add slue-vp_nel config to slue-phase-2.py (#5)

- add slue-vp_nel config to slue-phase-2.py (5a0039a8d7ec6b09041e7ab16c1039f23cc007dc)


Co-authored-by: Ankita Pasd <ankitap@users.noreply.huggingface.co>

slue-phase-2.py

@@ -3,6 +3,7 @@ import os
 import csv
 import ast
 import gzip
+import json
 
 import datasets
 from datasets.utils.logging import get_logger
@@ -14,6 +15,7 @@ _URL = "https://asappresearch.github.io/slue-toolkit/"
 _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",
 }
 
 _LICENSE = """
@@ -56,6 +58,11 @@ For questions from the other 4 datasets, their question texts, answer strings, a
 
 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.
 =======================================================
+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. 
+=======================================================
 
 """
 
@@ -97,6 +104,26 @@ def load_word2time(word2time_file):
                 )
     return word2time
 
+def parse_nel_time_spans(nel_timestamps):
+    nel_timestamps = ast.literal_eval(nel_timestamps)
+    return [
+        {
+            "ne_label": ne,
+            "start_char_idx": start,
+            "char_offset": off,
+            "start_sec": t0,
+            "end_sec": t1,
+        }
+        for ne, start, off, t0, t1 in nel_timestamps
+    ]
+
+def read_word_timestamps(word_alignments_fn):
+    data = json.loads(open(word_alignments_fn).read())
+    return [
+        {"word": word, "start_sec": start, "end_sec": end}
+        for word, start, end in data["timestamps"]
+    ]
+
 class SLUE2Config(datasets.BuilderConfig):
     """BuilderConfig for SLUE."""
 
@@ -128,6 +155,10 @@ class SLUE2(datasets.GeneratorBasedBuilder):
             name="sqa5",
             description="SLUE-SQA-5 set which includes Spoken Question Answering task.",
         ),
+        SLUE2Config(
+            name="vp_nel",
+            description="SLUE-VP-NEL set with named entity labels and time-stamps.",
+        ),
     ]
 
     def _info(self):
@@ -175,6 +206,30 @@ class SLUE2(datasets.GeneratorBasedBuilder):
                         }
                     ),
             }
+        elif self.config.name == "vp_nel":
+            features = {
+                "id": datasets.Value("string"),
+                "split": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "speaker_id": datasets.Value("string"),
+                "normalized_text": datasets.Value("string"),
+                "word_timestamps": datasets.Sequence(
+                    {
+                        "word": datasets.Value("string"),
+                        "start_sec": datasets.Value("float64"),
+                        "end_sec": datasets.Value("float64"),
+                    }
+                ),
+                "normalized_nel": datasets.Sequence(
+                    {
+                        "ne_label": datasets.Value("string"),
+                        "start_char_idx": datasets.Value("int32"),
+                        "char_offset": datasets.Value("int32"),
+                        "start_sec": datasets.Value("float64"),
+                        "end_sec": datasets.Value("float64"),
+                    }
+                ),
+            }
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(features),
@@ -194,33 +249,42 @@ class SLUE2(datasets.GeneratorBasedBuilder):
         data_dir = os.path.join(dl_dir, config_name)
         print(data_dir)
 
-        splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir or "", f"{config_name}_fine-tune.tsv"
-                    ),
-                    "data_dir": data_dir,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir or "", f"{config_name}_dev.tsv"),
-                    "data_dir": data_dir,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir or "", f"{config_name}_test_blind.tsv"
-                    ),
-                    "data_dir": data_dir,
-                },
-            ),
-        ]
+        splits = []
+        if self.config.name in ["hvb", "sqa5"]:
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            data_dir or "", f"{config_name}_fine-tune.tsv"
+                        ),
+                        "data_dir": data_dir,
+                    },
+                )
+            )
+        if self.config.name in ["hvb", "sqa5", "vp_nel"]:
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            data_dir or "", f"{config_name}_dev.tsv"
+                        ),
+                        "data_dir": data_dir,
+                    },
+                ),
+            )
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": os.path.join(
+                            data_dir or "", f"{config_name}_test_blind.tsv"
+                        ),
+                        "data_dir": data_dir,
+                    },
+                ),
+            )
         if self.config.name == "sqa5":
             splits.append(
                 datasets.SplitGenerator(
@@ -288,4 +352,25 @@ class SLUE2(datasets.GeneratorBasedBuilder):
                             "word2time": load_word2time(word2time_file),
                             "answer_spans": parse_qa_answer_spans(row.get("answer_spans", "[]")),
                     }
+                elif self.config.name == "slue_nel":
+                    split = "test" if "test" in filepath else "dev"
+                    utt_id = row["id"]
+                    word_alignments_fn = os.path.join(
+                        data_dir, "word_timestamps", split, f"{utt_id}.json"
+                    )
+                    audio_file = os.path.join(
+                        data_dir,
+                        split,
+                        f"{utt_id}.ogg",
+                    )
+                    example = {
+                        "id": utt_id,
+                        "audio": audio_file,
+                        "speaker_id": row["speaker_id"],
+                        "text": row["normalized_text"],
+                        "ne_timestamps": parse_nel_time_spans(
+                            row.get("normalized_nel", "[]")
+                        ),
+                        "word_timestamps": read_word_timestamps(word_alignments_fn),
+                    }
                 yield idx, example