Upload indspeech_newstra_ethnicsr.py with huggingface_hub

indspeech_newstra_ethnicsr.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 itertools import chain
+from pathlib import Path
+from random import sample
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{sakti-cocosda-2013,
+    title = "Towards Language Preservation: Design and Collection of Graphemically Balanced and Parallel Speech Corpora of {I}ndonesian Ethnic Languages",
+    author = "Sakti, Sakriani and Nakamura, Satoshi",
+    booktitle = "Proc. Oriental COCOSDA",
+    year = "2013",
+    address = "Gurgaon, India"
+}
+
+@inproceedings{sakti-sltu-2014,
+  title = "Recent progress in developing grapheme-based speech recognition for {I}ndonesian ethnic languages: {J}avanese, {S}undanese, {B}alinese and {B}ataks",
+  author = "Sakti, Sakriani and Nakamura, Satoshi",
+  booktitle = "Proc. 4th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2014)",
+  year = "2014",
+  pages = "46--52",
+  address = "St. Petersburg, Russia"
+}
+
+@inproceedings{novitasari-sltu-2020,
+  title = "Cross-Lingual Machine Speech Chain for {J}avanese, {S}undanese, {B}alinese, and {B}ataks Speech Recognition and Synthesis",
+  author = "Novitasari, Sashi and Tjandra, Andros and Sakti, Sakriani and Nakamura, Satoshi",
+  booktitle = "Proc. Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
+  year = "2020",
+  pages = "131--138",
+  address = "Marseille, France"
+}
+"""
+
+_DATASETNAME = "indspeech_newstra_ethnicsr"
+_DESCRIPTION = """\
+INDspeech_NEWSTRA_EthnicSR is a collection of graphemically balanced and parallel speech corpora of four major Indonesian ethnic languages: Javanese, Sundanese, Balinese, and Bataks. It was developed in 2013 by the Nara Institute of Science and Technology (NAIST, Japan) [Sakti et al., 2013]. The data has been used to develop Indonesian ethnic speech recognition in supervised learning [Sakti et al., 2014] and semi-supervised learning [Novitasari et al., 2020] based on Machine Speech Chain framework [Tjandra et al., 2020].
+"""
+
+_HOMEPAGE = "https://github.com/s-sakti/data_indsp_newstra_ethnicsr"
+_LANGUAGES = ["sun", "jav", "btk", "ban"]
+_LOCAL = False
+_LICENSE = "CC-BY-NC-SA 4.0"
+
+_lst_TYPE = ["traEth", "traInd"]
+_lst_LANG = {"Bli": "BALI", "Btk": "BATAK", "Jaw": "JAWA", "Snd": "SUNDA"}
+_lst_STD_LANG = {"ban": "Bli", "btk": "Btk", "jav": "Jaw", "sun": "Snd"}
+_lst_HEAD_1_TRAIN = "https://raw.githubusercontent.com/s-sakti/data_indsp_newstra_ethnicsr/main/lst/dataset1_train_news_"
+_lst_HEAD_1_TEST = ["https://raw.githubusercontent.com/s-sakti/data_indsp_newstra_ethnicsr/main/lst/dataset1_test_" + ltype + "_" for ltype in _lst_TYPE]
+_lst_HEAD_2 = "https://raw.githubusercontent.com/s-sakti/data_indsp_newstra_ethnicsr/main/lst/dataset2_"
+_sp_TEMPLATE = "https://raw.githubusercontent.com/s-sakti/data_indsp_newstra_ethnicsr/main/speech/16kHz/"  # {lang}/Ind0{index}_{gender}_{lang_code}.zip"
+_txt_TEMPLATE = "https://github.com/s-sakti/data_indsp_newstra_ethnicsr/raw/main/text/utts_transcript/"  # {lang}/Ind0{index}_{gender}_{lang_code}.zip"
+
+_URLS = {
+    "dataset1_train": {llang.lower(): [_lst_HEAD_1_TRAIN + llang + ".lst"] for llang in _lst_LANG},
+    "dataset1_test": {llang.lower(): [head1test + llang + ".lst" for head1test in _lst_HEAD_1_TEST] for llang in _lst_LANG},
+    "dataset2_train": {llang.lower(): [_lst_HEAD_2 + "train_news_" + llang + ".lst"] for llang in _lst_LANG},
+    "dataset2_test": {llang.lower(): [_lst_HEAD_2 + "test_news_" + llang + ".lst"] for llang in _lst_LANG},
+    "speech": {llang.lower(): [_sp_TEMPLATE + _lst_LANG[llang] + "/Ind" + str(idx).zfill(3) + "_" + ("M" if idx % 2 == 0 else "F") + "_" + llang + ".zip" for idx in range(1, 11)] for llang in _lst_LANG},
+    "transcript": {llang.lower(): [_txt_TEMPLATE + _lst_LANG[llang] + "/Ind" + str(idx).zfill(3) + "_" + ("M" if idx % 2 == 0 else "F") + "_" + llang + ".zip" for idx in range(1, 11)] for llang in _lst_LANG},
+}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+def nusantara_config_constructor(lang, schema, version, overlap):
+    if lang == "":
+        raise ValueError(f"Invalid lang {lang}")
+
+    if schema != "source" and schema != "nusantara_sptext":
+        raise ValueError(f"Invalid schema: {schema}")
+
+    return NusantaraConfig(
+        name="indspeech_newstra_ethnicsr_{overlap}_{lang}_{schema}".format(lang=lang, schema=schema, overlap=overlap),
+        version=datasets.Version(version),
+        description="indspeech_newstra_ethnicsr {schema} schema for {lang} language with {overlap}ping dataset".format(lang=_lst_LANG[_lst_STD_LANG[lang]], schema=schema, overlap=overlap),
+        schema=schema,
+        subset_id="indspeech_newstra_ethnicsr_{overlap}".format(overlap=overlap),
+    )
+
+class INDspeechNEWSTRAEthnicSR(datasets.GeneratorBasedBuilder):
+    """
+    The dataset contains 2 sub-datasets
