Datasets:
Tasks:
Translation
Size:
1K - 10K
| # 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. | |
| """Filtered Tamil ASR corpus collected from common_voice 11, fleurs, openslr65, openslr127 and ucla corpora filtered for duration between 5 - 25 secs""" | |
| import json | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @misc{mile_1, | |
| doi = {10.48550/ARXIV.2207.13331}, | |
| url = {https://arxiv.org/abs/2207.13331}, | |
| author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A}, | |
| title = {Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada}, | |
| publisher = {arXiv}, | |
| year = {2022}, | |
| } | |
| @misc{mile_2, | |
| doi = {10.48550/ARXIV.2207.13333}, | |
| url = {https://arxiv.org/abs/2207.13333}, | |
| author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A}, | |
| title = {Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada}, | |
| publisher = {arXiv}, | |
| year = {2022}, | |
| } | |
| @inproceedings{he-etal-2020-open, | |
| title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}}, | |
| author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot}, | |
| booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, | |
| month = may, | |
| year = {2020}, | |
| address = {Marseille, France}, | |
| publisher = {European Language Resources Association (ELRA)}, | |
| pages = {6494--6503}, | |
| url = {https://www.aclweb.org/anthology/2020.lrec-1.800}, | |
| ISBN = "{979-10-95546-34-4}, | |
| } | |
| @misc{https://doi.org/10.48550/arxiv.2211.09536, | |
| doi = {10.48550/ARXIV.2211.09536}, | |
| url = {https://arxiv.org/abs/2211.09536}, | |
| author = {Kumar, Gokul Karthik and S, Praveen and Kumar, Pratyush and Khapra, Mitesh M. and Nandakumar, Karthik}, | |
| keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering}, | |
| title = {Towards Building Text-To-Speech Systems for the Next Billion Users}, | |
| publisher = {arXiv}, | |
| year = {2022}, | |
| copyright = {arXiv.org perpetual, non-exclusive license} | |
| } | |
| @inproceedings{commonvoice:2020, | |
| author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, | |
| title = {Common Voice: A Massively-Multilingual Speech Corpus}, | |
| booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, | |
| pages = {4211--4215}, | |
| year = 2020 | |
| } | |
| @misc{https://doi.org/10.48550/arxiv.2205.12446, | |
| doi = {10.48550/ARXIV.2205.12446}, | |
| url = {https://arxiv.org/abs/2205.12446}, | |
| author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur}, | |
| keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering}, | |
| title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech}, | |
| publisher = {arXiv}, | |
| year = {2022}, | |
| copyright = {Creative Commons Attribution 4.0 International} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The corpus contains roughly 1000 hours of audio and trasncripts in Tamil language. The transcripts have beedn de-duplicated using exact match deduplication. | |
| """ | |
| _HOMEPAGE = "" | |
| _LICENSE = "https://creativecommons.org/licenses/" | |
| _METADATA_URLS = { | |
| "train": "data/train.jsonl", | |
| "test": "data/test.jsonl" | |
| } | |
| _URLS = { | |
| "train": "data/train.tar.gz", | |
| "test": "data/test.tar.gz", | |
| } | |
| class TamilASRCorpus(datasets.GeneratorBasedBuilder): | |
| """Tamil ASR Corpus contains transcribed speech corpus for training ASR systems for Tamil language.""" | |
| VERSION = datasets.Version("1.1.0") | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "audio": datasets.Audio(sampling_rate=16_000), | |
| "path": datasets.Value("string"), | |
| "sentence": datasets.Value("string"), | |
| "length": datasets.Value("float") | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| supervised_keys=("sentence", "label"), | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| metadata_paths = dl_manager.download(_METADATA_URLS) | |
| train_archive = dl_manager.download(_URLS["train"]) | |
| test_archive = dl_manager.download(_URLS["test"]) | |
| local_extracted_train_archive = dl_manager.extract(train_archive) if not dl_manager.is_streaming else None | |
| local_extracted_test_archive = dl_manager.extract(test_archive) if not dl_manager.is_streaming else None | |
| test_archive = dl_manager.download(_URLS["test"]) | |
| train_dir = "train" | |
| test_dir = "test" | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "metadata_path": metadata_paths["train"], | |
| "local_extracted_archive": local_extracted_train_archive, | |
| "path_to_clips": train_dir, | |
| "audio_files": dl_manager.iter_archive(train_archive), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "metadata_path": metadata_paths["test"], | |
| "local_extracted_archive": local_extracted_test_archive, | |
| "path_to_clips": test_dir, | |
| "audio_files": dl_manager.iter_archive(test_archive), | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, metadata_path, local_extracted_archive, path_to_clips, audio_files): | |
| """Yields examples as (key, example) tuples.""" | |
| examples = {} | |
| with open(metadata_path, encoding="utf-8") as f: | |
| for key, row in enumerate(f): | |
| data = json.loads(row) | |
| examples[data["path"]] = data | |
| 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: | |
| result = examples[path] | |
| path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path | |
| result["audio"] = {"path": path, "bytes": f.read()} | |
| result["path"] = path | |
| yield id_, result | |
| id_ += 1 | |
| elif inside_clips_dir: | |
| break |