Upload indspeech_teldialog_svcsr.py with huggingface_hub

indspeech_teldialog_svcsr.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-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_teldialog_svcsr"
+
+_DESCRIPTION = """\
+This is the first Indonesian speech dataset for small vocabulary continuous speech recognition (SVCSR).
+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. Furthermore, as all speakers utter the same sentences,
+it can also be used for voice conversion tasks.
+
+The text is based on a word vocabulary which is derived from some necessary dialog calls,
+such as dialog calls with the 119 emergency department, 108 telephone information department,
+and ticket reservation department. In total, it consists of 20,000 utterances (about 18 hours of speech) from the
+70-word dialog vocabulary of 100 sentences (including single word sentences) each uttered by 200 speakers
+(100 Females, 100 Males). The age is limited to middle age (20-40 years), but they present a wide range of spoken
+dialects from different ethnic groups. The recording is conducted in parallel for both clean and telephone speech,
+but we open only the clean speech due to quality issues on telephone speech.
+Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 16000 Hz.
+These utterances are equally split into training and test sets with 100 speakers (50 Females, 50 Males) in each set.
+"""
+
+_HOMEPAGE = "https://github.com/s-sakti/data_indsp_teldialog_svcsr/"
+
+_LICENSE = "CC-BY-NC-SA-4.0"
+
+_LANGUAGES = ["ind"]
+_LOCAL = False
+
+URL_TEMPLATE = "https://raw.githubusercontent.com/s-sakti/data_indsp_teldialog_svcsr/main/"
+_URLS = {
+    _DATASETNAME: {"lst": URL_TEMPLATE + "lst/", "speech": URL_TEMPLATE + "speech/", "text": URL_TEMPLATE + "text/"},
+}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class INDspeechTELDIALOGSVCSR(datasets.GeneratorBasedBuilder):
+    """
+    This is an Indonesian speech dataset on small vocabulary continuous speech recognition (SVCSR) from necessary
+    dialog calls. The dataset loader is designed for speech recognition task.
+    There are 20000 utterances (train: 10000, test:10000) uttered by 200 speakers (50 male 50 female each in train and
+    test).
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="indspeech_teldialog_svcsr_source",
+            version=SOURCE_VERSION,
+            description="indspeech_teldialog_svcsr source schema",
+            schema="source",
+            subset_id="indspeech_teldialog_svcsr",
+        ),
+        NusantaraConfig(
+            name="indspeech_teldialog_svcsr_nusantara_sptext",
+            version=NUSANTARA_VERSION,
+            description="indspeech_teldialog_svcsr Nusantara schema",
+            schema="nusantara_sptext",
+            subset_id="indspeech_teldialog_svcsr",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "indspeech_teldialog_svcsr_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        # Create the source schema; this schema will keep all keys/information/labels as close to the original dataset as possible.
+
+        # You can arbitrarily nest lists and dictionaries.
+        # For iterables, use lists over tuples or `datasets.Sequence`
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "speaker_id": datasets.Value("string"),
+                    "gender_id": datasets.Value("string"),
+                    "utterance_id": 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,
+            task_templates=[datasets.AutomaticSpeechRecognition(audio_column="audio", transcription_column="sentences")],
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = {
+            "spk_data": {"train": dl_manager.download_and_extract(os.path.join(urls["lst"], "train_spk.lst")), "test": dl_manager.download_and_extract(os.path.join(urls["lst"], "test_spk.lst"))},
+            "wav_data": {"train": dl_manager.download_and_extract(os.path.join(urls["lst"], "train_wav.lst")), "test": dl_manager.download_and_extract(os.path.join(urls["lst"], "test_wav.lst"))},
+            "txt_data": dl_manager.download_and_extract(os.path.join(urls["text"], "text.zip")),
+        }
+        speakers = {}
+        with open(data_dir["spk_data"]["train"], "r") as f:
+            speakers["train"] = [sp.replace("\n", "") for sp in f.readlines()]
+        f.close()
+        with open(data_dir["spk_data"]["test"], "r") as f:
+            speakers["test"] = [sp.replace("\n", "") for sp in f.readlines()]
+        f.close()
+        data_dir["speech_path"] = {
+            "train": {sp: dl_manager.download_and_extract(os.path.join(urls["speech"], "train", sp + ".zip")) for sp in speakers["train"]},
+            "test": {sp: dl_manager.download_and_extract(os.path.join(urls["speech"], "test", sp + ".zip")) for sp in speakers["test"]},
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["wav_data"]["train"],
+                    "audio_path": data_dir["speech_path"]["train"],
+                    "text_path": data_dir["txt_data"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["wav_data"]["test"],
+                    "audio_path": data_dir["speech_path"]["test"],
+                    "text_path": data_dir["txt_data"],
+                    "split": "test",
+                },
+            ),
+        ]
+
+    @staticmethod
+    def text_process(utterance_txt_dir):
+        with open(utterance_txt_dir + ".ANS", "r") as f:
+            lines = [x.replace("\n", "") for x in f.readlines()]
+        f.close()
+        return " ".join(lines)
+
+    def _generate_examples(self, filepath: Path, audio_path, text_path: Path, split: str) -> Tuple[int, Dict]:
+        with open(filepath, "r") as f:
+            filelist = [x.replace("\n", "") for x in f.readlines()]
+        f.close()
+        for fn in filelist:
+            speaker_id = fn[:3]
+            gender_id = fn[:1]
+            utterance_id = fn[4:8]
+            _id = fn.replace(".wav", "")
+            text = self.text_process(os.path.join(text_path, utterance_id))
+            if self.config.schema == "source":
+                yield _id, {
+                    "speaker_id": speaker_id,
+                    "gender_id": gender_id,
+                    "utterance_id": utterance_id,
+                    "audio": os.path.join(audio_path[speaker_id], fn),
+                    "text": text,
+                }
+
+            elif self.config.schema == "nusantara_sptext":
+                yield _id, {
+                    "id": _id,
+                    "speaker_id": speaker_id,
+                    "text": text,
+                    "path": os.path.join(audio_path[speaker_id], fn),
+                    "audio": os.path.join(audio_path[speaker_id], fn),
+                    "metadata": {
+                        "speaker_age": None,
+                        "speaker_gender": gender_id,
+                    },
+                }