' in am_pm.lower() else ' am'
+    return result
+
+
+def _expand_measurement(m):
+    num_str, unit = m.groups()
+    is_neg = num_str.startswith('-')
+    if is_neg: num_str = num_str[1:]
+    if '/' in num_str:
+        num_word = _expand_fraction(re.match(_fraction_re, num_str))
+        is_plural = True
+    else:
+        num_word = _number_to_words_custom(num_str)
+        is_plural = float(num_str) != 1
+    unit_word = _measurement_map[unit][1] if is_plural else _measurement_map[unit][0]
+    result = f"{num_word} {unit_word}"
+    return f"minus {result}" if is_neg else result
+
+
+def _expand_currency(m):
+    symbol, amount_str = (m.group(1), m.group(2)) if m.group(1) else (m.group(4), m.group(3))
+    amount_str = (amount_str or "").replace(",", "")
+    if amount_str.startswith('.'): amount_str = '0' + amount_str
+    major_map = {"$": ("dollar", "dollars"), "£": ("pound", "pounds"), "€": ("euro", "euros")}
+    minor_map = {"$": ("cent", "cents"), "£": ("penny", "pence"), "€": ("cent", "cents")}
+    major_singular, major_plural = major_map.get(symbol, ("", ""))
+    parts = amount_str.split('.')
+    major_val = int(parts[0]) if parts[0] else 0
+    minor_val = int(parts[1].ljust(2, '0')) if len(parts) > 1 and parts[1] else 0
+    result = []
+    if major_val > 0:
+        result.append(f"{_number_to_words_custom(major_val)} {major_singular if major_val == 1 else major_plural}")
+    if minor_val > 0:
+        minor_singular, minor_plural = minor_map.get(symbol, ("", ""))
+        result.append(f"{_number_to_words_custom(minor_val)} {minor_singular if minor_val == 1 else minor_plural}")
+    return " and ".join(result) or f"zero {major_plural}"
+
+
+def _expand_decimal_number(m):
+    num_str = m.group(1)
+    parts = num_str.split('.')
+    integer_part = _number_to_words_custom(parts[0])
+    fractional_part = ' '.join(_number_to_words_custom(digit) for digit in parts[1])
+    return f"{integer_part} point {fractional_part}"
+
+
+def _expand_date(m):
+    month, day, year = m.groups()
+    month_word = month_name[int(month)]
+    day_word = _ordinal_custom(day)
+    year_num = int(year)
+    if len(year) == 2: year_num += 2000 if year_num < 50 else 1900
+    return f"{month_word} {day_word}, {_expand_number_positive(str(year_num))}"
+
+
+def _expand_fraction(m):
+    n, d = int(m.group(1)), int(m.group(2))
+    if d == 0: return m.group(0)
+    common_fractions = {(1, 2): "one half", (1, 4): "one quarter", (3, 4): "three quarters"}
+    if (n, d) in common_fractions: return common_fractions[(n, d)]
+    return f"{_number_to_words_custom(n)} over {_number_to_words_custom(d)}"
+
+
+def _expand_ordinal_word(m):
+    return _ordinal_custom(m.group(0)[:-2])
+
+
+def _expand_number(m):
+    num_str = m.group(0)
+    if num_str.startswith('-'): return f"minus {_expand_number_positive(num_str[1:])}"
+    return _expand_number_positive(num_str)
+
+
+def _expand_number_positive(num_str):
+    num = int(num_str)
+    if 2000 <= num < 2010: return f"two thousand and {_number_to_words_custom(num % 100)}"
+    if 1100 <= num < 2100 and num % 100 != 0:
+        return f"{_number_to_words_custom(num // 100)} {_number_to_words_custom(num % 100)}"
+    return _number_to_words_custom(num_str)
+
+
+def _expand_acronym(m): return " ".join(m.group(0))
+
+
+def normalize(text):
+    text = "".join(char for char in unicodedata.normalize("NFD", text) if unicodedata.category(char) != "Mn")
+    text = re.sub(r"@", " at ", text)
+    for regex, replacement in _abbreviations: text = regex.sub(replacement, text)
+    text = re.sub(_currency_suffix_re, _expand_currency_suffix, text)
+    text = re.sub(_phone_re, _expand_phone_number, text)
+    text = re.sub(_dimension_re, _expand_dimension, text)
+    text = re.sub(_roman_re, _expand_roman, text)
+    text = re.sub(_decade_re, _expand_decade, text)
+    text = re.sub(_score_re, _expand_score, text)
+    text = re.sub(_date_re, _expand_date, text)
+    text = re.sub(_time_re, _expand_time, text)
+    text = re.sub(_ordinal_number_re, _convert_ordinal, text)
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_currency_re, _expand_currency, text)
+    text = re.sub(_measurement_re, _expand_measurement, text)
+    text = re.sub(_fraction_re, _expand_fraction, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_number, text)
+    text = re.sub(_ordinal_re, _expand_ordinal_word, text)
+    text = re.sub(_alphanumeric_re, _expand_alphanumeric, text)
+    text = re.sub(_acronym_re, _expand_acronym, text)
+    text = re.sub(_number_re, _expand_number, text)
+    text = text.lower()
+    text = re.sub(r"%", " percent", text)
+    domain_re = re.compile(r'\b([a-z0-9-]+)\.([a-z]{2,})\b')
+    while domain_re.search(text): text = domain_re.sub(r'\1 dot \2', text)
+    text = re.sub(r"[^a-z0-9'.,?!:;-]", " ", text)
+    text = re.sub(r"\s+", " ", text)
+    return text.strip()
diff --git a/genie_tts/G2P/English/WordSegment.py b/genie_tts/G2P/English/WordSegment.py
new file mode 100644
index 0000000000000000000000000000000000000000..0359d9ca60ad76fbecc4ad2ba92d635942c6b959
--- /dev/null
+++ b/genie_tts/G2P/English/WordSegment.py
@@ -0,0 +1,143 @@
+import io
+import math
+import os
+from typing import List, Iterator, Tuple, Dict
+
+from ...Core.Resources import English_G2P_DIR
+
+
+class WordSegmenter:
+    """
+    Contains the core logic for word segmentation, adapted from the original library.
+    """
+    ALPHABET = set('abcdefghijklmnopqrstuvwxyz0123456789')
+    TOTAL = 1024908267229.0
+    LIMIT = 24
+
+    def __init__(self):
+        self.unigrams: Dict[str, float] = {}
+        self.bigrams: Dict[str, float] = {}
+        self.words: List[str] = []
+        self.total: float = 0.0
+
+    def load(self, data_directory: str):
+        """
+        Load unigram, bigram, and word counts from the specified data directory.
+        This is the main modification from the original library.
+        """
+        unigrams_path = os.path.join(data_directory, 'unigrams.txt')
+        bigrams_path = os.path.join(data_directory, 'bigrams.txt')
+        words_path = os.path.join(data_directory, 'words.txt')
+
+        for file_path in [unigrams_path, bigrams_path, words_path]:
+            if not os.path.exists(file_path):
+                raise FileNotFoundError(
+                    f"Word segmentation data file not found: {file_path}. "
+                    "Please ensure the data directory is correct."
+                )
+
+        self.unigrams.update(self._parse(unigrams_path))
+        self.bigrams.update(self._parse(bigrams_path))
+        with io.open(words_path, encoding='utf-8') as reader:
+            self.words.extend(reader.read().splitlines())
+
+        self.total = self.TOTAL
+
+    @staticmethod
+    def _parse(filename: str) -> Dict[str, float]:
+        """Read `filename` and parse tab-separated file of word and count pairs."""
+        with io.open(filename, encoding='utf-8') as reader:
+            # CORRECTED LINE: The generator now defines `line` before the comprehension uses it.
+            lines = (line.split('\t') for line in reader)
+            return {word: float(number) for word, number in lines if len(word) > 0 and len(number) > 0}
+
+    def score(self, word: str, previous: str = None) -> float:
+        """Score `word` in the context of `previous` word."""
+        if previous is None:
+            if word in self.unigrams:
+                return self.unigrams[word] / self.total
+            return 10.0 / (self.total * 10 ** len(word))
+
+        bigram = f'{previous} {word}'
+        if bigram in self.bigrams and previous in self.unigrams:
+            return self.bigrams[bigram] / self.total / self.score(previous)
+
+        return self.score(word)
+
+    def isegment(self, text: str) -> Iterator[str]:
+        """Return iterator of words that is the best segmenation of `text`."""
+        memo = {}
+
+        def search(text: str, previous: str = '<s>') -> Tuple[float, List[str]]:
+            if text == '':
+                return 0.0, []
+
+            def candidates() -> Iterator[Tuple[float, List[str]]]:
+                for prefix, suffix in self._divide(text):
+                    prefix_score = math.log10(self.score(prefix, previous))
+
+                    pair = (suffix, prefix)
+                    if pair not in memo:
+                        memo[pair] = search(suffix, prefix)
+                    suffix_score, suffix_words = memo[pair]
+
+                    yield prefix_score + suffix_score, [prefix] + suffix_words
+
+            return max(candidates())
+
+        clean_text = self._clean(text)
+
+        # Original logic to avoid recursion limits by chunking
+        size = 250
+        prefix = ''
+        if len(clean_text) > size:
+            for offset in range(0, len(clean_text), size):
+                chunk = clean_text[offset:(offset + size)]
+                _, chunk_words = search(prefix + chunk)
+
+                if len(chunk_words) > 5:
+                    prefix = ''.join(chunk_words[-5:])
+                    del chunk_words[-5:]
+                else:  # handle case where chunk is small
+                    prefix = ''.join(chunk_words)
+                    chunk_words = []
+
+                for word in chunk_words:
+                    yield word
+
+            _, prefix_words = search(prefix)
+            for word in prefix_words:
+                yield word
+        else:
+            _, words = search(clean_text)
+            for word in words:
+                yield word
+
+    def segment(self, text: str) -> List[str]:
+        """Return list of words that is the best segmenation of `text`."""
+        return list(self.isegment(text))
+
+    def _divide(self, text: str) -> Iterator[Tuple[str, str]]:
+        """Yield `(prefix, suffix)` pairs from `text`."""
+        for pos in range(1, min(len(text), self.LIMIT) + 1):
+            yield text[:pos], text[pos:]
+
+    @classmethod
+    def _clean(cls, text: str) -> str:
+        """Return `text` lower-cased with non-alphanumeric characters removed."""
+        text_lower = text.lower()
+        return ''.join(letter for letter in text_lower if letter in cls.ALPHABET)
+
+
+# --- Public Interface ---
+# Create a single instance to be used by the importing module.
+
+_segmenter = WordSegmenter()
+_segmenter.load(os.path.join(English_G2P_DIR, 'wordsegment'))
+
+
+def segment_text(text: str) -> List[str]:
+    """
+    Public function to segment a text string into a list of words.
+    """
+    return _segmenter.segment(text)
diff --git a/genie_tts/G2P/English/__init__.py b/genie_tts/G2P/English/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/genie_tts/G2P/Japanese/JapaneseG2P.py b/genie_tts/G2P/Japanese/JapaneseG2P.py
new file mode 100644
index 0000000000000000000000000000000000000000..94362fc4b62601530de7b08699e43e91eaa4e591
--- /dev/null
+++ b/genie_tts/G2P/Japanese/JapaneseG2P.py
@@ -0,0 +1,150 @@
+# -*- coding: utf-8 -*-
+"""
+用于纯日语的 G2P。
+"""
+import re
+import pyopenjtalk
+from typing import List
+from ..SymbolsV2 import symbols_v2, symbol_to_id_v2
+
+# 匹配连续的标点符号
+_CONSECUTIVE_PUNCTUATION_RE = re.compile(r"([,./?!~…・])\1+")
+
+# 匹配需要转换为日语读法的特殊符号
+_SYMBOLS_TO_JAPANESE = [
+    (re.compile("%"), "パーセント"),
+    (re.compile("％"), "パーセント"),
+]
+
+# 匹配日语字符（汉字、假名、全角字母数字等）
+_JAPANESE_CHARACTERS_RE = re.compile(
+    r"[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
+)
+
+# 匹配非日语字符（标点、空格等）
+_JAPANESE_MARKS_RE = re.compile(
+    r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
+)
+
+
+class JapaneseG2P:
+    """
+    一个简化的、封装好的日语Grapheme-to-Phoneme（字素到音素）转换器。
+
+    本版本假设 pyopenjtalk 库已安装，并且不使用任何用户自定义词典。
+    它专注于提供一个纯粹、高效的文本到音素转换接口。
+    """
+
+    @staticmethod
+    def _text_normalize(text: str) -> str:
+        """对输入文本进行基础的规范化处理。"""
+        for regex, replacement in _SYMBOLS_TO_JAPANESE:
+            text = re.sub(regex, replacement, text)
+        text = _CONSECUTIVE_PUNCTUATION_RE.sub(r"\1", text)
+        text = text.lower()
+        return text
+
+    @staticmethod
+    def _post_replace_phoneme(ph: str) -> str:
+        """对单个音素或标点进行后处理替换。"""
+        rep_map = {
+            "：": ",", "；": ",", "，": ",", "。": ".",
+            "！": "!", "？": "?", "\n": ".", "·": ",",
+            "、": ",", "...": "…",
+        }
+        return rep_map.get(ph, ph)
+
+    @staticmethod
+    def _numeric_feature_by_regex(regex: str, s: str) -> int:
+        """从OpenJTalk标签中提取数值特征。"""
+        match = re.search(regex, s)
+        return int(match.group(1)) if match else -50
+
+    @staticmethod
+    def _pyopenjtalk_g2p_prosody(text: str) -> List[str]:
+        """使用pyopenjtalk提取音素及韵律符号。"""
+        labels = pyopenjtalk.make_label(pyopenjtalk.run_frontend(text))
+        phones = []
+        for n, lab_curr in enumerate(labels):
+            p3 = re.search(r"-(.*?)\+", lab_curr).group(1)
+            if p3 in "AEIOU":
+                p3 = p3.lower()
+
+            if p3 == "sil":
+                if n == 0:
+                    phones.append("^")
+                elif n == len(labels) - 1:
+                    e3 = JapaneseG2P._numeric_feature_by_regex(r"!(\d+)_", lab_curr)
+                    phones.append("?" if e3 == 1 else "$")
+                continue
+            elif p3 == "pau":
+                phones.append("_")
+                continue
+            else:
+                phones.append(p3)
+
+            a1 = JapaneseG2P._numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr)
+            a2 = JapaneseG2P._numeric_feature_by_regex(r"\+(\d+)\+", lab_curr)
+            a3 = JapaneseG2P._numeric_feature_by_regex(r"\+(\d+)/", lab_curr)
+            f1 = JapaneseG2P._numeric_feature_by_regex(r"/F:(\d+)_", lab_curr)
+            lab_next = labels[n + 1] if n + 1 < len(labels) else ""
+            a2_next = JapaneseG2P._numeric_feature_by_regex(r"\+(\d+)\+", lab_next)
+
+            if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl":
+                phones.append("#")
+            elif a1 == 0 and a2_next == a2 + 1 and a2 != f1:
+                phones.append("]")
+            elif a2 == 1 and a2_next == 2:
+                phones.append("[")
+
+        return phones
+
+    @staticmethod
+    def g2p(text: str, with_prosody: bool = True) -> List[str]:
+        """
+        将日语文本转换为音素序列。
+
+        Args:
+            text (str): 待转换的日语文本。
+            with_prosody (bool): 是否在输出中包含韵律符号。默认为 True。
+
+        Returns:
+            List[str]: 音素和符号的列表。
+        """
+        if not text.strip():
+            return []
+
+        # 1. 文本规范化
+        norm_text = JapaneseG2P._text_normalize(text)
+
+        # 2. 使用标点符号分割字符串，得到日语文本片段
+        japanese_segments = _JAPANESE_MARKS_RE.split(norm_text)
+        punctuation_marks = _JAPANESE_MARKS_RE.findall(norm_text)
+
+        phonemes = []
+        for i, segment in enumerate(japanese_segments):
+            if segment:
+                if with_prosody:  # 移除分析结果中句首(^)/句尾($)的符号，因为我们按片段处理
+                    phones = JapaneseG2P._pyopenjtalk_g2p_prosody(segment)[1:-1]
+                else:
+                    phones = pyopenjtalk.g2p(segment).split(" ")
+                phonemes.extend(phones)
+
+            # 将对应的标点符号添加回来
+            if i < len(punctuation_marks):
+                mark = punctuation_marks[i].strip()
+                if mark:
+                    phonemes.append(mark)
+
+        # 3. 对最终列表中的每个元素进行后处理（主要转换全角标点）
+        processed_phonemes = [JapaneseG2P._post_replace_phoneme(p) for p in phonemes]
+
+        return processed_phonemes
+
+
+def japanese_to_phones(text: str) -> List[int]:
+    phones = JapaneseG2P.g2p(text)
+    phones = [ph for ph in phones if ph in symbols_v2]
+    # print(phones)
+    phones = [symbol_to_id_v2[ph] for ph in phones]
+    return phones
diff --git a/genie_tts/G2P/Japanese/__init__.py b/genie_tts/G2P/Japanese/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/genie_tts/G2P/SymbolsV2.py b/genie_tts/G2P/SymbolsV2.py
new file mode 100644
index 0000000000000000000000000000000000000000..37b24b76aa2fdbaa198ba6f15e2cc9af7503631e
--- /dev/null
+++ b/genie_tts/G2P/SymbolsV2.py
@@ -0,0 +1,119 @@
+# -*- coding: utf-8 -*-
+
+from typing import List, Dict
+
+# -------------------------
+# 基础符号集定义
+# -------------------------
+
+# 标点和特殊分隔符
+PUNCTUATION = ["!", "?", "…", ",", "."]
