VarBase.__getitem__ work for np.int64, np.longlong; but __setitem_varbase__...

VarBase.__getitem__ work for np.int64, np.longlong; but __setitem_varbase__ not support paddle.int16/set_value op not support

deepspeech/exps/u2/model.py

@@ -568,26 +568,25 @@ class U2Tester(U2Trainer):
                 ctc_probs = ctc_probs.squeeze(0)
                 target = target.squeeze(0)
                 alignment = ctc_utils.forced_align(ctc_probs, target)
-                logger.info("align ids", key[0], alignment)
+                logger.info(f"align ids: {key[0]} {alignment}")
                 fout.write('{} {}\n'.format(key[0], alignment))
 
                 # 3. gen praat
                 # segment alignment
                 align_segs = text_grid.segment_alignment(alignment)
-                logger.info("align tokens", key[0], align_segs)
+                logger.info(f"align tokens: {key[0]}, {align_segs}")
                 # IntervalTier, List["start end token\n"]
                 subsample = utility.get_subsample(self.config)
                 tierformat = text_grid.align_to_tierformat(
                     align_segs, subsample, token_dict)
                 # write tier
-                align_output_path = os.path.join(
-                    os.path.dirname(self.args.result_file), "align")
-                tier_path = os.path.join(align_output_path, key[0] + ".tier")
-                with open(tier_path, 'w') as f:
+                align_output_path = Path(self.args.result_file).parent / "align"
+                align_output_path.mkdir(parents=True, exist_ok=True)
+                tier_path = align_output_path / (key[0] + ".tier")
+                with tier_path.open('w') as f:
                     f.writelines(tierformat)
                 # write textgrid
-                textgrid_path = os.path.join(align_output_path,
-                                             key[0] + ".TextGrid")
+                textgrid_path = align_output_path / (key[0] + ".TextGrid")
                 second_per_frame = 1. / (1000. /
                                          stride_ms)  # 25ms window, 10ms stride
                 second_per_example = (
@@ -595,7 +594,7 @@ class U2Tester(U2Trainer):
                 text_grid.generate_textgrid(
                     maxtime=second_per_example,
                     intervals=tierformat,
-                    output=textgrid_path)
+                    output=str(textgrid_path))
 
     def run_align(self):
         self.resume_or_scratch()

deepspeech/exps/u2_kaldi/model.py

@@ -545,9 +545,8 @@ class U2Tester(U2Trainer):
         self.model.eval()
         logger.info(f"Align Total Examples: {len(self.align_loader.dataset)}")
 
-        stride_ms = self.config.collater.stride_ms
-        token_dict = self.args.char_list
-
+        stride_ms = self.align_loader.collate_fn.stride_ms
+        token_dict = self.align_loader.collate_fn.vocab_list
         with open(self.args.result_file, 'w') as fout:
             # one example in batch
             for i, batch in enumerate(self.align_loader):
@@ -564,26 +563,25 @@ class U2Tester(U2Trainer):
                 ctc_probs = ctc_probs.squeeze(0)
                 target = target.squeeze(0)
                 alignment = ctc_utils.forced_align(ctc_probs, target)
-                logger.info("align ids", key[0], alignment)
+                logger.info(f"align ids: {key[0]} {alignment}")
                 fout.write('{} {}\n'.format(key[0], alignment))
 
                 # 3. gen praat
                 # segment alignment
                 align_segs = text_grid.segment_alignment(alignment)
-                logger.info("align tokens", key[0], align_segs)
+                logger.info(f"align tokens: {key[0]}, {align_segs}")
                 # IntervalTier, List["start end token\n"]
                 subsample = utility.get_subsample(self.config)
                 tierformat = text_grid.align_to_tierformat(
                     align_segs, subsample, token_dict)
                 # write tier
-                align_output_path = os.path.join(
-                    os.path.dirname(self.args.result_file), "align")
-                tier_path = os.path.join(align_output_path, key[0] + ".tier")
-                with open(tier_path, 'w') as f:
+                align_output_path = Path(self.args.result_file).parent / "align"
+                align_output_path.mkdir(parents=True, exist_ok=True)
+                tier_path = align_output_path / (key[0] + ".tier")
+                with tier_path.open('w') as f:
                     f.writelines(tierformat)
                 # write textgrid
-                textgrid_path = os.path.join(align_output_path,
-                                             key[0] + ".TextGrid")
+                textgrid_path = align_output_path / (key[0] + ".TextGrid")
                 second_per_frame = 1. / (1000. /
                                          stride_ms)  # 25ms window, 10ms stride
                 second_per_example = (
@@ -591,7 +589,7 @@ class U2Tester(U2Trainer):
                 text_grid.generate_textgrid(
                     maxtime=second_per_example,
                     intervals=tierformat,
-                    output=textgrid_path)
+                    output=str(textgrid_path))
 
