Update dataset_script.py

dataset_script.py

@@ -1,154 +1,154 @@
-# 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']
-        ],
-    },
-}
-
-
-# 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="original", version=VERSION,
-                               description="All original utterances, speaker id and transcription from Openslr Large Nepali ASR Dataset"),
-        datasets.BuilderConfig(name="cleaned", 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 = "original"
-
-    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"),
-                "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,
-        )
-
-    def _split_generators(self, dl_manager):
-        index_file = dl_manager.download(_URLS[self.config.name]['index_file'])
-        zip_paths = [item for sublist in [
-            dl_manager.download(
-                _URLS[self.config.name]['zipfiles'][i:i+4]
-            ) for i in range(0, len(_URLS[self.config.name]['zipfiles']), 4)
-        ] for item in sublist]
-        audio_paths = dict(zip([url[-5] for url in _URLS[self.config.name]["zipfiles"]],
-                           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):
-                if self.config.name == 'cleaned':
-                    path = os.path.join(
-                        audio_paths[row['utterance_id'][0]], 'cleaned',
-                        'asr_nepali', 'data', row['utterance_id'][:2],
-                        f"{row['utterance_id']}.flac"
-                    )
-                else:
-                    path = os.path.join(
-                        audio_paths[row['utterance_id'][0]],
-                        'asr_nepali', 'data', row['utterance_id'][:2],
-                        f"{row['utterance_id']}.flac"
-                    )
-                yield key, {
-                    "utterance_id": row['utterance_id'],
-                    "speaker_id": row['speaker_id'],
-                    "utterance": path,
-                    "transcription": row['transcription'],
-                    "num_frames": int(row['num_frames']),
-                }
+# 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/rishi70612/openslr-nepali-asr-cleaned/resolve/main/data/utt_spk_text_clean.tsv",
+        "zipfiles": [
+            f"https://huggingface.co/datasets/rishi70612/openslr-nepali-asr-cleaned/resolve/main/data/asr_nepali_{k}.zip"
+            for k in [*range(10), *'abcdef']
+        ],
+    },
+    'original': {
+        "index_file": "https://huggingface.co/datasets/rishi70612/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']
+        ],
+    },
+}
+
+
+# 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="original", version=VERSION,
+                               description="All original utterances, speaker id and transcription from Openslr Large Nepali ASR Dataset"),
+        datasets.BuilderConfig(name="cleaned", 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 = "original"
+
+    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"),
+                "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,
+        )
+
+    def _split_generators(self, dl_manager):
+        index_file = dl_manager.download(_URLS[self.config.name]['index_file'])
+        zip_paths = [item for sublist in [
+            dl_manager.download(
+                _URLS[self.config.name]['zipfiles'][i:i+4]
+            ) for i in range(0, len(_URLS[self.config.name]['zipfiles']), 4)
+        ] for item in sublist]
+        audio_paths = dict(zip([url[-5] for url in _URLS[self.config.name]["zipfiles"]],
+                           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):
+                if self.config.name == 'cleaned':
+                    path = os.path.join(
+                        audio_paths[row['utterance_id'][0]], 'cleaned',
+                        'asr_nepali', 'data', row['utterance_id'][:2],
+                        f"{row['utterance_id']}.flac"
+                    )
+                else:
+                    path = os.path.join(
+                        audio_paths[row['utterance_id'][0]],
+                        'asr_nepali', 'data', row['utterance_id'][:2],
+                        f"{row['utterance_id']}.flac"
+                    )
+                yield key, {
+                    "utterance_id": row['utterance_id'],
+                    "speaker_id": row['speaker_id'],
+                    "utterance": path,
+                    "transcription": row['transcription'],
+                    "num_frames": int(row['num_frames']),
+                }