+PUNCTUATION_SYMBOLS = ["!", "?", "…", ",", ".", "-", "SP", "SP2", "SP3", "UNK"]
+
+# 中文普通话（Pinyin）符号
+# 声母
+PINYIN_INITIALS = [
+    "AA", "EE", "OO", "b", "c", "ch", "d", "f", "g", "h", "j", "k", "l",
+    "m", "n", "p", "q", "r", "s", "sh", "t", "w", "x", "y", "z", "zh",
+]
+# 基础韵母 (不带声调)
+PINYIN_FINALS_BASE = [
+    "E", "En", "a", "ai", "an", "ang", "ao", "e", "ei", "en", "eng", "er",
+    "i", "i0", "ia", "ian", "iang", "iao", "ie", "in", "ing", "iong",
+    "ir", "iu", "o", "ong", "ou", "u", "ua", "uai", "uan", "uang", "ui",
+    "un", "uo", "v", "van", "ve", "vn",
+]
+
+# 日语 (Romaji) 符号
+JAPANESE_SYMBOLS = [
+    "I", "N", "U", "a", "b", "by", "ch", "cl", "d", "dy", "e", "f", "g",
+    "gy", "h", "hy", "i", "j", "k", "ky", "m", "my", "n", "ny", "o", "p",
+    "py", "r", "ry", "s", "sh", "t", "ts", "u", "v", "w", "y", "z",
+]
+
+# 英语 (ARPAbet) 符号
+ARPABET_SYMBOLS = {
+    "AH0", "S", "AH1", "EY2", "AE2", "EH0", "OW2", "UH0", "NG", "B", "G",
+    "AY0", "M", "AA0", "F", "AO0", "ER2", "UH1", "IY1", "AH2", "DH", "IY0",
+    "EY1", "IH0", "K", "N", "W", "IY2", "T", "AA1", "ER1", "EH2", "OY0",
+    "UH2", "UW1", "Z", "AW2", "AW1", "V", "UW2", "AA2", "ER", "AW0",
+    "UW0", "R", "OW1", "EH1", "ZH", "AE0", "IH2", "IH", "Y", "JH", "P",
+    "AY1", "EY0", "OY2", "TH", "HH", "D", "ER0", "CH", "AO1", "AE1",
+    "AO2", "OY1", "AY2", "IH1", "OW0", "L", "SH",
+}
+
+# 韩语 (Hangul) 符号
+KOREAN_SYMBOLS = "ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ空停"
+
+# 粤语 (Jyutping/Yale) 符号
+CANTONESE_SYMBOLS = {
+    "Yeot3", "Yip1", "Yyu3", "Yeng4", "Yut5", "Yaan5", "Ym5", "Yaan6", "Yang1", "Yun4",
+    "Yon2", "Yui5", "Yun2", "Yat3", "Ye", "Yeot1", "Yoeng5", "Yoek2", "Yam2", "Yeon6",
+    "Yu6", "Yiu3", "Yaang6", "Yp5", "Yai4", "Yoek4", "Yit6", "Yam5", "Yoeng6", "Yg1",
+    "Yk3", "Yoe4", "Yam3", "Yc", "Yyu4", "Yyut1", "Yiu4", "Ying3", "Yip3", "Yaap3",
+    "Yau3", "Yan4", "Yau1", "Yap4", "Yk6", "Yok3", "Yai1", "Yeot6", "Yan2", "Yoek6",
+    "Yt1", "Yoi1", "Yit5", "Yn4", "Yaau3", "Yau4", "Yuk6", "Ys", "Yuk", "Yin6",
+    "Yung6", "Ya", "You", "Yaai5", "Yau5", "Yoi3", "Yaak3", "Yaat3", "Ying2", "Yok5",
+    "Yeng2", "Yyut3", "Yam1", "Yip5", "You1", "Yam6", "Yaa5", "Yi6", "Yek4", "Yyu2",
+    "Yuk5", "Yaam1", "Yang2", "Yai", "Yiu6", "Yin4", "Yok4", "Yot3", "Yui2", "Yeoi5",
+    "Yyun6", "Yyu5", "Yoi5", "Yeot2", "Yim4", "Yeoi2", "Yaan1", "Yang6", "Yong1", "Yaang4",
+    "Yung5", "Yeon1", "Yin2", "Ya3", "Yaang3", "Yg", "Yk2", "Yaau5", "Yut1", "Yt5",
+    "Yip4", "Yung4", "Yj", "Yong3", "Ya1", "Yg6", "Yaau6", "Yit3", "Yun3", "Ying1",
+    "Yn2", "Yg4", "Yl", "Yp3", "Yn3", "Yak1", "Yang5", "Yoe6", "You2", "Yap2",
+    "Yak2", "Yt3", "Yot5", "Yim2", "Yi1", "Yn6", "Yaat5", "Yaam3", "Yoek5", "Ye3",
+    "Yeon4", "Yaa2", "Yu3", "Yim6", "Ym", "Yoe3", "Yaai2", "Ym2", "Ya6", "Yeng6",
+    "Yik4", "Yot4", "Yaai4", "Yyun3", "Yu1", "Yoeng1", "Yaap2", "Yuk3", "Yoek3", "Yeng5",
+    "Yeoi1", "Yiu2", "Yok1", "Yo1", "Yoek1", "Yoeng2", "Yeon5", "Yiu1", "Yoeng4", "Yuk2",
+    "Yat4", "Yg5", "Yut4", "Yan6", "Yin3", "Yaa6", "Yap1", "Yg2", "Yoe5", "Yt4",
+    "Ya5", "Yo4", "Yyu1", "Yak3", "Yeon2", "Yong4", "Ym1", "Ye2", "Yaang5", "Yoi2",
+    "Yeng3", "Yn", "Yyut4", "Yau", "Yaak2", "Yaan4", "Yek2", "Yin1", "Yi5", "Yoe2",
+    "Yei5", "Yaat6", "Yak5", "Yp6", "Yok6", "Yei2", "Yaap1", "Yyut5", "Yi4", "Yim1",
+    "Yk5", "Ye4", "Yok2", "Yaam6", "Yat2", "Yon6", "Yei3", "Yyu6", "Yeot5", "Yk4",
+    "Yai6", "Yd", "Yg3", "Yei6", "Yau2", "Yok", "Yau6", "Yung3", "Yim5", "Yut6",
+    "Yit1", "Yon3", "Yat1", "Yaam2", "Yyut2", "Yui6", "Yt2", "Yek6", "Yt", "Ye6",
+    "Yang3", "Ying6", "Yaau1", "Yeon3", "Yng", "Yh", "Yang4", "Ying5", "Yaap6", "Yoeng3",
+    "Yyun4", "You3", "Yan5", "Yat5", "Yot1", "Yun1", "Yi3", "Yaa1", "Yaap4", "You6",
+    "Yaang2", "Yaap5", "Yaa3", "Yaak6", "Yeng1", "Yaak1", "Yo5", "Yoi4", "Yam4", "Yik1",
+    "Ye1", "Yai5", "Yung1", "Yp2", "Yui4", "Yaak4", "Yung2", "Yak4", "Yaat4", "Yeoi4",
+    "Yut2", "Yin5", "Yaau4", "Yap6", "Yb", "Yaam4", "Yw", "Yut3", "Yong2", "Yt6",
+    "Yaai6", "Yap5", "Yik5", "Yun6", "Yaam5", "Yun5", "Yik3", "Ya2", "Yyut6", "Yon4",
+    "Yk1", "Yit4", "Yak6", "Yaan2", "Yuk1", "Yai2", "Yik2", "Yaat2", "Yo3", "Ykw",
+    "Yn5", "Yaa", "Ye5", "Yu4", "Yei1", "Yai3", "Yyun5", "Yip2", "Yaau2", "Yiu5",
+    "Ym4", "Yeoi6", "Yk", "Ym6", "Yoe1", "Yeoi3", "Yon", "Yuk4", "Yaai3", "Yaa4",
+    "Yot6", "Yaang1", "Yei4", "Yek1", "Yo", "Yp", "Yo6", "Yp4", "Yan3", "Yoi",
+    "Yap3", "Yek3", "Yim3", "Yz", "Yot2", "Yoi6", "Yit2", "Yu5", "Yaan3", "Yan1",
+    "Yon5", "Yp1", "Yong5", "Ygw", "Yak", "Yat6", "Ying4", "Yu2", "Yf", "Ya4",
+    "Yon1", "You4", "Yik6", "Yui1", "Yaat1", "Yeot4", "Yi2", "Yaai1", "Yek5", "Ym3",
+    "Yong6", "You5", "Yyun1", "Yn1", "Yo2", "Yip6", "Yui3", "Yaak5", "Yyun2"
+}
+
+
+def _generate_pinyin_finals_with_tones(base_finals, num_tones=5):
+    """根据基础韵母和声调数量，自动生成带声调的韵母列表。"""
+    finals_with_tones = []
+    for tone in range(1, num_tones + 1):
+        for final in base_finals:
+            finals_with_tones.append(f"{final}{tone}")
+    return finals_with_tones
+
+
+def create_master_symbol_list():
+    pinyin_finals = _generate_pinyin_finals_with_tones(PINYIN_FINALS_BASE)
+
+    main_symbols = set()
+    main_symbols.add("_")  # 添加下划线符号
+    main_symbols.update(PINYIN_INITIALS)
+    main_symbols.update(pinyin_finals)
+    main_symbols.update(JAPANESE_SYMBOLS)
+    main_symbols.update(PUNCTUATION_SYMBOLS)
+    main_symbols.update(ARPABET_SYMBOLS)
+
+    master_list = sorted(list(main_symbols))
+    master_list.extend(["[", "]"])
+    master_list.extend(sorted(list(KOREAN_SYMBOLS)))
+    master_list.extend(sorted(list(CANTONESE_SYMBOLS)))
+    return master_list
+
+
+symbols_v2: List[str] = create_master_symbol_list()
+symbol_to_id_v2: Dict[str, int] = {s: i for i, s in enumerate(symbols_v2)}
diff --git a/genie_tts/G2P/__init__.py b/genie_tts/G2P/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/genie_tts/GUI/AudioPlayer.py b/genie_tts/GUI/AudioPlayer.py
new file mode 100644
index 0000000000000000000000000000000000000000..76e615ce9985896bd2efca8a199fd2d54a649dc7
--- /dev/null
+++ b/genie_tts/GUI/AudioPlayer.py
@@ -0,0 +1,94 @@
+import sounddevice as sd
+import threading
+import queue
+from typing import Union, Optional, Callable
+import numpy as np
+import os
+import soundfile as sf
+
+
+def run_in_sub_thread(func) -> Callable[..., threading.Thread]:
+    def wrapper(*args, **kwargs) -> threading.Thread:
+        thread = threading.Thread(target=func, args=args, kwargs=kwargs)
+        thread.daemon = True
+        thread.start()
+        return thread
+
+    return wrapper
+
+
+class AudioPlayer:
+    CHUNK_SIZE: int = 1024
+
+    def __init__(self):
+        self._task_queue: queue.Queue[bytes | str] = queue.Queue()
+        self._worker_thread: Optional[threading.Thread] = None
+        self._stop_event: threading.Event = threading.Event()
+        self._start_worker()
+
+    def _start_worker(self):
+        """启动工作线程（如果它尚未运行或已关闭）。"""
+        if self._worker_thread and self._worker_thread.is_alive():
+            return
+        self._stop_event.clear()
+        self._worker_thread = self._playback_worker()
+
+    @run_in_sub_thread
+    def _playback_worker(self) -> None:
+        while not self._stop_event.is_set():
+            try:
+                task: str = self._task_queue.get(timeout=0.1)
+            except queue.Empty:
+                continue
+
+            stream = None
+            try:
+                if isinstance(task, str) and os.path.isfile(task):
+                    with sf.SoundFile(task, 'r') as f:
+                        if sd is not None:
+                            stream = sd.OutputStream(
+                                samplerate=f.samplerate,
+                                channels=f.channels,
+                                dtype='float32',
+                            )
+                            stream.start()
+                        while not self._stop_event.is_set():
+                            chunk = f.read(self.CHUNK_SIZE, dtype='float32')
+                            if not chunk.any():
+                                break
+                            stream.write(chunk)
+            except Exception as e:
+                if isinstance(e, sf.SoundFileError):
+                    print(f"无法读取或解析音频文件: {task}, 错误: {e}")
+                else:
+                    print(f"播放时发生错误: {e}")
+            finally:
+                if stream:
+                    stream.stop()
+                    stream.close()
+                self._task_queue.task_done()
+
+    def play(self, source: Union[str, np.ndarray]):
+        """将音频源加入播放队列。"""
+        self._start_worker()
+        self._task_queue.put(source)
+
+    def stop(self):
+        """停止播放并清空播放队列。"""
+        self._stop_event.set()
+        if self._worker_thread and self._worker_thread.is_alive():
+            self._worker_thread.join()
+        self._stop_event.clear()
+
+        with self._task_queue.mutex:
+            self._task_queue.queue.clear()
+            while self._task_queue.unfinished_tasks > 0:
+                self._task_queue.task_done()
+
+    def wait(self):
+        """阻塞，直到队列中所有任务都播放完成。"""
+        self._task_queue.join()
+
+    def close(self):
+        """永久关闭播放器并释放资源。"""
+        self.stop()
diff --git a/genie_tts/GUI/ConverterWidget.py b/genie_tts/GUI/ConverterWidget.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1d6bbf8596e1c6aebe67dfd328992dada328614
--- /dev/null
+++ b/genie_tts/GUI/ConverterWidget.py
@@ -0,0 +1,204 @@
+import sys
+import os
+import datetime
+
+from PySide6.QtWidgets import (
+    QApplication, QWidget, QVBoxLayout, QPushButton, QTextEdit, QFileDialog,
+    QListView, QTreeView, QAbstractItemView
+)
+from PySide6.QtCore import Signal, QObject, QSettings, QThread
+
+from ..Converter.Converter import convert
+from ..Converter.v2.Converter import find_ckpt_and_pth
+
+
+def get_timestamp_msg(message: str, level: str = "INFO") -> str:
+    """辅助函数：生成类似 Logging 格式的带时间戳字符串"""
+    now = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    return f"{now} - {level} - {message}"
+
+
+class Worker(QObject):
+    finished = Signal()
+    log_signal = Signal(str)
+
+    def __init__(self, folders):
+        super().__init__()
+        self.folders = folders
+
+    def log(self, message: str, level: str = "INFO"):
+        """内部辅助方法，用于格式化并发送日志"""
+        formatted_msg = get_timestamp_msg(message, level)
+        self.log_signal.emit(formatted_msg)
+
+    def run(self):
+        """执行转换任务"""
+        try:
+            root_output_dir = os.path.abspath("./Output")
+            for folder in self.folders:
+                character_name: str = os.path.basename(folder)
+                output_dir: str = os.path.join(root_output_dir, character_name)
+                if os.path.exists(output_dir):
+                    self.log(f'输出文件夹 {output_dir} 已存在，将覆盖内容。', "WARNING")
+                torch_ckpt_path, torch_pth_path = find_ckpt_and_pth(folder)
+                if not torch_ckpt_path or not torch_pth_path:
+                    self.log(f'无法处理文件夹 {folder} 。请保证文件夹内有 GPT—SOVITS V2 导出的 .pth 和 .ckpt 模型。',
+                             "ERROR")
+                    continue
+                self.log(f'正在处理 {folder} 。')
+                # 调用转换逻辑
+                convert(torch_ckpt_path, torch_pth_path, output_dir)
+                self.log(f'{folder} 处理完成。')  # 可选：提示完成
+            os.startfile(root_output_dir)
+        except Exception as e:
+            self.log(f"任务执行过程中发生未捕获异常: {str(e)}", "ERROR")
+        finally:
+            self.finished.emit()
+
+
+class ConverterWidget(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.setWindowTitle('GENIE Converter (PySide6 Version)')
+        self.resize(1280, 720)
+
+        self.settings = QSettings("MyCompany", "GENIE Converter")
+
+        main_layout = QVBoxLayout(self)
+        main_layout.setContentsMargins(20, 20, 20, 20)
+        main_layout.setSpacing(15)
+
+        self.folder_button = QPushButton('📂 选择一个或多个文件夹')
+        self.folder_button.setFixedHeight(40)
+        self.folder_button.clicked.connect(self.open_folder_dialog)
+
+        self.log_display = QTextEdit()
+        self.log_display.setReadOnly(True)
+
+        main_layout.addWidget(self.folder_button)
+        main_layout.addWidget(self.log_display)
+
+        self.apply_stylesheet()
+
+        self.thread = None
+        self.worker = None
+
+        self.append_formatted_log("欢迎使用 GENIE Converter!")