+    Dataset 1 has 2250/1000 train/test samples per language
+    Dataset 2 has another 1600/50 train/test per language
+    The 'overlap' keyword in the dataset-name combines both sub-datasets, while 'nooverlap' will only use dataset 1
+    """
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [nusantara_config_constructor(lang, "source", _SOURCE_VERSION, overlap) for lang in _lst_STD_LANG for overlap in ["overlap","nooverlap"]] +\
+                      [nusantara_config_constructor(lang, "nusantara_sptext", _NUSANTARA_VERSION, overlap) for lang in _lst_STD_LANG for overlap in ["overlap","nooverlap"]]
+
+    DEFAULT_CONFIG_NAME = "indspeech_newstra_ethnicsr_jav_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),
+                    "text": datasets.Value("string"),
+                    "gender": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "nusantara_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]:
+        lang = _lst_STD_LANG[self.config.name.split("_")[4]].lower()
+        ds1_train_urls = _URLS["dataset1_train"][lang]
+        ds1_test_urls = _URLS["dataset1_test"][lang]
+        ds2_train_urls = _URLS["dataset2_train"][lang]
+        ds2_test_urls = _URLS["dataset2_test"][lang]
+        sp_urls = _URLS["speech"][lang]
+        txt_urls = _URLS["transcript"][lang]
+
+        ds1_train_dir = [Path(dl_manager.download_and_extract(ds1_train_url)) for ds1_train_url in ds1_train_urls]
+        ds1_test_dir = [Path(dl_manager.download_and_extract(ds1_test_url)) for ds1_test_url in ds1_test_urls]
+        ds2_train_dir = [Path(dl_manager.download_and_extract(ds2_train_url)) for ds2_train_url in ds2_train_urls]
+        ds2_test_dir = [Path(dl_manager.download_and_extract(ds2_test_url)) for ds2_test_url in ds2_test_urls]
+        sp_dir = {str(Path(sp_url).name)[:-4]: os.path.join(Path(dl_manager.download_and_extract(sp_url)), str(Path(sp_url).name))[:-4] for sp_url in sp_urls}
+        txt_dir = {str(Path(txt_url).name)[:-4]: os.path.join(Path(dl_manager.download_and_extract(txt_url)), str(Path(txt_url).name))[:-4] for txt_url in txt_urls}
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": {
+                        "dataset1": ds1_train_dir,
+                        "dataset2": ds2_train_dir,
+                        "speech": sp_dir,
+                        "transcript": txt_dir,
+                    },
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": {
+                        "dataset1": ds1_test_dir,
+                        "dataset2": ds2_test_dir,
+                        "speech": sp_dir,
+                        "transcript": txt_dir,
+                    },
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        sample_list=[]
+        if self.config.name.split("_")[3] == "nooverlap":
+            sample_list = [open(samples).read().splitlines() for samples in filepath["dataset1"]]  # +filepath["dataset2"]]
+            sample_list = list(chain(*sample_list))
+        elif self.config.name.split("_")[3] == "overlap":
+            sample_list = [open(samples).read().splitlines() for samples in filepath["dataset1"]+filepath["dataset2"]]
+            sample_list = list(chain(*sample_list))
+
+        for id, row in enumerate(sample_list):
+            if self.config.schema == "source":
+                ex = {
+                    "id": str(id),
+                    "speaker_id": str(Path(row).parent).split("/")[1],
+                    "path": os.path.join(filepath["speech"][str(Path(row).parent).split("/")[1]], str(Path(row).name) + ".wav"),
+                    "audio": os.path.join(filepath["speech"][str(Path(row).parent).split("/")[1]], str(Path(row).name) + ".wav"),
+                    "text": open(os.path.join(filepath["transcript"][str(Path(row).parent).split("/")[1]], str(Path(row).name) + ".txt"), "r").read().splitlines()[0],
+                    "gender": str(Path(row).parent).split("/")[1].split("_")[1],
+                }
+                yield id, ex
+
+            elif self.config.schema == "nusantara_sptext":
+                ex = {
+                    "id": str(id),
+                    "speaker_id": str(Path(row).parent).split("/")[1],
+                    "path": os.path.join(filepath["speech"][str(Path(row).parent).split("/")[1]], str(Path(row).name) + ".wav"),
+                    "audio": os.path.join(filepath["speech"][str(Path(row).parent).split("/")[1]], str(Path(row).name) + ".wav"),
+                    "text": open(os.path.join(filepath["transcript"][str(Path(row).parent).split("/")[1]], str(Path(row).name) + ".txt"), "r").read().splitlines()[0],
+                    "metadata": {
+                        "speaker_age": None,
+                        "speaker_gender": str(Path(row).parent).split("/")[1].split("_")[1],
+                    },
+                }
+                yield id, ex