Upload biulders.py

biulders.py

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+# 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)
+
+_URL = "https://huggingface.co/datasets/SumitMdhr/SANT-ASR/resolve/main/"
+_URLS = {
+    "zipfile": _URL + "CLEAN_DATA.zip",
+    "index_file": _URL + "metedata1.csv",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class OpenslrNepaliAsrCleaned(datasets.GeneratorBasedBuilder):
+    """End Silences Removed Large Nepali ASR Dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "original"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "utterance_id": datasets.Value("string"),
+                "speaker_id": datasets.Value("string"),
+                "utterance": datasets.Audio(),
+                "transcription": datasets.Value("string"),
+                "num_frames": datasets.Value("int32"),
+            }
+        )
+        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["zipfiles"])
+        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"],
+                    "speaker_id": row["speaker_id"],
+                    "utterance": path,
+                    "transcription": row["transcription"],
+                    "num_frames": int(row["num_frames"]),
+                }