+        self.append_formatted_log("支持将 GPT—SOVITS V2/V2ProPlus 模型导出为 GENIE 引擎所需的格式。")
+        self.append_formatted_log("请选择一个或多个文件夹，每个文件夹中包含一对 .pth 和 .ckpt 文件。")
+        self.append_formatted_log("您可以使用 Ctrl 或 Shift 键来进行多选。\n")
+
+    def apply_stylesheet(self):
+        self.setStyleSheet("""
+            QWidget {
+                background-color: #2b2b2b;
+                color: #f0f0f0;
+                font-family: 'Segoe UI', 'Microsoft YaHei', 'Arial';
+                font-size: 14px;
+            }
+            QPushButton {
+                background-color: #007bff;
+                color: white;
+                border: none;
+                padding: 10px;
+                border-radius: 5px;
+                font-weight: bold;
+            }
+            QPushButton:hover { background-color: #0056b3; }
+            QPushButton:pressed { background-color: #004494; }
+            QPushButton:disabled {
+                background-color: #555;
+                color: #aaa;
+            }
+            QTextEdit {
+                background-color: #1e1e1e;
+                border: 1px solid #444;
+                border-radius: 5px;
+                padding: 8px;
+                font-family: 'Consolas', 'Courier New', monospace;
+            }
+            QScrollBar:vertical {
+                border: none; background: #2b2b2b; width: 12px; margin: 0;
+            }
+            QScrollBar::handle:vertical {
+                background: #555; min-height: 20px; border-radius: 6px;
+            }
+            QScrollBar::handle:vertical:hover { background: #007bff; }
+            QScrollBar::add-line:vertical, QScrollBar::sub-line:vertical { height: 0px; }
+        """)
+
+    def append_log(self, text: str):
+        """直接给主线程调用的日志打印方法"""
+        self.log_display.append(text)
+        self.scroll_to_bottom()
+
+    def append_formatted_log(self, text: str, level="INFO"):
+        """给主线程调用的带格式日志方法"""
+        msg = get_timestamp_msg(text, level)
+        self.append_log(msg)
+
+    def scroll_to_bottom(self):
+        self.log_display.verticalScrollBar().setValue(
+            self.log_display.verticalScrollBar().maximum()
+        )
+
+    def open_folder_dialog(self):
+        last_dir = self.settings.value("last_dir", "")
+
+        dialog = QFileDialog(self, '请选择文件夹', str(last_dir))
+        dialog.setFileMode(QFileDialog.FileMode.Directory)
+        dialog.setOption(QFileDialog.Option.DontUseNativeDialog, True)
+
+        list_view = dialog.findChild(QListView, 'listView')
+        if list_view:
+            list_view.setSelectionMode(QAbstractItemView.SelectionMode.ExtendedSelection)
+
+        tree_view = dialog.findChild(QTreeView)
+        if tree_view:
+            tree_view.setSelectionMode(QAbstractItemView.SelectionMode.ExtendedSelection)
+
+        if dialog.exec():
+            selected_folders = dialog.selectedFiles()
+            if selected_folders:
+                self.run_conversion_task(selected_folders)
+                self.settings.setValue("last_dir", os.path.dirname(selected_folders[0]))
+
+    def run_conversion_task(self, folders):
+        self.folder_button.setEnabled(False)
+        self.folder_button.setText("🔄 转换中，请稍候...")
+
+        self.thread = QThread()
+        self.worker = Worker(folders)
+        self.worker.moveToThread(self.thread)
+
+        # 连接线程控制信号
+        self.thread.started.connect(self.worker.run)
+        self.worker.finished.connect(self.on_conversion_finished)
+        self.worker.log_signal.connect(self.append_log)
+
+        self.thread.start()
+
+    def on_conversion_finished(self):
+        self.thread.quit()
+        self.thread.wait()
+        self.worker.deleteLater()
+        self.thread.deleteLater()
+        self.thread = None
+        self.worker = None
+
+        self.folder_button.setEnabled(True)
+        self.folder_button.setText("📂 选择一个或多个文件夹")
+        self.append_formatted_log("所有任务已完成。", "INFO")
+
+    def closeEvent(self, event):
+        if self.thread is not None and self.thread.isRunning():
+            self.thread.quit()
+            self.thread.wait()
+        super().closeEvent(event)
+
+
+def start_gui() -> None:
+    app = QApplication(sys.argv)
+    window = ConverterWidget()
+    window.show()
+    sys.exit(app.exec())
diff --git a/genie_tts/GUI/GUI.py b/genie_tts/GUI/GUI.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2b4e4408dc8d8567ce30e4bb3ada1f494d62185
--- /dev/null
+++ b/genie_tts/GUI/GUI.py
@@ -0,0 +1,649 @@
+import sys
+import os
+import shutil
+from typing import List, Optional, TextIO, Any
+import uuid
+
+import soundfile as sf
+import numpy as np
+
+from PySide6.QtWidgets import (
+    QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QPushButton, QLabel, QLineEdit, QTextEdit,
+    QFileDialog, QGroupBox, QScrollArea, QMessageBox, QTabWidget, QFormLayout, QFrame,
+)
+from PySide6.QtCore import (
+    Qt, Signal, Slot, QObject,
+)
+from PySide6.QtGui import QTextCursor, QCloseEvent
+
+from ..Utils.TextSplitter import TextSplitter
+from .Utils import (
+    generate_output_filenames, FileSelectorWidget, FileSelectionMode, MyComboBox, sanitize_filename,
+    MyTextEdit
+)
+from .AudioPlayer import AudioPlayer
+from .PresetManager import PresetManager
+from .ServerManager import InferenceWorker
+from .ConverterWidget import ConverterWidget
+
+"""
+抄自 Genie CUDA Runtime
+"""
+
+CACHE_DIR = './UserData/Cache/GenieGUI'
+os.makedirs(CACHE_DIR, exist_ok=True)
+
+
+# ==================== 后台工作线程 ====================
+
+class LogRedirector(QObject):
+    """重定向 stdout 到 Signal"""
+    textWritten = Signal(str)
+
+    def __init__(self):
+        super().__init__()
+        self._old_stdout: TextIO = sys.stdout
+
+    def write(self, text: Any):
+        text = str(text)
+        self.textWritten.emit(text)
+        if self._old_stdout is not None:
+            self._old_stdout.write(text)
+
+    def flush(self):
+        pass
+
+
+# ==================== UI 组件实现 ====================
+
+class PreviewItemWidget(QFrame):
+    """单条音频预览组件"""
+
+    def __init__(
+            self,
+            index: int,
+            text: str,
+            file_path: str,
+            player: AudioPlayer,
+            parent: QWidget = None
+    ):
+        super().__init__(parent)
+        self.text: str = text
+        self.file_path: str = file_path
+        self.player: AudioPlayer = player
+        self.setFrameShape(QFrame.Shape.StyledPanel)
+        self.setFrameShadow(QFrame.Shadow.Raised)
+
+        layout = QHBoxLayout(self)
+        layout.setContentsMargins(5, 5, 5, 5)
+
+        # 编号
+        lbl_id = QLabel(f"#{index}")
+        lbl_id.setFixedWidth(40)
+        lbl_id.setStyleSheet("font-weight: bold; color: #555;")
+
+        # 文本
+        lbl_text = QLabel(text)
+        lbl_text.setFixedWidth(240)
+        lbl_text.setToolTip(text)
+
+        # 按钮 - 播放
+        btn_play = QPushButton("▶ 播放")
+        btn_play.setFixedWidth(80)
+        btn_play.clicked.connect(self._play_audio)
+
+        # 按钮 - 保存
+        btn_save = QPushButton("⬇ 保存")
+        btn_save.setFixedWidth(80)
+        btn_save.clicked.connect(self._save_file)
+
+        # 按钮 - 删除 (新增)
+        btn_del = QPushButton("🗑 删除")
+        btn_del.setFixedWidth(80)
+        btn_del.setStyleSheet("color: #ff4d4d;")  # 以此区分删除按钮
+        btn_del.clicked.connect(self._delete_item)
+
+        layout.addWidget(lbl_id)
+        layout.addWidget(lbl_text, 1)  # Stretch
+        layout.addWidget(btn_play)
+        layout.addWidget(btn_save)
+        layout.addWidget(btn_del)  # 添加到布局
+
+    def _play_audio(self):
+        # 播放前先停止其他播放
+        self.player.stop()
+        self.player.play(self.file_path)
+
+    def _save_file(self):
+        filename = sanitize_filename(self.text)
+        save_path, _ = QFileDialog.getSaveFileName(
+            self, "保存音频", f"{filename}.wav", "WAV Audio (*.wav)"
+        )
+        if save_path:
+            try:
+                shutil.copy(self.file_path, save_path)
+                QMessageBox.information(self, "成功", "文件保存成功！")
+            except Exception as e:
+                QMessageBox.critical(self, "错误", f"保存失败: {e}")
+
+    def _delete_item(self):
+        """删除当前条目及对应的文件"""
+        # 1. 停止播放
+        self.player.stop()
+
+        # 2. 尝试删除物理文件 (避免垃圾堆积)
+        try:
+            if os.path.exists(self.file_path):
+                os.remove(self.file_path)
+                print(f"[INFO] 已删除文件: {self.file_path}")
+        except Exception as e:
+            print(f"[WARN] 删除文件失败: {e}")
+
+        # 3. 从界面移除自身
+        self.deleteLater()
+
+
+class LogWidget(QWidget):
+    """日志显示Tab"""
+
+    def __init__(self, parent: QWidget = None):
+        super().__init__(parent)
+        layout = QVBoxLayout(self)
+        self.text_edit: QTextEdit = QTextEdit()
+        self.text_edit.setReadOnly(True)
+        self.text_edit.setStyleSheet(
+            "background-color: #1e1e1e;"
+            "color: #ecf0f1;"
+            "font-family: Consolas;"
+            "font-size: 12pt;"
+        )
+        layout.addWidget(self.text_edit)
+
+    @Slot(str)
+    def append_log(self, text: str):
+        self.text_edit.moveCursor(QTextCursor.MoveOperation.End)
+        self.text_edit.insertPlainText(text)
+        self.text_edit.moveCursor(QTextCursor.MoveOperation.End)
+
+
+class TTSWidget(QWidget):
+    """TTS 主交互界面"""
+
+    def __init__(self, player: AudioPlayer, parent: QWidget = None):
+        super().__init__(parent)
+        self.player: AudioPlayer = player
+        self.splitter: TextSplitter = TextSplitter()
+        self.current_gen_id: int = 0
+        self.current_worker: Optional[InferenceWorker] = None
+
+        main_layout = QVBoxLayout(self)
+
+        # ---------------- 顶部：预设管理器 ----------------
+        self.preset_manager = PresetManager(
+            presets_file='./UserData/GenieGuiConfig.json',
+            state_getter=self.get_ui_state,
+        )
+        self.preset_manager.sig_load_state.connect(self.apply_ui_state)
+        main_layout.addWidget(self.preset_manager)
+
+        # ---------------- 中间：滚动设置区 ----------------
+        scroll = QScrollArea()
+        scroll.setWidgetResizable(True)
+        content_widget = QWidget()
+
+        content_layout = QHBoxLayout(content_widget)
+        left_column_layout = QVBoxLayout()
+        right_column_layout = QVBoxLayout()
+
+        # ==================== 左侧列内容 ====================
+
+        # 模型设置组
+        group_model = QGroupBox("模型设置")
+        self.layout_model = QFormLayout()
+        self.combo_model_type = MyComboBox()
+        self.combo_model_type.addItems(["Genie-TTS"])
+        self.combo_model_type.currentTextChanged.connect(self._update_model_ui_visibility)
+        self.combo_model_type.setEnabled(False)
+        self.file_gpt = FileSelectorWidget("gpt_path", FileSelectionMode.FILE, "Checkpoints (*.ckpt)")
+        self.file_vits = FileSelectorWidget("vits_path", FileSelectionMode.FILE, "Models (*.pth)")
+        self.file_genie = FileSelectorWidget("genie_dir", FileSelectionMode.DIRECTORY)
+        self.file_gpt.pathChanged.connect(self._on_gpt_path_changed)
+        self.file_vits.pathChanged.connect(self._on_vits_path_changed)
+        self.layout_model.addRow("模型类型:", self.combo_model_type)
+        self.layout_model.addRow("GPT模型 (.ckpt):", self.file_gpt)
+        self.layout_model.addRow("VITS模型 (.pth):", self.file_vits)
+        self.layout_model.addRow("Genie模型目录:", self.file_genie)
+        group_model.setLayout(self.layout_model)
+        # 参考音频组
+        group_ref = QGroupBox("参考音频")
+        layout_ref = QFormLayout()
+        self.file_ref_audio = FileSelectorWidget("ref_audio", FileSelectionMode.FILE, "Audio (*.wav *.mp3)")
+        self.input_ref_text = QLineEdit()
+        self.input_ref_text.setPlaceholderText("请输入参考音频对应的文本...")
+        btn_play_ref = QPushButton("▶️")
+        btn_play_ref.setFixedWidth(30)
+        btn_play_ref.clicked.connect(self._play_ref_audio)
+        hbox_ref_text = QHBoxLayout()
+        hbox_ref_text.addWidget(self.input_ref_text)
+        hbox_ref_text.addWidget(btn_play_ref)
+        layout_ref.addRow("音频文件:", self.file_ref_audio)
+        layout_ref.addRow("音频文本:", hbox_ref_text)
+        group_ref.setLayout(layout_ref)
+
+        left_column_layout.addWidget(group_model)
+        left_column_layout.addWidget(group_ref)
+        left_column_layout.addStretch()
+
+        # ==================== 右侧列内容 ====================
+
+        # === 推理设置组 ===
+        group_infer = QGroupBox("推理参数")
+        layout_infer = QFormLayout()
+        self.combo_device = MyComboBox()
+        self.combo_device.addItems(["CPU"])
+        self.combo_device.setEnabled(False)
+        self.combo_quality = MyComboBox()
+        self.combo_quality.addItems(["质量优先"])
+        self.combo_quality.setEnabled(False)
+        self.combo_split = MyComboBox()
+        self.combo_split.addItems(["不切分", "智能切分", "按行切分"])
+        self.combo_mode = MyComboBox()
+        self.combo_mode.addItems(["串行推理"])
+        self.combo_mode.setEnabled(False)
+        self.combo_lang = MyComboBox()
+        self.combo_lang.addItems(["Chinese", "English", "Japanese"])
+        layout_infer.addRow("推理设备:\n(重启生效)", self.combo_device)
+        layout_infer.addRow("推理需求:", self.combo_quality)
+        layout_infer.addRow("分句方式:", self.combo_split)
+        layout_infer.addRow("推理模式:", self.combo_mode)
+        layout_infer.addRow("目标语言:", self.combo_lang)
+        group_infer.setLayout(layout_infer)
+
+        # === 自动保存组 ===
+        group_save = QGroupBox("自动保存设置")
+        self.layout_save = QFormLayout()
+        self.combo_save_mode = MyComboBox()
+        self.combo_save_mode.addItems(["禁用自动保存", "保存为单个文件", "保存为多个文件"])
+        self.combo_save_mode.currentIndexChanged.connect(self._update_save_ui_state)
+        default_out_path = os.path.join(os.path.expanduser("~"), "Desktop", "Genie 输出语音")
+        self.file_out_dir = FileSelectorWidget("out_dir", FileSelectionMode.DIRECTORY)
+        self.file_out_dir.set_path(default_out_path)
+        self.layout_save.addRow("保存方式:", self.combo_save_mode)
+        self.layout_save.addRow("输出文件夹:", self.file_out_dir)
+        group_save.setLayout(self.layout_save)
+
+        right_column_layout.addWidget(group_infer)
+        right_column_layout.addWidget(group_save)
+        right_column_layout.addStretch()
+
+        content_layout.addLayout(left_column_layout, 1)
+        content_layout.addLayout(right_column_layout, 1)
+        scroll.setWidget(content_widget)
+        main_layout.addWidget(scroll, 5)
+
+        # ==================== 底部：输入控制 + 输出预览 ====================
+
+        # 创建底部容器 widget
+        bottom_widget = QWidget()
+        bottom_layout = QHBoxLayout(bottom_widget)
+        bottom_layout.setContentsMargins(0, 0, 0, 0)  # 去除边距让它贴合
+
+        # --- 输入控制组 ---
+        group_input = QGroupBox("目标文本")
+        layout_input = QVBoxLayout()
+        self.text_input = MyTextEdit()
+        self.text_input.setPlaceholderText("请输入要合成的目标文本...")
