Delete loading script

arabic_speech_corpus.py

@@ -1,145 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""Arabic Speech Corpus"""
-
-
-import os
-
-import datasets
-from datasets.tasks import AutomaticSpeechRecognition
-
-
-_CITATION = """\
-@phdthesis{halabi2016modern,
-  title={Modern standard Arabic phonetics for speech synthesis},
-  author={Halabi, Nawar},
-  year={2016},
-  school={University of Southampton}
-}
-"""
-
-_DESCRIPTION = """\
-This Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton.
-The corpus was recorded in south Levantine Arabic
-(Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice.
-Note that in order to limit the required storage for preparing this dataset, the audio
-is stored in the .flac format and is not converted to a float32 array. To convert, the audio
-file to a float32 array, please make use of the `.map()` function as follows:
-
-
-```python
-import soundfile as sf
-
-def map_to_array(batch):
-    speech_array, _ = sf.read(batch["file"])
-    batch["speech"] = speech_array
-    return batch
-
-dataset = dataset.map(map_to_array, remove_columns=["file"])
-```
-"""
-
-_URL = "http://en.arabicspeechcorpus.com/arabic-speech-corpus.zip"
-
-
-class ArabicSpeechCorpusConfig(datasets.BuilderConfig):
-    """BuilderConfig for ArabicSpeechCorpu."""
-
-    def __init__(self, **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.
-        """
-        super(ArabicSpeechCorpusConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
-
-
-class ArabicSpeechCorpus(datasets.GeneratorBasedBuilder):
-    """ArabicSpeechCorpus dataset."""
-
-    BUILDER_CONFIGS = [
-        ArabicSpeechCorpusConfig(name="clean", description="'Clean' speech."),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "file": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                    "audio": datasets.Audio(sampling_rate=48_000),
-                    "phonetic": datasets.Value("string"),
-                    "orthographic": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=("file", "text"),
-            homepage=_URL,
-            citation=_CITATION,
-            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download_and_extract(_URL)
-        archive_path = os.path.join(archive_path, "arabic-speech-corpus")
-        return [
-            datasets.SplitGenerator(name="train", gen_kwargs={"archive_path": archive_path}),
-            datasets.SplitGenerator(name="test", gen_kwargs={"archive_path": os.path.join(archive_path, "test set")}),
-        ]
-
-    def _generate_examples(self, archive_path):
-        """Generate examples from a Librispeech archive_path."""
-        lab_dir = os.path.join(archive_path, "lab")
-        wav_dir = os.path.join(archive_path, "wav")
-        if "test set" in archive_path:
-            phonetic_path = os.path.join(archive_path, "phonetic-transcript.txt")
-        else:
-            phonetic_path = os.path.join(archive_path, "phonetic-transcipt.txt")
-
-        orthographic_path = os.path.join(archive_path, "orthographic-transcript.txt")
-
-        phonetics = {}
-        orthographics = {}
-
-        with open(phonetic_path, "r", encoding="utf-8") as f:
-            for line in f:
-                wav_file, phonetic = line.split('"')[1::2]
-                phonetics[wav_file] = phonetic
-
-        with open(orthographic_path, "r", encoding="utf-8") as f:
-            for line in f:
-                wav_file, orthographic = line.split('"')[1::2]
-                orthographics[wav_file] = orthographic
-
-        for _id, lab_name in enumerate(sorted(os.listdir(lab_dir))):
-            lab_path = os.path.join(lab_dir, lab_name)
-            lab_text = open(lab_path, "r", encoding="utf-8").read()
-
-            wav_name = lab_name[:-4] + ".wav"
-            wav_path = os.path.join(wav_dir, wav_name)
-
-            example = {
-                "file": wav_path,
-                "audio": wav_path,
-                "text": lab_text,
-                "phonetic": phonetics[wav_name],
-                "orthographic": orthographics[wav_name],
-            }
-            yield str(_id), example