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mswc.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 os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{mazumder2021mswc,
+    author = {Mazumder, Mark and Chitlangia, Sharad and Banbury, Colby and Kang, Yiping and Ciro, Juan and Achorn, Keith and Galvez,
+    Daniel and Sabini, Mark and Mattson, Peter and Kanter, David and Diamos, Greg and Warden, Pete and Meyer, Josh and Janapa Reddi,
+    Vijay},
+    booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
+    editor = {J. Vanschoren and S. Yeung},
+    pages = {},
+    publisher = {Curran},
+    title = {Multilingual Spoken Words Corpus},
+    url = {https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/fe131d7f5a6b38b23cc967316c13dae2-Paper-round2.pdf},
+    volume = {1},
+    year = {2021}
+}
+"""
+
+_DATASETNAME = "mswc"
+
+_DESCRIPTION = """\
+Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken words in 50 languages collectively spoken by over 5 billion people, for academic research and commercial applications in keyword spotting and spoken term search.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/MLCommons/ml_spoken_words"
+
+_LANGUAGES = ["cnh", "ind", "vie"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LANGUAGE_NAME_MAP = {
+    "cnh": "cnh",
+    "ind": "id",
+    "vie": "vi",
+}
+
+_FORMATS = ["wav", "opus"]
+
+_LICENSE = Licenses.CC_BY_4_0.value
+
+_LOCAL = False
+
+_URLS = "https://huggingface.co/datasets/MLCommons/ml_spoken_words/resolve/refs%2Fconvert%2Fparquet/{lang}_{format}/{split}/0000.parquet?download=true"
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+_SUPPORTED_SCHEMA_STRINGS = [f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class MSWC(datasets.GeneratorBasedBuilder):
+    """
+    Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken words in 50 languages collectively spoken by over 5 billion people, for academic research and commercial applications in keyword spotting and spoken term search.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = []
+
+    for language in _LANGUAGES:
+        for format in _FORMATS:
+            subset_id = f"{_DATASETNAME}_{language}_{format}"
+            BUILDER_CONFIGS.append(
+                SEACrowdConfig(name=f"{subset_id}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=subset_id),
+            )
+
+    seacrowd_schema_config: list[SEACrowdConfig] = []
+
+    for seacrowd_schema in _SUPPORTED_SCHEMA_STRINGS:
+        for language in _LANGUAGES:
+            for format in _FORMATS:
+                subset_id = f"{_DATASETNAME}_{language}_{format}"
+                seacrowd_schema_config.append(
+                    SEACrowdConfig(
+                        name=f"{subset_id}_{seacrowd_schema}",
+                        version=SEACROWD_VERSION,
+                        description=f"{_DATASETNAME} {seacrowd_schema} schema",
+                        schema=f"{seacrowd_schema}",
+                        subset_id=subset_id,
+                    )
+                )
+
+    BUILDER_CONFIGS.extend(seacrowd_schema_config)
+
+    DEFAULT_CONFIG_NAME = f"{_LANGUAGES[0]}_{_FORMATS[0]}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        _, _, format = str(self.config.subset_id).split("_")
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "is_valid": datasets.Value("bool"),
+                    "language": datasets.ClassLabel(num_classes=3),
+                    "speaker_id": datasets.Value("string"),
+                    "gender": datasets.ClassLabel(num_classes=4),
+                    "keyword": datasets.Value("string"),
+                    "audio": datasets.Audio(decode=False, sampling_rate=16000 if format == "wav" else 48000),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.SPEECH_RECOGNITION]).lower()}":
+            features = schemas.speech_text_features
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+        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."""
+
+        split_names = ["train", "validation", "test"]
+
+        result = []
+
+        _, language, format = str(self.config.subset_id).split("_")
+
+        for split_name in split_names:
+            path = dl_manager.download_and_extract(_URLS.format(split=split_name, lang=_LANGUAGE_NAME_MAP[language], format=format))
+
+            result.append(
+                datasets.SplitGenerator(
+                    name=split_name,
+                    gen_kwargs={
+                        "path": path,
+                        "split": split_name,
+                        "language": language,
+                        "format": format,
+                    },
+                ),
+            )
+
+        return result
+
+    def _generate_examples(self, path: Path, split: str, language: str, format: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        idx = 0
+
+        if self.config.schema == "source":
+            df = pd.read_parquet(path)
+
+            for _, row in df.iterrows():
+                yield idx, row.to_dict()
+                idx += 1
+
+        elif self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.SPEECH_RECOGNITION]).lower()}":
+            df = pd.read_parquet(path)
+
+            base_folder = os.path.dirname(path)
+            base_folder = os.path.join(base_folder, _DATASETNAME, language, format, split)
+
+            if not os.path.exists(base_folder):
+                os.makedirs(base_folder)
+
+            audio_paths = []
+
+            for _, row in df.iterrows():
+                audio_dict = row["audio"]
+                file_name = audio_dict["path"]
+
+                path = os.path.join(base_folder, file_name)
+
+                audio_dict["path"] = path
+
+                with open(path, "wb") as f:
+                    f.write(audio_dict["bytes"])
+
+                audio_paths.append(path)
+
+            df.rename(columns={"label": "text"}, inplace=True)
+
+            df["path"] = audio_paths
+
+            df["id"] = df.index + idx
+            df = df.assign(text="").astype({"text": "str"})
+            df = df.assign(metadata=[{"speaker_age": 0, "speaker_gender": gender} for gender in df["gender"]]).astype({"metadata": "object"})
+
+            df.drop(columns=["file", "is_valid", "language", "gender", "keyword"], inplace=True)
+
+            for _, row in df.iterrows():
+                yield idx, row.to_dict()
+                idx += 1
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")