Upload thai_elderly_speech.py with huggingface_hub

thai_elderly_speech.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.
+
+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA,
+                                      Licenses, Tasks)
+
+_CITATION = ""  # no dataset/paper citation found
+
+_DATASETNAME = "thai_elderly_speech"
+
+_DESCRIPTION = """\
+The Thai Elderly Speech dataset by Data Wow and VISAI Version 1 dataset aims at
+advancing Automatic Speech Recognition (ASR) technology specifically for the
+elderly population. Researchers can use this dataset to advance ASR technology
+for healthcare and smart home applications. The dataset consists of 19,200 audio
+files, totaling 17 hours and 11 minutes of recorded speech. The files are
+divided into 2 categories: Healthcare (relating to medical issues and services
+in 30 medical categories) and Smart Home (relating to smart home devices in 7
+household contexts). The dataset contains 5,156 unique sentences spoken by 32
+seniors (10 males and 22 females), aged 57-60 years old (average age of 63
+years).
+"""
+
+_HOMEPAGE = "https://github.com/VISAI-DATAWOW/Thai-Elderly-Speech-dataset/releases/tag/v1.0.0"
+
+_LANGUAGES = ["tha"]
+_SUBSETS = ["healthcare", "smarthome"]
+
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+_LOCAL = False
+
+_URLS = [
+    "https://github.com/VISAI-DATAWOW/Thai-Elderly-Speech-dataset/releases/download/v1.0.0/Dataset.zip.001",
+    "https://github.com/VISAI-DATAWOW/Thai-Elderly-Speech-dataset/releases/download/v1.0.0/Dataset.zip.002",
+    "https://github.com/VISAI-DATAWOW/Thai-Elderly-Speech-dataset/releases/download/v1.0.0/Dataset.zip.003",
+]
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_TO_TEXT_TRANSLATION]
+_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}"  # sptext
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ThaiElderlySpeechDataset(datasets.GeneratorBasedBuilder):
+    """A speech dataset from elderly Thai speakers."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = []
+    for subset in _SUBSETS:
+        BUILDER_CONFIGS += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{subset}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} {subset} source schema",
+                schema="source",
+                subset_id=subset,
+            ),
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA}",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} {subset} SEACrowd schema",
+                schema=_SEACROWD_SCHEMA,
+                subset_id=subset,
+            ),
+        ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_healthcare_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "filename": datasets.Value("string"),
+                    "transcription": datasets.Value("string"),
+                    "speaker": {
+                        "id": datasets.Value("string"),
+                        "age": datasets.Value("int32"),
+                        "gender": datasets.Value("string"),
+                    },
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]]  # ssp_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."""
+        zip_files = list(map(Path, dl_manager.download(_URLS)))
+        zip_combined = zip_files[0].parent / "thai_elderly_speech.zip"
+
+        with open(str(zip_combined), "wb") as out_file:
+            for zip_file in zip_files:
+                with open(str(zip_file), "rb") as in_file:
+                    out_file.write(in_file.read())
+
+        data_file = Path(dl_manager.extract(zip_combined)) / "Dataset"
+        subset_id = self.config.subset_id
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "speaker_file": data_file / "speaker_demography.json",
+                    "audio_dir": data_file / subset_id.title() / "Record",
+                    "transcript_file": data_file / subset_id.title() / "transcription.json",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, speaker_file: Path, audio_dir: Path, transcript_file: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        # read speaker information
+        with open(speaker_file, "r", encoding="utf-8") as f:
+            speaker_info = json.load(f)
+            speaker_dict = {speaker["speaker_id"]: {"age": speaker["age"], "gender": speaker["gender"]} for speaker in speaker_info}
+
+        # read transcript information
+        with open(transcript_file, "r", encoding="utf-8") as f:
+            annotations = json.load(f)
+
+        for idx, instance in enumerate(annotations):
+            transcript = instance["transcript"]
+
+            speaker_id = instance["speaker_id"]
+            speaker_info = speaker_dict[int(speaker_id)]
+
+            filename = instance["filename"]
+            audio_file = str(audio_dir / (filename + ".wav"))
+
+            if self.config.schema == "source":
+                yield idx, {
+                    "audio": audio_file,
+                    "filename": filename,
+                    "transcription": transcript,
+                    "speaker": {
+                        "id": speaker_id,
+                        "age": speaker_info["age"],
+                        "gender": speaker_info["gender"],
+                    },
+                }
+            elif self.config.schema == _SEACROWD_SCHEMA:
+                yield idx, {
+                    "id": idx,
+                    "path": audio_file,
+                    "audio": audio_file,
+                    "text": transcript,
+                    "speaker_id": speaker_id,
+                    "metadata": {
+                        "speaker_age": speaker_info["age"],
+                        "speaker_gender": speaker_info["gender"],
+                    },
+                }