Delete mailabs_speech_dataset.py

mailabs_speech_dataset.py

@@ -1,91 +0,0 @@
-
-# This script for Hugging Face's datasets library was written by Théo Gigant
-
-
-import csv
-import json
-import os
-import wave
-
-import datasets
-
-_CITATION = """\
-
-"""
-
-_DESCRIPTION = """\
-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.
-
-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.
-
-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.
-
-
-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.
-
-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).
-"""
-
-_HOMEPAGE = "https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/"
-
-_LICENSE = ""
-
-_URLS = {
-    "fr": "https://data.solak.de/data/Training/stt_tts/fr_FR.tgz",
-}
-
-class MAILABSSpeechDataset(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("0.9.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="fr", version=VERSION, description=""),
-    ]
-
-    DEFAULT_CONFIG_NAME = "fr"
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "sentence": datasets.Value("string"),
-                "audio": datasets.features.Audio(sampling_rate=16_000),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        urls = _URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "datapath": data_dir
-                },
-            ),
-        ]
-
-    def _generate_examples(self, datapath):
-        key = 0
-        try :
-          for gender in ["male", "female", "mix"]:
-              for name in os.listdir(os.path.join(datapath, "fr_FR", gender)):
-                  for book in os.listdir(os.path.join(datapath, "fr_FR", gender, name)):
-                      with open(os.path.join(datapath, "fr_FR", gender, name, book, "metadata.csv"), encoding="utf-8") as meta:
-                          for line in meta.readlines():
-                              line = line.split("|")
-                              filename = f"{line[0]}.wav"
-                              local_path = os.path.join("fr_FR", gender, name, book, "wavs", filename)
-                              yield key, {
-                                  "sentence": line[1],
-                                  "audio": os.path.join(datapath, local_path)
-                                }
-                              key += 1
-        except:
-          pass