add slue-sqa5 loading script

slue-phase-2.py

@@ -13,6 +13,7 @@ _URL = "https://asappresearch.github.io/slue-toolkit/"
 
 _DL_URLS = {
     "slue-hvb": "data/slue-hvb_blind.zip",
+    "slue-sqa5": "data/slue-sqa5_blind.zip",
 }
 
 _LICENSE = """
@@ -40,6 +41,21 @@ SLUE-HVB dataset contains a subset of the Gridspace-Stanford Harper Valley speec
 
 Additionally, we provide dialog act classification annotation and it is covered with the same license as CC-BY-4.0.
 =======================================================
+SLUE-SQA-5 Dataset
+
+SLUE-SQA-5 Dataset contains question texts and answer strings (question_text, normalized_question_text, and answer_spans column in .tsv files) from these datasets,
+            * SQuAD1.1            (for questions whose question_id starts with ‘squad-’)
+            * Natural Questions            (for questions whose question_id starts with ‘nq-’)
+            * WebQuestions            (for questions whose question_id starts with ‘wq-’)
+            * CuratedTREC            (for questions whose question_id starts with ‘trec-’)
+            * TriviaQA            (for questions whose question_id starts with ‘triviaqa-’)
+Additionally, we provide audio recordings (.wav files in “question” directories) of these questions.
+
+For questions from TriviaQA (questions whose question_id starts with ‘triviaqa-’), their question texts, answer strings, and audio recordings are licensed with the same Apache License 2.0 as TriviaQA (for more detail, please refer to https://github.com/mandarjoshi90/triviaqa/blob/master/LICENSE).
+For questions from the other 4 datasets, their question texts, answer strings, and audio recordings are licensed with Creative Commons Attribution-ShareAlike 4.0 International license.
+
+SLUE-SQA-5 also contains a subset of Spoken Wikipedia, including the audios placed in “document” directories and their transcripts (document_text and normalized_document_text column in .tsv files). Additionally, we provide the text-to-speech alignments (.txt files in “word2time” directories).These contents are licensed with the same Creative Commons (CC BY-SA 4.0) license as Spoken Wikipedia.
+=======================================================
 
 """
 
@@ -56,6 +72,31 @@ _DESCRIPTION = """\
 Spoken Language Understanding Evaluation (SLUE) benchmark Phase 2.
 """
 
+def parse_qa_answer_spans(answer_spans):
+    answer_spans = ast.literal_eval(answer_spans)
+    return [{"answer": a, "start_second": s, "end_second": e} for a, s, e in answer_spans]
+
+def load_word2time(word2time_file):
+    word2time = []
+    with open(word2time_file, "r") as f:
+        for line in f.readlines():
+            entity = line.strip().split('\t')
+            if len(entity)==1:
+                word = entity[0]
+                normalized_word, start_sec, end_sec = "", -1.0, -1.0
+            else:
+                word, normalized_word, start_sec, end_sec = entity
+                start_sec, end_sec = float(start_sec), float(end_sec)
+            word2time.append(
+                    {
+                        "word": word,
+                        "normalized_word": normalized_word,
+                        "start_second": start_sec,
+                        "end_second": end_sec,
+                    }
+                )
+    return word2time
+
 class SLUE2Config(datasets.BuilderConfig):
     """BuilderConfig for SLUE."""
 
@@ -83,6 +124,10 @@ class SLUE2(datasets.GeneratorBasedBuilder):
             name="hvb",
             description="SLUE-HVB set.",
         ),
+        SLUE2Config(
+            name="sqa5",
+            description="SLUE-SQA-5 set which includes Spoken Question Answering task.",
+        ),
     ]
 
     def _info(self):
@@ -102,6 +147,34 @@ class SLUE2(datasets.GeneratorBasedBuilder):
                     datasets.Value("string"),
                 ),
             }
+        elif self.config.name == "sqa5":
+            features = {
+                    "question_id": datasets.Value("string"),
+                    "question_audio": datasets.Audio(sampling_rate=16_000),
+                    "question_speaker_id": datasets.Value("string"),
+                    "raw_question_text": datasets.Value("string"),
+                    "normalized_question_text": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "document_audio": datasets.Audio(sampling_rate=16_000),
+                    "document_speaker_id": datasets.Value("string"),
+                    "raw_document_text": datasets.Value("string"),
+                    "normalized_document_text": datasets.Value("string"),
+                    "word2time": datasets.Sequence(
+                        {
+                            "word": datasets.Value("string"),
+                            "normalized_word": datasets.Value("string"),
+                            "start_second": datasets.Value("float64"),
+                            "end_second": datasets.Value("float64"),
+                        }
+                    ),
+                    "answer_spans": datasets.Sequence(
+                        {
+                            "answer": datasets.Value("string"),
+                            "start_second": datasets.Value("float64"),
+                            "end_second": datasets.Value("float64"),
+                        }
+                    ),
+            }
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(features),
@@ -148,13 +221,28 @@ class SLUE2(datasets.GeneratorBasedBuilder):
                 },
             ),
         ]
+        if self.config.name == "sqa5":
+            splits.append(
+                datasets.SplitGenerator(
+                    name="verified_test",
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            data_dir or "", f"{config_name}_verified-test_blind.tsv"
+                        ),
+                        "data_dir": data_dir,
+                    },
+                )                                                                     
+            )
         return splits
 
     def _generate_examples(self, filepath, data_dir):
         logger.info(f"generating examples from = {filepath}")
 
         with open(filepath) as f:
-            reader = csv.DictReader(f, delimiter="\t")
+            if self.config.name == "sqa5":
+                reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            else:
+                reader = csv.DictReader(f, delimiter="\t")
 
             for idx, row in enumerate(reader):
                 if self.config.name == "hvb":
@@ -176,4 +264,28 @@ class SLUE2(datasets.GeneratorBasedBuilder):
                         "intent": row["intent"],
                         "dialog_acts": eval(row.get("dialog_acts", "[]")),
                     }
-                yield idx, example
+                elif self.config.name == "sqa5":
+                    question_audio_file = os.path.join(
+                            data_dir, row["split"], "question", row["question_id"] + ".wav"
+                            )
+                    document_audio_file = os.path.join(
+                            data_dir, row["split"], "document", row["document_id"] + ".wav"
+                            )
+                    word2time_file = os.path.join(
+                            data_dir, row["split"], "word2time", row["document_id"] + ".txt"
+                            )
+                    example = {
+                            "question_id": row["question_id"],
+                            "question_audio": question_audio_file,
+                            "question_speaker_id": row["question_speaker_id"],
+                            "raw_question_text": row["question_text"],
+                            "normalized_question_text": row["normalized_question_text"],
+                            "document_id": row["document_id"],
+                            "document_audio": document_audio_file,
+                            "document_speaker_id": row["document_speaker_id"],
+                            "raw_document_text": row["document_text"],
+                            "normalized_document_text": row["normalized_document_text"],
+                            "word2time": load_word2time(word2time_file),
+                            "answer_spans": parse_qa_answer_spans(row.get("answer_spans", "[]")),
+                    }
+                yield idx, example