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import torch
def ctc_forced_align(
log_probs: torch.Tensor,
targets: torch.Tensor,
input_lengths: torch.Tensor,
target_lengths: torch.Tensor,
blank: int = 0,
ignore_id: int = -1,
) -> torch.Tensor:
"""Align a CTC label sequence to an emission.
Args:
log_probs (Tensor): log probability of CTC emission output.
Tensor of shape `(B, T, C)`. where `B` is the batch size, `T` is the input length,
`C` is the number of characters in alphabet including blank.
targets (Tensor): Target sequence. Tensor of shape `(B, L)`,
where `L` is the target length.
input_lengths (Tensor):
Lengths of the inputs (max value must each be <= `T`). 1-D Tensor of shape `(B,)`.
target_lengths (Tensor):
Lengths of the targets. 1-D Tensor of shape `(B,)`.
blank_id (int, optional): The index of blank symbol in CTC emission. (Default: 0)
ignore_id (int, optional): The index of ignore symbol in CTC emission. (Default: -1)
"""
targets[targets == ignore_id] = blank
batch_size, input_time_size, _ = log_probs.size()
bsz_indices = torch.arange(batch_size, device=input_lengths.device)
_t_a_r_g_e_t_s_ = torch.cat(
(
torch.stack((torch.full_like(targets, blank), targets), dim=-1).flatten(start_dim=1),
torch.full_like(targets[:, :1], blank),
),
dim=-1,
)
diff_labels = torch.cat(
(
torch.as_tensor([[False, False]], device=targets.device).expand(batch_size, -1),
_t_a_r_g_e_t_s_[:, 2:] != _t_a_r_g_e_t_s_[:, :-2],
),
dim=1,
)
neg_inf = torch.tensor(float("-inf"), device=log_probs.device, dtype=log_probs.dtype)
padding_num = 2
padded_t = padding_num + _t_a_r_g_e_t_s_.size(-1)
best_score = torch.full((batch_size, padded_t), neg_inf, device=log_probs.device, dtype=log_probs.dtype)
best_score[:, padding_num + 0] = log_probs[:, 0, blank]
best_score[:, padding_num + 1] = log_probs[bsz_indices, 0, _t_a_r_g_e_t_s_[:, 1]]
backpointers = torch.zeros((batch_size, input_time_size, padded_t), device=log_probs.device, dtype=targets.dtype)
for t in range(1, input_time_size):
prev = torch.stack(
(best_score[:, 2:], best_score[:, 1:-1], torch.where(diff_labels, best_score[:, :-2], neg_inf))
)
prev_max_value, prev_max_idx = prev.max(dim=0)
best_score[:, padding_num:] = log_probs[:, t].gather(-1, _t_a_r_g_e_t_s_) + prev_max_value
backpointers[:, t, padding_num:] = prev_max_idx
l1l2 = best_score.gather(
-1, torch.stack((padding_num + target_lengths * 2 - 1, padding_num + target_lengths * 2), dim=-1)
)
path = torch.zeros((batch_size, input_time_size), device=best_score.device, dtype=torch.long)
path[bsz_indices, input_lengths - 1] = padding_num + target_lengths * 2 - 1 + l1l2.argmax(dim=-1)
for t in range(input_time_size - 1, 0, -1):
target_indices = path[:, t]
prev_max_idx = backpointers[bsz_indices, t, target_indices]
path[:, t - 1] += target_indices - prev_max_idx
alignments = _t_a_r_g_e_t_s_.gather(dim=-1, index=(path - padding_num).clamp(min=0))
return alignments
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