+        self.text_input.setFixedHeight(300)
+        self.btn_start = QPushButton("开始推理")
+        self.btn_start.setFixedHeight(40)
+        self.btn_start.setStyleSheet("""
+            QPushButton {
+                background-color: #4CAF50; 
+                color: white; 
+                font-weight: bold; 
+                border-radius: 5px;
+            }
+            QPushButton:hover { background-color: #45a049; }
+            QPushButton:disabled { background-color: #cccccc; }
+        """)
+        self.btn_start.clicked.connect(self._start_inference)
+        layout_input.addWidget(self.text_input)
+        layout_input.addWidget(self.btn_start)
+        group_input.setLayout(layout_input)
+
+        # --- 输出预览组 ---
+        group_preview = QGroupBox("输出音频预览")
+        preview_layout = QVBoxLayout()
+        self.preview_scroll = QScrollArea()
+        self.preview_scroll.setWidgetResizable(True)
+        self.preview_container = QWidget()
+        self.preview_list_layout = QVBoxLayout(self.preview_container)
+        self.preview_list_layout.setAlignment(Qt.AlignmentFlag.AlignTop)
+        self.preview_scroll.setWidget(self.preview_container)
+        preview_layout.addWidget(self.preview_scroll)
+        group_preview.setLayout(preview_layout)
+
+        bottom_layout.addWidget(group_input, 1)
+        bottom_layout.addWidget(group_preview, 1)
+        main_layout.addWidget(bottom_widget, 3)
+
+        self.apply_ui_state(self.preset_manager.current_preset_data)
+
+    # ==================== 状态管理接口 (供 PresetManager 调用) ====================
+
+    @property
+    def current_preset_name(self) -> str:
+        return self.preset_manager.current_preset_name
+
+    @property
+    def current_preset_data(self) -> dict:
+        return self.preset_manager.current_preset_data
+
+    def get_ui_state(self) -> dict:
+        """收集当前UI状态为字典"""
+        return {
+            "model_type": self.combo_model_type.currentText(),
+            "gpt_path": self.file_gpt.get_path(),
+            "vits_path": self.file_vits.get_path(),
+            "genie_dir": self.file_genie.get_path(),
+            "ref_audio": self.file_ref_audio.get_path(),
+            "ref_text": self.input_ref_text.text(),
+            "device": self.combo_device.currentText().lower(),
+            "quality": self.combo_quality.currentText(),
+            "split": self.combo_split.currentText(),
+            "mode": self.combo_mode.currentText(),
+            "lang": self.combo_lang.currentText(),
+            "save_mode": self.combo_save_mode.currentText(),
+            "out_dir": self.file_out_dir.get_path()
+        }
+
+    @Slot(dict)
+    def apply_ui_state(self, data: dict) -> None:
+        """将字典数据应用到UI"""
+
+        def set_combo_text(combo: MyComboBox, text: str) -> None:
+            index = combo.findText(text)
+            if index >= 0:
+                combo.setCurrentIndex(index)
+
+        set_combo_text(self.combo_model_type, data.get("model_type", ""))
+        self.file_gpt.set_path(data.get("gpt_path", ""), block_signals=True)
+        self.file_vits.set_path(data.get("vits_path", ""), block_signals=True)
+        self.file_genie.set_path(data.get("genie_dir", ""))
+        self.file_ref_audio.set_path(data.get("ref_audio", ""))
+        self.input_ref_text.setText(data.get("ref_text", ""))
+
+        set_combo_text(self.combo_device, data.get("device", ""))
+        set_combo_text(self.combo_quality, data.get("quality", ""))
+        set_combo_text(self.combo_split, data.get("split", ""))
+        set_combo_text(self.combo_mode, data.get("mode", ""))
+        set_combo_text(self.combo_lang, data.get("lang", ""))
+        set_combo_text(self.combo_save_mode, data.get("save_mode", ""))
+
+        self.file_out_dir.set_path(data.get("out_dir", ""))
+
+        # 确保UI显隐状态正确
+        self._update_model_ui_visibility()
+        self._update_save_ui_state()
+
+    # ==================== UI 逻辑处理 ====================
+
+    def _update_model_ui_visibility(self, *args) -> None:
+        """根据模型类型控制文件选择器的显隐"""
+        is_gpt = self.combo_model_type.currentText() == "GPT-SoVITS"
+        self.layout_model.setRowVisible(self.file_gpt, is_gpt)
+        self.layout_model.setRowVisible(self.file_vits, is_gpt)
+        self.layout_model.setRowVisible(self.file_genie, not is_gpt)
+
+    @Slot(str)
+    def _on_gpt_path_changed(self, path: str):
+        if path and os.path.exists(path) and not self.file_vits.get_path():
+            self._try_auto_fill_sibling(path, ".pth", self.file_vits)
+
+    @Slot(str)
+    def _on_vits_path_changed(self, path: str):
+        if path and os.path.exists(path) and not self.file_gpt.get_path():
+            self._try_auto_fill_sibling(path, ".ckpt", self.file_gpt)
+
+    @staticmethod
+    def _try_auto_fill_sibling(current_path: str, target_ext: str, target_widget: FileSelectorWidget):
+        try:
+            directory = os.path.dirname(current_path)
+            if not os.path.exists(directory):
+                return
+            for f in os.listdir(directory):
+                if f.lower().endswith(target_ext.lower()):
+                    full_path = os.path.join(directory, f)
+                    target_widget.set_path(full_path)
+                    print(f"[INFO] 自动关联模型文件: {full_path}")
+                    break
+        except Exception as e:
+            print(f"[WARN] 自动关联文件失败: {e}")
+
+    def _update_save_ui_state(self) -> None:
+        enabled = self.combo_save_mode.currentText() != "禁用自动保存"
+        self.layout_save.setRowVisible(self.file_out_dir, enabled)
+
+    def _play_ref_audio(self) -> None:
+        path = self.file_ref_audio.get_path()
+        if os.path.exists(path):
+            self.player.stop()
+            self.player.play(path)
+        else:
+            QMessageBox.warning(self, "错误", "参考音频文件不存在")
+
+    def _get_split_texts(self, text: str) -> List[str]:
+        method = self.combo_split.currentText()
+        if method == "不切分":
+            return [text]
+        elif method == "按行切分":
+            return [line.strip() for line in text.split('\n') if line.strip()]
+        elif method == "智能切分":
+            return self.splitter.split(text)
+        return [text]
+
+    def _start_inference(self) -> None:
+        text = self.text_input.toPlainText().strip()
+        if not text:
+            QMessageBox.warning(self, "提示", "请输入目标文本")
+            return
+
+        ref_path = self.file_ref_audio.get_path()
+        ref_text = self.input_ref_text.text().strip()
+        if not ref_path or not ref_text:
+            QMessageBox.warning(self, "提示", "请设置参考音频")
+            return
+
+        if not self.file_genie.get_path():
+            QMessageBox.warning(self, "提示", "请选择Genie模型目录")
+            return
+
+        out_dir = self.file_out_dir.get_path()
+        save_mode = self.combo_save_mode.currentText()
+        if not out_dir and save_mode != "禁用自动保存":
+            desktop = os.path.join(os.path.expanduser("~"), "Desktop", "Genie Output")
+            self.file_out_dir.set_path(desktop)
+            print(f"[INFO] 未设置输出文件夹, 将在桌面创建!")
+
+        self.btn_start.setEnabled(False)
+        self.btn_start.setText("推理中...")
+        self._chain_import_model()
+
+    # ==================== 推理链式调用 ====================
+
+    def _chain_import_model(self) -> None:
+        req = {
+            "character_name": self.current_preset_name,
+            "onnx_model_dir": self.file_genie.get_path(),
+            "language": self.combo_lang.currentText(),
+        }
+        worker = InferenceWorker(req, mode="load_character")
+        worker.finished.connect(lambda s, m, d: self._on_import_finished(s, m))
+        worker.start()
+        self.current_worker = worker
+
+    @Slot(bool, str)
+    def _on_import_finished(self, success: bool, msg: str) -> None:
+        if not success:
+            self._reset_ui_state()
+            QMessageBox.critical(self, "模型加载失败", msg)
+            return
+        print(f"[INFO] {msg}")
+        self._chain_set_ref()
+
+    def _chain_set_ref(self) -> None:
+        req = {
+            "character_name": self.current_preset_name,
+            "audio_path": self.file_ref_audio.get_path(),
+            "audio_text": self.input_ref_text.text().strip(),
+            "language": self.combo_lang.currentText(),
+        }
+        worker = InferenceWorker(req, mode="set_reference_audio")
+        worker.finished.connect(lambda s, m, d: self._on_set_ref_finished(s, m))
+        worker.start()
+        self.current_worker = worker
+
+    @Slot(bool, str)
+    def _on_set_ref_finished(self, success: bool, msg: str) -> None:
+        if not success:
+            self._reset_ui_state()
+            QMessageBox.critical(self, "设置参考音频失败", msg)
+            return
+        print(f"[INFO] {msg}")
+        self._chain_tts()
+
+    def _chain_tts(self) -> None:
+        text_full = self.text_input.toPlainText().strip()
+        text_list = self._get_split_texts(text_full)
+
+        print(f"[INFO] 开始串行推理, 分句结果: {text_list}")
+        self._process_serial_step(0, text_list, [], 32000)
+
+    def _process_serial_step(
+            self,
+            index: int,
+            text_list: List[str],
+            audio_accumulator: List[np.ndarray],
+            sample_rate: int
+    ) -> None:
+        # 1. 终止条件：所有句子处理完毕
+        if index >= len(text_list):
+            save_mode = self.combo_save_mode.currentText()
+            out_dir = self.file_out_dir.get_path()
+
+            if out_dir:
+                os.makedirs(out_dir, exist_ok=True)
+
+            if audio_accumulator and save_mode != "保存为多个文件":
+                full_text = ''.join(text_list)
+                full_audio = np.concatenate(audio_accumulator, axis=0)
+                if save_mode == "保存为单个文件":
+                    target_names = generate_output_filenames(folder=out_dir, original_texts=[full_text])
+                    save_path = os.path.join(out_dir, target_names[0])
+                else:  # "禁用自动保存"
+                    save_path = os.path.join(CACHE_DIR, f"{uuid.uuid4().hex}.wav")
+                sf.write(save_path, data=full_audio, samplerate=sample_rate, subtype='PCM_16')
+                self._add_to_preview(full_text, save_path)
+
+            print(f"\n[INFO] 串行推理全部完成，共 {len(text_list)} 句。")
+            self._reset_ui_state()
+            return
+
+        # 2. 递归进行：发起当前句子的请求
+        req = {
+            "character_name": self.current_preset_name,
+            "text": text_list[index],
+        }
+        worker = InferenceWorker(req, mode="tts")
+        worker.finished.connect(
+            lambda s, m, d: self._on_serial_step_finished(s, m, d, index, text_list, audio_accumulator)
+        )
+        worker.start()
+        self.current_worker = worker
+
+    @Slot(bool, str, object, int, object, object, object)
+    def _on_serial_step_finished(
+            self,
+            success: bool,
+            msg: str,
+            return_data: dict,
+            index: int,
+            text_list: List[str],
+            audio_accumulator: List[np.ndarray]
+    ) -> None:
+        if not success:
+            self._reset_ui_state()
+            QMessageBox.critical(self, "推理失败", f"第 {index + 1} 句出错: {msg}")
+            return
+
+        sr = return_data.get("sample_rate", 32000)
+        audio_list = return_data.get("audio_list", [])
+        save_mode = self.combo_save_mode.currentText()
+        out_dir = self.file_out_dir.get_path()
+        if out_dir:
+            os.makedirs(out_dir, exist_ok=True)
+
+        if audio_list:
+            audio_accumulator.append(audio_list[0])
+            if save_mode == "保存为多个文件":
+                target_names = generate_output_filenames(folder=out_dir, original_texts=[text_list[index]])
+                save_path = os.path.join(out_dir, target_names[0])
+                sf.write(save_path, data=audio_list[0], samplerate=sr, subtype='FLOAT')
+                self._add_to_preview(text_list[index], save_path)
+        else:
+            print(f"[WARN] 第 {index + 1} 句返回空音频")
+
+        # 继续处理下一句
+        self._process_serial_step(index + 1, text_list, audio_accumulator, sr)
+
+    def _add_to_preview(self, text: str, path: str) -> None:
+        item = PreviewItemWidget(self.current_gen_id, text, path, self.player)
+        self.preview_list_layout.insertWidget(0, item)
+        self.current_gen_id += 1
+
+    def _reset_ui_state(self) -> None:
+        self.btn_start.setEnabled(True)
+        self.btn_start.setText("开始推理")
+
+    def closeEvent(self, event: QCloseEvent) -> None:
+        # 委托 PresetManager 处理保存逻辑
+        self.preset_manager.shutdown()
+        super().closeEvent(event)
+
+
+class MainWindow(QMainWindow):
+    def __init__(self):
+        super().__init__()
+        self.setWindowTitle("Genie TTS Inference GUI")
+        self.resize(1300, 900)
+
+        # 初始化音频播放器
+        self.player: AudioPlayer = AudioPlayer()
+
+        # 初始化日志重定向
+        self.log_widget: LogWidget = LogWidget()
+        sys.stdout = LogRedirector()
+        sys.stdout.textWritten.connect(self.log_widget.append_log)
+
+        # 初始化主界面
+        self.tabs: QTabWidget = QTabWidget()
+        self.tts_widget = TTSWidget(self.player)
+        self.conv_widget = ConverterWidget()
+
+        self.tabs.addTab(self.log_widget, "GUI Log")
+        self.tabs.addTab(self.tts_widget, "TTS Inference")
+        self.tabs.addTab(self.conv_widget, "Converter")
+        self.tabs.setCurrentIndex(1)  # 默认显示TTS页
+
+        self.setCentralWidget(self.tabs)
+
+    def closeEvent(self, event: QCloseEvent) -> None:
+        if os.path.exists(CACHE_DIR):
+            shutil.rmtree(CACHE_DIR)
+        if hasattr(self, 'player'):
+            self.player.stop()
+        # 线程安全退出后，再恢复 stdout
+        sys.stdout = sys.__stdout__
+        if hasattr(self, 'tts_widget'):
+            self.tts_widget.closeEvent(event)
+        event.accept()
diff --git a/genie_tts/GUI/PresetManager.py b/genie_tts/GUI/PresetManager.py
new file mode 100644
index 0000000000000000000000000000000000000000..15f25131bfcfb19f10cea7267651241c8f2be497
--- /dev/null
+++ b/genie_tts/GUI/PresetManager.py
@@ -0,0 +1,188 @@
+import os
+import json
+from typing import Callable, Optional, Dict
+
+from PySide6.QtWidgets import (
+    QWidget, QVBoxLayout, QHBoxLayout, QPushButton, QLabel,
+    QComboBox, QGroupBox, QMessageBox, QInputDialog
+)
+from PySide6.QtCore import Signal, QSettings, Slot
+
+
+class PresetManager(QWidget):
+    # 信号：通知主界面加载数据
+    sig_load_state = Signal(dict)
+
+    def __init__(self,
+                 presets_file: str,
+                 state_getter: Callable[[], dict],
+                 parent: QWidget = None):
+        super().__init__(parent)
+        self.presets_file: str = presets_file
+        self.state_getter: Callable[[], dict] = state_getter
+        self.presets: Dict[str, dict] = {}
+        self.current_preset_name: Optional[str] = None
+        self._init_ui()
+        self._load_from_disk()
+        # 初始化完成后，应用一次初始状态
+        self._apply_initial_preset()
+
+    def _init_ui(self):
+        layout = QVBoxLayout(self)
+        layout.setContentsMargins(0, 0, 0, 0)
