Initial commit: Diffutslator translation model

.gitattributes

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+*.pt filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: Diffutslator
+emoji: 🌐
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# Diffutslator 扩散翻译器
+
+基于扩散模型的机器翻译系统。模型在翻译过程中可视化语言嵌入空间的渐变。
+
+## 功能
+
+- 中英文互译
+- 可调节DDIM推理步数
+- 可视化扩散过程
+
+## 使用方法
+
+1. 输入要翻译的文本
+2. 选择翻译方向（或自动检测）
+3. 调整DDIM步数（越多质量越高，速度越慢）
+4. 点击翻译

app.py

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+"""
+Diffutslator Hugging Face Space 应用
+基于扩散模型的机器翻译演示
+"""
+
+import os
+import sys
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+import gradio as gr
+from typing import Optional, Tuple, List
+from dataclasses import dataclass, field
+import json
+
+
+# ==================== 配置（与config.py保持一致，用于加载检查点）====================
+@dataclass
+class ModelConfig:
+    d_model: int = 256
+    n_heads: int = 4
+    n_layers: int = 4
+    d_ff: int = 512
+    max_len: int = 128
+    dropout: float = 0.1
+    vocab_size_zh: int = 8000
+    vocab_size_en: int = 8000
+    pad_token: str = "<pad>"
+    sos_token: str = "<sos>"
+    eos_token: str = "<eos>"
+    unk_token: str = "<unk>"
+    mask_token: str = "<mask>"
+
+
+@dataclass
+class DiffusionConfig:
+    timesteps: int = 1000
+    ddim_steps: int = 50
+    beta_start: float = 0.0001
+    beta_end: float = 0.02
+    length_noise_scale: float = 0.3
+
+
+@dataclass
+class TrainingConfig:
+    batch_size: int = 64
+    gradient_accumulation: int = 1
+    learning_rate: float = 1e-4
+    weight_decay: float = 0.01
+    warmup_steps: int = 500
+    epochs: int = 10
+    save_every: int = 1
+    eval_every: int = 100
+    quick_mode: bool = False
+    quick_samples: int = 1000
+    checkpoint_dir: str = "checkpoints"
+    resume: Optional[str] = None
+
+
+@dataclass
+class DataConfig:
+    tatoeba_path: str = ""
+    cveto_zh_path: str = ""
+    cveto_en_path: str = ""
+    max_samples: Optional[int] = None
+    min_len: int = 2
+    max_len: int = 128
+    use_cache: bool = True
+    cache_dir: str = ".cache"
+
+
+@dataclass
+class Config:
+    model: ModelConfig = field(default_factory=ModelConfig)
+    diffusion: DiffusionConfig = field(default_factory=DiffusionConfig)
+    training: TrainingConfig = field(default_factory=TrainingConfig)
+    data: DataConfig = field(default_factory=DataConfig)
+    project_dir: str = ""
+
+
+# 创建一个假的config模块，用于加载检查点时反序列化
+class _FakeConfigModule:
+    Config = Config
+    ModelConfig = ModelConfig
+    DiffusionConfig = DiffusionConfig
+    TrainingConfig = TrainingConfig
+    DataConfig = DataConfig
+
+
+# 将假模块注入sys.modules
+sys.modules['config'] = _FakeConfigModule()
+
+
+# ==================== 分词器 ====================
+import re
+
+class Tokenizer:
+    """BPE分词器（与tokenizer.py兼容）"""
+    
+    def __init__(self, vocab_size: int = 8000, lang: str = "zh"):
+        self.vocab_size = vocab_size
+        self.lang = lang
+        
+        # 特殊token
+        self.pad_token = "<pad>"
+        self.sos_token = "<sos>"
+        self.eos_token = "<eos>"
+        self.unk_token = "<unk>"
+        self.mask_token = "<mask>"
+        
+        self.special_tokens = [self.pad_token, self.sos_token, self.eos_token, self.unk_token, self.mask_token]
+        
+        # 词表
+        self.token_to_id: dict = {}
+        self.id_to_token: dict = {}
+        
+        # BPE合并规则
+        self.merges: list = []
+        self.bpe_ranks: dict = {}
+    
+    @property
+    def vocab_size_actual(self) -> int:
+        return len(self.token_to_id)
+    
+    @property
+    def pad_id(self) -> int:
+        return self.token_to_id[self.pad_token]
+    
+    @property
+    def sos_id(self) -> int:
+        return self.token_to_id[self.sos_token]
+    
+    @property
+    def eos_id(self) -> int:
+        return self.token_to_id[self.eos_token]
+    
+    @property
+    def unk_id(self) -> int:
+        return self.token_to_id[self.unk_token]
+    
+    def _is_chinese(self, char: str) -> bool:
+        return '\u4e00' <= char <= '\u9fff'
+    
+    def _pre_tokenize(self, text: str) -> List[str]:
+        """预分词"""
+        if self.lang == "zh":
+            tokens = []
+            current = ""
+            for char in text:
+                if self._is_chinese(char):
+                    if current:
+                        tokens.append(current)
+                        current = ""
+                    tokens.append(char)
+                elif char.isalnum():
+                    current += char.lower()
+                else:
+                    if current:
+                        tokens.append(current)
+                        current = ""
+                    if char.strip():
+                        tokens.append(char)
+            if current:
+                tokens.append(current)
+            return tokens
+        else:
+            text = text.lower()
+            tokens = re.findall(r"\w+|[^\w\s]", text)
+            return tokens
+    
+    def _get_pairs(self, word: tuple) -> set:
+        """获取词中的所有相邻字符对"""
+        pairs = set()
+        prev = word[0]
+        for char in word[1:]:
+            pairs.add((prev, char))
+            prev = char
+        return pairs
+    
+    def _apply_bpe(self, token: str) -> List[str]:
+        """对单个token应用BPE"""
+        if not token:
+            return []
+        
+        word = tuple(token) + ('</w>',)
+        
+        while True:
+            pairs = self._get_pairs(word)
+            if not pairs:
+                break
+            
+            # 找到rank最高的pair
+            min_pair = None
+            min_rank = float('inf')
+            for pair in pairs:
+                rank = self.bpe_ranks.get(pair, float('inf'))
+                if rank < min_rank:
+                    min_rank = rank
+                    min_pair = pair
+            
+            if min_pair is None or min_rank == float('inf'):
+                break
+            
+            # 合并
+            new_word = []
+            i = 0
+            while i < len(word):
+                if i < len(word) - 1 and word[i] == min_pair[0] and word[i + 1] == min_pair[1]:
+                    new_word.append(min_pair[0] + min_pair[1])
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            word = tuple(new_word)
+        
+        return [t for t in word if t != '</w>']
+    
+    def encode(self, text: str, add_sos: bool = True, add_eos: bool = True) -> List[int]:
+        """编码文本为token id序列"""
+        tokens = self._pre_tokenize(text)
+        
+        ids = []
+        if add_sos:
+            ids.append(self.sos_id)
+        
+        for token in tokens:
+            bpe_tokens = self._apply_bpe(token)
+            for t in bpe_tokens:
+                ids.append(self.token_to_id.get(t, self.unk_id))
+        
+        if add_eos:
+            ids.append(self.eos_id)
+        
+        return ids
+    
+    def decode(self, ids: List[int], skip_special: bool = True) -> str:
+        """解码token id序列为文本"""
+        tokens = []
+        for id in ids:
+            token = self.id_to_token.get(id, self.unk_token)
+            if skip_special and token in self.special_tokens:
+                continue
+            token = token.replace('</w>', '')
+            if token:
+                tokens.append(token)
+        
+        if self.lang == "en":
+            text = ' '.join(tokens)
+            text = re.sub(r'\s+([.,!?;:\'\"])', r'\1', text)
+            text = re.sub(r'([.,!?;:])([a-zA-Z])', r'\1 \2', text)
+            text = re.sub(r'\s+', ' ', text).strip()
+        else:
+            text = ''.join(tokens)
+        
+        return text
+    
+    @classmethod
+    def load(cls, path: str) -> "Tokenizer":
+        """加载分词器"""
+        with open(path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        
+        tokenizer = cls(vocab_size=data["vocab_size"], lang=data["lang"])
+        tokenizer.token_to_id = data["token_to_id"]
+        tokenizer.id_to_token = {int(k): v for k, v in data["id_to_token"].items()}
+        tokenizer.merges = [tuple(m) for m in data["merges"]]
+        tokenizer.bpe_ranks = {pair: i for i, pair in enumerate(tokenizer.merges)}
+        tokenizer.special_tokens = data["special_tokens"]
+        
+        return tokenizer
+
+
+# ==================== 模型组件 ====================
+class PositionalEncoding(nn.Module):
+    """正弦位置编码"""
+    
+    def __init__(self, d_model: int, max_len: int = 128, dropout: float = 0.1):
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.register_buffer("pe", pe)
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.pe[:, :x.size(1), :]
+        return self.dropout(x)
+
+
+class SinusoidalTimeEmbedding(nn.Module):
+    """时间步的正弦嵌入（用于扩散）"""
+    
+    def __init__(self, d_model: int):
+        super().__init__()
+        self.d_model = d_model
+    
+    def forward(self, t: torch.Tensor) -> torch.Tensor:
+        t = t.float().unsqueeze(-1)
+        half_dim = self.d_model // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=t.device) * -emb)
+        emb = t * emb.unsqueeze(0)
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+        return emb
+
+
+class LanguageEmbedding(nn.Module):
+    """语言特定的嵌入层"""
+    
+    def __init__(self, vocab_size: int, d_model: int, max_len: int = 128, dropout: float = 0.1):
+        super().__init__()
+        self.d_model = d_model
+        self.token_embedding = nn.Embedding(vocab_size, d_model)
+        self.position_encoding = PositionalEncoding(d_model, max_len, dropout)
+        self.length_embedding = nn.Embedding(max_len + 1, d_model)
+        self.scale = math.sqrt(d_model)
+    
+    def forward(self, token_ids: torch.Tensor, lengths: Optional[torch.Tensor] = None) -> torch.Tensor:
+        x = self.token_embedding(token_ids) * self.scale
+        x = self.position_encoding(x)
+        if lengths is not None:
+            len_emb = self.length_embedding(lengths)
+            x = x + len_emb.unsqueeze(1)
+        return x
+
+
+class DualLanguageEmbedding(nn.Module):
+    """双语嵌入层"""
+    
+    def __init__(self, vocab_size_zh: int, vocab_size_en: int, d_model: int, max_len: int = 128, dropout: float = 0.1):
+        super().__init__()
+        self.d_model = d_model
+        self.zh_embedding = LanguageEmbedding(vocab_size_zh, d_model, max_len, dropout)
+        self.en_embedding = LanguageEmbedding(vocab_size_en, d_model, max_len, dropout)
+    
+    def forward(self, token_ids: torch.Tensor, lang: str, lengths: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if lang == 'zh':
+            return self.zh_embedding(token_ids, lengths)
+        else:
+            return self.en_embedding(token_ids, lengths)
+
+
+class OutputProjection(nn.Module):
+    """输出投影层"""
+    
+    def __init__(self, d_model: int, vocab_size: int):
+        super().__init__()
+        self.projection = nn.Linear(d_model, vocab_size, bias=False)
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.projection(x)
+
+
+class DualOutputProjection(nn.Module):
+    """双语输出投影层"""
+    
+    def __init__(self, d_model: int, vocab_size_zh: int, vocab_size_en: int):
+        super().__init__()
+        self.zh_projection = OutputProjection(d_model, vocab_size_zh)
+        self.en_projection = OutputProjection(d_model, vocab_size_en)
+    
+    def forward(self, x: torch.Tensor, lang: str) -> torch.Tensor:
+        if lang == 'zh':
+            return self.zh_projection(x)
+        else:
+            return self.en_projection(x)
+
+
+class MultiHeadAttention(nn.Module):
+    """多头自注意力"""
+    
+    def __init__(self, d_model: int, n_heads: int, dropout: float = 0.1):
+        super().__init__()
+        assert d_model % n_heads == 0
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.d_k = d_model // n_heads
+        self.w_q = nn.Linear(d_model, d_model)
+        self.w_k = nn.Linear(d_model, d_model)
+        self.w_v = nn.Linear(d_model, d_model)
+        self.w_o = nn.Linear(d_model, d_model)
+        self.dropout = nn.Dropout(dropout)
+    
+    def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        batch_size = q.size(0)
+        q = self.w_q(q).view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2)
+        k = self.w_k(k).view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2)
+        v = self.w_v(v).view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2)
+        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, float('-inf'))
+        attn = F.softmax(scores, dim=-1)
+        attn = self.dropout(attn)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)
+        return self.w_o(out)
+
+
+class FeedForward(nn.Module):
+    """前馈网络"""
+    
+    def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1):
+        super().__init__()
+        self.w1 = nn.Linear(d_model, d_ff)
+        self.w2 = nn.Linear(d_ff, d_model)
+        self.dropout = nn.Dropout(dropout)
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.dropout(self.w2(F.gelu(self.w1(x))))
+
+
+class TransformerBlock(nn.Module):
+    """Transformer块"""
+    
+    def __init__(self, d_model: int, n_heads: int, d_ff: int, dropout: float = 0.1):
+        super().__init__()
+        self.attn = MultiHeadAttention(d_model, n_heads, dropout)
+        self.ff = FeedForward(d_model, d_ff, dropout)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+    
+    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        x = x + self.dropout(self.attn(self.norm1(x), self.norm1(x), self.norm1(x), mask))
+        x = x + self.dropout(self.ff(self.norm2(x)))
+        return x
+
+
+class DualNoisePredictor(nn.Module):
+    """双语言噪声预测器"""
+    
+    def __init__(self, d_model: int = 256, n_heads: int = 4, n_layers: int = 4, d_ff: int = 512, max_len: int = 128, dropout: float = 0.1):
+        super().__init__()
+        self.d_model = d_model
+        
+        # 时间步嵌入（共享）
+        self.time_embedding = SinusoidalTimeEmbedding(d_model)
+        self.time_mlp = nn.Sequential(
+            nn.Linear(d_model, d_model * 4),
+            nn.GELU(),
+            nn.Linear(d_model * 4, d_model),
+        )
+        
+        # 语言特定的输入投影
+        self.zh_input_proj = nn.Linear(d_model, d_model)
+        self.en_input_proj = nn.Linear(d_model, d_model)
+        
+        # 共享Transformer层
+        self.layers = nn.ModuleList([
+            TransformerBlock(d_model, n_heads, d_ff, dropout)
+            for _ in range(n_layers)
+        ])
+        
+        # 语言特定的输出投影
+        self.zh_output_proj = nn.Linear(d_model, d_model)
+        self.en_output_proj = nn.Linear(d_model, d_model)
+        
+        self.output_norm = nn.LayerNorm(d_model)
+    
+    def forward(self, x_t: torch.Tensor, t: torch.Tensor, lang: str = "zh", mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        # 时间步嵌入
+        t_emb = self.time_embedding(t)
+        t_emb = self.time_mlp(t_emb)
+        
+        # 语言特定输入投影
+        if lang == "zh":
+            x = self.zh_input_proj(x_t)
+        else:
+            x = self.en_input_proj(x_t)
+        
+        # 添加时间信息
+        x = x + t_emb.unsqueeze(1)
+        
+        # 共享Transformer
+        for layer in self.layers:
+            x = layer(x, mask)
+        
+        # 输出归一化
+        x = self.output_norm(x)
+        
+        # 语言特定输出投影
+        if lang == "zh":
+            noise_pred = self.zh_output_proj(x)
+        else:
+            noise_pred = self.en_output_proj(x)
+        
+        return noise_pred
+
+
+class LanguageSwitcher(nn.Module):
+    """语言切换分类器"""
+    
+    def __init__(self, d_model: int = 256, hidden_dim: int = 128, dropout: float = 0.1):
+        super().__init__()
+        self.global_pool = nn.AdaptiveAvgPool1d(1)
+        self.classifier = nn.Sequential(
+            nn.Linear(d_model, hidden_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, 2),
+        )
+    
+    def forward(self, x_t: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if mask is not None:
+            x_t = x_t * mask.unsqueeze(-1)
+        x = x_t.transpose(1, 2)
+        x = self.global_pool(x).squeeze(-1)
+        logits = self.classifier(x)
+        return logits
+    
+    def predict(self, x_t: torch.Tensor, mask: Optional[torch.Tensor] = None) -> Tuple[str, float]:
+        self.eval()
+        with torch.no_grad():
+            logits = self.forward(x_t, mask)
+            probs = F.softmax(logits, dim=-1)
+            zh_prob = probs[0, 0].item()
+            en_prob = probs[0, 1].item()
+            if zh_prob > en_prob:
+                return "zh", zh_prob
+            else:
+                return "en", en_prob
+
+
+# ==================== 扩散过程 ====================
+class Diffusion:
+    def __init__(self, config: DiffusionConfig):
+        self.config = config
+        self.timesteps = config.timesteps
+        
+        # Beta schedule (linear)
+        betas = torch.linspace(config.beta_start, config.beta_end, self.timesteps)
+        alphas = 1.0 - betas
+        alphas_cumprod = torch.cumprod(alphas, dim=0)
+        
+        self.register_buffer("betas", betas)
+        self.register_buffer("alphas", alphas)
+        self.register_buffer("alphas_cumprod", alphas_cumprod)
+        self.register_buffer("sqrt_alphas_cumprod", torch.sqrt(alphas_cumprod))
+        self.register_buffer("sqrt_one_minus_alphas_cumprod", torch.sqrt(1 - alphas_cumprod))
+    
+    def register_buffer(self, name: str, tensor: torch.Tensor):
+        setattr(self, name, tensor)
+    
+    def q_sample(self, x_0: torch.Tensor, t: torch.Tensor, noise: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
+        if noise is None:
+            noise = torch.randn_like(x_0)
+        sqrt_alpha = self.sqrt_alphas_cumprod[t]
+        sqrt_one_minus_alpha = self.sqrt_one_minus_alphas_cumprod[t]
+        x_t = sqrt_alpha.view(-1, 1, 1) * x_0 + sqrt_one_minus_alpha.view(-1, 1, 1) * noise
+        return x_t, noise
+    
+    def p_sample(self, x_t: torch.Tensor, t: torch.Tensor, predicted_noise: torch.Tensor) -> torch.Tensor:
+        beta = self.betas[t]
+        sqrt_one_minus_alpha = self.sqrt_one_minus_alphas_cumprod[t]
+        sqrt_recip_alpha = 1.0 / torch.sqrt(self.alphas[t])
+        
+        # 去噪
+        x_0_pred = sqrt_recip_alpha.view(-1, 1, 1) * (x_t - sqrt_one_minus_alpha.view(-1, 1, 1) * predicted_noise)
+        
+        # 添加噪声（除了最后一步）
+        if t[0] > 0:
+            noise = torch.randn_like(x_t)
+            x_prev = x_0_pred + torch.sqrt(beta).view(-1, 1, 1) * noise
+        else:
+            x_prev = x_0_pred
+        
+        return x_prev
+
+
+class DDIMSampler:
+    def __init__(self, diffusion: Diffusion, ddim_steps: int = 50):
+        self.diffusion = diffusion
+        self.ddim_steps = ddim_steps
+        
+        # 选择均匀分布的时间步，从高到低（从噪声到干净）
+        c = self.diffusion.timesteps // ddim_steps
+        ddim_timesteps = [i * c for i in range(ddim_steps)]
+        self.ddim_timesteps = torch.tensor(list(reversed(ddim_timesteps)))
+    
+    def ddim_step(self, x_t: torch.Tensor, t: int, t_prev: int, 
+                  predicted_noise: torch.Tensor, eta: float = 0.0) -> torch.Tensor:
+        """DDIM单步"""
+        alpha_t = self.diffusion.alphas_cumprod[t]
+        alpha_prev = self.diffusion.alphas_cumprod[t_prev] if t_prev >= 0 else torch.tensor(1.0)
+        
+        # 预测 x_0
+        x_0_pred = (x_t - torch.sqrt(1 - alpha_t) * predicted_noise) / torch.sqrt(alpha_t)
+        
+        # 方差
+        sigma = eta * torch.sqrt((1 - alpha_prev) / (1 - alpha_t)) * torch.sqrt(1 - alpha_t / alpha_prev)
+        
+        # DDIM更新
+        dir_xt = torch.sqrt(1 - alpha_prev - sigma ** 2) * predicted_noise
+        
+        if t_prev >= 0:
+            noise = torch.randn_like(x_t)
+            x_prev = torch.sqrt(alpha_prev) * x_0_pred + dir_xt + sigma * noise
+        else:
+            x_prev = x_0_pred
+        
+        return x_prev
+
+
+# ==================== 翻译器 ====================
+class Translator:
+    def __init__(self, model_dir: str = "."):
+        self.device = torch.device("cpu")
+        
+        # 配置
+        self.model_config = ModelConfig()
+        self.diffusion_config = DiffusionConfig()
+        
+        # 加载分词器
+        self.zh_tokenizer = Tokenizer.load(os.path.join(model_dir, "tokenizer_zh.json"))
+        self.en_tokenizer = Tokenizer.load(os.path.join(model_dir, "tokenizer_en.json"))
+        
+        # 初始化模型
+        self.embedding = DualLanguageEmbedding(
+            vocab_size_zh=self.zh_tokenizer.vocab_size_actual,
+            vocab_size_en=self.en_tokenizer.vocab_size_actual,
+            d_model=self.model_config.d_model,
+            max_len=self.model_config.max_len,
+            dropout=0.0,
+        )
+        
+        self.output_proj = DualOutputProjection(
+            d_model=self.model_config.d_model,
+            vocab_size_zh=self.zh_tokenizer.vocab_size_actual,
+            vocab_size_en=self.en_tokenizer.vocab_size_actual,
+        )
+        
+        self.model = DualNoisePredictor(
+            d_model=self.model_config.d_model,
+            n_heads=self.model_config.n_heads,
+            n_layers=self.model_config.n_layers,
+            d_ff=self.model_config.d_ff,
+            max_len=self.model_config.max_len,
+            dropout=0.0,
+        )
+        self.switcher = LanguageSwitcher(
+            d_model=self.model_config.d_model,
+            hidden_dim=self.model_config.d_model // 2,
+            dropout=0.0,
+        )
+        
+        self.diffusion = Diffusion(self.diffusion_config)
+        
+        # 加载权重
+        self._load_checkpoint(os.path.join(model_dir, "best.pt"))
+    
+    def _load_checkpoint(self, path: str):
+        state = torch.load(path, map_location=self.device, weights_only=False)
+        self.embedding.load_state_dict(state['embedding'])
+        self.output_proj.load_state_dict(state['output_proj'])
+        self.model.load_state_dict(state['model'])
+        self.switcher.load_state_dict(state['switcher'])
+        print(f"已加载模型: {path}")
+    
+    def _encode(self, text: str, lang: str) -> torch.Tensor:
+        if lang == "zh":
+            ids = self.zh_tokenizer.encode(text, add_sos=True, add_eos=True)
+        else:
+            ids = self.en_tokenizer.encode(text, add_sos=True, add_eos=True)
+        return torch.tensor(ids, dtype=torch.long).unsqueeze(0)
+    
+    def _decode(self, ids: torch.Tensor, lang: str) -> str:
+        ids = ids[0].tolist()
+        if lang == "zh":
+            return self.zh_tokenizer.decode(ids, skip_special=True)
+        else:
+            return self.en_tokenizer.decode(ids, skip_special=True)
+    
+    def _embed_to_tokens(self, x: torch.Tensor, lang: str) -> torch.Tensor:
+        logits = self.output_proj(x, lang)
+        return logits.argmax(dim=-1)
+    
+    @torch.no_grad()
+    def translate(
+        self,
+        text: str,
+        source_lang: str,
+        ddim_steps: int = 50,
+        show_process: bool = False,
+    ) -> Tuple[str, List[str]]:
+        """翻译文本，返回结果和中间过程"""
+        self.model.eval()
+        self.embedding.eval()
+        self.output_proj.eval()
+        self.switcher.eval()
+        
+        target_lang = "en" if source_lang == "zh" else "zh"
+        
+        # 更新DDIM步数
+        self.diffusion_config.ddim_steps = ddim_steps
+        ddim_sampler = DDIMSampler(self.diffusion, ddim_steps)
+        
+        # 编码源语言
+        source_ids = self._encode(text, source_lang)
+        source_len = torch.tensor([source_ids.size(1)])
+        
+        # 嵌入源语言
+        source_emb = self.embedding(source_ids, source_lang, source_len)
+        
+        # 前向扩散到纯噪声
+        batch_size = source_emb.size(0)
+        t_full = torch.full((batch_size,), self.diffusion_config.timesteps - 1, dtype=torch.long)
+        noise = torch.randn_like(source_emb)
+        x_t, _ = self.diffusion.q_sample(source_emb, t_full, noise)
+        
+        # DDIM反向扩散
+        timesteps = ddim_sampler.ddim_timesteps
+        total_steps = len(timesteps)
+        switch_point = total_steps // 2
+        
+        process_steps = []
+        
+        for i, t in enumerate(timesteps[:-1]):
+            t_prev = timesteps[i + 1]
+            
+            # 语言切换
+            if i < switch_point:
+                current_lang = source_lang
+            else:
+                current_lang = target_lang
+            
+            # 预测噪声
+            t_tensor = torch.full((x_t.size(0),), t.item(), dtype=torch.long)
+            predicted_noise = self.model(x_t, t_tensor, lang=current_lang)
+            
+            # 记录过程
+            if show_process and i % max(1, total_steps // 10) == 0:
+                current_ids = self._embed_to_tokens(x_t, current_lang)
+                current_text = self._decode(current_ids, current_lang)
+                process_steps.append(f"Step {t.item()}: {current_text[:50]}")
+            
+            # DDIM步骤
+            x_t = ddim_sampler.ddim_step(x_t, t.item(), t_prev.item(), predicted_noise, eta=0.0)
+        
+        # 最终解码
+        final_ids = self._embed_to_tokens(x_t, target_lang)
+        result = self._decode(final_ids, target_lang)
+        
+        return result, process_steps
+
+
+# ==================== Gradio 应用 ====================
+def create_app():
+    # 加载模型
+    print("正在加载模型...")
+    # 使用脚本所在目录作为模型目录
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    translator = Translator(model_dir=script_dir)
+    print("模型加载完成!")
