[utils] use force_align of torchaudio (#2597)

* [utils] use force_align of torchaudio

* rounded outputs of force_align

* remove empty line

wenet/cli/model.py

@@ -128,9 +128,9 @@ def _decode(self,
             for i, x in enumerate(res.tokens):
                 tokens_info.append({
                     'token': self.char_dict[x],
-                    'start': times[i][0],
-                    'end': times[i][1],
-                    'confidence': res.tokens_confidence[i]
+                    'start': round(times[i][0], 3),
+                    'end': round(times[i][1], 3),
+                    'confidence': round(res.tokens_confidence[i], 2)
                 })
             result['tokens'] = tokens_info
         return result

wenet/cli/paraformer_model.py

@@ -56,9 +56,9 @@ def transcribe(self, audio_file: str, tokens_info: bool = False) -> dict:
             for i, x in enumerate(res.tokens):
                 tokens_info.append({
                     'token': self.tokenizer.char_dict[x],
-                    'start': times[i][0],
-                    'end': times[i][1],
-                    'confidence': res.tokens_confidence[i]
+                    'start': round(times[i][0], 3),
+                    'end': round(times[i][1], 3),
+                    'confidence': round(res.tokens_confidence[i], 2)
                 })
             result['tokens'] = tokens_info
 

wenet/utils/ctc_utils.py

@@ -17,6 +17,7 @@
 import numpy as np
 
 import torch
+import torchaudio.functional as F
 
 
 def remove_duplicates_and_blank(hyp: List[int],
@@ -112,53 +113,10 @@ def force_align(ctc_probs: torch.Tensor, y: torch.Tensor, blank_id=0) -> list:
     Returns:
         torch.Tensor: alignment result
     """
-    ctc_probs = ctc_probs.cpu()
-    y = y.cpu()
-    y_insert_blank = insert_blank(y, blank_id)
-
-    log_alpha = torch.zeros((ctc_probs.size(0), len(y_insert_blank)))
-    log_alpha = log_alpha - float('inf')  # log of zero
-    state_path = torch.zeros((ctc_probs.size(0), len(y_insert_blank)),
-                             dtype=torch.int16) - 1  # state path
-
-    # init start state
-    log_alpha[0, 0] = ctc_probs[0][y_insert_blank[0]]
-    log_alpha[0, 1] = ctc_probs[0][y_insert_blank[1]]
-
-    for t in range(1, ctc_probs.size(0)):
-        for s in range(len(y_insert_blank)):
-            if y_insert_blank[s] == blank_id or s < 2 or y_insert_blank[
-                    s] == y_insert_blank[s - 2]:
-                candidates = torch.tensor(
-                    [log_alpha[t - 1, s], log_alpha[t - 1, s - 1]])
-                prev_state = [s, s - 1]
-            else:
-                candidates = torch.tensor([
-                    log_alpha[t - 1, s],
-                    log_alpha[t - 1, s - 1],
-                    log_alpha[t - 1, s - 2],
-                ])
-                prev_state = [s, s - 1, s - 2]
-            log_alpha[
-                t, s] = torch.max(candidates) + ctc_probs[t][y_insert_blank[s]]
-            state_path[t, s] = prev_state[torch.argmax(candidates)]
-
-    state_seq = -1 * torch.ones((ctc_probs.size(0), 1), dtype=torch.int16)
-
-    candidates = torch.tensor([
-        log_alpha[-1, len(y_insert_blank) - 1],
-        log_alpha[-1, len(y_insert_blank) - 2]
-    ])
-    final_state = [len(y_insert_blank) - 1, len(y_insert_blank) - 2]
-    state_seq[-1] = final_state[torch.argmax(candidates)]
-    for t in range(ctc_probs.size(0) - 2, -1, -1):
-        state_seq[t] = state_path[t + 1, state_seq[t + 1, 0]]
-
-    output_alignment = []
-    for t in range(0, ctc_probs.size(0)):
-        output_alignment.append(y_insert_blank[state_seq[t, 0]])
-
-    return output_alignment
+    ctc_probs = ctc_probs[None].cpu()
+    y = y[None].cpu()
+    alignments, _ = F.forced_align(ctc_probs, y, blank=blank_id)
+    return alignments[0]
 
 
 def get_blank_id(configs, symbol_table):