change the docstrings and comments to English

whisperx/alignment.py

@@ -403,33 +403,33 @@ def get_trellis(emission, tokens, blank_id=0):
 
 
 def get_wildcard_emission(frame_emission, tokens, blank_id):
-    """处理包含通配符的token emission分数（向量化版本）
+    """Processing token emission scores containing wildcards (vectorized version)
 
     Args:
-        frame_emission: 当前帧的emission概率向量
-        tokens: token索引列表
-        blank_id: blank token的ID
+        frame_emission: Emission probability vector for the current frame
+        tokens: List of token indices
+        blank_id: ID of the blank token
 
     Returns:
-        tensor: 每个token位置的最大概率分数
+        tensor: Maximum probability score for each token position
     """
     assert 0 <= blank_id < len(frame_emission)
 
-    # 将tokens转换为tensor（如果还不是的话）
+    # Convert tokens to a tensor if they are not already
     tokens = torch.tensor(tokens) if not isinstance(tokens, torch.Tensor) else tokens
 
-    # 创建mask来标识通配符位置
+    # Create a mask to identify wildcard positions
     wildcard_mask = (tokens == -1)
 
-    # 为非通配符位置获取分数
-    regular_scores = frame_emission[tokens.clamp(min=0)]  # clamp避免-1索引
+    # Get scores for non-wildcard positions
+    regular_scores = frame_emission[tokens.clamp(min=0)]  # clamp to avoid -1 index
 
-    # 创建掩码并计算最大值，不会修改frame_emission
-    max_valid_score = frame_emission.clone()  # 创建副本
-    max_valid_score[blank_id] = float('-inf')  # 在副本上操作
+    # Create a mask and compute the maximum value without modifying frame_emission
+    max_valid_score = frame_emission.clone()   # Create a copy
+    max_valid_score[blank_id] = float('-inf')  # Modify the copy to exclude the blank token
     max_valid_score = max_valid_score.max()
 
-    # 使用where操作来组合结果
+    # Use where operation to combine results
     result = torch.where(wildcard_mask, max_valid_score, regular_scores)
 
     return result
@@ -488,15 +488,26 @@ class Path:
 
 @dataclass
 class BeamState:
-    """beam search中的状态"""
-    token_index: int  # 当前token位置
-    time_index: int  # 当前时间步
-    score: float  # 累积分数
-    path: List[Point]  # 路径历史
+    """State in beam search."""
+    token_index: int   # Current token position
+    time_index: int    # Current time step
+    score: float       # Cumulative score
+    path: List[Point]  # Path history
 
 
 def backtrack_beam(trellis, emission, tokens, blank_id=0, beam_width=5):
-    """标准CTC beam search回溯实现
+    """Standard CTC beam search backtracking implementation.
+
+    Args:
+        trellis (torch.Tensor): The trellis (or lattice) of shape (T, N), where T is the number of time steps
+                                and N is the number of tokens (including the blank token).
+        emission (torch.Tensor): The emission probabilities of shape (T, N).
+        tokens (List[int]): List of token indices (excluding the blank token).
+        blank_id (int, optional): The ID of the blank token. Defaults to 0.
+        beam_width (int, optional): The number of top paths to keep during beam search. Defaults to 5.
+
+    Returns:
+        List[Point]: the best path
     """
     T, J = trellis.size(0) - 1, trellis.size(1) - 1
 
@@ -524,7 +535,7 @@ def backtrack_beam(trellis, emission, tokens, blank_id=0, beam_width=5):
             stay_score = trellis[t - 1, j]
             change_score = trellis[t - 1, j - 1] if j > 0 else float('-inf')
 
-            # Stay路径
+            # Stay
             if not math.isinf(stay_score):
                 new_path = beam.path.copy()
                 new_path.append(Point(j, t - 1, p_stay.exp().item()))
@@ -535,7 +546,7 @@ def backtrack_beam(trellis, emission, tokens, blank_id=0, beam_width=5):
                     path=new_path
                 ))
 
-            # Change路径
+            # Change
             if j > 0 and not math.isinf(change_score):
                 new_path = beam.path.copy()
                 new_path.append(Point(j - 1, t - 1, p_change.exp().item()))
@@ -546,7 +557,7 @@ def backtrack_beam(trellis, emission, tokens, blank_id=0, beam_width=5):
                     path=new_path
                 ))
 
-        # 只按分数排序，不需要去重
+        # sort by score
         beams = sorted(next_beams, key=lambda x: x.score, reverse=True)[:beam_width]
 
         if not beams: