Merge pull request #986 from bfs18/main

support timestamp for numbers.

whisperx/alignment.py

@@ -2,6 +2,7 @@
 Forced Alignment with Whisper
 C. Max Bain
 """
+import math
 
 from dataclasses import dataclass
 from typing import Iterable, Optional, Union, List
@@ -171,10 +172,17 @@ def align(
             elif char_ in model_dictionary.keys():
                 clean_char.append(char_)
                 clean_cdx.append(cdx)
+            else:
+                # add placeholder
+                clean_char.append('*')
+                clean_cdx.append(cdx)
 
         clean_wdx = []
         for wdx, wrd in enumerate(per_word):
-            if any([c in model_dictionary.keys() for c in wrd]):
+            if any([c in model_dictionary.keys() for c in wrd.lower()]):
+                clean_wdx.append(wdx)
+            else:
+                # index for placeholder
                 clean_wdx.append(wdx)
 
                 
@@ -222,7 +230,7 @@ def align(
             continue
 
         text_clean = "".join(segment_data[sdx]["clean_char"])
-        tokens = [model_dictionary[c] for c in text_clean]
+        tokens = [model_dictionary.get(c, -1) for c in text_clean]
 
         f1 = int(t1 * SAMPLE_RATE)
         f2 = int(t2 * SAMPLE_RATE)
@@ -255,7 +263,8 @@ def align(
                 blank_id = code
 
         trellis = get_trellis(emission, tokens, blank_id)
-        path = backtrack(trellis, emission, tokens, blank_id)
+        # path = backtrack(trellis, emission, tokens, blank_id)
+        path = backtrack_beam(trellis, emission, tokens, blank_id, beam_width=2)
 
         if path is None:
             print(f'Failed to align segment ("{segment["text"]}"): backtrack failed, resorting to original...')
@@ -264,7 +273,7 @@ def align(
 
         char_segments = merge_repeats(path, text_clean)
 
-        duration = t2 -t1
+        duration = t2 - t1
         ratio = duration * waveform_segment.size(0) / (trellis.size(0) - 1)
 
         # assign timestamps to aligned characters
@@ -371,70 +380,203 @@ def align(
 """
 source: https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html
 """
+
+
 def get_trellis(emission, tokens, blank_id=0):
     num_frame = emission.size(0)
     num_tokens = len(tokens)
 
-    # Trellis has extra diemsions for both time axis and tokens.
+    trellis = torch.zeros((num_frame, num_tokens))
-    # The extra dim for tokens represents <SoS> (start-of-sentence)
+    trellis[1:, 0] = torch.cumsum(emission[1:, blank_id], 0)
-    # The extra dim for time axis is for simplification of the code.
+    trellis[0, 1:] = -float("inf")
-    trellis = torch.empty((num_frame + 1, num_tokens + 1))
+    trellis[-num_tokens + 1:, 0] = float("inf")
-    trellis[0, 0] = 0
-    trellis[1:, 0] = torch.cumsum(emission[:, 0], 0)
-    trellis[0, -num_tokens:] = -float("inf")
-    trellis[-num_tokens:, 0] = float("inf")
 
-    for t in range(num_frame):
+    for t in range(num_frame - 1):
         trellis[t + 1, 1:] = torch.maximum(
             # Score for staying at the same token
             trellis[t, 1:] + emission[t, blank_id],
             # Score for changing to the next token
-            trellis[t, :-1] + emission[t, tokens],
+            # trellis[t, :-1] + emission[t, tokens[1:]],
+            trellis[t, :-1] + get_wildcard_emission(emission[t], tokens[1:], blank_id),
         )
     return trellis
 
+
+def get_wildcard_emission(frame_emission, tokens, blank_id):
+    """Processing token emission scores containing wildcards (vectorized version)
+
+    Args:
+        frame_emission: Emission probability vector for the current frame
+        tokens: List of token indices
+        blank_id: ID of the blank token
+
+    Returns:
+        tensor: Maximum probability score for each token position
+    """
+    assert 0 <= blank_id < len(frame_emission)
+
+    # Convert tokens to a tensor if they are not already
+    tokens = torch.tensor(tokens) if not isinstance(tokens, torch.Tensor) else tokens
+
+    # Create a mask to identify wildcard positions
+    wildcard_mask = (tokens == -1)
+
+    # Get scores for non-wildcard positions
+    regular_scores = frame_emission[tokens.clamp(min=0)]  # clamp to avoid -1 index
+
+    # 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()
+
+    # Use where operation to combine results
+    result = torch.where(wildcard_mask, max_valid_score, regular_scores)
+
+    return result
+
+
 @dataclass
 class Point:
     token_index: int
     time_index: int
     score: float
 
+
 def backtrack(trellis, emission, tokens, blank_id=0):
-    # Note:
+    t, j = trellis.size(0) - 1, trellis.size(1) - 1
-    # j and t are indices for trellis, which has extra dimensions
+
-    # for time and tokens at the beginning.
+    path = [Point(j, t, emission[t, blank_id].exp().item())]
-    # When referring to time frame index `T` in trellis,
+    while j > 0:
-    # the corresponding index in emission is `T-1`.
+        # Should not happen but just in case
-    # Similarly, when referring to token index `J` in trellis,
+        assert t > 0
-    # the corresponding index in transcript is `J-1`.
-    j = trellis.size(1) - 1
-    t_start = torch.argmax(trellis[:, j]).item()
 
-    path = []
-    for t in range(t_start, 0, -1):
         # 1. Figure out if the current position was stay or change
-        # Note (again):
+        # Frame-wise score of stay vs change
-        # `emission[J-1]` is the emission at time frame `J` of trellis dimension.
+        p_stay = emission[t - 1, blank_id]
-        # Score for token staying the same from time frame J-1 to T.
+        # p_change = emission[t - 1, tokens[j]]
-        stayed = trellis[t - 1, j] + emission[t - 1, blank_id]
+        p_change = get_wildcard_emission(emission[t - 1], [tokens[j]], blank_id)[0]
-        # Score for token changing from C-1 at T-1 to J at T.
-        changed = trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]
 
-        # 2. Store the path with frame-wise probability.
+        # Context-aware score for stay vs change
-        prob = emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
+        stayed = trellis[t - 1, j] + p_stay
-        # Return token index and time index in non-trellis coordinate.
+        changed = trellis[t - 1, j - 1] + p_change
-        path.append(Point(j - 1, t - 1, prob))
 
-        # 3. Update the token
+        # Update position
+        t -= 1
         if changed > stayed:
             j -= 1
-            if j == 0:
+
-                break
+        # Store the path with frame-wise probability.
-    else:
+        prob = (p_change if changed > stayed else p_stay).exp().item()
-        # failed
+        path.append(Point(j, t, prob))
-        return None
+
+    # Now j == 0, which means, it reached the SoS.
+    # Fill up the rest for the sake of visualization
+    while t > 0:
+        prob = emission[t - 1, blank_id].exp().item()
+        path.append(Point(j, t - 1, prob))
+        t -= 1
+
     return path[::-1]
 
+
+
+@dataclass
+class Path:
+    points: List[Point]
+    score: float
+
+
+@dataclass
+class BeamState:
+    """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):
+    """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
+
+    init_state = BeamState(
+        token_index=J,
+        time_index=T,
+        score=trellis[T, J],
+        path=[Point(J, T, emission[T, blank_id].exp().item())]
+    )
+
+    beams = [init_state]
+
+    while beams and beams[0].token_index > 0:
+        next_beams = []
+
+        for beam in beams:
+            t, j = beam.time_index, beam.token_index
+
+            if t <= 0:
+                continue
+
+            p_stay = emission[t - 1, blank_id]
+            p_change = get_wildcard_emission(emission[t - 1], [tokens[j]], blank_id)[0]
+
+            stay_score = trellis[t - 1, j]
+            change_score = trellis[t - 1, j - 1] if j > 0 else float('-inf')
+
+            # Stay
+            if not math.isinf(stay_score):
+                new_path = beam.path.copy()
+                new_path.append(Point(j, t - 1, p_stay.exp().item()))
+                next_beams.append(BeamState(
+                    token_index=j,
+                    time_index=t - 1,
+                    score=stay_score,
+                    path=new_path
+                ))
+
+            # 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()))
+                next_beams.append(BeamState(
+                    token_index=j - 1,
+                    time_index=t - 1,
+                    score=change_score,
+                    path=new_path
+                ))
+
+        # sort by score
+        beams = sorted(next_beams, key=lambda x: x.score, reverse=True)[:beam_width]
+
+        if not beams:
+            break
+
+    if not beams:
+        return None
+
+    best_beam = beams[0]
+    t = best_beam.time_index
+    j = best_beam.token_index
+    while t > 0:
+        prob = emission[t - 1, blank_id].exp().item()
+        best_beam.path.append(Point(j, t - 1, prob))
+        t -= 1
+
+    return best_beam.path[::-1]
+
+
 # Merge the labels
 @dataclass
 class Segment: