Delete builder.py

builder.py

@@ -1,135 +0,0 @@
-# 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.
-"""Leading and Trailing Silences Removed Large Nepali ASR Dataset"""
-
-import os
-import csv
-
-import datasets
-
-
-_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}
-}
-"""
-
-_DESCRIPTION = """\
-This data set contains transcribed audio data for Nepali. The data set consists of flac files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file.
-The data set has been manually quality checked, but there might still be errors.
-The audio files are sampled at rate of 16KHz, and leading and trailing silences are trimmed using torchaudio's voice activity detection.
-"""
-
-# Official homepage for the dataset
-_HOMEPAGE = "https://www.openslr.org/54/"
-
-# The licence for the dataset
-_LICENSE = "license:cc-by-sa-4.0"
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-
-
-# _URLS = {
-#     'cleaned': {
-#         "index_file": "https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/data/utt_spk_text_clean.tsv",
-#         "zipfiles": [
-#             f"https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/data/asr_nepali_{k}.zip"
-#             for k in [*range(10), *'abcdef']
-#         ],
-#     },
-#     'original': {
-#         "index_file": "https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/data/utt_spk_text_orig.tsv",
-#         "zipfiles": [
-#             f"https://www.openslr.org/resources/54/asr_nepali_{k}.zip"
-#             for k in [*range(10), *'abcdef']
-#         ],
-#     },
-# }
-_URL = "https://huggingface.co/datasets/SumitMdhr/ASR/resolve/main/"
-_URLS = {
-    "zipfile": _URL + "CLEAN_DATA.zip",
-    "index_file": _URL + "metedata1.tsv",
-}
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class NepaliAsr(datasets.GeneratorBasedBuilder):
-    """End Silences Removed Large Nepali ASR Dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "utterance_id": datasets.Value("string"),
-                "utterance": datasets.Audio(),
-                "transcription": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            # Here we define them above because they are different between the two configurations
-            features=features,
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-            task_templates=[
-                datasets.tasks.AutomaticSpeechRecognition(
-                    audio_column="utterance", transcription_column="transcription"
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        index_file = dl_manager.download(_URLS["index_file"])
-        zip_paths = dl_manager.download(_URLS["zipfile"])
-        audio_paths = dl_manager.extract(zip_paths)
-        for path in zip_paths:
-            if os.path.exists(path):
-                os.remove(path)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "index_file": index_file,
-                    "audio_paths": audio_paths,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, index_file, audio_paths):
-        with open(index_file, encoding="utf-8") as f:
-            reader = csv.DictReader(f , delimiter="\t")
-            for key, row in enumerate(reader):
-                path = os.path.join(audio_paths, "CLEAN_DATA", row['utterance_id'])
-                yield key, {
-                    "utterance_id": row["utterance_id"],
-                    "utterance": path,
-                    "transcription": row["transcription"],
-                }