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import gzip |
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import json |
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import re |
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import os |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = """\ |
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This data set contains multi-speaker high quality transcribed audio data for Sinhalese. The data set consists of wave files, and a TSV file. |
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The file si_lk.lines.txt contains a FileID, which in tern contains the UserID and the Transcription of audio in the file. |
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The data set has been manually quality checked, but there might still be errors. |
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This dataset was collected by Google in Sri Lanka. |
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""" |
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_CITATION = """ |
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@inproceedings{kjartansson-etal-tts-sltu2018, |
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title = {{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}}, |
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author = {Keshan Sodimana and Knot Pipatsrisawat and Linne Ha and Martin Jansche and Oddur Kjartansson and Pasindu De Silva and Supheakmungkol Sarin}, |
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booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, |
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year = {2018}, |
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address = {Gurugram, India}, |
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month = aug, |
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pages = {66--70}, |
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URL = {http://dx.doi.org/10.21437/SLTU.2018-14} |
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} |
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""" |
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_URL = "https://www.openslr.org/30/" |
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_DATA_URL = "https://www.openslr.org/resources/30/si_lk.tar.gz" |
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_LICENSE = "https://www.openslr.org/resources/30/LICENSE.txt" |
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_LANGUAGES = [ |
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"si", |
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] |
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class SiTTSConfig(datasets.BuilderConfig): |
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"""BuilderConfig for SiTTS.""" |
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def __init__(self, *args, languages, **kwargs): |
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"""BuilderConfig for SiTTS. |
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Args: |
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languages (:obj:`List[str]`): list of languages to load |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__( |
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*args, name="+".join(languages), **kwargs, |
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) |
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self.languages = languages |
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class SiTTS(datasets.GeneratorBasedBuilder): |
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"""SiTTS, a manually quality checked, Sinhala multi-speaker TTS corpora.""" |
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BUILDER_CONFIGS = [SiTTSConfig(languages=[lang]) for lang in _LANGUAGES] |
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BUILDER_CONFIG_CLASS = SiTTSConfig |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"sentence": datasets.Value("string"), |
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"file_path": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage=_URL, |
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citation=_CITATION, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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abs_path_to_clips = dl_manager.download_and_extract(_DATA_URL) |
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abs_path_to_data = dl_manager.download(f"{_URL}si_lk.lines.txt") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": os.path.join(abs_path_to_data, "si_lk.lines.txt"), |
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"path_to_clips": abs_path_to_clips, |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, path_to_clips): |
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data_fields = list(self._info().features.keys()) |
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path_idx = data_fields.index("file_path") |
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with open(filepath, encoding="utf-8") as f: |
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lines = f.readlines() |
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for id_, line in enumerate(lines): |
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sentence = re.findall(r'"(.*?)"', line)[0].strip() |
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file_path = "{0}.wav".format(re.findall(r"(sin_[^\s]+)", line)[0]) |
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field_values = [sentence, file_path] |
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field_values[path_idx] = os.path.join( |
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path_to_clips, field_values[path_idx] |
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) |
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if len(field_values) < len(data_fields): |
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field_values += (len(data_fields) - len(field_values)) * ["''"] |
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yield id_, {key: value for key, value in zip(data_fields, field_values)} |
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