+    
+    def translate_text(text: str, language: str, ddim_steps: int, show_process: bool):
+        if not text.strip():
+            return "", []
+        
+        # 自动检测或手动选择
+        if language == "自动检测":
+            if any('\u4e00' <= c <= '\u9fff' for c in text):
+                source_lang = "zh"
+            else:
+                source_lang = "en"
+        else:
+            source_lang = "zh" if language == "中文 → 英文" else "en"
+        
+        try:
+            result, process = translator.translate(
+                text, source_lang, ddim_steps, show_process
+            )
+            process_text = "\n".join(process) if process else "（过程未显示）"
+            return result, process_text
+        except Exception as e:
+            return f"翻译出错: {str(e)}", ""
+    
+    # 创建界面
+    with gr.Blocks(
+        title="Diffutslator",
+        theme=gr.themes.Soft(),
+        css="""
+        .output-box { min-height: 100px; }
+        .process-box { font-family: monospace; font-size: 12px; }
+        """
+    ) as app:
+        gr.Markdown(
+            """
+            # Diffutslator 扩散翻译器
+            
+            基于扩散模型的机器翻译系统，可视化翻译过程中的语言渐变。
+            """
+        )
+        
+        with gr.Row():
+            with gr.Column(scale=2):
+                input_text = gr.Textbox(
+                    label="输入文本",
+                    placeholder="输入要翻译的中文或英文...",
+                    lines=5,
+                )
+                
+                with gr.Row():
+                    language = gr.Dropdown(
+                        choices=["自动检测", "中文 → 英文", "英文 → 中文"],
+                        value="自动检测",
+                        label="翻译方向",
+                    )
+                    
+                    ddim_steps = gr.Slider(
+                        minimum=10,
+                        maximum=100,
+                        value=50,
+                        step=5,
+                        label="DDIM步数",
+                        info="步数越多质量越高，速度越慢",
+                    )
+                
+                show_process = gr.Checkbox(
+                    value=False,
+                    label="显示扩散过程",
+                    info="显示翻译中间步骤（会增加推理时间）",
+                )
+                
+                translate_btn = gr.Button("翻译", variant="primary", size="lg")
+            
+            with gr.Column(scale=2):
+                output_text = gr.Textbox(
+                    label="翻译结果",
+                    lines=5,
+                    interactive=False,
+                    elem_classes=["output-box"],
+                )
+                
+                process_text = gr.Textbox(
+                    label="扩散过程",
+                    lines=5,
+                    interactive=False,
+                    visible=False,
+                    elem_classes=["process-box"],
+                )
+        
+        # 示例
+        gr.Examples(
+            examples=[
+                ["你好，世界！", "自动检测"],
+                ["Hello, how are you today?", "自动检测"],
+                ["机器学习正在改变世界。", "中文 → 英文"],
+                ["The quick brown fox jumps over the lazy dog.", "英文 → 中文"],
+            ],
+            inputs=[input_text, language],
+        )
+        
+        # 事件处理
+        def toggle_process(show):
+            return gr.Textbox(visible=show)
+        
+        show_process.change(
+            fn=toggle_process,
+            inputs=[show_process],
+            outputs=[process_text],
+        )
+        
+        translate_btn.click(
+            fn=translate_text,
+            inputs=[input_text, language, ddim_steps, show_process],
+            outputs=[output_text, process_text],
+        )
+        
+        # 回车提交
+        input_text.submit(
+            fn=translate_text,
+            inputs=[input_text, language, ddim_steps, show_process],
+            outputs=[output_text, process_text],
+        )
+    
+    return app
+
+
+if __name__ == "__main__":
+    app = create_app()
+    app.launch()

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:283c995651aa11ebde09858ad5002cd932c1dd6dd5ede16be733c16cbb5c4c55
+size 47986610

requirements.txt

@@ -0,0 +1,2 @@
+gradio>=4.0.0
+torch>=2.0.0

tokenizer_zh.json

@@ -0,0 +1,5631 @@
+{
+  "vocab_size": 8000,
+  "lang": "zh",
+  "token_to_id": {
+    "<pad>": 0,
+    "<sos>": 1,
+    "<eos>": 2,
+    "<unk>": 3,
+    "<mask>": 4,
+    "!</w>": 5,
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+    "jack</w>": 37,
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+    "muiriel": 48,
+    "muiriel</w>": 49,
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+    "nancy</w>": 51,
+    "ncy</w>": 52,
+    "om</w>": 53,
+    "te</w>": 54,
+    "tom</w>": 55,
+    "uir": 56,
+    "y</w>": 57,
+    "“</w>": 58,
+    "”</w>": 59,
+    "…</w>": 60,
+    "、</w>": 61,
+    "。</w>": 62,
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+    "七</w>": 64,
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+    "下</w>": 68,
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+    "中</w>": 76,
+    "丰</w>": 77,
+    "为</w>": 78,
+    "举</w>": 79,
+    "久</w>": 80,
+    "么</w>": 81,
+    "义</w>": 82,
+    "之</w>": 83,
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+    "乐</w>": 85,
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+    "624": "疑</w>",
+    "625": "疯</w>",
+    "626": "病</w>",
+    "627": "痛</w>",
+    "628": "瘋</w>",
+    "629": "發</w>",
+    "630": "白</w>",
+    "631": "百</w>",
+    "632": "的</w>",
+    "633": "盐</w>",
+    "634": "盖</w>",
+    "635": "盛</w>",
+    "636": "目</w>",
+    "637": "直</w>",
+    "638": "相</w>",
+    "639": "盹</w>",
+    "640": "看</w>",
+    "641": "真</w>",
+    "642": "眠</w>",
+    "643": "眼</w>",
