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struct_amb_ind.py

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+import os
+from itertools import chain
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{widiaputri-etal-5641,
+  author = {Widiaputri, Ruhiyah Faradishi and Purwarianti, Ayu and Lestari, Dessi Puji and Azizah, Kurniawati and Tanaya, Dipta and Sakti, Sakriani},
+  title = {Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian},
+  booktitle = {Proceedings of the EMNLP 2023},
+  year = {2023}
+}
+"""
+
+_DATASETNAME = "struct_amb_ind"
+
+_DESCRIPTION = """
+This dataset contains the first Indonesian speech dataset for structurally ambiguous utterances and each of transcription and two disambiguation texts.
+The structurally ambiguous sentences were adapted from Types 4,5,6, and 10 of Types Of Syntactic Ambiguity in English by [Taha et al., 1983].
+For each chosen type, 100 structurally ambiguous sentences in Indonesian were made by crowdsourcing.
+Each Indonesian ambiguous sentence has two possible interpretations, resulting in two disambiguation text outputs for each ambiguous sentence.
+Each disambiguation text is made up of two sentences. All of the sentences have been checked by linguists.
+"""
+
+_HOMEPAGE = "https://github.com/ha3ci-lab/struct_amb_ind"
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = True  # get the audio data externally from https://drive.google.com/drive/folders/1QeaptstBgwGYO6THGkZHHViExrogCMUj
+_LANGUAGES = ["ind"]
+
+_URL_TEMPLATES = {
+    "keys": "https://raw.githubusercontent.com/ha3ci-lab/struct_amb_ind/main/keys/train_dev_test_spk_keys/",
+    "text": "https://raw.githubusercontent.com/ha3ci-lab/struct_amb_ind/main/text/",
+}
+
+_URLS = {
+    "split_train": _URL_TEMPLATES["keys"] + "train_spk",
+    "split_dev": _URL_TEMPLATES["keys"] + "dev_spk",
+    "split_test": _URL_TEMPLATES["keys"] + "test_spk",
+    "text_transcript": _URL_TEMPLATES["text"] + "ID_amb_disam_transcript.txt",
+}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class StructAmbInd(datasets.GeneratorBasedBuilder):
+    """
+    This dataset contains the first Indonesian speech dataset for structurally ambiguous utterances and each of transcription and two disambiguation texts.
+    This dataloader does NOT contain the additional training data for as mentioned in the _HOMEPAGE, as it is already implemented in the dataloader "indspeech_news_lvcsr".
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_sptext",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_sptext",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "amb_transcript": datasets.Value("string"),
+                    "disam_text": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_sptext":
+            features = schemas.speech_text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        # The data_dir configuration is required ONLY for the audio_urls.
+        if self.config.data_dir is None:
+            raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.")
+        else:
+            data_dir = self.config.data_dir
+
+        # load the local audio folders
+        audio_urls = [data_dir + "/" + f"{gender}{_id:02}.zip" for gender in ["F", "M"] for _id in range(1, 12, 1)]
+        audio_files_dir = [Path(dl_manager.extract(audio_url)) / audio_url.split("/")[-1][:-4] for audio_url in audio_urls]
+        # load the speaker splits and transcript
+        split_train = Path(dl_manager.download(_URLS["split_train"]))
+        split_dev = Path(dl_manager.download(_URLS["split_dev"]))
+        split_test = Path(dl_manager.download(_URLS["split_test"]))
+        text_transcript = Path(dl_manager.download(_URLS["text_transcript"]))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"split": split_train, "transcript": text_transcript, "audio_files_dir": audio_files_dir},
+            ),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"split": split_dev, "transcript": text_transcript, "audio_files_dir": audio_files_dir}),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"split": split_test, "transcript": text_transcript, "audio_files_dir": audio_files_dir},
+            ),
+        ]
+
+    def _generate_examples(self, split: Path, transcript: Path, audio_files_dir: List[Path]) -> Tuple[int, Dict]:
+        speaker_ids = open(split, "r").readlines()
+        speaker_ids = [id.replace("\n", "") for id in speaker_ids]
+        speech_folders = [audio_folder for audio_folder in audio_files_dir if audio_folder.name.split("/")[-1] in speaker_ids]
+        speech_files = list(chain(*[list(map((str(speech_folder) + "/").__add__, os.listdir(speech_folder))) for speech_folder in speech_folders]))
+
+        transcript = open(transcript, "r").readlines()
+        transcript = [sent.replace("\n", "").split("|") for sent in transcript]
+        transcript_dict = {sent[0]: {"amb_transcript": sent[1], "disam_text": sent[2]} for sent in transcript}
+
+        for key, aud_file in enumerate(speech_files):
+            aud_id = aud_file.split("/")[-1][:-4]
+            aud_info = aud_id.split("_")
+            if self.config.schema == "source":
+                row = {
+                    "id": aud_id,
+                    "speaker_id": aud_info[1],
+                    "path": aud_file,
+                    "audio": aud_file,
+                    "amb_transcript": transcript_dict[aud_id]["amb_transcript"],
+                    "disam_text": transcript_dict[aud_id]["disam_text"],
+                }
+                yield key, row
+            elif self.config.schema == "seacrowd_sptext":
+                row = {
+                    "id": aud_id,
+                    "path": aud_file,
+                    "audio": aud_file,
+                    "text": transcript_dict[aud_id]["amb_transcript"],
+                    "speaker_id": aud_info[1],
+                    "metadata": {
+                        "speaker_age": None,
+                        "speaker_gender": aud_info[1][0],
+                    },
+                }
+                yield key, row
+            else:
+                raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.")