+
+        group = QGroupBox("预设管理 (自动保存)")
+        h_layout = QHBoxLayout()
+
+        self.lbl_info = QLabel("当前:")
+
+        self.combo_presets = QComboBox()
+        self.combo_presets.textActivated.connect(self._on_preset_switch_triggered)
+
+        btn_new = QPushButton("新建")
+        btn_new.clicked.connect(self.create_preset)
+
+        # === 新增：重命名按钮 ===
+        btn_rename = QPushButton("重命名")
+        btn_rename.clicked.connect(self.rename_preset)
+        # =====================
+
+        btn_del = QPushButton("删除")
+        btn_del.clicked.connect(self.delete_preset)
+
+        h_layout.addWidget(self.lbl_info)
+        h_layout.addWidget(self.combo_presets, 1)
+        h_layout.addWidget(btn_new)
+        h_layout.addWidget(btn_rename)  # 添加到布局
+        h_layout.addWidget(btn_del)
+
+        group.setLayout(h_layout)
+        layout.addWidget(group)
+
+    @property
+    def current_preset_data(self) -> dict:
+        return self.presets.get(self.current_preset_name, {})
+
+    def _load_from_disk(self):
+        """从磁盘加载 JSON"""
+        if os.path.exists(self.presets_file):
+            try:
+                with open(self.presets_file, 'r', encoding='utf-8') as f:
+                    self.presets = json.load(f)
+            except Exception as e:
+                print(f"[ERROR] 预设文件损坏: {e}")
+                self.presets = {}
+
+        if not self.presets:
+            self.presets = {"Default": {}}
+
+        self.combo_presets.clear()
+        self.combo_presets.addItems(list(self.presets.keys()))
+
+    def _save_to_disk(self):
+        """写入磁盘"""
+        try:
+            os.makedirs(os.path.dirname(self.presets_file), exist_ok=True)
+            with open(self.presets_file, 'w', encoding='utf-8') as f:
+                json.dump(self.presets, f, indent=4, ensure_ascii=False)
+        except Exception as e:
+            print(f"[ERROR] 保存预设失败: {e}")
+
+    def _apply_initial_preset(self):
+        """初始化时恢复上次的选择"""
+        last_used = QSettings("MyTTS", "GUI").value("last_preset", "Default")
+        if last_used not in self.presets and self.combo_presets.count() > 0:
+            last_used = self.combo_presets.itemText(0)
+        self.current_preset_name = last_used
+        self.combo_presets.setCurrentText(last_used)
+        self._load_preset_data(last_used)
+
+    @Slot(str)
+    def _on_preset_switch_triggered(self, new_preset_name: str):
+        if new_preset_name == self.current_preset_name:
+            return
+        if self.current_preset_name:
+            self._save_current_state_to_memory(self.current_preset_name)
+        self._load_preset_data(new_preset_name)
+        self.current_preset_name = new_preset_name
+        QSettings("MyTTS", "GUI").setValue("last_preset", new_preset_name)
+        self._save_to_disk()
+
+    def _save_current_state_to_memory(self, preset_name: str):
+        """调用回调获取主界面状态，并更新到内存字典"""
+        if self.state_getter and preset_name in self.presets:
+            current_data = self.state_getter()
+            self.presets[preset_name] = current_data
+
+    def _load_preset_data(self, preset_name: str):
+        """发送信号给主界面加载数据"""
+        data = self.presets.get(preset_name, {})
+        self.sig_load_state.emit(data)
+        print(f"[INFO] 已加载预设: {preset_name}")
+
+    # ================= 公共接口 =================
+
+    def create_preset(self):
+        """新建预设"""
+        if self.current_preset_name:
+            self._save_current_state_to_memory(self.current_preset_name)
+
+        name, ok = QInputDialog.getText(self, "新建预设", "名称:")
+        if ok and name:
+            if name in self.presets:
+                QMessageBox.warning(self, "警告", "预设名已存在")
+                return
+            self.presets[name] = {}
+            self.combo_presets.addItem(name)
+            self.combo_presets.setCurrentText(name)
+            self.current_preset_name = name
+            self._save_to_disk()
+            self._load_preset_data(name)
+            print(f"[INFO] 已创建预设: {name}")
+
+    def rename_preset(self):
+        """重命名当前预设"""
+        current_name = self.current_preset_name
+        if not current_name:
+            return
+
+        # 先保存当前状态到内存，确保重命名时带走的是最新数据
+        self._save_current_state_to_memory(current_name)
+
+        new_name, ok = QInputDialog.getText(self, "重命名预设", "新名称:", text=current_name)
+        if ok and new_name and new_name != current_name:
+            if new_name in self.presets:
+                QMessageBox.warning(self, "警告", "预设名已存在")
+                return
+            # 迁移数据
+            self.presets[new_name] = self.presets.pop(current_name)
+            self.current_preset_name = new_name
+            # 更新下拉框显示的文本（更新当前选中的这一项）
+            current_index = self.combo_presets.currentIndex()
+            self.combo_presets.setItemText(current_index, new_name)
+            # 更新配置记录
+            QSettings("MyTTS", "GUI").setValue("last_preset", new_name)
+            self._save_to_disk()
+            print(f"[INFO] 已重命名预设: {current_name} -> {new_name}")
+
+    def delete_preset(self):
+        """删除当前预设"""
+        target = self.current_preset_name
+        if len(self.presets) <= 1:
+            QMessageBox.warning(self, "禁止", "至少保留一个预设")
+            return
+
+        if QMessageBox.StandardButton.Yes == QMessageBox.question(self, "确认", f"删除 '{target}'?"):
+            del self.presets[target]
+            self.combo_presets.removeItem(self.combo_presets.currentIndex())
+            new_name = self.combo_presets.currentText()
+            self.current_preset_name = new_name
+            self._load_preset_data(new_name)
+            self._save_to_disk()
+            print(f"[INFO] 已删除预设: {target}")
+
+    def shutdown(self):
+        """关闭时触发"""
+        if self.current_preset_name:
+            self._save_current_state_to_memory(self.current_preset_name)
+        self._save_to_disk()
diff --git a/genie_tts/GUI/ServerManager.py b/genie_tts/GUI/ServerManager.py
new file mode 100644
index 0000000000000000000000000000000000000000..7928b80aba69632032eff0a25143faebe7c719c1
--- /dev/null
+++ b/genie_tts/GUI/ServerManager.py
@@ -0,0 +1,61 @@
+from PySide6.QtCore import Signal, QThread
+
+from ..Utils.Shared import context
+from ..Internal import load_character, set_reference_audio
+from ..Core.Inference import tts_client
+from ..ModelManager import model_manager
+
+
+class InferenceWorker(QThread):
+    """执行推理任务的 Worker"""
+    finished = Signal(bool, str, object)  # success, message, data
+
+    def __init__(self, request_data: dict, mode: str):
+        super().__init__()
+        self.req: dict = request_data
+        self.mode: str = mode
+
+    def run(self) -> None:
+        try:
+            if self.mode == 'load_character':
+                load_character(
+                    character_name=self.req['character_name'],
+                    onnx_model_dir=self.req['onnx_model_dir'],
+                    language=self.req['language'],
+                )
+                self.finished.emit(True, "导入角色完成", None)
+
+            elif self.mode == 'set_reference_audio':
+                set_reference_audio(
+                    character_name=self.req['character_name'],
+                    audio_path=self.req['audio_path'],
+                    audio_text=self.req['audio_text'],
+                    language=self.req['language'],
+                )
+                self.finished.emit(True, "设置参考音频完成", None)
+
+            elif self.mode == 'tts':
+                gsv_model = model_manager.get(self.req['character_name'])
+                tts_client.stop_event.clear()
+                audio_chunk = tts_client.tts(
+                    text=self.req['text'],
+                    prompt_audio=context.current_prompt_audio,
+                    encoder=gsv_model.T2S_ENCODER,
+                    first_stage_decoder=gsv_model.T2S_FIRST_STAGE_DECODER,
+                    stage_decoder=gsv_model.T2S_STAGE_DECODER,
+                    vocoder=gsv_model.VITS,
+                    prompt_encoder=gsv_model.PROMPT_ENCODER,
+                    language=gsv_model.LANGUAGE,
+                )
+                audio_chunk = audio_chunk.squeeze()
+                try:
+                    return_data = {
+                        "sample_rate": 32000,
+                        "audio_list": [audio_chunk],
+                    }
+                    self.finished.emit(True, "推理完成", return_data)
+                except Exception as e:
+                    self.finished.emit(False, f"数据解析失败: {e}", None)
+
+        except Exception as e:
+            self.finished.emit(False, f"请求异常: {str(e)}", None)
diff --git a/genie_tts/GUI/Utils.py b/genie_tts/GUI/Utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c801e2b4635bcba08c40aa64926b9ad408252727
--- /dev/null
+++ b/genie_tts/GUI/Utils.py
@@ -0,0 +1,257 @@
+import os
+import re
+from enum import Enum
+from datetime import datetime
+import socket
+from typing import List
+
+from PySide6.QtWidgets import (
+    QWidget, QHBoxLayout, QPushButton, QLineEdit, QFileDialog, QMessageBox, QComboBox, QTextEdit
+)
+from PySide6.QtCore import (Qt, Signal, QSettings, Property, QObject, QEvent, QMimeData)
+from PySide6.QtGui import QFont
+
+
+def sanitize_filename(filename: str, replacement: str = '') -> str:
+    """
+    将文本清理为合法的 Windows 文件名。
+
+    Args:
+        filename (str): 原始文件名。
+        replacement (str): 非法字符的替换字符，默认为空，建议使用 "_"。
+
+    Returns:
+        str: 清理后的文件名。
+    """
+    # 1. 去除非法字符
+    # \/:*?"<>| 是标准非法字符
+    # \x00-\x1f 是控制字符 (如换行符、制表符等)，Windows 也不允许
+    cleaned = re.sub(r'[\\/:*?"<>|\x00-\x1f]', replacement, filename)
+
+    # 2. 去除首尾的空格和点
+    # Windows 文件名不能以空格或点结尾，也不能以空格开头(虽然允许但通常不推荐)
+    cleaned = cleaned.strip().rstrip('.')
+
+    # 3. 处理 Windows 保留文件名 (CON, PRN, AUX, NUL, COM1-9, LPT1-9)
+    # 这些名字不论加什么扩展名都是非法的 (例如 con.txt 也是非法的)
+    reserved_names = {
+        "CON", "PRN", "AUX", "NUL",
+        "COM1", "COM2", "COM3", "COM4", "COM5", "COM6", "COM7", "COM8", "COM9",
+        "LPT1", "LPT2", "LPT3", "LPT4", "LPT5", "LPT6", "LPT7", "LPT8", "LPT9"
+    }
+
+    # 如果文件名(全大写)是保留字，或者文件名是保留字+扩展名(如 con.txt)，则加下划线前缀
+    filename_upper = cleaned.upper()
+    file_stem = filename_upper.split('.')[0]  # 获取不带后缀的主文件名
+
+    if filename_upper in reserved_names or file_stem in reserved_names:
+        cleaned = "_" + cleaned
+
+    # 4. 处理空文件名 (如果输入全是乱码被删光了)
+    if not cleaned:
+        cleaned = "unnamed_file"
+
+    # 5. 限制长度 (Windows API 通常限制 255 字符，但在某些路径下更短)
+    cleaned = truncate_text(cleaned)
+
+    return cleaned
+
+
+def is_port_free(port: int) -> bool:
+    """返回端口是否可用"""
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        try:
+            s.bind(('', port))
+        except OSError:
+            return False
+    return True
+
+
+def find_free_port(preferred: int = 8000) -> int:
+    # 先尝试 preferred 端口
+    if is_port_free(preferred):
+        return preferred
+
+    # 否则用系统自动分配
+    s = socket.socket()
+    s.bind(('', 0))
+    port = s.getsockname()[1]
+    s.close()
+    return port
+
+
+def truncate_text(text: str, max_len: int = 30) -> str:
+    """
+    按指定宽度截断文本
+    """
+    result = ""
+    cur_len = 0
+    for ch in text:
+        char_len = 2 if ('\u4e00' <= ch <= '\u9fff') else 1
+        if cur_len + char_len > max_len:
+            break
+        result += ch
+        cur_len += char_len
+    return result
+
+
+def generate_output_filenames(folder: str, original_texts: List[str]) -> List[str]:
+    """
+    批量生成文件名：
+    输入 original_texts 列表
+    输出等长 filenames 列表
+    """
+    today = datetime.now().strftime("%Y%m%d")
+    pattern = re.compile(rf'^\[{today}]\[(\d{{3}})]')
+
+    # ① 查找当天现存最大编号
+    max_n = 0
+    if os.path.isdir(folder):
+        for name in os.listdir(folder):
+            m = pattern.match(name)
+            if m:
+                n = int(m.group(1))
+                max_n = max(max_n, n)
+
+    filenames = []
+    cur_n = max_n
+
+    # ② 依次生成新文件名
+    for text in original_texts:
+        cur_n += 1
+        n_str = f"{cur_n:03d}"
+
+        cleaned = sanitize_filename(text)
+
+        filename = f"[{today}][{n_str}]{cleaned}.wav"
+        filenames.append(filename)
+
+    return filenames
+
+
+# ==================== 通用组件 ====================
+
+class FileSelectionMode(Enum):
+    FILE = 0
+    DIRECTORY = 1
+
+
+class FileSelectorWidget(QWidget):
+    """一个包含行编辑和浏览按钮的复合控件，支持文件和文件夹选择。（原样引用并稍作适配）"""
+    pathChanged = Signal(str)
+
+    def __init__(
+            self,
+            setting_key: str,
+            selection_mode: FileSelectionMode = FileSelectionMode.DIRECTORY,
+            file_filter: str = "All Files (*)",
+            parent: QWidget = None,
+    ):
+        super().__init__(parent)
+        self.setting_key: str = setting_key
+        self.selection_mode: FileSelectionMode = selection_mode
+        self.file_filter: str = file_filter
+
+        # 使用 QSettings 模拟 STUDIO_SETTINGS
+        self.settings = QSettings("MyTTS", "GUI")
+
+        layout = QHBoxLayout(self)
+        layout.setContentsMargins(0, 0, 0, 0)
+        layout.setSpacing(5)
+
+        self.path_edit: QLineEdit = QLineEdit()
+        self.path_edit.setReadOnly(True)
+        self.path_edit.setPlaceholderText("未选择路径")
+
+        self.browse_button: QPushButton = QPushButton("📁")
+        self.browse_button.setCursor(Qt.CursorShape.PointingHandCursor)
+        default_font = QFont()
+        default_font.setPointSize(10)
+        self.browse_button.setFont(default_font)
+        self.browse_button.setFixedSize(30, 30)
+
+        self.clear_button: QPushButton = QPushButton("❌")
+        self.clear_button.setCursor(Qt.CursorShape.PointingHandCursor)
+        self.clear_button.setFont(default_font)
+        self.clear_button.setFixedSize(30, 30)
+
+        layout.addWidget(self.path_edit)
+        layout.addWidget(self.browse_button)
+        layout.addWidget(self.clear_button)
+
+        self.browse_button.clicked.connect(self._open_dialog)
+        self.clear_button.clicked.connect(self._clear_path)
+        self.path_edit.textChanged.connect(self.pathChanged)
+
+    def _open_dialog(self):
+        path = self.path_edit.text()
+        if path and os.path.exists(path):
+            start_path = path
+        else:
+            # 【修改点】默认路径改为 Desktop
+            desktop_path = os.path.join(os.path.expanduser("~"), "Desktop")
+            start_path = str(self.settings.value(
+                f"last_path_{self.setting_key}", defaultValue=desktop_path
+            ))
+
+        if self.selection_mode == FileSelectionMode.DIRECTORY:
+            selected_path = QFileDialog.getExistingDirectory(
+                self, "选择文件夹", start_path
+            )
+        else:
+            selected_path, _ = QFileDialog.getOpenFileName(
+                self, "选择文件", start_path, self.file_filter
+            )
+
+        if selected_path:
+            self.set_path(selected_path)
+            parent_path = os.path.dirname(selected_path)
+            if parent_path:
+                self.settings.setValue(
+                    f"last_path_{self.setting_key}", parent_path)
+
+    def _clear_path(self):