+    "644": "着</w>",
+    "645": "睛</w>",
+    "646": "睡</w>",
+    "647": "知</w>",
+    "648": "短</w>",
+    "649": "石</w>",
+    "650": "码</w>",
+    "651": "破</w>",
+    "652": "确</w>",
+    "653": "碎</w>",
+    "654": "示</w>",
+    "655": "社</w>",
+    "656": "祝</w>",
+    "657": "神</w>",
+    "658": "票</w>",
+    "659": "福</w>",
+    "660": "离</w>",
+    "661": "私</w>",
+    "662": "种</w>",
+    "663": "秘</w>",
+    "664": "移</w>",
+    "665": "程</w>",
+    "666": "空</w>",
+    "667": "窗</w>",
+    "668": "窜</w>",
+    "669": "站</w>",
+    "670": "童</w>",
+    "671": "笑</w>",
+    "672": "笔</w>",
+    "673": "笛</w>",
+    "674": "第</w>",
+    "675": "笼</w>",
+    "676": "等</w>",
+    "677": "筑</w>",
+    "678": "答</w>",
+    "679": "简</w>",
+    "680": "籍</w>",
+    "681": "粗</w>",
+    "682": "精</w>",
+    "683": "糕</w>",
+    "684": "糟</w>",
+    "685": "素</w>",
+    "686": "索</w>",
+    "687": "給</w>",
+    "688": "經</w>",
+    "689": "總</w>",
+    "690": "红</w>",
+    "691": "纪</w>",
+    "692": "纯</w>",
+    "693": "纸</w>",
+    "694": "线</w>",
+    "695": "绅</w>",
+    "696": "终</w>",
+    "697": "经</w>",
+    "698": "结</w>",
+    "699": "给</w>",
+    "700": "统</w>",
+    "701": "绿</w>",
+    "702": "缺</w>",
+    "703": "网</w>",
+    "704": "罗</w>",
+    "705": "罚</w>",
+    "706": "置</w>",
+    "707": "美</w>",
+    "708": "群</w>",
+    "709": "習</w>",
+    "710": "老</w>",
+    "711": "考</w>",
+    "712": "者</w>",
+    "713": "而</w>",
+    "714": "耍</w>",
+    "715": "耗</w>",
+    "716": "职</w>",
+    "717": "肯</w>",
+    "718": "胖</w>",
+    "719": "能</w>",
+    "720": "脑</w>",
+    "721": "脚</w>",
+    "722": "脸</w>",
+    "723": "腾</w>",
+    "724": "腿</w>",
+    "725": "自</w>",
+    "726": "至</w>",
+    "727": "船</w>",
+    "728": "艰</w>",
+    "729": "色</w>",
+    "730": "艺</w>",
+    "731": "花</w>",
+    "732": "苏</w>",
+    "733": "英</w>",
+    "734": "茶</w>",
+    "735": "药</w>",
+    "736": "落</w>",
+    "737": "著</w>",
+    "738": "虑</w>",
+    "739": "虾</w>",
+    "740": "蜂</w>",
+    "741": "蝴</w>",
+    "742": "蝶</w>",
+    "743": "蠢</w>",
+    "744": "血</w>",
+    "745": "行</w>",
+    "746": "衣</w>",
+    "747": "表</w>",
+    "748": "被</w>",
+    "749": "裡</w>",
+    "750": "要</w>",
+    "751": "覆</w>",
+    "752": "覺</w>",
+    "753": "见</w>",
+    "754": "观</w>",
+    "755": "规</w>",
+    "756": "视</w>",
+    "757": "觉</w>",
+    "758": "解</w>",
+    "759": "言</w>",
+    "760": "計</w>",
+    "761": "試</w>",
+    "762": "話</w>",
+    "763": "該</w>",
+    "764": "誓</w>",
+    "765": "說</w>",
+    "766": "請</w>",
+    "767": "讀</w>",
+    "768": "變</w>",
+    "769": "计</w>",
+    "770": "订</w>",
+    "771": "认</w>",
+    "772": "让</w>",
+    "773": "训</w>",
+    "774": "议</w>",
+    "775": "记</w>",
+    "776": "讲</w>",
+    "777": "讶</w>",
+    "778": "许</w>",
+    "779": "论</w>",
+    "780": "设</w>",
+    "781": "访</w>",
+    "782": "证</w>",
+    "783": "评</w>",
+    "784": "识</w>",
+    "785": "诉</w>",
+    "786": "试</w>",
+    "787": "诗</w>",
+    "788": "诚</w>",
+    "789": "话</w>",
+    "790": "该</w>",
+    "791": "语</w>",
+    "792": "误</w>",
+    "793": "说</w>",
+    "794": "请</w>",
+    "795": "诺</w>",
+    "796": "读</w>",
+    "797": "课</w>",
+    "798": "谁</w>",
+    "799": "谈</w>",
+    "800": "谎</w>",
+    "801": "谢</w>",
+    "802": "象</w>",
+    "803": "賺</w>",
+    "804": "负</w>",
+    "805": "货</w>",
+    "806": "购</w>",
+    "807": "贷</w>",
+    "808": "费</w>",
+    "809": "赛</w>",
+    "810": "赢</w>",
+    "811": "走</w>",
+    "812": "赶</w>",
+    "813": "起</w>",
+    "814": "趕</w>",
+    "815": "趣</w>",
+    "816": "足</w>",
+    "817": "跑</w>",
+    "818": "跟</w>",
+    "819": "路</w>",
+    "820": "踢</w>",
+    "821": "躲</w>",
+    "822": "較</w>",
+    "823": "车</w>",
+    "824": "轨</w>",
+    "825": "转</w>",
+    "826": "轻</w>",
+    "827": "较</w>",
+    "828": "辆</w>",
+    "829": "辈</w>",
+    "830": "辜</w>",
+    "831": "辩</w>",
+    "832": "达</w>",
+    "833": "迅</w>",
+    "834": "过</w>",
+    "835": "近</w>",
+    "836": "还</w>",
+    "837": "这</w>",
+    "838": "进</w>",
+    "839": "远</w>",
+    "840": "迟</w>",
+    "841": "述</w>",
+    "842": "迷</w>",
+    "843": "迹</w>",
+    "844": "送</w>",
+    "845": "适</w>",
+    "846": "逃</w>",
+    "847": "选</w>",
+    "848": "透</w>",
+    "849": "递</w>",
+    "850": "途</w>",
+    "851": "這</w>",
+    "852": "通</w>",
+    "853": "速</w>",
+    "854": "造</w>",
+    "855": "進</w>",
+    "856": "過</w>",
+    "857": "道</w>",
+    "858": "遛</w>",
+    "859": "遠</w>",
+    "860": "邀</w>",
+    "861": "那</w>",
+    "862": "邻</w>",
+    "863": "部</w>",
+    "864": "都</w>",
+    "865": "酒</w>",
+    "866": "采</w>",
+    "867": "里</w>",
+    "868": "重</w>",
+    "869": "金</w>",
+    "870": "钟</w>",
+    "871": "钱</w>",
+    "872": "铁</w>",
+    "873": "铃</w>",
+    "874": "铭</w>",
+    "875": "银</w>",
+    "876": "销</w>",
+    "877": "错</w>",
+    "878": "镜</w>",
+    "879": "長</w>",
+    "880": "长</w>",
+    "881": "間</w>",
+    "882": "问</w>",
+    "883": "间</w>",
+    "884": "闻</w>",
+    "885": "阅</w>",
+    "886": "阐</w>",
+    "887": "防</w>",
+    "888": "阳</w>",
+    "889": "附</w>",
+    "890": "限</w>",
+    "891": "除</w>",
+    "892": "险</w>",
+    "893": "随</w>",
+    "894": "隻</w>",
+    "895": "难</w>",
+    "896": "雨</w>",
+    "897": "雪</w>",
+    "898": "零</w>",
+    "899": "雹</w>",
+    "900": "需</w>",
+    "901": "震</w>",
+    "902": "露</w>",
+    "903": "非</w>",
+    "904": "靠</w>",
+    "905": "面</w>",
+    "906": "音</w>",
+    "907": "題</w>",
+    "908": "项</w>",
+    "909": "须</w>",
+    "910": "顾</w>",
+    "911": "预</w>",
+    "912": "题</w>",
+    "913": "风</w>",
+    "914": "飞</w>",
+    "915": "食</w>",
+    "916": "餐</w>",
+    "917": "饭</w>",
