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voxlingua.py

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+"""
+This new update refers to the this HF dataloader script
+https://huggingface.co/datasets/csebuetnlp/xlsum/blob/main/xlsum.py
+while conforming to SEACrowd schema
+"""
+
+import os
+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 TASK_TO_SCHEMA, Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{valk2021slt,
+  title={{VoxLingua107}: a Dataset for Spoken Language Recognition},
+  author={J{\"o}rgen Valk and Tanel Alum{\"a}e},
+  booktitle={Proc. IEEE SLT Workshop},
+  year={2021},
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ceb", "ind", "jav", "khm", "lao", "zlm", "mya", "sun", "tha", "tgl", "vie", "war"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LANG_TO_DATASOURCE_LANG = {
+    "ceb": "ceb",
+    "ind": "id",
+    "jav": "jw",
+    "khm": "km",
+    "lao": "lo",
+    "zlm": "ms",
+    "mya": "my",
+    "sun": "su",
+    "tha": "th",
+    "tgl": "tl",
+    "vie": "vi",
+    "war": "war"}
+
+_DATASETNAME = "voxlingua"
+
+_DESCRIPTION = """\
+VoxLingua107 is a comprehensive speech dataset designed for training spoken language identification models.
+It comprises short speech segments sourced from YouTube videos, labeled based on the language indicated in the video
+title and description. The dataset covers 107 languages and contains a total of 6628 hours of speech data,
+averaging 62 hours per language. However, the actual amount of data per language varies significantly.
+Additionally, there is a separate development set consisting of 1609 speech segments from 33 languages,
+validated by at least two volunteers to ensure the accuracy of language representation.
+"""
+
+_HOMEPAGE = "https://bark.phon.ioc.ee/voxlingua107/"
+
+_LICENSE = Licenses.CC_BY_4_0.value
+
+_URLS = "https://bark.phon.ioc.ee/voxlingua107/{identifier}.zip"
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_LANGUAGE_IDENTIFICATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+def construct_configs() -> List[SEACrowdConfig]:
+    """
+    The function `construct_configs` constructs a list of SEACrowdConfig objects, and returns the list.
+
+    output:
+        a list of `SEACrowdConfig` objects.
+    """
+
+    # set output var
+    config_list = []
+
+    # construct zipped arg for config instantiation
+    CONFIG_SUFFIXES_FOR_TASK = [TASK_TO_SCHEMA.get(task).lower() for task in _SUPPORTED_TASKS]
+    TASKS_AND_CONFIG_SUFFIX_PAIRS = list(zip(_SUPPORTED_TASKS, CONFIG_SUFFIXES_FOR_TASK))
+
+    # implement source schema
+    version, config_name_prefix = _SOURCE_VERSION, "source"
+    config_list += [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{config_name_prefix}",
+            version=datasets.Version(version),
+            description=f"{_DATASETNAME} {config_name_prefix} schema",
+            schema=f"{config_name_prefix}",
+            subset_id=f"{config_name_prefix}",
+        )
+    ]
+
+    # implement SEACrowd schema
+    version, config_name_prefix = _SEACROWD_VERSION, "seacrowd"
+    for task_obj, config_name_suffix in TASKS_AND_CONFIG_SUFFIX_PAIRS:
+        config_list += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{config_name_prefix}_{config_name_suffix}",
+                version=datasets.Version(version),
+                description=f"{_DATASETNAME} {config_name_prefix} schema for {task_obj.name}",
+                schema=f"{config_name_prefix}_{config_name_suffix}",
+                subset_id=f"{config_name_prefix}_{config_name_suffix}",
+            )
+        ]
+    return config_list
+
+
+class VoxLinguaDataset(datasets.GeneratorBasedBuilder):
+    """Speech Lang ID on dataset VoxLingua."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = construct_configs()
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            # since the source only contains audio folder structure,
+            # we will define it using simplified ver of SEACrowd speech_features schema
+            features = datasets.Features({
+                "id": datasets.Value("string"),
+                "path": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "labels": datasets.ClassLabel(names=_LANGUAGES)})
+
+        elif self.config.schema == "seacrowd_speech":
+            features = schemas.speech_features(label_names=_LANGUAGES)
+
+        else:
+            raise ValueError(f"Unexpected self.config.schema of {self.config.schema}!")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        # since this is a Speech LID, all languages must be downloaded in a single lists
+        # for train data, the identifier is a lang_code defined in `_LANG_TO_DATASOURCE_LANG`
+        train_url_list = [_URLS.format(identifier=_LANG_TO_DATASOURCE_LANG[lang_val]) for lang_val in _LANGUAGES]
+        train_data_dir = dl_manager.download_and_extract(train_url_list)
+
+        # for val data, the `dev.zip` doesn't contain any data indicated in _LANGUAGES
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_data_dir,
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: List[Path]) -> Tuple[int, Dict]:
+
+        # this is defined as -1 so that in the first loop it will have value of 0
+        example_idx = -1
+
+        for idx, child_path in enumerate(filepath):
+            # check for 2 things:
+
+            # 1. for every filepath list element (which contain 1 lang data), it will contain only 1 subdir and named its lang code in source
+            first_level_rel_dir = os.listdir(child_path)
+            expected_lang_label = _LANG_TO_DATASOURCE_LANG[_LANGUAGES[idx]]
+            assert first_level_rel_dir == [expected_lang_label], f"The structure of path is unexpected! Expected {[expected_lang_label]} got: {first_level_rel_dir}"
+
+            # 2. within the first_level_dir, all of them are file (no directory)
+            first_level_dir = os.path.join(child_path, first_level_rel_dir[0])
+            second_level_dir = os.listdir(first_level_dir)
+            assert not all(os.path.isdir(expected_file) for expected_file in second_level_dir), f"Found directory within folder {first_level_dir}!"
+
+            # extract sound data with format ".wav"
+            wav_files = [os.path.join(first_level_dir, file) for file in second_level_dir if file.endswith(".wav")]
+
+            if self.config.schema == "source":
+                for _fp in wav_files:
+                    example_idx += 1
+                    ex = {"id": example_idx, "path": _fp, "audio": _fp, "labels": _LANGUAGES.index(expected_lang_label)}
+                    yield example_idx, ex
+
+            elif self.config.schema == "seacrowd_speech":
+                for _fp in wav_files:
+                    example_idx += 1
+                    # audio = {"path": file, "bytes": file.read()}
+                    ex = {
+                        "id": example_idx,
+                        "path": _fp,
+                        "audio": _fp,
+                        "speaker_id": "",
+                        "labels": _LANGUAGES.index(expected_lang_label),
+                        "metadata": {
+                            # unavailable, filled with default val
+                            "speaker_age": -1,
+                            "speaker_gender": "",
+                        },
+                    }
+                    yield example_idx, ex
+
+            else:
+                raise ValueError(f"Invalid config schema of {self.config.schema}!")