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openslr.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
+import re
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{kjartansson18_sltu,
+  author={Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha},
+  title={{Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali}},
+  year=2018,
+  booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)},
+  pages={52--55},
+  doi={10.21437/SLTU.2018-11}
+}
+"""
+
+_DATASETNAME = "openslr"
+
+_DESCRIPTION = """\
+This data set contains transcribed high-quality audio of Javanese, Sundanese, Burmese, Khmer. This data set\
+come from 3 different projects under OpenSLR initiative
+"""
+
+_HOMEPAGE = "https://www.openslr.org/resources.php"
+
+_LANGUAGES = ["mya", "jav", "sun", "khm"]
+
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+_LOCAL = False
+
+_RESOURCES = {
+    "SLR35": {
+        "language": "jav",
+        "files": [
+            "asr_javanese_0.zip",
+            "asr_javanese_1.zip",
+            "asr_javanese_2.zip",
+            "asr_javanese_3.zip",
+            "asr_javanese_4.zip",
+            "asr_javanese_5.zip",
+            "asr_javanese_6.zip",
+            "asr_javanese_7.zip",
+            "asr_javanese_8.zip",
+            "asr_javanese_9.zip",
+            "asr_javanese_a.zip",
+            "asr_javanese_b.zip",
+            "asr_javanese_c.zip",
+            "asr_javanese_d.zip",
+            "asr_javanese_e.zip",
+            "asr_javanese_f.zip",
+        ],
+        "index_files": ["asr_javanese/utt_spk_text.tsv"] * 16,
+        "data_dirs": ["asr_javanese/data"] * 16,
+    },
+    "SLR36": {
+        "language": "sun",
+        "files": [
+            "asr_sundanese_0.zip",
+            "asr_sundanese_1.zip",
+            "asr_sundanese_2.zip",
+            "asr_sundanese_3.zip",
+            "asr_sundanese_4.zip",
+            "asr_sundanese_5.zip",
+            "asr_sundanese_6.zip",
+            "asr_sundanese_7.zip",
+            "asr_sundanese_8.zip",
+            "asr_sundanese_9.zip",
+            "asr_sundanese_a.zip",
+            "asr_sundanese_b.zip",
+            "asr_sundanese_c.zip",
+            "asr_sundanese_d.zip",
+            "asr_sundanese_e.zip",
+            "asr_sundanese_f.zip",
+        ],
+        "index_files": ["asr_sundanese/utt_spk_text.tsv"] * 16,
+        "data_dirs": ["asr_sundanese/data"] * 16,
+    },
+    "SLR41": {
+        "language": "jav",
+        "files": ["jv_id_female.zip", "jv_id_male.zip"],
+        "index_files": ["jv_id_female/line_index.tsv", "jv_id_male/line_index.tsv"],
+        "data_dirs": ["jv_id_female/wavs", "jv_id_male/wavs"],
+    },
+    "SLR42": {
+        "language": "khm",
+        "files": ["km_kh_male.zip"],
+        "index_files": ["km_kh_male/line_index.tsv"],
+        "data_dirs": ["km_kh_male/wavs"],
+    },
+    "SLR44": {
+        "language": "sun",
+        "files": ["su_id_female.zip", "su_id_male.zip"],
+        "index_files": ["su_id_female/line_index.tsv", "su_id_male/line_index.tsv"],
+        "data_dirs": ["su_id_female/wavs", "su_id_male/wavs"],
+    },
+    "SLR80": {
+        "language": "mya",
+        "files": ["my_mm_female.zip"],
+        "index_files": ["line_index.tsv"],
+        "data_dirs": [""],
+    },
+}
+_URLS = {_DATASETNAME: "https://openslr.org/resources/{subset}"}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class OpenSLRDataset(datasets.GeneratorBasedBuilder):
+    """This data set contains transcribed high-quality audio of Javanese, Sundanese, Burmese, Khmer. This data set
+    come from 3 different projects under OpenSLR initiative"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(name=f"{_DATASETNAME}_{subset}_{_RESOURCES[subset]['language']}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}")
+        for subset in _RESOURCES.keys()
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_{_RESOURCES[subset]['language']}_seacrowd_sptext", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME} SEACrowd schema", schema="seacrowd_sptext", subset_id=f"{_DATASETNAME}"
+        )
+        for subset in _RESOURCES.keys()
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_SLR41_jav_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=48_000),
+                    "sentence": 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]:
+        """Returns SplitGenerators."""
+        subset = self.config.name.split("_")[1]
+        urls = [f"{_URLS[_DATASETNAME].format(subset=subset[3:])}/{file}" for file in _RESOURCES[subset]["files"]]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        path_to_indexs = [os.path.join(path, f"{_RESOURCES[subset]['index_files'][i]}") for i, path in enumerate(data_dir)]
+        path_to_datas = [os.path.join(path, f"{_RESOURCES[subset]['data_dirs'][i]}") for i, path in enumerate(data_dir)]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": [path_to_indexs, path_to_datas],
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        subset = self.config.name.split("_")[1]
+        path_to_indexs, path_to_datas = filepath[0], filepath[1]
+        counter = -1
+        if subset in ["SLR35", "SLR36"]:
+            sentence_index = {}
+            for i, path_to_index in enumerate(path_to_indexs):
+                with open(path_to_index, encoding="utf-8") as f:
+                    lines = f.readlines()
+                    for id_, line in enumerate(lines):
+                        field_values = re.split(r"\t\t?", line.strip())
+                        filename, user_id, sentence = field_values
+                        sentence_index[filename] = sentence
+                for path_to_data in sorted(Path(path_to_datas[i]).rglob("*.flac")):
+                    filename = path_to_data.stem
+                    if path_to_data.stem not in sentence_index:
+                        continue
+                    path = str(path_to_data.resolve())
+                    sentence = sentence_index[filename]
+                    counter += 1
+                    if self.config.schema == "source":
+                        example = {"path": path, "audio": path, "sentence": sentence}
+                    elif self.config.schema == "seacrowd_sptext":
+                        example = {
+                            "id": counter,
+                            "path": path,
+                            "audio": path,
+                            "text": sentence,
+                            "speaker_id": user_id,
+                            "metadata": {
+                                "speaker_age": None,
+                                "speaker_gender": None,
+                            },
+                        }
+                    yield counter, example
+        else:
+            for i, path_to_index in enumerate(path_to_indexs):
+                geneder = "female" if "female" in path_to_index else "male"
+                with open(path_to_index, encoding="utf-8") as f:
+                    lines = f.readlines()
+                    for id_, line in enumerate(lines):
+                        # Following regexs are needed to normalise the lines, since the datasets
+                        # are not always consistent and have bugs:
+                        line = re.sub(r"\t[^\t]*\t", "\t", line.strip())
+                        field_values = re.split(r"\t\t?", line)
+                        if len(field_values) != 2:
+                            continue
+                        filename, sentence = field_values
+                        path = os.path.join(path_to_datas[i], f"{filename}.wav")
+                        counter += 1
+                        if self.config.schema == "source":
+                            example = {"path": path, "audio": path, "sentence": sentence}
+                        elif self.config.schema == "seacrowd_sptext":
+                            example = {
+                                "id": counter,
+                                "path": path,
+                                "audio": path,
+                                "text": sentence,
+                                "speaker_id": None,
+                                "metadata": {
+                                    "speaker_age": None,
+                                    "speaker_gender": geneder,
+                                },
+                            }
+                        yield counter, example