     def run_align(self):
         self.resume_or_scratch()

deepspeech/exps/u2_st/model.py

@@ -595,26 +595,25 @@ class U2STTester(U2STTrainer):
                 ctc_probs = ctc_probs.squeeze(0)
                 target = target.squeeze(0)
                 alignment = ctc_utils.forced_align(ctc_probs, target)
-                logger.info("align ids", key[0], alignment)
+                logger.info(f"align ids: {key[0]} {alignment}")
                 fout.write('{} {}\n'.format(key[0], alignment))
 
                 # 3. gen praat
                 # segment alignment
                 align_segs = text_grid.segment_alignment(alignment)
-                logger.info("align tokens", key[0], align_segs)
+                logger.info(f"align tokens: {key[0]}, {align_segs}")
                 # IntervalTier, List["start end token\n"]
                 subsample = utility.get_subsample(self.config)
                 tierformat = text_grid.align_to_tierformat(
                     align_segs, subsample, token_dict)
                 # write tier
-                align_output_path = os.path.join(
-                    os.path.dirname(self.args.result_file), "align")
-                tier_path = os.path.join(align_output_path, key[0] + ".tier")
-                with open(tier_path, 'w') as f:
+                align_output_path = Path(self.args.result_file).parent / "align"
+                align_output_path.mkdir(parents=True, exist_ok=True)
+                tier_path = align_output_path / (key[0] + ".tier")
+                with tier_path.open('w') as f:
                     f.writelines(tierformat)
                 # write textgrid
-                textgrid_path = os.path.join(align_output_path,
-                                             key[0] + ".TextGrid")
+                textgrid_path = align_output_path / (key[0] + ".TextGrid")
                 second_per_frame = 1. / (1000. /
                                          stride_ms)  # 25ms window, 10ms stride
                 second_per_example = (
@@ -622,7 +621,7 @@ class U2STTester(U2STTrainer):
                 text_grid.generate_textgrid(
                     maxtime=second_per_example,
                     intervals=tierformat,
-                    output=textgrid_path)
+                    output=str(textgrid_path))
 
     def run_align(self):
         self.resume_or_scratch()

deepspeech/utils/ctc_utils.py

@@ -86,8 +86,10 @@ def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
     log_alpha = paddle.zeros(
         (ctc_probs.size(0), len(y_insert_blank)))  #(T, 2L+1)
     log_alpha = log_alpha - float('inf')  # log of zero
+
+    # self.__setitem_varbase__(item, value) When assign a value to a paddle.Tensor, the data type of the paddle.Tensor not support int16
     state_path = (paddle.zeros(
-        (ctc_probs.size(0), len(y_insert_blank)), dtype=paddle.int16) - 1
+        (ctc_probs.size(0), len(y_insert_blank)), dtype=paddle.int32) - 1
                   )  # state path, Tuple((T, 2L+1))
 
     # init start state
@@ -111,8 +113,8 @@ def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
             log_alpha[t, s] = paddle.max(candidates) + ctc_probs[t][
                 y_insert_blank[s]]
             state_path[t, s] = prev_state[paddle.argmax(candidates)]
-
-    state_seq = -1 * paddle.ones((ctc_probs.size(0), 1), dtype=paddle.int16)
+    # self.__setitem_varbase__(item, value) When assign a value to a paddle.Tensor, the data type of the paddle.Tensor not support int16
+    state_seq = -1 * paddle.ones((ctc_probs.size(0), 1), dtype=paddle.int32)
 
     candidates = paddle.to_tensor([
         log_alpha[-1, len(y_insert_blank) - 1],  # Sb