UPLOAD SLUE_TED

data/slue-ted_dev.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a5c679d3c9dcdd20ea90b1ee0a57d8d176e31264e9499d35f7013f624ac93c02
+size 5692022240

data/slue-ted_test_blind.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33fbdad35f0557ce9cb0ce12abf1c20f8c9851d271b689d1b787b6ec072292c4
+size 5972328793

data/slue-ted_train.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b17e2c27e8ceac6b7ede9ba639833130710337515265dedd35f66e0aff2b670e
+size 46727678707

slue-phase-2.py

@@ -16,6 +16,7 @@ _DL_URLS = {
     "slue-hvb": "data/slue-hvb_blind.zip",
     "slue-sqa5": "data/slue-sqa5_blind.zip",
     "slue-vp_nel": "data/slue-vp_nel_blind.zip",
+    "slue-ted": "data/slue-ted",
 }
 
 _LICENSE = """
@@ -63,7 +64,10 @@ SLUE-vp_nel Dataset
 SLUE-vp_nel includes word-level time stamps for dev and test splits of the SLUE-voxpopuli corpus.
 For the dev split, the dataset also contains named entity annotations and corresponding time-stamps in a tsv format. 
 =======================================================
+SLUE-TED Dataset
 
+SLUE-TED Dataset contains TED Talk audios along with the associated abstracts and title, which were concatenated to create reference summaries. This corpus is licensed with the same Creative Commons (CC BY–NC–ND 4.0 International) license as TED talks. For further information, please refer to the details provided below.
+=======================================================
 """
 
 _CITATION = """\
@@ -161,6 +165,10 @@ class SLUE2(datasets.GeneratorBasedBuilder):
             name="vp_nel",
             description="SLUE-vp_nel set with named entity labels and time-stamps.",
         ),
+        SLUE2Config(
+            name="ted",
+            description="SLUE-TED set which includes Speech Summarisation task",
+        ),
     ]
 
     def _info(self):
@@ -231,6 +239,15 @@ class SLUE2(datasets.GeneratorBasedBuilder):
                     }
                 ),
             }
+        elif self.config.name == "ted":
+            features = {
+                "id": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "speaker": datasets.Value("string"),
+                "transcript": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "abstract": datasets.Value("string"),
+            }
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(features),
@@ -245,9 +262,13 @@ class SLUE2(datasets.GeneratorBasedBuilder):
     ) -> List[datasets.SplitGenerator]:
 
         config_name = f"slue-{self.config.name}"
-
-        dl_dir = dl_manager.download_and_extract(_DL_URLS[config_name])
-        data_dir = os.path.join(dl_dir, config_name)
+        if config_name=="slue-ted":
+            train_dl_dir = dl_manager.download_and_extract(_DL_URLS[config_name]+"_train.zip")
+            valid_dl_dir = dl_manager.download_and_extract(_DL_URLS[config_name]+"_dev.zip")
+            test_dl_dir = dl_manager.download_and_extract(_DL_URLS[config_name]+"_test_blind.zip")
+        else:
+            dl_dir = dl_manager.download_and_extract(_DL_URLS[config_name])
+            data_dir = os.path.join(dl_dir, config_name)
 
         splits = []
         if self.config.name in ["hvb", "sqa5"]:
@@ -262,6 +283,40 @@ class SLUE2(datasets.GeneratorBasedBuilder):
                     },
                 )
             )
+        if self.config.name in ["ted"]:
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            os.path.join(train_dl_dir, config_name) or "", f"{config_name}_fine-tune.tsv"
+                        ),
+                        "data_dir": os.path.join(train_dl_dir, config_name),
+                    },
+                )
+            )
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            os.path.join(valid_dl_dir, config_name+"_dev") or "", f"{config_name}_dev.tsv"
+                        ),
+                        "data_dir": os.path.join(valid_dl_dir, config_name+"_dev"),
+                    },
+                ),
+            )
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            os.path.join(test_dl_dir, config_name+"_test") or "", f"{config_name}_test_blind.tsv"
+                        ),
+                        "data_dir": os.path.join(test_dl_dir, config_name+"_test"),
+                    },
+                ),
+            )
         if self.config.name in ["hvb", "sqa5", "vp_nel"]:
             splits.append(
                 datasets.SplitGenerator(
@@ -374,4 +429,18 @@ class SLUE2(datasets.GeneratorBasedBuilder):
                         ),
                         "word_timestamps": read_word_timestamps(word_alignments_fn),
                     }
+                if self.config.name == "ted":
+                    split = "test" if "test" in filepath else "dev" if "dev" in filepath else "fine-tune"
+                    audio_file = os.path.join(
+                        data_dir, split,
+                        row["id"] + ".flac"
+                    )
+                    example = {
+                        "id": row["id"],
+                        "audio": audio_file,
+                        "speaker": row["speaker"],
+                        "transcript": row["transcript"],
+                        "title": eval(row.get("title", "[]")),
+                        "abstract": eval(row.get("abstract", "[]")),
+                    }
                 yield idx, example