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genie_tts/Audio/Audio.py

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+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

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,
+            })

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)

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)

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__()

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)

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)

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)

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()

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)

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()

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()

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")

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()

genie_tts/Data/v2/Keys/t2s_onnx_keys.txt

@@ -0,0 +1,291 @@
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genie_tts/Data/v2ProPlus/Models/prompt_encoder_fp32.onnx

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+oid sha256:de17d88fdfe9598f9d25710b7299bccac3c0a79851cd8085fc210492260cc0b9
+size 44533

genie_tts/Data/v2ProPlus/Models/vits_fp32.onnx

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+oid sha256:9f9cb0882bbd028e73e5b54b94b54f53370a0f65605ca7dee4e4f1ab5edac89c
+size 1613193

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)

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

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

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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+
+traditional_characters = 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鐃鐄鐇鐏鐒鐓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])

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

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"])+"
+    )

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���
+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

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))

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

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

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

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

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()

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)

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��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

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)}

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()