audio-based TN fix for empty pred_text/text (#92)

ekmb · pre-commit-ci[bot] · web-flow · commit 9ddd2b3111ac · 2023-08-15T12:07:34.000-07:00
* fix for empty pred_text Signed-off-by: Evelina <ebakhturina@nvidia.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add unittests Signed-off-by: Evelina <ebakhturina@nvidia.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix path Signed-off-by: Evelina <ebakhturina@nvidia.com> * fix path Signed-off-by: Evelina <ebakhturina@nvidia.com> * fix pytest Signed-off-by: Evelina <ebakhturina@nvidia.com> --------- Signed-off-by: Evelina <ebakhturina@nvidia.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
diff --git a/Jenkinsfile b/Jenkinsfile
@@ -57,7 +57,6 @@ pipeline {
     }
 
 
-
     stage('L0: Create EN TN/ITN Grammars') {
       when {
         anyOf {
@@ -67,7 +66,11 @@ pipeline {
       }
       failFast true
       parallel {
-
+        stage('L0: Test utils') {
+          steps {
+            sh 'CUDA_VISIBLE_DEVICES="" pytest tests/nemo_text_processing/audio_based_utils/ --cpu'
+          }
+        }
         stage('L0: En TN grammars') {
           steps {
             sh 'CUDA_VISIBLE_DEVICES="" python nemo_text_processing/text_normalization/normalize.py --text="1" --cache_dir ${EN_TN_CACHE}'
diff --git a/nemo_text_processing/text_normalization/normalize_with_audio.py b/nemo_text_processing/text_normalization/normalize_with_audio.py
@@ -141,7 +141,7 @@ def normalize(
         Returns:
             normalized text options (usually there are multiple ways of normalizing a given semiotic class)
         """
-        if pred_text is None or self.tagger is None:
+        if pred_text is None or pred_text == "" or self.tagger is None:
             return self.normalize_non_deterministic(
                 text=text, n_tagged=n_tagged, punct_post_process=punct_post_process, verbose=verbose
             )
@@ -156,6 +156,7 @@ def normalize(
         semiotic_spans, pred_text_spans, norm_spans, text_with_span_tags_list, masked_idx_list = get_alignment(
             text, det_norm, pred_text, verbose=False
         )
+
         sem_tag_idx = 0
         for cur_semiotic_span, cur_pred_text, cur_deter_norm in zip(semiotic_spans, pred_text_spans, norm_spans):
             if len(cur_semiotic_span) == 0:
diff --git a/nemo_text_processing/text_normalization/utils_audio_based.py b/nemo_text_processing/text_normalization/utils_audio_based.py
@@ -24,8 +24,7 @@
 
 def _get_alignment(a: str, b: str) -> Dict:
     """
-
-    Construscts alignment between a and b
+    Constructs alignment between a and b
 
     Returns:
         a dictionary, where keys are a's word index and values is a Tuple that contains span from b, and whether it
@@ -62,7 +61,7 @@ def _get_alignment(a: str, b: str) -> Dict:
 
 def adjust_boundaries(norm_raw_diffs: Dict, norm_pred_diffs: Dict, raw: str, norm: str, pred_text: str, verbose=False):
     """
-    Adjust alignement boundaries by taking norm--raw texts and norm--pred_text alignements, and creating raw-pred_text
+    Adjust alignment boundaries by taking norm--raw texts and norm--pred_text alignments, and creating raw-pred_text alignment
         alignment.
 
     norm_raw_diffs: output of _get_alignment(norm, raw)
@@ -92,10 +91,12 @@ def adjust_boundaries(norm_raw_diffs: Dict, norm_pred_diffs: Dict, raw: str, nor
             raw_text_mask_idx: [1, 4]
     """
 
-    adjusted = []
+    raw_pred_spans = []
     word_id = 0
     while word_id < len(norm.split()):
         norm_raw, norm_pred = norm_raw_diffs[word_id], norm_pred_diffs[word_id]
+        # if there is a mismatch in norm_raw and norm_pred, expand the boundaries of the shortest mismatch to align with the longest one
+        # e.g., norm_raw = (1, 2, 'match') norm_pred = (1, 5, 'non-match') => expand norm_raw until the next matching sequence or the end of string to align with norm_pred
         if (norm_raw[2] == MATCH and norm_pred[2] == NONMATCH) or (norm_raw[2] == NONMATCH and norm_pred[2] == MATCH):
             mismatched_id = word_id
             non_match_raw_start = norm_raw[0]
@@ -114,20 +115,21 @@ def adjust_boundaries(norm_raw_diffs: Dict, norm_pred_diffs: Dict, raw: str, nor
             if not done:
                 non_match_raw_end = len(raw.split())
                 non_match_pred_end = len(pred_text.split())
-            adjusted.append(
+            raw_pred_spans.append(
                 (
                     mismatched_id,
                     (non_match_raw_start, non_match_raw_end, NONMATCH),
                     (non_match_pred_start, non_match_pred_end, NONMATCH),
                 )
             )
         else:
-            adjusted.append((word_id, norm_raw, norm_pred))
+            raw_pred_spans.append((word_id, norm_raw, norm_pred))
         word_id += 1
 