+    "918": "饿</w>",
+    "919": "首</w>",
+    "920": "马</w>",
+    "921": "驶</w>",
+    "922": "验</w>",
+    "923": "骑</w>",
+    "924": "骗</w>",
+    "925": "高</w>",
+    "926": "鬼</w>",
+    "927": "鱼</w>",
+    "928": "鲍</w>",
+    "929": "鲜</w>",
+    "930": "麻</w>",
+    "931": "麼</w>",
+    "932": "點</w>",
+    "933": "鼠</w>",
+    "934": "龙</w>",
+    "935": "﹐</w>",
+    "936": "！</w>",
+    "937": "，</w>",
+    "938": "？</w>"
+  },
+  "merges": [
+    [
+      "。",
+      "</w>"
+    ],
+    [
+      "我",
+      "</w>"
+    ],
+    [
+      "的",
+      "</w>"
+    ],
+    [
+      "了",
+      "</w>"
+    ],
+    [
+      "他",
+      "</w>"
+    ],
+    [
+      "是",
+      "</w>"
+    ],
+    [
+      "你",
+      "</w>"
+    ],
+    [
+      "这",
+      "</w>"
+    ],
+    [
+      "一",
+      "</w>"
+    ],
+    [
+      "，",
+      "</w>"
+    ],
+    [
+      "不",
+      "</w>"
+    ],
+    [
+      "在",
+      "</w>"
+    ],
+    [
+      "们",
+      "</w>"
+    ],
+    [
+      "有",
+      "</w>"
+    ],
+    [
+      "个",
+      "</w>"
+    ],
+    [
+      "？",
+      "</w>"
+    ],
+    [
+      "她",
+      "</w>"
+    ],
+    [
+      "很",
+      "</w>"
+    ],
+    [
+      "会",
+      "</w>"
+    ],
+    [
+      "去",
+      "</w>"
+    ],
+    [
+      "人",
+      "</w>"
+    ],
+    [
+      "要",
+      "</w>"
+    ],
+    [
+      "来",
+      "</w>"
+    ],
+    [
+      "生",
+      "</w>"
+    ],
+    [
+      "得",
+      "</w>"
+    ],
+    [
+      "上",
+      "</w>"
+    ],
+    [
+      "天",
+      "</w>"
+    ],
+    [
+      "就",
+      "</w>"
+    ],
+    [
+      "子",
+      "</w>"
+    ],
+    [
+      "到",
+      "</w>"
+    ],
+    [
+      "车",
+      "</w>"
+    ],
+    [
+      "么",
+      "</w>"
+    ],
+    [
+      "吗",
+      "</w>"
+    ],
+    [
+      "没",
+      "</w>"
+    ],
+    [
+      "里",
+      "</w>"
+    ],
+    [
+      "能",
+      "</w>"
+    ],
+    [
+      "想",
+      "</w>"
+    ],
+    [
+      "大",
+      "</w>"
+    ],
+    [
+      "可",
+      "</w>"
+    ],
+    [
+      "说",
+      "</w>"
+    ],
+    [
+      "那",
+      "</w>"
+    ],
+    [
+      "什",
+      "</w>"
+    ],
+    [
+      "下",
+      "</w>"
+    ],
+    [
+      "对",
+      "</w>"
+    ],
+    [
+      "看",
+      "</w>"
+    ],
+    [
+      "多",
+      "</w>"
+    ],
+    [
+      "！",
+      "</w>"
+    ],
+    [
+      "喜",
+      "</w>"
+    ],
+    [
+      "以",
+      "</w>"
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+    [
+      "学",
+      "</w>"
+    ],
+    [
+      "过",
+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
+    ],
+    [
+      "日",
+      "</w>"
+    ],
+    [
+      "家",
+      "</w>"
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+    [
+      "事",
+      "</w>"
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+    [
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+      "</w>"
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+      "</w>"
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+      "</w>"
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+    [
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+      "</w>"
+    ],
+    [
+      "欢",
+      "</w>"
+    ],
+    [
+      "时",
+      "</w>"
+    ],
+    [
+      "也",
+      "</w>"
+    ],
+    [
+      "道",
+      "</w>"
+    ],
+    [
+      "问",
+      "</w>"
+    ],
+    [
+      "开",
+      "</w>"
+    ],
+    [
+      "和",
+      "</w>"
+    ],
+    [
+      "孩",
+      "</w>"
+    ],
+    [
+      "出",
+      "</w>"
+    ],
+    [
+      "快",
+      "</w>"
+    ],
+    [
+      "常",
+      "</w>"
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+    [
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+      "</w>"
+    ],
+    [
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+      "</w>"
+    ],
+    [
+      "如",
+      "</w>"
+    ],
+    [
+      "无",
+      "</w>"
+    ],
+    [
+      "法",
+      "</w>"
+    ],
+    [
+      "地",
+      "</w>"
+    ],
+    [
+      "比",
+      "</w>"
+    ],
+    [
+      "回",
+      "</w>"
+    ],
+    [
+      "果",
+      "</w>"
+    ],
+    [
+      "“",
+      "</w>"
+    ],
+    [
+      "样",
+      "</w>"
+    ],
+    [
+      "”",
+      "</w>"
+    ],
+    [
+      "試",
+      "</w>"
+    ],
+    [
+      "从",
+      "</w>"
+    ],
+    [
+      "把",
+      "</w>"
+    ],
+    [
+      "做",
+      "</w>"
+    ],
+    [
+      "老",
+      "</w>"
+    ],
+    [
+      "?",
+      "</w>"
+    ],
+    [
+      "听",
+      "</w>"
+    ],
+    [
+      "本",
+      "</w>"
+    ],
+    [
+      "爸",
+      "</w>"
+    ],
+    [
+      "妈",
+      "</w>"
+    ],
+    [
+      "还",
+      "</w>"
+    ],
+    [
+      "這",
+      "</w>"
+    ],
+    [
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+      "</w>"
+    ],
+    [
+      "用",
+      "</w>"
+    ],
+    [
+      "话",
+      "</w>"
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+      "旅",
+      "</w>"
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+      "明",
+      "</w>"
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+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
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+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
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+    [
+      "游",
+      "</w>"
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+    [
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+      "</w>"
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+      "</w>"
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+    [
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+      "</w>"
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+      "</w>"
