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indspeech_news_tts.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
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{sakti-tts-cocosda-2008,
+    title = "Development of HMM-based Indonesian Speech Synthesis",
+    author = "Sakti, Sakriani and Maia, Ranniery and Sakai, Shinsuke and Nakamura, Satoshi",
+    booktitle = "Proc. Oriental COCOSDA",
+    year = "2008",
+    pages = "215--220"
+    address = "Kyoto, Japan"
+}
+
+@inproceedings{sakti-tts-malindo-2010,
+    title = "Quality and Intelligibility Assessment of Indonesian HMM-Based Speech Synthesis System",
+    author = "Sakti, Sakriani and Sakai, Shinsuke and Isotani, Ryosuke and Kawai, Hisashi and Nakamura, Satoshi",
+    booktitle = "Proc. MALINDO",
+    year = "2010",
+    pages = "51--57"
+    address = "Jakarta, Indonesia"
+}
+
+@article{sakti-s2st-csl-2013,
+    title = "{A-STAR}: Toward Tranlating Asian Spoken Languages",
+    author = "Sakti, Sakriani and Paul, Michael and Finch, Andrew and Sakai, Shinsuke and Thang, Tat Vu, and Kimura, Noriyuki 
+    and Hori, Chiori and Sumita, Eiichiro and Nakamura, Satoshi and Park, Jun and Wutiwiwatchai, Chai and Xu, Bo and Riza, Hammam 
+    and Arora, Karunesh and Luong, Chi Mai and Li, Haizhou",
+    journal = "Special issue on Speech-to-Speech Translation, Computer Speech and Language Journal",
+    volume = "27",
+    number ="2",
+    pages = "509--527",
+    year = "2013",
+    publisher = "Elsevier"
+}
+"""
+
+_DATASETNAME = "INDspeech_NEWS_TTS"
+_LANGUAGES = ["ind"]
+
+_DESCRIPTION = """\
+INDspeech_NEWS_TTS is a speech dataset for developing an Indonesian text-to-speech synthesis system. The data was developed by Advanced Telecommunication Research Institute International (ATR) Japan under the the Asian speech translation advanced research (A-STAR) project [Sakti et al., 2013].
+"""
+_HOMEPAGE = "https://github.com/s-sakti/data_indsp_news_tts"
+
+_LICENSE = "CC-BY-NC-SA 4.0"
+
+_TRAIN_TASKS = {"120": "Orig_trainset_120min.lst", "60": "Orig_trainset_60min.lst", "30": "Orig_trainset_30min.lst", "12": "Orig_trainset_12min.lst", "ZR": "ZRChallenge_trainset.lst"}
+
+_TEST_TASKS = {
+    "MOS": "Orig_testset_MOS.lst",
+    # "SUS": "Orig_testset_SUS.lst",
+    "ZR": "ZRChallenge_testset.lst",
+}
+
+_URLS = {"lst_": "https://github.com/s-sakti/data_indsp_news_tts/raw/main/lst/", "speech_": "https://github.com/s-sakti/data_indsp_news_tts/raw/main/speech/", "text_": "https://github.com/s-sakti/data_indsp_news_tts/raw/main/text/orig_transcript"}
+
+_SUPPORTED_TASKS = [Tasks.TEXT_TO_SPEECH]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+_LOCAL = False
+
+
+def nusantara_config_constructor(schema, version, train_task, test_task):
+
+    if schema != "source" and schema != "nusantara_sptext":
+        raise ValueError(f"Invalid schema: {schema}")
+
+    return NusantaraConfig(
+        name="indspeech_news_tts_{tr_task}_{ts_task}_{schema}".format(schema=schema, tr_task=train_task, ts_task=test_task),
+        version=datasets.Version(version),
+        description="indspeech_news_tts {schema} schema for {tr_task} train and {ts_task} test task".format(schema=schema, tr_task=train_task, ts_task=test_task),
+        schema=schema,
+        subset_id="indspeech_news_tts_{tr_task}_{ts_task}".format(tr_task=train_task, ts_task=test_task),
+    )
+
+
+class INDspeechNEWSTTS(datasets.GeneratorBasedBuilder):
+    """
+    Tasks:
+    Original = Train [120, 60, 30, 12], Test [MOS, SUS]
+    ZR = Train, Test
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = (
+        [nusantara_config_constructor("source", _SOURCE_VERSION, train, test) for train in ["12", "30", "60", "120"] for test in ["MOS"]]
+        + [nusantara_config_constructor("nusantara_sptext", _NUSANTARA_VERSION, train, test) for train in ["12", "30", "60", "120"] for test in ["MOS"]]
+        + [nusantara_config_constructor("source", _SOURCE_VERSION, "ZR", "ZR")]
+        + [nusantara_config_constructor("nusantara_sptext", _NUSANTARA_VERSION, "ZR", "ZR")]
+    )
+
+    DEFAULT_CONFIG_NAME = "indspeech_news_tts_120_MOS_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]:
+        """Returns SplitGenerators."""
+        tr_task = self.config.name.split("_")[3]  # [12,30,60,120,ZR]
+        ts_task = self.config.name.split("_")[4]  # [MOS, ZR]
+
+        lst_train_dir = Path(dl_manager.download_and_extract(_URLS["lst_"] + _TRAIN_TASKS[tr_task]))
+        lst_test_dir = Path(dl_manager.download_and_extract(_URLS["lst_"] + _TEST_TASKS[ts_task]))
+        txt_dir = Path(dl_manager.download_and_extract(_URLS["text_"]))
+        speech_dir = {"SPK00_" + str(spk).zfill(2) + "00": Path(dl_manager.download_and_extract(_URLS["speech_"] + "SPK00_" + str(spk).zfill(2) + "00.zip") + "/SPK00_" + str(spk).zfill(2) + "00") for spk in range(0, 21)}
+        # print(os.listdir(speech_dir["SPK00_1500"]))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": {"samples": lst_train_dir, "text": txt_dir, "speech": speech_dir},
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": {"samples": lst_test_dir, "text": txt_dir, "speech": speech_dir},
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        samples = open(filepath["samples"], "r").read().splitlines()
+
+        transcripts = {}
+        with open(filepath["text"]) as file:
+            for line in file:
+                key, text = line.replace("\n", "").split("\t")
+                transcripts[key] = text
+
+        for key, id in enumerate(samples):
+            spk_id, gender, speech_id = id.split("_")
+            spk_group = speech_id[:2]
+
+            if self.config.schema == "source":
+                example = {
+                    "id": id,
+                    "speaker_id": spk_id,
+                    "path": os.path.join(filepath["speech"]["SPK00_" + spk_group + "00"], id + ".wav"),
+                    "audio": os.path.join(filepath["speech"]["SPK00_" + spk_group + "00"], id + ".wav"),
+                    "text": transcripts[id],
+                    "gender": gender,
+                }
+                yield key, example
+            elif self.config.schema == "nusantara_sptext":
+                example = {
+                    "id": str(id),
+                    "speaker_id": spk_id,
+                    "path": os.path.join(filepath["speech"]["SPK00_" + spk_group + "00"], id + ".wav"),
+                    "audio": os.path.join(filepath["speech"]["SPK00_" + spk_group + "00"], id + ".wav"),
+                    "text": transcripts[id],
+                    "metadata": {
+                        "speaker_age": None,
+                        "speaker_gender": gender,
+                    },
+                }
+                yield key, example