-    adjusted2 = []
+    # aggregate neighboring spans with the same status
+    spans_merged_neighbors = []
     last_status = None
-    for idx, item in enumerate(adjusted):
+    for idx, item in enumerate(raw_pred_spans):
         if last_status is None:
             last_status = item[1][2]
             raw_start = item[1][0]
@@ -139,7 +141,7 @@ def adjust_boundaries(norm_raw_diffs: Dict, norm_pred_diffs: Dict, raw: str, nor
             raw_end = item[1][1]
             pred_text_end = item[2][1]
         else:
-            adjusted2.append(
+            spans_merged_neighbors.append(
                 [[norm_span_start, item[0]], [raw_start, raw_end], [pred_text_start, pred_text_end], last_status]
             )
             last_status = item[1][2]
@@ -152,13 +154,13 @@ def adjust_boundaries(norm_raw_diffs: Dict, norm_pred_diffs: Dict, raw: str, nor
     if last_status == item[1][2]:
         raw_end = item[1][1]
         pred_text_end = item[2][1]
-        adjusted2.append(
+        spans_merged_neighbors.append(
             [[norm_span_start, item[0]], [raw_start, raw_end], [pred_text_start, pred_text_end], last_status]
         )
     else:
-        adjusted2.append(
+        spans_merged_neighbors.append(
             [
-                [adjusted[idx - 1][0], len(norm.split())],
+                [raw_pred_spans[idx - 1][0], len(norm.split())],
                 [item[1][0], len(raw.split())],
                 [item[2][0], len(pred_text.split())],
                 item[1][2],
@@ -171,10 +173,10 @@ def adjust_boundaries(norm_raw_diffs: Dict, norm_pred_diffs: Dict, raw: str, nor
 
     # increase boundaries between raw and pred_text if some spans contain empty pred_text
     extended_spans = []
-    adjusted3 = []
+    raw_norm_spans_corrected_for_pred_text = []
     idx = 0
-    while idx < len(adjusted2):
-        item = adjusted2[idx]
+    while idx < len(spans_merged_neighbors):
+        item = spans_merged_neighbors[idx]
 
         cur_semiotic = " ".join(raw_list[item[1][0] : item[1][1]])
         cur_pred_text = " ".join(pred_text_list[item[2][0] : item[2][1]])
@@ -186,8 +188,8 @@ def adjust_boundaries(norm_raw_diffs: Dict, norm_pred_diffs: Dict, raw: str, nor
         # if cur_pred_text is an empty string
         if item[2][0] == item[2][1]:
             # for the last item
-            if idx == len(adjusted2) - 1 and len(adjusted3) > 0:
-                last_item = adjusted3[-1]
+            if idx == len(spans_merged_neighbors) - 1 and len(raw_norm_spans_corrected_for_pred_text) > 0:
+                last_item = raw_norm_spans_corrected_for_pred_text[-1]
                 last_item[0][1] = item[0][1]
                 last_item[1][1] = item[1][1]
                 last_item[2][1] = item[2][1]
@@ -196,29 +198,31 @@ def adjust_boundaries(norm_raw_diffs: Dict, norm_pred_diffs: Dict, raw: str, nor
                 raw_start, raw_end = item[0]
                 norm_start, norm_end = item[1]
                 pred_start, pred_end = item[2]
-                while idx < len(adjusted2) - 1 and not ((pred_end - pred_start) > 2 and adjusted2[idx][-1] == MATCH):
+                while idx < len(spans_merged_neighbors) - 1 and not (
+                    (pred_end - pred_start) > 2 and spans_merged_neighbors[idx][-1] == MATCH
+                ):
                     idx += 1
-                    raw_end = adjusted2[idx][0][1]
-                    norm_end = adjusted2[idx][1][1]
-                    pred_end = adjusted2[idx][2][1]
+                    raw_end = spans_merged_neighbors[idx][0][1]
+                    norm_end = spans_merged_neighbors[idx][1][1]
+                    pred_end = spans_merged_neighbors[idx][2][1]
                 cur_item = [[raw_start, raw_end], [norm_start, norm_end], [pred_start, pred_end], NONMATCH]
-                adjusted3.append(cur_item)
-                extended_spans.append(len(adjusted3) - 1)
+                raw_norm_spans_corrected_for_pred_text.append(cur_item)
+                extended_spans.append(len(raw_norm_spans_corrected_for_pred_text) - 1)
             idx += 1
         else:
-            adjusted3.append(item)
+            raw_norm_spans_corrected_for_pred_text.append(item)
             idx += 1
 
