Delete subakko.py

subakko.py

@@ -1,144 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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 datasets
-
-
-_CITATION = """\
-@inproceedings{luong-vu-2016-non,
-    title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
-    author = "Luong, Hieu-Thi  and
-      Vu, Hai-Quan",
-    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
-    month = dec,
-    year = "2016",
-    address = "Osaka, Japan",
-    publisher = "The COLING 2016 Organizing Committee",
-    url = "https://aclanthology.org/W16-5207",
-    pages = "51--55",
-}
-"""
-
-_DESCRIPTION = """\
-VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for
-Vietnamese Automatic Speech Recognition task.
-The corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of.
-We publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems.
-"""
-
-_HOMEPAGE = "https://doi.org/10.5281/zenodo.7068130"
-
-_LICENSE = "CC BY-NC-SA 4.0"
-
-# Source data: "https://zenodo.org/record/7068130/files/vivos.tar.gz"
-_DATA_URL = "https://huggingface.co/datasets/ahnafsamin/SUBAK.KO/resolve/main/Data/subakko.zip"
-
-_PROMPTS_URLS = {
-    "train": "https://huggingface.co/datasets/ahnafsamin/SUBAK.KO/resolve/main/Data/train.tar.xz",
-    "test": "https://huggingface.co/datasets/ahnafsamin/SUBAK.KO/resolve/main/Data/test.tar.xz",
-}
-
-
-class Subakko(datasets.GeneratorBasedBuilder):
-    """VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for
-    Vietnamese Automatic Speech Recognition task."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "speaker_id": datasets.Value("string"),
-                    "path": datasets.Value("string"),
-                    "audio": datasets.Audio(sampling_rate=16_000),
-                    "sentence": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        prompts_paths = dl_manager.download_and_extract(_PROMPTS_URLS)
-        archive = dl_manager.download(_DATA_URL)
-        train_dir = "/subakko"
-        test_dir = "/subakko"
-        print("I am samin")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "prompts_path": prompts_paths["train"],
-                    "path_to_clips": train_dir,
-                    "audio_files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "prompts_path": prompts_paths["test"],
-                    "path_to_clips": test_dir,
-                    "audio_files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, prompts_path, path_to_clips, audio_files):
-        """Yields examples as (key, example) tuples."""
-        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is here for legacy reason (tfds) and is not important in itself.
-        examples = {}
-        with open(prompts_path, encoding="utf-8") as f:
-            for row in f:
-                data = row.strip().split("\t", 1)
-                #speaker_id = data[0].split("_")[0]
-                audio_path = data[0]
-                examples[audio_path] = {
-                    "speaker_id": speaker_id,
-                    "path": audio_path,
-                    "sentence": data[1],
-                }
-        inside_clips_dir = False
-        id_ = 0
-        for path, f in audio_files:
-            if path.startswith(path_to_clips):
-                inside_clips_dir = True
-                if path in examples:
-                    audio = {"path": path, "bytes": f.read()}
-                    yield id_, {**examples[path], "audio": audio}
-                    id_ += 1
-            elif inside_clips_dir:
-                break