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import csv
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import json
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import os
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import wave
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import datasets
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_CITATION = """\
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"""
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_DESCRIPTION = """\
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The M-AILABS Speech Dataset is the first large dataset that we are providing free-of-charge, freely usable as training data for speech recognition and speech synthesis.
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Most of the data is based on LibriVox and Project Gutenberg. The training data consist of nearly thousand hours of audio and the text-files in prepared format.
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A transcription is provided for each clip. Clips vary in length from 1 to 20 seconds and have a total length of approximately shown in the list (and in the respective info.txt-files) below.
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The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded by the LibriVox project and is also in the public domain – except for Ukrainian.
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Ukrainian audio was kindly provided either by Nash Format or Gwara Media for machine learning purposes only (please check the data info.txt files for details).
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"""
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_HOMEPAGE = "https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/"
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_LICENSE = ""
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_URLS = {
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"fr": "https://data.solak.de/data/Training/stt_tts/fr_FR.tgz",
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}
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class MAILABSSpeechDataset(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("0.9.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="fr", version=VERSION, description=""),
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]
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DEFAULT_CONFIG_NAME = "fr"
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def _info(self):
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features = datasets.Features(
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{
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"sentence": datasets.Value("string"),
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"audio": datasets.features.Audio(sampling_rate=16_000),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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urls = _URLS[self.config.name]
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data_dir = dl_manager.download_and_extract(urls)
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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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"datapath": data_dir
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},
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),
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]
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def _generate_examples(self, datapath):
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key = 0
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for gender in ["male", "female", "mix"]:
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for name in os.listdir(os.path.join(datapath, "fr_FR", gender)):
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try :
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for book in os.listdir(os.path.join(datapath, "fr_FR", gender, name)):
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try:
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with open(os.path.join(datapath, "fr_FR", gender, name, book, "metadata.csv"), encoding="utf-8") as meta:
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for line in meta.readlines():
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line = line.split("|")
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filename = f"{line[0]}.wav"
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local_path = os.path.join("fr_FR", gender, name, book, "wavs", filename)
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yield key, {
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"sentence": line[1],
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"audio": os.path.join(datapath, local_path)
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}
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key +=1
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except:
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pass
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except:
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pass
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