     semiotic_spans = []
     norm_spans = []
     pred_texts = []
     raw_text_masked = ""
-    for idx, item in enumerate(adjusted3):
+    for idx, item in enumerate(raw_norm_spans_corrected_for_pred_text):
         cur_semiotic = " ".join(raw_list[item[1][0] : item[1][1]])
         cur_pred_text = " ".join(pred_text_list[item[2][0] : item[2][1]])
         cur_norm_span = " ".join(norm_list[item[0][0] : item[0][1]])
 
-        if idx == len(adjusted3) - 1:
+        if idx == len(raw_norm_spans_corrected_for_pred_text) - 1:
             cur_norm_span = " ".join(norm_list[item[0][0] : len(norm_list)])
         if (item[-1] == NONMATCH and cur_semiotic != cur_norm_span) or (idx in extended_spans):
             raw_text_masked += " " + SEMIOTIC_TAG
@@ -233,24 +237,31 @@ def adjust_boundaries(norm_raw_diffs: Dict, norm_pred_diffs: Dict, raw: str, nor
 
     if verbose:
         print("+" * 50)
-        print("adjusted:")
-        for item in adjusted2:
+        print("raw_pred_spans:")
+        for item in spans_merged_neighbors:
             print(f"{raw.split()[item[1][0]: item[1][1]]} -- {pred_text.split()[item[2][0]: item[2][1]]}")
 
         print("+" * 50)
-        print("adjusted2:")
-        for item in adjusted2:
+        print("spans_merged_neighbors:")
+        for item in spans_merged_neighbors:
             print(f"{raw.split()[item[1][0]: item[1][1]]} -- {pred_text.split()[item[2][0]: item[2][1]]}")
         print("+" * 50)
-        print("adjusted3:")
-        for item in adjusted3:
+        print("raw_norm_spans_corrected_for_pred_text:")
+        for item in raw_norm_spans_corrected_for_pred_text:
             print(f"{raw.split()[item[1][0]: item[1][1]]} -- {pred_text.split()[item[2][0]: item[2][1]]}")
         print("+" * 50)
 
     return semiotic_spans, pred_texts, norm_spans, raw_text_masked_list, raw_text_mask_idx
 
 
-def get_alignment(raw, norm, pred_text, verbose: bool = False):
+def get_alignment(raw: str, norm: str, pred_text: str, verbose: bool = False):
+    """
+    Aligns raw text with deterministically normalized text and ASR output, finds semiotic spans
+    """
+    for value in [raw, norm, pred_text]:
+        if value is None or value == "":
+            return [], [], [], [], []
+
     norm_pred_diffs = _get_alignment(norm, pred_text)
     norm_raw_diffs = _get_alignment(norm, raw)
 
@@ -271,8 +282,9 @@ def get_alignment(raw, norm, pred_text, verbose: bool = False):
 
 
 if __name__ == "__main__":
-    raw = 'This is #4 ranking on G.S.K.T.'
-    pred_text = 'this iss for ranking on g k p'
+    raw = 'This is a #4 ranking on G.S.K.T.'
+    pred_text = 'this iss p k for ranking on g k p'
     norm = 'This is nubmer four ranking on GSKT'
 
-    get_alignment(raw, norm, pred_text, True)
+    output = get_alignment(raw, norm, pred_text, True)
+    print(output)
diff --git a/tests/nemo_text_processing/audio_based_utils/__init__.py b/tests/nemo_text_processing/audio_based_utils/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  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.
diff --git a/tests/nemo_text_processing/audio_based_utils/test_audio_based_utils.py b/tests/nemo_text_processing/audio_based_utils/test_audio_based_utils.py
@@ -0,0 +1,35 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  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.
+
+import pytest
+from nemo_text_processing.text_normalization.utils_audio_based import get_alignment
+
+
+class TestAudioBasedTNUtils:
+    @pytest.mark.run_only_on('CPU')
+    @pytest.mark.unit
+    def test_default(self):
+        raw = 'This is #4 ranking on G.S.K.T.'
+        pred_text = 'this iss for ranking on g k p'
+        norm = 'This is nubmer four ranking on GSKT'
+
+        output = get_alignment(raw, norm, pred_text, True)
+        reference = (
+            ['is #4', 'G.S.K.T.'],
+            ['iss for', 'g k p'],
+            ['is nubmer four', 'GSKT'],
+            ['This', '[SEMIOTIC_SPAN]', 'ranking', 'on', '[SEMIOTIC_SPAN]'],
+            [1, 4],
+        )
+        assert output == reference
