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ucla_phonetic.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This dataset contains audio recordings and phonetic transcriptions of word utterances for various low-resource SEA languages.
+Each language has a directory of text and audio files, with the latter forming one data subset.
+The dataset is prepared from the online UCLA phonetic dataset, which contains 7000 utterances across 100 low-resource languages, phonetically aligned using various automatic approaches, and manually fixed for misalignments.
+"""
+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 Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{li2021multilingual,
+  title={Multilingual phonetic dataset for low resource speech recognition},
+  author={Li, Xinjian and Mortensen, David R and Metze, Florian and Black, Alan W},
+  booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={6958--6962},
+  year={2021},
+  organization={IEEE}
+}
+"""
+
+_DATASETNAME = "ucla_phonetic"
+
+_DESCRIPTION = """\
+This dataset contains audio recordings and phonetic transcriptions of word utterances for various low-resource SEA languages.
+Each language has a directory of text and audio files, with the latter forming one data subset.
+The dataset is prepared from the online UCLA phonetic dataset, which contains 7000 utterances across 100 low-resource languages, phonetically aligned using various automatic approaches, and manually fixed for misalignments.
+"""
+
+_HOMEPAGE = "https://github.com/xinjli/ucla-phonetic-corpus"
+
+_LANGUAGES = ["ace", "brv", "hil", "hni", "ilo", "khm", "mak", "mya", "pam"]
+
+_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
+
+_LOCAL = False
+
+_DATA_URL = "https://github.com/xinjli/ucla-phonetic-corpus/releases/download/v1.0/data.tar.gz"
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+def seacrowd_config_constructor(lang, schema, version):
+    if lang not in _LANGUAGES:
+        raise ValueError(f"Invalid lang {lang}")
+
+    if schema not in ["source", "seacrowd_sptext"]:
+        raise ValueError(f"Invalid schema: {schema}")
+
+    return SEACrowdConfig(
+        name=f"ucla_phonetic_{lang}_{schema}",
+        version=datasets.Version(version),
+        description=f"UCLA Phonetic {schema} for {lang}",
+        schema=schema,
+        subset_id=f"{lang}_{schema}",
+    )
+
+
+class UCLAPhoneticDataset(datasets.GeneratorBasedBuilder):
+    """This dataset contains audio recordings and phonetic transcriptions of word utterances for various low-resource SEA languages."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = (
+        [
+            SEACrowdConfig(
+                name="ucla_phonetic_source",
+                version=datasets.Version(_SOURCE_VERSION),
+                description="UCLA Phonetic source for ace",
+                schema="source",
+                subset_id="ace_source",
+            ),
+            SEACrowdConfig(
+                name="ucla_phonetic_seacrowd_sptext",
+                version=datasets.Version(_SOURCE_VERSION),
+                description="UCLA Phonetic seacrowd+sptext for ace",
+                schema="seacrowd_sptext",
+                subset_id="ace_seacrowd_sptext",
+            ),
+        ]
+        + [seacrowd_config_constructor(lang, "source", _SOURCE_VERSION) for lang in _LANGUAGES]
+        + [seacrowd_config_constructor(lang, "seacrowd_sptext", _SEACROWD_VERSION) for lang in _LANGUAGES]
+    )
+
+    DEFAULT_CONFIG_NAME = "ucla_phonetic_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features({"id": datasets.Value("string"), "text": datasets.Value("string"), "audio": datasets.Audio(sampling_rate=16_000)})
+        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]:
+        """Returns SplitGenerators."""
+
+        lang, schema = self.config.subset_id.split("_", maxsplit=1)
+        data_dir = dl_manager.download_and_extract(_DATA_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "data", lang, "text.txt"),
+                    "audiopath": Path(os.path.join(data_dir, "data", lang, "audio")),
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, audiopath: Path) -> Tuple[int, Dict]:
+
+        audiofiles = {}
+        for audiofile in audiopath.iterdir():
+            audio_idx = os.path.basename(audiofile).split(".")[0]
+            audiofiles[audio_idx] = audiofile
+
+        if self.config.schema == "source":
+            for line_idx, line in enumerate(open(filepath)):
+                audio_idx, text = line.strip().split(maxsplit=1)
+                yield line_idx, {"id": line_idx, "text": text, "audio": str(audiofiles[audio_idx])}
+
+        elif self.config.schema == "seacrowd_sptext":
+            for line_idx, line in enumerate(open(filepath)):
+                audio_idx, text = line.strip().split(maxsplit=1)
+                yield line_idx, {"id": line_idx, "path": str(audiofiles[audio_idx]), "audio": str(audiofiles[audio_idx]), "text": text, "speaker_id": None, "metadata": {"speaker_age": None, "speaker_gender": None}}