+        if not self.path_edit.text():
+            return
+        reply = QMessageBox.question(self, '确认', '您确定要清空路径吗？',
+                                     QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No,
+                                     QMessageBox.StandardButton.No)
+        if reply == QMessageBox.StandardButton.Yes:
+            self.set_path("")
+
+    def get_path(self) -> str:
+        text = self.path_edit.text()
+        # 即使路径不存在（可能是输入时），也返回文本供逻辑判断，或者严格校验
+        return text
+
+    def set_path(self, path: str, block_signals: bool = False):
+        if block_signals:
+            self.path_edit.blockSignals(True)
+        self.path_edit.setText(path)
+        if block_signals:
+            self.path_edit.blockSignals(False)
+
+    path = Property(str, fget=get_path, fset=set_path, notify=pathChanged)  # type: ignore
+
+
+class WheelEventFilter(QObject):
+    def eventFilter(self, obj, event):
+        if event.type() == QEvent.Type.Wheel and isinstance(obj, QComboBox):
+            return True  # 阻止默认滚轮行为
+        return super().eventFilter(obj, event)
+
+
+class MyComboBox(QComboBox):
+    def __init__(self, parent: QWidget = None):
+        super().__init__(parent)
+        self._wheelFilter = WheelEventFilter()
+        self.installEventFilter(self._wheelFilter)
+
+
+class MyTextEdit(QTextEdit):
+    def insertFromMimeData(self, source: QMimeData) -> None:
+        # 仅取纯文本
+        if source.hasText():
+            self.insertPlainText(source.text())
+        else:
+            super().insertFromMimeData(source)
diff --git a/genie_tts/GetPhonesAndBert.py b/genie_tts/GetPhonesAndBert.py
new file mode 100644
index 0000000000000000000000000000000000000000..c181a948a162341643864c289fbdc81b6a281850
--- /dev/null
+++ b/genie_tts/GetPhonesAndBert.py
@@ -0,0 +1,34 @@
+import numpy as np
+from typing import Tuple
+from .Utils.Constants import BERT_FEATURE_DIM
+from .ModelManager import model_manager
+
+
+def get_phones_and_bert(prompt_text: str, language: str = 'japanese') -> Tuple[np.ndarray, np.ndarray]:
+    if language.lower() == 'english':
+        from .G2P.English.EnglishG2P import english_to_phones
+        phones = english_to_phones(prompt_text)
+        text_bert = np.zeros((len(phones), BERT_FEATURE_DIM), dtype=np.float32)
+    elif language.lower() == 'chinese':
+        from .G2P.Chinese.ChineseG2P import chinese_to_phones
+        text_clean, _, phones, word2ph = chinese_to_phones(prompt_text)
+        if model_manager.load_roberta_model():
+            encoded = model_manager.roberta_tokenizer.encode(text_clean)
+            input_ids = np.array([encoded.ids], dtype=np.int64)
+            attention_mask = np.array([encoded.attention_mask], dtype=np.int64)
+            ort_inputs = {
+                'input_ids': input_ids,
+                'attention_mask': attention_mask,
+                'repeats': np.array(word2ph, dtype=np.int64),
+            }
+            outputs = model_manager.roberta_model.run(None, ort_inputs)
+            text_bert = outputs[0].astype(np.float32)
+        else:
+            text_bert = np.zeros((len(phones), BERT_FEATURE_DIM), dtype=np.float32)
+    else:
+        from .G2P.Japanese.JapaneseG2P import japanese_to_phones
+        phones = japanese_to_phones(prompt_text)
+        text_bert = np.zeros((len(phones), BERT_FEATURE_DIM), dtype=np.float32)
+
+    phones_seq = np.array([phones], dtype=np.int64)
+    return phones_seq, text_bert
diff --git a/genie_tts/Internal.py b/genie_tts/Internal.py
new file mode 100644
index 0000000000000000000000000000000000000000..77d87595b1aee44698d906c20c1d9d81589a1cbe
--- /dev/null
+++ b/genie_tts/Internal.py
@@ -0,0 +1,395 @@
+# 请严格遵循导入顺序。
+# 1、环境变量。
+import os
+from os import PathLike
+
+os.environ["HF_HUB_ENABLE_PROGRESS_BAR"] = "1"
+
+# 2、Logging & Warnings。
+import logging
+import warnings
+
+warnings.filterwarnings("ignore", category=UserWarning, module="jieba_fast._compat")
+logging.basicConfig(level=logging.INFO, format="%(message)s", datefmt="[%X]")
+logger = logging.getLogger(__name__)
+
+# 3、ONNX。
+import onnxruntime
+
+onnxruntime.set_default_logger_severity(3)
+
+# 导入剩余库。
+
+from pathlib import Path
+import json
+import asyncio
+from typing import AsyncIterator, Optional, Union, Dict
+
+from .Audio.ReferenceAudio import ReferenceAudio
+from .Core.Resources import ensure_exists, Chinese_G2P_DIR, English_G2P_DIR
+from .Core.TTSPlayer import tts_player
+from .ModelManager import model_manager
+from .Utils.Shared import context
+from .Utils.Language import normalize_language
+from .PredefinedCharacter import download_chara, CHARA_LANG, CHARA_ALIAS_MAP
+
+# A module-level private dictionary to store reference audio configurations.
+_reference_audios: Dict[str, dict] = {}
+SUPPORTED_AUDIO_EXTS = {'.wav', '.flac', '.ogg', '.aiff', '.aif'}
+
+
+def check_onnx_model_dir(onnx_model_dir: Union[str, os.PathLike]) -> None:
+    """
+    Checks if the directory contains the necessary ONNX model files for Genie TTS (v2 or v2ProPlus).
+    Raises a FileNotFoundError with detailed instructions if validation fails.
+    """
+    model_path = Path(onnx_model_dir)
+
+    # 1. Check if directory exists
+    if not model_path.exists() or not model_path.is_dir():
+        raise FileNotFoundError(f"The model directory '{onnx_model_dir}' does not exist or is not a directory.")
+
+    # 2. Define required files
+    # Base files required by both v2 and v2ProPlus
+    required_base_files = {
+        "t2s_encoder_fp32.bin",
+        "t2s_encoder_fp32.onnx",
+        "t2s_first_stage_decoder_fp32.onnx",
+        "t2s_shared_fp16.bin",
+        "t2s_stage_decoder_fp32.onnx",
+        "vits_fp16.bin",
+        "vits_fp32.onnx"
+    }
+
+    # 3. Get current files in directory
+    existing_files = set(f.name for f in model_path.iterdir() if f.is_file())
+
+    # 4. Validate
+    # We check if the base files exist. If base files are missing, the model is definitely unusable.
+    if not required_base_files.issubset(existing_files):
+        missing = required_base_files - existing_files
+
+        # Construct detailed error message
+        error_msg = (
+            f"\n\n[Genie Error] Invalid ONNX model directory: '{model_path}'\n"
+            "===============================================================\n"
+            f"Missing base files: {', '.join(missing)}\n"
+            "A valid model folder must contain at least the following files.\n"
+            "1. [v2 Base] (Required for all models):\n"
+            "   - t2s_encoder_fp32.bin\n"
+            "   - t2s_encoder_fp32.onnx\n"
+            "   - t2s_first_stage_decoder_fp32.onnx\n"
+            "   - t2s_shared_fp16.bin\n"
+            "   - t2s_stage_decoder_fp32.onnx\n"
+            "   - vits_fp16.bin\n"
+            "   - vits_fp32.onnx\n"
+            "2. [v2ProPlus Additions] (Required for v2pp features):\n"
+            "   - prompt_encoder_fp16.bin\n"
+            "   - prompt_encoder_fp32.onnx\n"
+            "===============================================================\n"
+        )
+        raise FileNotFoundError(error_msg)
+
+
+def load_character(
+        character_name: str,
+        onnx_model_dir: Union[str, PathLike],
+        language: str,
+) -> None:
+    """
+    Loads a character model from an ONNX model directory.
+
+    Args:
+        character_name (str): The name to assign to the loaded character.
+        onnx_model_dir (str | PathLike): The directory path containing the ONNX model files.
+        language (str): The language of the character model.
+    """
+    check_onnx_model_dir(onnx_model_dir)
+
+    language = normalize_language(language)
+    if language not in ['Japanese', 'English', 'Chinese']:
+        raise ValueError('Unknown language')
+
+    if language == 'Chinese':
+        ensure_exists(Chinese_G2P_DIR, "Chinese_G2P_DIR")
+    elif language == 'English':
+        ensure_exists(English_G2P_DIR, "English_G2P_DIR")
+
+    model_path: str = os.fspath(onnx_model_dir)
+    model_manager.load_character(
+        character_name=character_name,
+        model_dir=model_path,
+        language=language,
+    )
+
+
+def unload_character(
+        character_name: str,
+) -> None:
+    """
+    Unloads a previously loaded character model to free up resources.
+
+    Args:
+        character_name (str): The name of the character to unload.
+    """
+    model_manager.remove_character(
+        character_name=character_name,
+    )
+
+
+def set_reference_audio(
+        character_name: str,
+        audio_path: Union[str, PathLike],
+        audio_text: str,
+        language: str = None,
+) -> None:
+    """
+    Sets the reference audio for a character to be used for voice cloning.
+
+    This must be called for a character before using 'tts' or 'tts_async'.
+
+    Args:
+        character_name (str): The name of the character.
+        audio_path (str | PathLike): The file path to the reference audio (e.g., a WAV file).
+        audio_text (str): The transcript of the reference audio.
+        language (str): The language of the reference audio.
+    """
+    audio_path: str = os.fspath(audio_path)
+
+    # 检查文件后缀是否支持
+    ext = os.path.splitext(audio_path)[1].lower()
+    if ext not in SUPPORTED_AUDIO_EXTS:
+        logger.error(
+            f"Audio format '{ext}' is not supported. Only the following formats are supported: {SUPPORTED_AUDIO_EXTS}"
+        )
+        return
+
+    if language is None:
+        gsv_model = model_manager.get(character_name)
+        if gsv_model:
+            language = gsv_model.LANGUAGE
+        else:
+            raise ValueError('No language specified')
+    language = normalize_language(language)
+    if language not in ['Japanese', 'English', 'Chinese']:
+        raise ValueError('Unknown language')
+
+    _reference_audios[character_name] = {
+        'audio_path': audio_path,
+        'audio_text': audio_text,
+        'language': language,
+    }
+    # print(_reference_audios[character_name])
+    context.current_prompt_audio = ReferenceAudio(
+        prompt_wav=audio_path,
+        prompt_text=audio_text,
+        language=language,
+    )
+
+
+async def tts_async(
+        character_name: str,
+        text: str,
+        play: bool = False,
+        split_sentence: bool = False,
+        save_path: Union[str, PathLike, None] = None,
+) -> AsyncIterator[bytes]:
+    """
+    Asynchronously generates speech from text and yields audio chunks.
+
+    This function returns an async iterator that provides the audio data in
+    real-time as it's being generated.
+
+    Args:
+        character_name (str): The name of the character to use for synthesis.
+        text (str): The text to be synthesized into speech.
+        play (bool, optional): If True, plays the audio as it's generated. Defaults to False.
+        split_sentence (bool, optional): If True, splits the text into sentences for synthesis. Defaults to False.
+        save_path (str | PathLike | None, optional): If provided, saves the generated audio to this file path. Defaults to None.
+
+    Yields:
+        bytes: A chunk of the generated audio data.
+
+    Raises:
+        ValueError: If 'set_reference_audio' has not been called for the character.
+    """
+    if character_name not in _reference_audios:
+        raise ValueError("Please call 'set_reference_audio' first to set the reference audio.")
+
+    if save_path:
+        save_path = os.fspath(save_path)
+        parent_dir = os.path.dirname(save_path)
+        if parent_dir:
+            os.makedirs(parent_dir, exist_ok=True)
+
+    # 1. 创建 asyncio 队列和获取当前事件循环
+    stream_queue: asyncio.Queue[Union[bytes, None]] = asyncio.Queue()
+    loop = asyncio.get_running_loop()
+
+    # 2. 定义回调函数，用于在线程和 asyncio 之间安全地传递数据
+    def tts_chunk_callback(c: Optional[bytes]):
+        """This callback is called from the TTS worker thread."""
+        loop.call_soon_threadsafe(stream_queue.put_nowait, c)
+
+    # 设置 TTS 上下文
+    context.current_speaker = character_name
+    context.current_prompt_audio = ReferenceAudio(
+        prompt_wav=_reference_audios[character_name]['audio_path'],
+        prompt_text=_reference_audios[character_name]['audio_text'],
+        language=_reference_audios[character_name]['language'],
+    )
+
+    # 3. 使用新的回调接口启动 TTS 会话
+    tts_player.start_session(
+        play=play,
+        split=split_sentence,
+        save_path=save_path,
+        chunk_callback=tts_chunk_callback,
+    )
+
+    # 馈送文本并通知会话结束
+    tts_player.feed(text)
+    tts_player.end_session()
+
+    # 4. 从队列中异步读取数据并产生
+    while True:
+        chunk = await stream_queue.get()
+        if chunk is None:
+            break
+        yield chunk
+
+
+def tts(
+        character_name: str,
+        text: str,
+        play: bool = False,
+        split_sentence: bool = True,
+        save_path: Union[str, PathLike, None] = None,
+) -> None:
+    """
+    Synchronously generates speech from text.
+
+    This is a blocking function that will not return until the entire TTS
+    process is complete.
+
+    Args:
+        character_name (str): The name of the character to use for synthesis.
+        text (str): The text to be synthesized into speech.
+        play (bool, optional): If True, plays the audio.
+        split_sentence (bool, optional): If True, splits the text into sentences for synthesis.
+        save_path (str | PathLike | None, optional): If provided, saves the generated audio to this file path. Defaults to None.
+    """
+    if character_name not in _reference_audios:
+        logger.error("Please call 'set_reference_audio' first to set the reference audio.")
+        return
+
+    if save_path:
+        save_path = os.fspath(save_path)
+        parent_dir = os.path.dirname(save_path)
+        if parent_dir:
+            os.makedirs(parent_dir, exist_ok=True)
+
+    context.current_speaker = character_name
+    context.current_prompt_audio = ReferenceAudio(
+        prompt_wav=_reference_audios[character_name]['audio_path'],
+        prompt_text=_reference_audios[character_name]['audio_text'],
+        language=_reference_audios[character_name]['language'],
+    )
+
+    tts_player.start_session(
+        play=play,
+        split=split_sentence,
+        save_path=save_path,
+    )
+    tts_player.feed(text)
+    tts_player.end_session()
+    tts_player.wait_for_tts_completion()
+
+
+def wait_for_playback_done() -> None:
+    """
+    Wait until all TTS tasks have finished processing and playback has fully completed.
+    """
+    tts_player.wait_for_playback_done()
+
+
+def stop() -> None:
+    """
+    Stops the currently playing text-to-speech audio.
+    """
+    tts_player.stop()
+
+
+def convert_to_onnx(
+        torch_ckpt_path: Union[str, PathLike],
+        torch_pth_path: Union[str, PathLike],
+        output_dir: Union[str, PathLike],
+) -> None:
+    """
+    Converts PyTorch model checkpoints to the ONNX format.
+
+    This function requires PyTorch to be installed.
+
+    Args:
+        torch_ckpt_path (str | PathLike): The path to the T2S model (.ckpt) file.
+        torch_pth_path (str | PathLike): The path to the VITS model (.pth) file.
