Create cleaned_jv_openslr_10k.py

cleaned_jv_openslr_10k.py

@@ -0,0 +1,172 @@
+# 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 csv
+import os
+from pathlib import Path
+from typing import List
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{kjartansson-etal-sltu2018,
+    title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese,  Sinhala, Nepali, and Bangladeshi Bengali}},
+    author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha},
+    booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
+    year  = {2018},
+    address = {Gurugram, India},
+    month = aug,
+    pages = {52--55},
+    URL   = {http://dx.doi.org/10.21437/SLTU.2018-11},
+  }
+"""
+
+_DATASETNAME = "jv_id_asr"
+
+_DESCRIPTION = """\
+This data set contains transcribed audio data for Javanese. The data set consists of wave files, and a TSV file.
+The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file.
+The data set has been manually quality checked, but there might still be errors.
+This dataset was collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia.
+"""
+
+_HOMEPAGE = "http://openslr.org/35/"
+_LANGUAGES = ["jav"]
+_LOCAL = False
+
+_LICENSE = "Attribution-ShareAlike 4.0 International"
+
+_URLs = {
+    "jv_id_asr_train": "https://univindonesia-my.sharepoint.com/:u:/g/personal/bimasena_putra_office_ui_ac_id/EV7Bg0Ik0t5OmlQhDSsiW84BXgTTJwEgqnuv1E-RAzcYfw?e=46bm2H&download=1",
+    "jv_id_asr_dev": "https://univindonesia-my.sharepoint.com/:u:/g/personal/bimasena_putra_office_ui_ac_id/EfnpOTriq6VNiiPpYYoV5TEBIhHE77NNSo2jcM-dXvJyiQ?e=SFtYUY&download=1",
+    "jv_id_asr_test": "https://univindonesia-my.sharepoint.com/:u:/g/personal/bimasena_putra_office_ui_ac_id/EQWc1L4e5RVJuwgg4BZssooBqn8cxAlbwsHdJG-_OUBpVQ?e=tcqwYc&download=1",
+}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class JvIdASR(datasets.GeneratorBasedBuilder):
+    """Javanese ASR training data set containing ~185K utterances."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="jv_id_asr_source",
+            version=SOURCE_VERSION,
+            description="jv_id_asr source schema",
+            schema="source",
+            subset_id="jv_id_asr",
+        ),
+        SEACrowdConfig(
+            name="jv_id_asr_seacrowd_sptext",
+            version=SEACROWD_VERSION,
+            description="jv_id_asr Nusantara schema",
+            schema="seacrowd_sptext",
+            subset_id="jv_id_asr",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "jv_id_asr_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"),
+                }
+            )
+        elif self.config.schema == "seacrowd_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]:
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": dl_manager.download_and_extract(_URLs["jv_id_asr_train"])},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": dl_manager.download_and_extract(_URLs["jv_id_asr_dev"])},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": dl_manager.download_and_extract(_URLs["jv_id_asr_test"])},
+            )
+        ]
+
+    def _generate_examples(self, filepath: str):
+        # Load the DatasetDict arrow file
+        filtered_dataset = datasets.load_from_disk("jv_dataset_2")  # Replace with the actual path
+
+        # Combine IDs from all splits into a set
+        filtered_ids = set()
+        for split in ["train", "validation", "test"]:
+            filtered_ids.update(filtered_dataset[split]["id"])
+
+        # Path to the TSV file
+        tsv_file = os.path.join(filepath, "asr_javanese", "utt_spk_text.tsv")
+        with open(tsv_file, "r") as f:
+            tsv_file = csv.reader(f, delimiter="\t")
+            for line in tsv_file:
+                audio_id, sp_id, text = line[0], line[1], line[2]
+                wav_path = os.path.join(filepath, "asr_javanese", "data", "{}".format(audio_id[:2]), "{}.flac".format(audio_id))
+
+                # Filter based on the IDs
+                if audio_id in filtered_ids and os.path.exists(wav_path):
+                    if self.config.schema == "source":
+                        ex = {
+                            "id": audio_id,
+                            "speaker_id": sp_id,
+                            "path": wav_path,
+                            "audio": wav_path,
+                            "text": text,
+                        }
+                        yield audio_id, ex
+                    elif self.config.schema == "seacrowd_sptext":
+                        ex = {
+                            "id": audio_id,
+                            "speaker_id": sp_id,
+                            "path": wav_path,
+                            "audio": wav_path,
+                            "text": text,
+                            "metadata": {
+                                "speaker_age": None,
+                                "speaker_gender": None,
+                            },
+                        }
+                        yield audio_id, ex
+        f.close()