Upload indspeech_digit_cdsr.py with huggingface_hub

indspeech_digit_cdsr.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 cgitb import text
+from itertools import chain
+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-icslp-2004,
+    title = "Indonesian Speech Recognition for Hearing and Speaking Impaired People",
+    author = "Sakti, Sakriani and Hutagaol, Paulus and Arman, Arry Akhmad and Nakamura, Satoshi",
+    booktitle = "Proc. International Conference on Spoken Language Processing (INTERSPEECH - ICSLP)",
+    year = "2004",
+    pages = "1037--1040"
+    address = "Jeju Island, Korea"
+}
+"""
+_DATASETNAME = "indspeech_digit_cdsr"
+_LANGUAGES = ["ind"]
+_DESCRIPTION = """\
+INDspeech_DIGIT_CDSR is the first Indonesian speech dataset for connected digit speech recognition (CDSR). The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan and Bandung Institute of Technology (ITB) under the Asia-Pacific Telecommunity (APT) project in 2004 [Sakti et al., 2004]. Although it was originally developed for a telecommunication system for hearing and speaking impaired people, it can be used for other applications, i.e., automatic call centers that recognize telephone numbers.
+"""
+
+_HOMEPAGE = "https://github.com/s-sakti/data_indsp_digit_cdsr"
+_LOCAL = False
+_LICENSE = "CC-BY-NC-SA-4.0"
+
+_TMP_URL = {
+    "lst": "https://raw.githubusercontent.com/s-sakti/data_indsp_digit_cdsr/main/lst/",
+    "text": "https://github.com/s-sakti/data_indsp_digit_cdsr/raw/main/text/",
+    "speech": "https://github.com/s-sakti/data_indsp_digit_cdsr/raw/main/speech/",
+}
+
+_URLS = {
+    "lst": {
+        "train_spk": _TMP_URL["lst"] + "train_spk.lst",
+        "train_fname": _TMP_URL["lst"] + "train_fname.lst",
+        "test_spk": [_TMP_URL["lst"] + "test" + str(i) + "_spk.lst" for i in range(1, 5)],
+        "test_fname": [_TMP_URL["lst"] + "test" + str(i) + "_fname.lst" for i in range(1, 5)],
+    },
+    "train": {"speech": _TMP_URL["speech"] + "train/", "text": _TMP_URL["text"] + "train/"},
+    "test": {"speech": _TMP_URL["speech"] + "test", "text": _TMP_URL["text"] + "test"},
+}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class INDspeechDIGITCDSR(datasets.GeneratorBasedBuilder):
+    """Indonesian speech dataset for connected digit speech recognition"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="indspeech_digit_cdsr_source",
+            version=SOURCE_VERSION,
+            description="indspeech_digit_cdsr source schema",
+            schema="source",
+            subset_id="indspeech_digit_cdsr",
+        ),
+        NusantaraConfig(
+            name="indspeech_digit_cdsr_nusantara_sptext",
+            version=NUSANTARA_VERSION,
+            description="indspeech_digit_cdsr Nusantara schema",
+            schema="nusantara_sptext",
+            subset_id="indspeech_digit_cdsr",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "indspeech_digit_cdsr_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "speaker_id": datasets.Value("string"),
+                    "gender": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "text": 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."""
+        lst_train_spk = Path(dl_manager.download_and_extract(_URLS["lst"]["train_spk"]))
+        lst_train_fname = Path(dl_manager.download_and_extract(_URLS["lst"]["train_fname"]))
+        lst_test_spk = [Path(dl_manager.download_and_extract(url)) for url in _URLS["lst"]["test_spk"]]
+        lst_test_fname = [Path(dl_manager.download_and_extract(url)) for url in _URLS["lst"]["test_fname"]]
+
+        fnames = {"test": []}
+        speech = {"test": {}}
+        text = {"test": {}}
+
+        with open(lst_train_spk, "r") as f:
+            speakers = [spk.replace("\n", "") for spk in f.readlines()]
+            tmp_speech = [Path(dl_manager.download_and_extract(_URLS["train"]["speech"] + spk + ".zip")) for spk in speakers]
+            tmp_text = [Path(dl_manager.download_and_extract(_URLS["train"]["text"] + spk + ".zip")) for spk in speakers]
+            speech["train"] = {speech[:-4]: os.path.join(spk, speech) for spk in tmp_speech for speech in os.listdir(spk)}
+            text["train"] = {text[:-4]: os.path.join(spk, text) for spk in tmp_text for text in os.listdir(spk)}
+        f.close()
+
+        with open(lst_train_fname, "r") as f:
+            fnames["train"] = [fname.replace("\n", "") for fname in f.readlines()]
+        f.close()
+
+        for i in range(1, 5):
+            with open(lst_test_fname[i - 1], "r") as f:
+                fnames["test"].append([spk.replace("\n", "") for spk in f.readlines()])
+            f.close()
+
+            with open(lst_test_spk[i - 1], "r") as f:
+                speakers = [spk.replace("\n", "") for spk in f.readlines()]
+                tmp_speech = [Path(dl_manager.download_and_extract(_URLS["test"]["speech"] + str(i) + "/" + spk + ".zip")) for spk in speakers]
+                tmp_text = [Path(dl_manager.download_and_extract(_URLS["test"]["text"] + str(i) + "/" + spk + ".zip")) for spk in speakers]
+                tmp_dict_speech = {speech[:-4]: os.path.join(spk, speech) for spk in tmp_speech for speech in os.listdir(spk)}
+                tmp_dict_text = {text[:-4]: os.path.join(spk, text) for spk in tmp_text for text in os.listdir(spk)}
+            f.close()
+
+            for k, v in tmp_dict_speech.items():
+                if k in speech["test"]:
+                    continue
+                else:
+                    speech["test"][k] = v
+
+            for k, v in tmp_dict_text.items():
+                if k in text["test"]:
+                    continue
+                else:
+                    text["test"][k] = v
+
+        fnames["test"] = list(chain(*fnames["test"]))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": {
+                        "fnames": fnames["train"],
+                        "speech": speech["train"],
+                        "text": text["train"],
+                    },
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": {
+                        "fnames": fnames["test"],
+                        "speech": speech["test"],
+                        "text": text["test"],
+                    },
+                    "split": "test",
+                },
+            ),
+        ]
+
+    @staticmethod
+    def text_process(utterance_path):
+        with open(utterance_path, "r") as f:
+            w = [r.replace("\n", "") for r in f.readlines()]
+        f.close()
+        return " ".join(w[1:-1])
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        had_used = []
+        for key, example in enumerate(filepath["fnames"]):
+            if example not in had_used:
+                had_used.append(example)
+                spk_id, _ = example.split("_")
+                if self.config.schema == "source":
+                    yield key, {
+                        "id": example,
+                        "speaker_id": spk_id,
+                        "gender": spk_id[0],
+                        "path": filepath["speech"][example],
+                        "audio": filepath["speech"][example],
+                        "text": self.text_process(filepath["text"][example]),
+                    }
+
+                elif self.config.schema == "nusantara_sptext":
+                    yield key, {
+                        "id": example,
+                        "speaker_id": spk_id,
+                        "text": self.text_process(filepath["text"][example]),
+                        "path": filepath["speech"][example],
+                        "audio": filepath["speech"][example],
+                        "metadata": {
+                            "speaker_age": None,
+                            "speaker_gender": spk_id[0],
+                        },
+                    }
+            else:
+                continue