add data loader script

openslr-nepali-asr-cleaned.py

@@ -0,0 +1,127 @@
+# 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 csv
+import json
+import os
+
+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 = {
+    "index_file": "https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/data/utt_spk_text.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']
+    ],
+}
+
+
+# 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")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="all", version=VERSION,
+                               description="All cleaned utterances, speaker id and transcription from Openslr Large Nepali ASR Dataset"),
+    ]
+
+    # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "utterance_id": datasets.Value("string"),
+                "speaker_id": datasets.Value("string"),
+                "utterance": datasets.Audio(sampling_rate=16000),
+                "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'])
+        audio_paths = dict(zip([url[-5] for url in _URLS["zipfiles"]],
+                           dl_manager.download_and_extract(_URLS['zipfiles'])))
+        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 = f"{audio_paths[row['Utterance'][0]]}/cleaned/asr_nepali/data/{row['Utterance'][:2]}/{row['Utterance']}.flac"
+                yield key, {
+                    "utterance_id": row['Utterance'],
+                    "speaker_id": row['Speaker'],
+                    "utterance": path,
+                    "transcription": row['Text'],
+                }