+        output_dir (str | PathLike): The directory where the ONNX models will be saved.
+    """
+    try:
+        import torch
+    except ImportError:
+        logger.error("❌ PyTorch is not installed. Please run `pip install torch` first.")
+        return
+
+    from .Converter.Converter import convert
+
+    torch_ckpt_path = os.fspath(torch_ckpt_path)
+    torch_pth_path = os.fspath(torch_pth_path)
+    output_dir = os.fspath(output_dir)
+
+    convert(
+        torch_pth_path=torch_pth_path,
+        torch_ckpt_path=torch_ckpt_path,
+        output_dir=output_dir,
+    )
+
+
+def clear_reference_audio_cache() -> None:
+    """
+    Clears the cache of reference audio data.
+    """
+    ReferenceAudio.clear_cache()
+
+
+def load_predefined_character(character_name: str) -> None:
+    """
+    Download and load a predefined character model for TTS inference.
+    """
+    character_name = character_name.lower().strip()
+    if character_name not in CHARA_ALIAS_MAP:
+        logger.error(f"No predefined character model found for {character_name}")
+        return
+    character_name = CHARA_ALIAS_MAP[character_name]
+
+    save_path = download_chara(character_name)
+    model_manager.load_character(
+        character_name=character_name,
+        model_dir=os.path.join(save_path, 'tts_models'),
+        language=CHARA_LANG[character_name],
+    )
+
+    with open(os.path.join(save_path, "prompt_wav.json"), "r", encoding="utf-8") as f:
+        prompt_wav_dict: Dict[str, Dict[str, str]] = json.load(f)
+
+    audio_text = prompt_wav_dict["Normal"]["text"]
+    audio_path = os.path.join(save_path, "prompt_wav", prompt_wav_dict["Normal"]["wav"])
+    _reference_audios[character_name] = {
+        'audio_path': audio_path,
+        'audio_text': audio_text,
+        'language': CHARA_LANG[character_name],
+    }
+    context.current_prompt_audio = ReferenceAudio(
+        prompt_wav=audio_path,
+        prompt_text=audio_text,
+        language=CHARA_LANG[character_name],
+    )
diff --git a/genie_tts/ModelManager.py b/genie_tts/ModelManager.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3e84bf65f6f378922fe88191e5ba1561a848e48
--- /dev/null
+++ b/genie_tts/ModelManager.py
@@ -0,0 +1,324 @@
+"""
+不再新建 .bin 文件。
+修改后内存: 6448 MB
+修改前内存: 5952 MB
+"""
+
+import gc
+import logging
+import os
+from dataclasses import dataclass
+from typing import Optional, List, Dict
+
+import numpy as np
+import onnx
+import onnxruntime
+from onnxruntime import InferenceSession
+from tokenizers import Tokenizer
+
+from .Core.Resources import (HUBERT_MODEL_DIR, SV_MODEL, ROBERTA_MODEL_DIR)
+from .Utils.Utils import LRUCacheDict
+
+onnxruntime.set_default_logger_severity(3)
+logger = logging.getLogger(__name__)
+
+
+class GSVModelFile:
+    T2S_ENCODER_FP32: str = 't2s_encoder_fp32.onnx'
+
+    T2S_FIRST_STAGE_DECODER_FP32: str = 't2s_first_stage_decoder_fp32.onnx'
+    T2S_FIRST_STAGE_DECODER_FP16: str = 't2s_first_stage_decoder_fp16.onnx'
+    T2S_STAGE_DECODER_FP32: str = 't2s_stage_decoder_fp32.onnx'
+    T2S_STAGE_DECODER_FP16: str = 't2s_stage_decoder_fp16.onnx'
+    T2S_DECODER_WEIGHT_FP16: str = 't2s_shared_fp16.bin'
+
+    VITS_FP32: str = 'vits_fp32.onnx'
+    VITS_WEIGHT_FP16: str = 'vits_fp16.bin'
+
+    PROMPT_ENCODER: str = 'prompt_encoder_fp32.onnx'
+    PROMPT_ENCODER_WEIGHT_FP16: str = 'prompt_encoder_fp16.bin'
+
+    HUBERT_MODEL = os.path.join(HUBERT_MODEL_DIR, "chinese-hubert-base.onnx")
+    HUBERT_MODEL_WEIGHT_FP16 = os.path.join(HUBERT_MODEL_DIR, "chinese-hubert-base_weights_fp16.bin")
+
+    ROBERTA_MODEL = os.path.join(ROBERTA_MODEL_DIR, 'RoBERTa.onnx')
+    ROBERTA_TOKENIZER = os.path.join(ROBERTA_MODEL_DIR, 'roberta_tokenizer')
+
+
+@dataclass
+class GSVModel:
+    LANGUAGE: str
+    T2S_ENCODER: InferenceSession
+    T2S_FIRST_STAGE_DECODER: InferenceSession
+    T2S_STAGE_DECODER: InferenceSession
+    VITS: InferenceSession
+    PROMPT_ENCODER: Optional[InferenceSession] = None
+    PROMPT_ENCODER_PATH: Optional[str] = None
+
+
+def load_session_with_fp16_conversion(
+        onnx_path: str,
+        fp16_bin_path: str,
+        providers: List[str],
+        sess_options: Optional[onnxruntime.SessionOptions] = None
+) -> InferenceSession:
+    """
+    通用函数：读取 ONNX 和 FP16 权重文件，在内存中将权重转换为 FP32，
+    注入到 ONNX 模型中并加载 InferenceSession，不产生临时文件。
+    """
+    if not os.path.exists(onnx_path):
+        raise FileNotFoundError(f"ONNX Model not found: {onnx_path}")
+    if not os.path.exists(fp16_bin_path):
+        raise FileNotFoundError(f"FP16 Weight file not found: {fp16_bin_path}")
+
+    model_proto = onnx.load(onnx_path, load_external_data=False)
+    fp16_data = np.fromfile(fp16_bin_path, dtype=np.float16)
+    fp32_data = fp16_data.astype(np.float32)
+    fp32_bytes = fp32_data.tobytes()
+
+    # 遍历并修补模型中的 External Data Initializers
+    for tensor in model_proto.graph.initializer:
+        # 检查该 Tensor 是否使用外部数据
+        if tensor.data_location == onnx.TensorProto.EXTERNAL:
+            offset = 0
+            length = 0
+            # 解析外部数据信息
+            for entry in tensor.external_data:
+                if entry.key == 'offset':
+                    offset = int(entry.value)
+                elif entry.key == 'length':
+                    length = int(entry.value)
+
+            if offset + length > len(fp32_bytes):
+                logger.warning(
+                    f"Tensor {tensor.name} requested a data range that exceeds the size of the provided bin file. "
+                    f"Offset: {offset}, Length: {length}, Buffer: {len(fp32_bytes)}"
+                )
+                continue
+
+            tensor_data = fp32_bytes[offset: offset + length]
+            tensor.raw_data = tensor_data
+
+            del tensor.external_data[:]
+            tensor.data_location = onnx.TensorProto.DEFAULT
+
+    try:
+        session = InferenceSession(
+            model_proto.SerializeToString(),
+            providers=providers,
+            sess_options=sess_options
+        )
+        return session
+    except Exception as e:
+        logger.error(f"Failed to load in-memory model {os.path.basename(onnx_path)}: {e}")
+        raise e
+
+
+class ModelManager:
+    def __init__(self):
+        capacity_str = os.getenv('Max_Cached_Character_Models', '3')
+        self.character_to_model: Dict[str, Dict[str, Optional[InferenceSession]]] = LRUCacheDict(
+            capacity=int(capacity_str)
+        )
+        self.character_to_language: Dict[str, str] = {}
+        self.character_model_paths: Dict[str, str] = {}
+        self.providers = ["CPUExecutionProvider"]
+
+        self.cn_hubert: Optional[InferenceSession] = None
+        self.speaker_verification_model: Optional[InferenceSession] = None
+        self.roberta_model: Optional[InferenceSession] = None
+        self.roberta_tokenizer: Optional[Tokenizer] = None
+
+    def load_roberta_model(self, model_path: str = GSVModelFile.ROBERTA_MODEL) -> bool:
+        if self.roberta_model is not None:
+            return True
+        if not os.path.exists(model_path):
+            # logger.warning(f'RoBERTa model does not exist: {model_path}. BERT features will not be used.')
+            return False
+        try:
+            self.roberta_model = onnxruntime.InferenceSession(
+                model_path,
+                providers=self.providers,
+            )
+            self.roberta_tokenizer = Tokenizer.from_file(
+                os.path.join(GSVModelFile.ROBERTA_TOKENIZER, 'tokenizer.json')
+            )
+            logger.info(f"Successfully loaded RoBERTa model.")
+            return True
+        except Exception as e:
+            logger.error(
+                f"Error: Failed to load ONNX model '{GSVModelFile.ROBERTA_MODEL}'.\n"
+                f"Details: {e}"
+            )
+        return False
+
+    def load_sv_model(self, model_path: str = SV_MODEL) -> bool:
+        if self.speaker_verification_model is not None:
+            return True
+        try:
+            self.speaker_verification_model = onnxruntime.InferenceSession(
+                model_path,
+                providers=self.providers,
+            )
+            logger.info(f"Successfully loaded Speaker Verification model.")
+            return True
+        except Exception as e:
+            logger.error(
+                f"Error: Failed to load ONNX model '{SV_MODEL}'.\n"
+                f"Details: {e}"
+            )
+        return False
+
+    def load_cn_hubert(self, model_path: str = GSVModelFile.HUBERT_MODEL) -> bool:
+        if self.cn_hubert is not None:
+            return True
+        try:
+            # Hubert 也应用内存转换逻辑
+            if model_path == GSVModelFile.HUBERT_MODEL and os.path.exists(GSVModelFile.HUBERT_MODEL_WEIGHT_FP16):
+                self.cn_hubert = load_session_with_fp16_conversion(
+                    model_path,
+                    GSVModelFile.HUBERT_MODEL_WEIGHT_FP16,
+                    self.providers
+                )
+            else:
+                self.cn_hubert = onnxruntime.InferenceSession(
+                    model_path,
+                    providers=self.providers,
+                )
+            logger.info("Successfully loaded CN_HuBERT model.")
+            return True
+        except Exception as e:
+            logger.error(
+                f"Error: Failed to load ONNX model '{GSVModelFile.HUBERT_MODEL}'.\n"
+                f"Details: {e}"
+            )
+        return False
+
+    def get(self, character_name: str) -> Optional[GSVModel]:
+        character_name = character_name.lower()
+        language = self.character_to_language.get(character_name, 'Japanese')
+        if character_name in self.character_to_model:
+            model_map: dict = self.character_to_model[character_name]
+            # 简化获取逻辑
+            t2s_first_stage_decoder = model_map.get(GSVModelFile.T2S_FIRST_STAGE_DECODER_FP32) or \
+                                      model_map.get(GSVModelFile.T2S_FIRST_STAGE_DECODER_FP16)
+            t2s_stage_decoder = model_map.get(GSVModelFile.T2S_STAGE_DECODER_FP32) or \
+                                model_map.get(GSVModelFile.T2S_STAGE_DECODER_FP16)
+            prompt_encoder_path = os.path.join(self.character_model_paths[character_name], GSVModelFile.PROMPT_ENCODER)
+
+            return GSVModel(
+                LANGUAGE=language,
+                T2S_ENCODER=model_map[GSVModelFile.T2S_ENCODER_FP32],
+                T2S_FIRST_STAGE_DECODER=t2s_first_stage_decoder,
+                T2S_STAGE_DECODER=t2s_stage_decoder,
+                VITS=model_map[GSVModelFile.VITS_FP32],
+                PROMPT_ENCODER=model_map[GSVModelFile.PROMPT_ENCODER],
+                PROMPT_ENCODER_PATH=prompt_encoder_path,
+            )
+        if character_name in self.character_model_paths:
+            model_dir = self.character_model_paths[character_name]
+            if self.load_character(character_name, model_dir, language=language):
+                return self.get(character_name)
+            else:
+                del self.character_model_paths[character_name]
+                return None
+        return None
+
+    def has_character(self, character_name: str) -> bool:
+        character_name = character_name.lower()
+        return character_name in self.character_model_paths
+
+    def load_character(
+            self,
+            character_name: str,
+            model_dir: str,
+            language: str,
+    ) -> bool:
+        """
+        加载角色模型，如果需要，在内存中动态转换 FP16 权重。
+        """
+        character_name = character_name.lower()
+        if character_name in self.character_to_model:
+            _ = self.character_to_model[character_name]
+            return True
+
+        model_dict: Dict[str, Optional[InferenceSession]] = {}
+
+        # 定义 ONNX 文件到 FP16 Bin 文件的映射关系
+        onnx_to_fp16_map = {
+            GSVModelFile.T2S_FIRST_STAGE_DECODER_FP32: GSVModelFile.T2S_DECODER_WEIGHT_FP16,
+            GSVModelFile.T2S_STAGE_DECODER_FP32: GSVModelFile.T2S_DECODER_WEIGHT_FP16,
+            GSVModelFile.VITS_FP32: GSVModelFile.VITS_WEIGHT_FP16,
+            GSVModelFile.PROMPT_ENCODER: GSVModelFile.PROMPT_ENCODER_WEIGHT_FP16
+        }
+
+        # 确定需要加载的模型列表
+        model_files_to_load = [
+            GSVModelFile.T2S_ENCODER_FP32,
+            GSVModelFile.VITS_FP32,
+            GSVModelFile.PROMPT_ENCODER,
+        ]
+
+        fp32_decoders = [GSVModelFile.T2S_FIRST_STAGE_DECODER_FP32, GSVModelFile.T2S_STAGE_DECODER_FP32]
+        model_files_to_load.extend(fp32_decoders)
+
+        try:
+            for model_file in model_files_to_load:
+                model_path = os.path.normpath(os.path.join(model_dir, model_file))
+
+                # 设置 Session Options
+                sess_options = onnxruntime.SessionOptions()
+                sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+
+                if os.path.exists(model_path):
+                    fp16_bin_name = onnx_to_fp16_map.get(model_file)
+                    fp16_bin_path = os.path.join(model_dir, fp16_bin_name) if fp16_bin_name else None
+
+                    if fp16_bin_path and os.path.exists(fp16_bin_path):
+                        model_dict[model_file] = load_session_with_fp16_conversion(
+                            model_path, fp16_bin_path, self.providers, sess_options
+                        )
+                    else:
+                        model_dict[model_file] = onnxruntime.InferenceSession(
+                            model_path,
+                            providers=self.providers,
+                            sess_options=sess_options,
+                        )
+                elif model_file == GSVModelFile.PROMPT_ENCODER:
+                    model_dict[model_file] = None
+                else:
+                    raise FileNotFoundError(f'文件 {model_path} 不存在！')
+
+            # 日志信息
+            is_v2pp = model_dict[GSVModelFile.PROMPT_ENCODER] is not None
+            logger.info(
+                f"Character {character_name.capitalize()} loaded successfully.\n"
+                f"- Model Path: {model_dir}\n"
+                f"- Model Type: {'V2ProPlus' if is_v2pp else 'V2'}"
+            )
+
+            self.character_to_model[character_name] = model_dict
+            self.character_to_language[character_name] = language
+            self.character_model_paths[character_name] = model_dir
+            return True
+
+        except Exception as e:
+            logger.error(
+                f"Error: Failed to load ONNX model '{model_dir}'.\n"
+                f"Details: {e}"
+            )
+            return False
+
+    def remove_all_character(self) -> None:
+        self.character_to_model.clear()
+        gc.collect()
+
+    def remove_character(self, character_name: str) -> None:
+        character_name = character_name.lower()
+        if character_name in self.character_to_model:
+            del self.character_to_model[character_name]
+            gc.collect()
+            logger.info(f"Character {character_name.capitalize()} removed successfully.")
