diff --git a/deepspeech/exps/u2/model.py b/deepspeech/exps/u2/model.py
index f166a071..6da0c3bd 100644
--- a/deepspeech/exps/u2/model.py
+++ b/deepspeech/exps/u2/model.py
@@ -34,9 +34,11 @@ from deepspeech.models.u2 import U2Model
 from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
 from deepspeech.training.scheduler import WarmupLR
 from deepspeech.training.trainer import Trainer
+from deepspeech.utils import ctc_utils
 from deepspeech.utils import error_rate
 from deepspeech.utils import layer_tools
 from deepspeech.utils import mp_tools
+from deepspeech.utils import text_grid
 from deepspeech.utils.log import Log
 
 logger = Log(__name__).getlog()
@@ -483,6 +485,67 @@ class U2Tester(U2Trainer):
         except KeyboardInterrupt:
             sys.exit(-1)
 
+    @paddle.no_grad()
+    def align(self):
+        if self.config.decoding.batch_size > 1:
+            logger.fatal('alignment mode must be running with batch_size == 1')
+            sys.exit(1)
+
+        # xxx.align
+        assert self.args.result_file
+
+        self.model.eval()
+        logger.info(f"Align Total Examples: {len(self.test_loader.dataset)}")
+
+        stride_ms = self.test_loader.dataset.stride_ms
+        token_dict = self.test_loader.dataset.vocab_list
+        with open(self.args.result_file, 'w') as fout:
+            for i, batch in enumerate(self.test_loader):
+                key, feat, feats_length, target, target_length = batch
+                # 1. Encoder
+                encoder_out, encoder_mask = self.model._forward_encoder(
+                    feat, feats_length)  # (B, maxlen, encoder_dim)
+                maxlen = encoder_out.size(1)
+                ctc_probs = self.model.ctc.log_softmax(
+                    encoder_out)  # (1, maxlen, vocab_size)
+
+                # 2. alignment
+                # print(ctc_probs.size(1))
+                ctc_probs = ctc_probs.squeeze(0)
+                target = target.squeeze(0)
+                alignment = ctc_utils.forced_align(ctc_probs, target)
+                print(alignment)
+                fout.write('{} {}\n'.format(key[0], alignment))
+
+                # 3. gen praat
+                # segment alignment
+                align_segs = text_grid.segment_alignment(alignment)
+                print(align_segs)
+                # IntervalTier, List["start end token\n"]
+                subsample = get_subsample(self.config)
+                tierformat = text_grid.align_to_tierformat(
+                    align_segs, subsample, token_dict)
+                tier_path = os.path.join(
+                    os.path.dirname(args.result_file), key[0] + ".tier")
+                with open(tier_path, 'w') as f:
+                    f.writelines(tierformat)
+
+                textgrid_path = s.path.join(
+                    os.path.dirname(args.result_file), key[0] + ".TextGrid")
+                second_per_frame = 1. / (1000. / stride_ms
+                                         )  # 25ms window, 10ms stride
+                text_grid.generate_textgrid(
+                    maxtime=(len(alignment) + 1) * subsample * second_per_frame,
+                    lines=tierformat,
+                    output=textgrid_path)
+
+    def run_align(self):
+        self.resume_or_scratch()
+        try:
+            self.align()
+        except KeyboardInterrupt:
+            sys.exit(-1)
+
     def load_inferspec(self):
         """infer model and input spec.
 
diff --git a/deepspeech/utils/ctc_utils.py b/deepspeech/utils/ctc_utils.py
index 73669fea..76c1898b 100644
--- a/deepspeech/utils/ctc_utils.py
+++ b/deepspeech/utils/ctc_utils.py
@@ -46,7 +46,7 @@ def remove_duplicates_and_blank(hyp: List[int], blank_id=0) -> List[int]:
     return new_hyp
 
 
-def insert_blank(label: np.ndarray, blank_id: int=0):
+def insert_blank(label: np.ndarray, blank_id: int=0) -> np.ndarray:
     """Insert blank token between every two label token.
 
     "abcdefg" -> "-a-b-c-d-e-f-g-"
@@ -67,7 +67,7 @@ def insert_blank(label: np.ndarray, blank_id: int=0):
 
 
 def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
-                 blank_id=0) -> list:
+                 blank_id=0) -> List[int]:
     """ctc forced alignment.
 
     https://distill.pub/2017/ctc/
@@ -77,7 +77,7 @@ def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
         y (paddle.Tensor): label id sequence tensor, 1d tensor (L)
         blank_id (int): blank symbol index
     Returns:
-        paddle.Tensor: best alignment result, (T).
+        List[int]: best alignment result, (T).
     """
     y_insert_blank = insert_blank(y, blank_id)
 
diff --git a/deepspeech/utils/text_grid.py b/deepspeech/utils/text_grid.py
new file mode 100644
index 00000000..9afed89e
--- /dev/null
+++ b/deepspeech/utils/text_grid.py
@@ -0,0 +1,125 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Dict
+from typing import List
+from typing import Text
+
+import textgrid
+
+
+def segment_alignment(alignment: List[int], blank_id=0) -> List[List[int]]:
+    """segment ctc alignment ids by continuous blank and repeat label.
+
+    Args:
+        alignment (List[int]): ctc alignment id sequence. e.g. [0, 0, 0, 1, 1, 1, 2, 0, 0, 3]
+        blank_id (int, optional): blank id. Defaults to 0.
+
+    Returns:
+        List[List[int]]: segment aligment id sequence. e.g. [[0, 0, 0, 1, 1, 1], [2], [0, 0, 3]]
+    """
+    # convert alignment to a praat format, which is a doing phonetics
+    # by computer and helps analyzing alignment
+    align_segs = []
+    # get frames level duration for each token
+    start = 0
+    end = 0
+    while end < len(alignment):
+        while end < len(alignment) and alignment[end] == blank_id:  # blank
+            end += 1
+        if end == len(alignment):
+            align_segs[-1].extend(alignment[start:])
+            break
+        end += 1
+        while end < len(alignment) and alignment[end - 1] == alignment[
+                end]:  # repeat label
+            end += 1
+        align_segs.append(alignment[start:end])
+        start = end
+    return align_segs
+
+
+def align_to_tierformat(align_segs: List[List[int]],
+                        subsample: int,
+                        token_dict: Dict[int, Text],
+                        blank_id=0) -> List[Text]:
+    """Generate textgrid.Interval format from alignment segmentations.
+
+    Args:
+        align_segs (List[List[int]]): segmented ctc alignment ids.
+        subsample (int): 25ms frame_length, 10ms hop_length, 1/subsample
+        token_dict (Dict[int, Text]): int -> str map.
+
+    Returns:
+        List[Text]: list of textgrid.Interval.
+    """
+    hop_length = 10  # ms
+    second_ms = 1000  # ms
+    frame_per_second = second_ms / hop_length  # 25ms frame_length, 10ms hop_length
+    second_per_frame = 1.0 / frame_per_second
+
+    begin = 0
+    duration = 0
+    tierformat = []
+
+    for idx, tokens in enumerate(align_segs):
+        token_len = len(tokens)
+        token = tokens[-1]
+        # time duration in second
+        duration = token_len * subsample * second_per_frame
+        if idx < len(align_segs) - 1:
+            print(f"{begin:.2f} {begin + duration:.2f} {token_dict[token]}")
+            tierformat.append(
+                f"{begin:.2f} {begin + duration:.2f} {token_dict[token]}\n")
+        else:
+            for i in tokens:
+                if i != blank_id:
+                    token = i
+                    break
+            print(f"{begin:.2f} {begin + duration:.2f} {token_dict[token]}")
+            tierformat.append(
+                f"{begin:.2f} {begin + duration:.2f} {token_dict[token]}\n")
+        begin = begin + duration
+
+    return tierformat
+
+
+def generate_textgrid(maxtime: float,
+                      intervals: List[Text],
+                      output: Text,
+                      name: Text='ali') -> None:
+    """Create alignment textgrid file.
+
+    Args:
+        maxtime (float): audio duartion.
+        intervals (List[Text]): ctc output alignment. e.g. "start-time end-time word" per item.
+        output (Text): textgrid filepath.
+        name (Text, optional): tier or layer name. Defaults to 'ali'.
+    """
+    # Download Praat: https://www.fon.hum.uva.nl/praat/
+    avg_interval = maxtime / (len(intervals) + 1)
+    print(f"average duration per {name}: {avg_interval}")
+    margin = 0.0001
+
+    tg = textgrid.TextGrid(maxTime=maxtime)
+    tier = textgrid.IntervalTier(name=name, maxTime=maxtime)
+
+    i = 0
+    for dur in intervals:
+        s, e, text = dur.split()
+        tier.add(minTime=float(s) + margin, maxTime=float(e), mark=text)
+
+    tg.append(tier)
+
+    tg.write(output)
+    print("successfully generator textgrid {}.".format(output))