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+    [
+      "它",
+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
+    ],
+    [
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+    [
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+      "</w>"
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+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
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+    [
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+      "</w>"
+    ],
+    [
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+      "</w>"
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+    [
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+    [
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+    [
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+    [
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+    ],
+    [
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+      "</w>"
+    ],
+    [
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+    [
+      "津",
+      "</w>"
+    ],
+    [
+      "音",
+      "</w>"
+    ],
+    [
+      "值",
+      "</w>"
+    ],
+    [
+      "睛",
+      "</w>"
+    ],
+    [
+      "k",
+      "e</w>"
+    ],
+    [
+      "ja",
+      "ke</w>"
+    ],
+    [
+      "扇",
+      "</w>"
+    ],
+    [
+      "窗",
+      "</w>"
+    ],
+    [
+      "叫",
+      "</w>"
+    ],
+    [
+      "ja",
+      "c"
+    ],
+    [
+      "k",
+      "</w>"
+    ],
+    [
+      "jac",
+      "k</w>"
+    ],
+    [
+      "幹",
+      "</w>"
+    ],
+    [
+      "鲍",
+      "</w>"
+    ],
+    [
+      "勃",
+      "</w>"
+    ],
+    [
+      "丰",
+      "</w>"
+    ],
+    [
+      "富",
+      "</w>"
+    ],
+    [
+      "答",
+      "</w>"
+    ],
+    [
+      "复",
+      "</w>"
+    ],
+    [
+      "悔",
+      "</w>"
+    ],
+    [
+      "概",
+      "</w>"
+    ],
+    [
+      "澄",
+      "</w>"
+    ],
+    [
+      "清",
+      "</w>"
+    ],
+    [
+      "价",
+      "</w>"
+    ],
+    [
+      "涨",
+      "</w>"
+    ],
+    [
+      "守",
+      "</w>"
+    ],
+    [
+      "诺",
+      "</w>"
+    ],
+    [
+      "顾",
+      "</w>"
+    ],
+    [
+      "迷",
+      "</w>"
+    ],
+    [
+      "社",
+      "</w>"
+    ],
+    [
+      "团",
+      "</w>"
+    ],
+    [
+      "抓",
+      "</w>"
+    ],
+    [
+      "鼠",
+      "</w>"
+    ],
+    [
+      "纪",
+      "</w>"
+    ],
+    [
+      "品",
+      "</w>"
+    ],
+    [
+      "阅",
+      "</w>"
+    ],
+    [
+      "饭",
+      "</w>"
+    ],
+    [
+      "购",
+      "</w>"
+    ],
+    [
+      "镜",
+      "</w>"
+    ],
+    [
+      "迅",
+      "</w>"
+    ],
+    [
+      "速",
+      "</w>"
+    ],
+    [
+      "窜",
+      "</w>"
+    ],
+    [
+      "入",
+      "</w>"
+    ],
+    [
+      "群",
+      "</w>"
+    ],
+    [
+      "耗",
+      "</w>"
+    ],
+    [
+      "气",
+      "</w>"
+    ],
+    [
+      "化",
+      "</w>"
+    ],
+    [
+      "附",
+      "</w>"
+    ],
+    [
+      "近",
+      "</w>"
+    ],
+    [
+      "张",
+      "</w>"
+    ],
+    [
+      "片",
+      "</w>"
+    ],
+    [
+      "童",
+      "</w>"
+    ],
+    [
+      "福",
+      "</w>"
+    ],
+    [
+      "药",
+      "</w>"
+    ],
+    [
+      "创",
+      "</w>"
+    ],
+    [
+      "迹",
+      "</w>"
+    ],
+    [
+      "厕",
+      "</w>"
+    ],
+    [
+      "冲",
+      "</w>"
+    ],
+    [
+      "轨",
+      "</w>"
+    ],
+    [
+      "1",
+      "8"
+    ],
+    [
+      "18",
+      "</w>"
+    ],
+    [
+      "环",
+      "</w>"
+    ],
+    [
+      "素",
+      "</w>"
+    ],
+    [
+      "5",
+      "6</w>"
+    ],
+    [
+      "粗",
+      "</w>"
+    ],
+    [
+      "趕",
+      "</w>"
+    ],
+    [
+      "久",
+      "</w>"
+    ],
+    [
+      "妻",
+      "</w>"
+    ],
+    [
+      "互",
+      "</w>"
+    ],
+    [
+      "助",
+      "</w>"
+    ],
+    [
+      "训",
+      "</w>"
+    ],
+    [
+      "脑",
+      "</w>"
+    ],
+    [
+      "戏",
+      "</w>"
+    ],
+    [
+      "散",
+      "</w>"
+    ],
+    [
+      "步",
+      "</w>"
+    ],
+    [
+      "油",
+      "</w>"
+    ],
+    [
+      "置",
+      "</w>"
+    ],
+    [
+      "债",
+      "</w>"
+    ],
+    [
+      "冷",
+      "</w>"
+    ],
+    [
+      "湖",
+      "</w>"
+    ],
+    [
+      "结",
+      "</w>"
+    ],
+    [
+      "首",
+      "</w>"
+    ],
+    [
+      "歌",
+      "</w>"
+    ],
+    [
+      "1",
+      "0"
+    ],
+    [
+      "10",
+      "0</w>"
+    ],
+    [
+      "万",
+      "</w>"
+    ],
+    [
+      "辆",
+      "</w>"
+    ],
+    [
+      "呢",
+      "</w>"
+    ],
+    [
+      "變",
+      "</w>"
+    ],
+    [
+      "卖",
+      "</w>"
+    ],
+    [
+      "栋",
+      "</w>"
+    ],
+    [
+      "灰",
+      "</w>"
+    ],
+    [
+      "楼",
+      "</w>"
+    ],
+    [
+      "毕",
+      "</w>"
+    ],
+    [
+      "索",
+      "</w>"
+    ],
+    [
+      "抱",
+      "</w>"
+    ],
+    [
+      "歉",
+      "</w>"
+    ],
+    [
+      "盛",
+      "</w>"
+    ],
+    [
+      "邀",
+      "</w>"
+    ],
+    [
+      "延",
+      "</w>"
+    ],
+    [
+      "误",
+      "</w>"
+    ],
+    [
+      "苏",
+      "</w>"
+    ],
+    [
+      "兰",
+      "</w>"
+    ],
+    [
+      "古",
+      "</w>"
+    ],
+    [
+      "堡",
+      "</w>"
+    ],
+    [
+      "谁",
+      "</w>"
+    ],
+    [
+      "纸",
+      "</w>"
+    ],
+    [
+      "杂",
+      "</w>"
+    ],
+    [
+      "志",
+      "</w>"
+    ],
+    [
+      "闻",
+      "</w>"
+    ],
+    [
+      "播",
+      "</w>"
+    ],
+    [
+      "奶",
+      "</w>"
+    ]
+  ],
+  "special_tokens": [
+    "<pad>",
+    "<sos>",
+    "<eos>",
+    "<unk>",
+    "<mask>"
+  ]
+}