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| """ OpenSLR Dataset""" |
|
|
| from __future__ import absolute_import, division, print_function |
|
|
| import os |
| import re |
| from pathlib import Path |
|
|
| import datasets |
| from datasets.tasks import AutomaticSpeechRecognition |
|
|
|
|
| _DATA_URL = "https://openslr.org/resources/{}" |
|
|
| _CITATION = """\ |
| SLR70, SLR71: |
| @inproceedings{guevara-rukoz-etal-2020-crowdsourcing, |
| title = {{Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech}}, |
| author = {Guevara-Rukoz, Adriana and Demirsahin, Isin and He, Fei and Chu, Shan-Hui Cathy and Sarin, |
| Supheakmungkol and Pipatsrisawat, Knot and Gutkin, Alexander and Butryna, Alena and Kjartansson, Oddur}, |
| booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, |
| year = {2020}, |
| month = may, |
| address = {Marseille, France}, |
| publisher = {European Language Resources Association (ELRA)}, |
| url = {https://www.aclweb.org/anthology/2020.lrec-1.801}, |
| pages = {6504--6513}, |
| ISBN = {979-10-95546-34-4}, |
| } |
| |
| """ |
|
|
| _DESCRIPTION = """\ |
| OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, |
| and software related to speech recognition. We intend to be a convenient place for anyone to put resources that |
| they have created, so that they can be downloaded publicly. |
| """ |
|
|
| _HOMEPAGE = "https://openslr.org/" |
|
|
| _LICENSE = "" |
|
|
| _RESOURCES = { |
| |
| |
| "SLR70": { |
| "Language": "Nigerian English", |
| "LongName": "Crowdsourced high-quality Nigerian English speech data set", |
| "Category": "Speech", |
| "Summary": "Data set which contains recordings of Nigerian English", |
| "Files": ["en_ng_female.zip", "en_ng_male.zip"], |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], |
| "DataDirs": ["", ""], |
| }, |
| "SLR71": { |
| "Language": "Chilean Spanish", |
| "LongName": "Crowdsourced high-quality Chilean Spanish speech data set", |
| "Category": "Speech", |
| "Summary": "Data set which contains recordings of Chilean Spanish", |
| "Files": ["es_cl_female.zip", "es_cl_male.zip"], |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], |
| "DataDirs": ["", ""], |
| |
| }, |
| |
| } |
|
|
|
|
| class OpenSlrConfig(datasets.BuilderConfig): |
| """BuilderConfig for OpenSlr.""" |
|
|
| def __init__(self, name, **kwargs): |
| """ |
| Args: |
| data_dir: `string`, the path to the folder containing the files in the |
| downloaded .tar |
| citation: `string`, citation for the data set |
| url: `string`, url for information about the data set |
| **kwargs: keyword arguments forwarded to super. |
| """ |
| self.language = kwargs.pop("language", None) |
| self.long_name = kwargs.pop("long_name", None) |
| self.category = kwargs.pop("category", None) |
| self.summary = kwargs.pop("summary", None) |
| self.files = kwargs.pop("files", None) |
| self.index_files = kwargs.pop("index_files", None) |
| self.data_dirs = kwargs.pop("data_dirs", None) |
| description = ( |
| f"Open Speech and Language Resources dataset in {self.language}. Name: {self.name}, " |
| f"Summary: {self.summary}." |
| ) |
| super(OpenSlrConfig, self).__init__(name=name, description=description, **kwargs) |
|
|
|
|
| class OpenSlr(datasets.GeneratorBasedBuilder): |
| DEFAULT_WRITER_BATCH_SIZE = 32 |
|
|
| BUILDER_CONFIGS = [ |
| OpenSlrConfig( |
| name=resource_id, |
| language=_RESOURCES[resource_id]["Language"], |
| long_name=_RESOURCES[resource_id]["LongName"], |
| category=_RESOURCES[resource_id]["Category"], |
| summary=_RESOURCES[resource_id]["Summary"], |
| files=_RESOURCES[resource_id]["Files"], |
| index_files=_RESOURCES[resource_id]["IndexFiles"], |
| data_dirs=_RESOURCES[resource_id]["DataDirs"], |
| ) |
| for resource_id in _RESOURCES.keys() |
| ] |
|
|
| def _info(self): |
| features = datasets.Features( |
| { |
| "path": datasets.Value("string"), |
| "audio": datasets.Audio(sampling_rate=48_000), |
| "sentence": datasets.Value("string"), |
| } |
| ) |
|
|
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="sentence")], |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| resource_number = self.config.name.replace("SLR", "") |
| urls = [f"{_DATA_URL.format(resource_number)}/{file}" for file in self.config.files] |
| if urls[0].endswith(".zip"): |
| dl_paths = dl_manager.download_and_extract(urls) |
| path_to_indexs = [os.path.join(path, f"{self.config.index_files[i]}") for i, path in enumerate(dl_paths)] |
| path_to_datas = [os.path.join(path, f"{self.config.data_dirs[i]}") for i, path in enumerate(dl_paths)] |
| archives = None |
| else: |
| archives = dl_manager.download(urls) |
| path_to_indexs = dl_manager.download(self.config.index_files) |
| path_to_datas = self.config.data_dirs |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "path_to_indexs": path_to_indexs, |
| "path_to_datas": path_to_datas, |
| "archive_files": [dl_manager.iter_archive(archive) for archive in archives] if archives else None, |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, path_to_indexs, path_to_datas, archive_files): |
| """Yields examples.""" |
|
|
| counter = -1 |
| for i, path_to_index in enumerate(path_to_indexs): |
| with open(path_to_index, encoding="utf-8") as f: |
| lines = f.readlines() |
| for id_, line in enumerate(lines): |
| |
| |
| line = re.sub(r"\t[^\t]*\t", "\t", line.strip()) |
| field_values = re.split(r"\t\t?", line) |
| if len(field_values) != 2: |
| continue |
| filename, sentence = field_values |
| |
| path = os.path.join(path_to_datas[i], f"{filename}.wav") |
| counter += 1 |
| yield counter, {"path": path, "audio": path, "sentence": sentence} |
|
|
|
|
