Upload m-ailabs_speech_dataset_fr.py

m-ailabs_speech_dataset_fr.py

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+
+# 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