+
+
+model_manager: ModelManager = ModelManager()
diff --git a/genie_tts/PredefinedCharacter.py b/genie_tts/PredefinedCharacter.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8ba07dd6b4a5eb4107e8a5a61f1b6f1d4ec82c8
--- /dev/null
+++ b/genie_tts/PredefinedCharacter.py
@@ -0,0 +1,39 @@
+from huggingface_hub import snapshot_download
+import os
+from typing import Dict
+
+CHARA_LANG: Dict[str, str] = {
+    'mika': 'Japanese',
+    'feibi': 'Chinese',
+    'thirtyseven': 'English',
+}
+CHARA_ALIAS_MAP: Dict[str, str] = {
+    "mika": "mika",
+    "misono mika": "mika",
+    "圣园未花": "mika",
+    "未花": "mika",
+    "みその みか": "mika",
+    "feibi": "feibi",
+    "菲比": "feibi",
+    "37": "thirtyseven",
+    "thirtyseven": "thirtyseven",
+}
+
+
+def download_chara(chara: str, version: str = "v2ProPlus") -> str:
+    local_dir = os.path.join("CharacterModels", version, chara)
+    if os.path.exists(local_dir):
+        print(f"✔ Model for '{chara}' already exists locally. Skipping download.")
+        return local_dir
+
+    print(f"🚀 Starting download of model for character '{chara}'. This may take a few moments... ⏳")
+    remote_path = f"CharacterModels/{version}/{chara}/*"
+    snapshot_download(
+        repo_id="High-Logic/Genie",
+        repo_type="model",
+        allow_patterns=remote_path,
+        local_dir=".",
+        local_dir_use_symlinks=True,  # 软链接
+    )
+    print(f"🎉 All model files for '{chara}' have been downloaded to '{os.path.abspath(local_dir)}' 📂")
+    return local_dir
diff --git a/genie_tts/Server.py b/genie_tts/Server.py
new file mode 100644
index 0000000000000000000000000000000000000000..458bccd4c2cce79f46ed85f99bebbb969821a587
--- /dev/null
+++ b/genie_tts/Server.py
@@ -0,0 +1,169 @@
+import asyncio
+import os
+from typing import AsyncIterator, Optional, Callable, Union, Dict
+import logging
+
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+
+from .Audio.ReferenceAudio import ReferenceAudio
+from .Core.TTSPlayer import tts_player
+from .ModelManager import model_manager
+from .Utils.Shared import context
+from .Utils.Language import normalize_language
+
+logger = logging.getLogger(__name__)
+
+_reference_audios: Dict[str, dict] = {}
+SUPPORTED_AUDIO_EXTS = {'.wav', '.flac', '.ogg', '.aiff', '.aif'}
+
+app = FastAPI()
+
+
+class CharacterPayload(BaseModel):
+    character_name: str
+    onnx_model_dir: str
+    language: str
+
+
+class UnloadCharacterPayload(BaseModel):
+    character_name: str
+
+
+class ReferenceAudioPayload(BaseModel):
+    character_name: str
+    audio_path: str
+    audio_text: str
+    language: str
+
+
+class TTSPayload(BaseModel):
+    character_name: str
+    text: str
+    split_sentence: bool = False
+    save_path: Optional[str] = None
+
+
+@app.post("/load_character")
+def load_character_endpoint(payload: CharacterPayload):
+    try:
+        model_manager.load_character(
+            character_name=payload.character_name,
+            model_dir=payload.onnx_model_dir,
+            language=normalize_language(payload.language),
+        )
+        return {"status": "success", "message": f"Character '{payload.character_name}' loaded."}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/unload_character")
+def unload_character_endpoint(payload: UnloadCharacterPayload):
+    try:
+        model_manager.remove_character(character_name=payload.character_name)
+        return {"status": "success", "message": f"Character '{payload.character_name}' unloaded."}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/set_reference_audio")
+def set_reference_audio_endpoint(payload: ReferenceAudioPayload):
+    ext = os.path.splitext(payload.audio_path)[1].lower()
+    if ext not in SUPPORTED_AUDIO_EXTS:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Audio format '{ext}' is not supported. Supported formats: {SUPPORTED_AUDIO_EXTS}",
+        )
+    _reference_audios[payload.character_name] = {
+        'audio_path': payload.audio_path,
+        'audio_text': payload.audio_text,
+        'language': normalize_language(payload.language),
+    }
+    return {"status": "success", "message": f"Reference audio for '{payload.character_name}' set."}
+
+
+def run_tts_in_background(
+        character_name: str,
+        text: str,
+        split_sentence: bool,
+        save_path: Optional[str],
+        chunk_callback: Callable[[Optional[bytes]], None]
+):
+    try:
+        context.current_speaker = character_name
+        context.current_prompt_audio = ReferenceAudio(
+            prompt_wav=_reference_audios[character_name]['audio_path'],
+            prompt_text=_reference_audios[character_name]['audio_text'],
+            language=_reference_audios[character_name]['language'],
+        )
+        tts_player.start_session(
+            play=False,
+            split=split_sentence,
+            save_path=save_path,
+            chunk_callback=chunk_callback,
+        )
+        tts_player.feed(text)
+        tts_player.end_session()
+        tts_player.wait_for_tts_completion()
+    except Exception as e:
+        logger.error(f"Error in TTS background task: {e}", exc_info=True)
+
+
+async def audio_stream_generator(queue: asyncio.Queue) -> AsyncIterator[bytes]:
+    while True:
+        chunk = await queue.get()
+        if chunk is None:
+            break
+        yield chunk
+
+
+@app.post("/tts")
+async def tts_endpoint(payload: TTSPayload):
+    if payload.character_name not in _reference_audios:
+        raise HTTPException(status_code=404, detail="Character not found or reference audio not set.")
+
+    loop = asyncio.get_running_loop()
+    stream_queue: asyncio.Queue[Union[bytes, None]] = asyncio.Queue()
+
+    def tts_chunk_callback(chunk: Optional[bytes]):
+        loop.call_soon_threadsafe(stream_queue.put_nowait, chunk)
+
+    loop.run_in_executor(
+        None,
+        run_tts_in_background,
+        payload.character_name,
+        payload.text,
+        payload.split_sentence,
+        payload.save_path,
+        tts_chunk_callback
+    )
+
+    return StreamingResponse(audio_stream_generator(stream_queue), media_type="audio/wav")
+
+
+@app.post("/stop")
+def stop_endpoint():
+    try:
+        tts_player.stop()
+        return {"status": "success", "message": "TTS stopped."}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/clear_reference_audio_cache")
+def clear_reference_audio_cache_endpoint():
+    try:
+        ReferenceAudio.clear_cache()
+        return {"status": "success", "message": "Reference audio cache cleared."}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+def start_server(host: str = "127.0.0.1", port: int = 8000, workers: int = 1):
+    uvicorn.run(app, host=host, port=port, workers=workers)
+
+
+if __name__ == "__main__":
+    start_server(host="0.0.0.0", port=8000, workers=1)
diff --git a/genie_tts/Utils/Constants.py b/genie_tts/Utils/Constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b134e10b7612f06b6cc548938b27cf745575d1a
--- /dev/null
+++ b/genie_tts/Utils/Constants.py
@@ -0,0 +1,2 @@
+BERT_FEATURE_DIM = 1024
+PACKAGE_NAME = "genie_tts"
diff --git a/genie_tts/Utils/Language.py b/genie_tts/Utils/Language.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d6f23aa8bdedcddd125c7400d51381ee3b5f1cc
--- /dev/null
+++ b/genie_tts/Utils/Language.py
@@ -0,0 +1,26 @@
+language_map = {
+    # Chinese
+    "chinese": "Chinese",
+    "zh": "Chinese",
+    "zh-cn": "Chinese",
+    "zh-tw": "Chinese",
+    "zh-hans": "Chinese",
+    "zh-hant": "Chinese",
+
+    # English
+    "english": "English",
+    "en": "English",
+    "en-us": "English",
+    "en-gb": "English",
+    "eng": "English",
+
+    # Japanese
+    "japanese": "Japanese",
+    "jp": "Japanese",
+    "ja": "Japanese",
+    "nihongo": "Japanese",
+}
+
+
+def normalize_language(lang: str) -> str:
+    return language_map.get(lang.lower(), lang)
diff --git a/genie_tts/Utils/Shared.py b/genie_tts/Utils/Shared.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f3166f5599d718478e9ee13d4177b75afbc1aa2
--- /dev/null
+++ b/genie_tts/Utils/Shared.py
@@ -0,0 +1,13 @@
+from typing import TYPE_CHECKING, Optional
+
+if TYPE_CHECKING:
+    from ..Audio.ReferenceAudio import ReferenceAudio
+
+
+class Context:
+    def __init__(self):
+        self.current_speaker: str = ''
+        self.current_prompt_audio: Optional['ReferenceAudio'] = None
+
+
+context: Context = Context()
diff --git a/genie_tts/Utils/TextSplitter.py b/genie_tts/Utils/TextSplitter.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9dca1045dbad88a5ed8a9315ac4d0c4307f76b8
--- /dev/null
+++ b/genie_tts/Utils/TextSplitter.py
@@ -0,0 +1,123 @@
+import re
+from typing import List, Set, Pattern
+
+
+class TextSplitter:
+    def __init__(self, max_len: int = 40, min_len: int = 5):
+        """
+        初始化文本切分器。
+
+        :param max_len: 软限制最大长度 (Effective Length)。超过此长度遇到分隔符时会切分。
+        :param min_len: 硬限制最小长度 (Effective Length)。小于此长度遇到终止符也不会切分。
+        """
+        self.max_len: int = max_len
+        self.min_len: int = min_len
+
+        # 1. 定义基础字符集合
+        # 只要标点块中包含这些字符，就视为 Ending (终止符)
+        self.end_chars: Set[str] = {
+            '。', '！', '？', '…',
+            '!', '?', '.'
+        }
+
+        # 2. 定义标点符号全集 (用于正则匹配和长度计算过滤)
+        self.all_puncts_chars: Set[str] = self.end_chars | {
+            '，', '、', '；', '：', '——',
+            ',', ';', ':',
+            '“', '”', '‘', '’', '"', "'",
+        }
+
+        # 3. 编译正则表达式
+        # 使用非捕获组 (?:) 配合 + 号，实现贪婪匹配连续标点
+        # sort + escape 确保正则安全且优先匹配长标点
+        sorted_puncts: List[str] = sorted(list(self.all_puncts_chars), key=len, reverse=True)
+        escaped_puncts: List[str] = [re.escape(p) for p in sorted_puncts]
+        self.pattern: Pattern = re.compile(f"((?:{'|'.join(escaped_puncts)})+)")
+
+    @staticmethod
+    def get_char_width(char: str) -> int:
+        """计算单字符宽度：ASCII算1，其他（中日韩）算2"""
+        return 1 if ord(char) < 128 else 2
+
+    def get_effective_len(self, text: str) -> int:
+        """
+        计算字符串的有效长度。
+        逻辑：跳过标点符号，仅计算内容字符。
+        例如："你好......" -> 有效长度为 4 (你好)，而不是 10。
+        """
+        length = 0
+        for char in text:
+            # 如果是标点符号集合里的字符，不计入长度
+            if char in self.all_puncts_chars:
+                continue
+            length += self.get_char_width(char)
+        return length
+
+    def is_terminator_block(self, block: str) -> bool:
+        """
+        判断一个标点块是否起到结束句子的作用。
+        只要块中包含任意一个结束字符（如句号），则视为结束块。
+        """
+        for char in block:
+            if char in self.end_chars:
+                return True
+        return False
+
+    def split(self, text: str) -> List[str]:
+        """核心切分逻辑"""
+        if not text:
+            return []
+
+        text = text.replace('\n', '')
+
+        # 正则切分，segments 格式如: ['你好', '......', '我是谁', '？！', '']
+        segments: List[str] = self.pattern.split(text)
+
+        sentences: List[str] = []
+        current_buffer: str = ""
+
+        for segment in segments:
+            if not segment:
+                continue
+
+            # 判断当前片段是否是标点块（通过首字符判断即可，正则保证了一致性）
+            is_punct_block = segment[0] in self.all_puncts_chars
+
+            if is_punct_block:
+                current_buffer += segment
+
+                # 计算缓冲区内容的【有效长度】
+                eff_len = self.get_effective_len(current_buffer)
+
+                # 判断逻辑
+                if self.is_terminator_block(segment):
+                    # Case B: 结束符号 -> 检查 min_len
+                    if eff_len >= self.min_len:
+                        sentences.append(current_buffer.strip())
+                        current_buffer = ""
+                    # else: 有效长度太短，合并到下一句
+                else:
+                    # Case A-B: 分隔符号 -> 检查 max_len
+                    if eff_len >= self.max_len:
+                        sentences.append(current_buffer.strip())
+                        current_buffer = ""
+                    # else: 没到最大长度，继续累积
+            else:
+                # 纯文本
+                current_buffer += segment
+
+        # 处理残留缓冲区
+        if current_buffer:
+            self._flush_buffer(sentences, current_buffer)
+
+        return sentences
+
+    def _flush_buffer(self, sentences: List[str], buffer: str):
+        candidate = buffer.strip()
+        if not candidate:
+            return
+        eff_len = self.get_effective_len(candidate)
+        if eff_len > 0:
+            sentences.append(candidate)
+        elif sentences:
+            sentences[-1] += candidate
diff --git a/genie_tts/Utils/UserData.py b/genie_tts/Utils/UserData.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d1bd517e02d35f683e92c3536fbc6027ab2b438
--- /dev/null
+++ b/genie_tts/Utils/UserData.py
@@ -0,0 +1,44 @@
+import json
+from pathlib import Path
+from typing import Dict, Any
+import logging
+import importlib.resources
+
+from .Constants import PACKAGE_NAME
+
+logger = logging.getLogger(__name__)
+
+
+class UserDataManager:
+    def __init__(self, file_path: str = "./UserData.json"):
+        self.file_path = Path(file_path)
+        self._data: Dict[str, Any] = self._load()
+
+    def _load(self) -> Dict[str, Any]:
+        if self.file_path.exists():
+            try:
+                with self.file_path.open('r', encoding='utf-8') as f:
+                    return json.load(f)
+            except (json.JSONDecodeError, IOError) as e:
+                logger.warning(f"Failed to read user data file {self.file_path}. Using empty configuration. Error: {e}")
+
+                return {}
+        return {}
+
+    def _save(self):
+        try:
+            with self.file_path.open('w', encoding='utf-8') as f:
+                json.dump(self._data, f, indent=4, ensure_ascii=False)  # type: ignore
+        except IOError as e:
+            logger.warning(f"Failed to write user data file {self.file_path}. Error: {e}")
+
+    def get(self, key: str, default: Any = None) -> Any:
+        return self._data.get(key, default)
+
+    def set(self, key: str, value: Any):
+        self._data[key] = value
+        self._save()
+
+
+userdata_file: str = str(importlib.resources.files(PACKAGE_NAME) / 'UserData.json')
+userdata_manager = UserDataManager(file_path=userdata_file)
diff --git a/genie_tts/Utils/Utils.py b/genie_tts/Utils/Utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1453655a72e22716f4e966ef418480497f0a314c
--- /dev/null
+++ b/genie_tts/Utils/Utils.py
@@ -0,0 +1,28 @@
+from collections import OrderedDict
+import queue
+
+
+class LRUCacheDict(OrderedDict):
+    def __init__(self, capacity):
+        super().__init__()
+        self.capacity = capacity
+
+    def __getitem__(self, key):
+        value = super().__getitem__(key)
+        self.move_to_end(key)  # 访问后移到末尾
+        return value
+
+    def __setitem__(self, key, value):
+        if key in self:
+            self.move_to_end(key)
+        super().__setitem__(key, value)
+        if len(self) > self.capacity:
+            self.popitem(last=False)  # 删除最旧的（第一个）
+
+
+def clear_queue(q: queue.Queue) -> None:
+    while not q.empty():
+        try:
+            q.get_nowait()
+        except queue.Empty:
+            break
diff --git a/genie_tts/Utils/__init__.py b/genie_tts/Utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/genie_tts/__init__.py b/genie_tts/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d910acdb31af65801d20206bbebfb55544af3570
--- /dev/null
+++ b/genie_tts/__init__.py
@@ -0,0 +1,29 @@
+from .Internal import (
+    load_character,
+    unload_character,
+    set_reference_audio,
+    tts_async,
+    tts,
+    stop,
+    convert_to_onnx,
+    clear_reference_audio_cache,
+    load_predefined_character,
+    wait_for_playback_done,
+)
+from .Server import start_server
+from .Core.Resources import download_genie_data
+
+__all__ = [
+    "load_character",
+    "unload_character",
+    "set_reference_audio",
+    "tts_async",
+    "tts",
+    "stop",
+    "convert_to_onnx",
+    "clear_reference_audio_cache",
+    "start_server",
+    "load_predefined_character",
+    "wait_for_playback_done",
+    'download_genie_data',
+]