Delete loading script

lj_speech.py

@@ -1,116 +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
-"""LJ automatic speech recognition dataset."""
-
-
-import csv
-import os
-
-import datasets
-from datasets.tasks import AutomaticSpeechRecognition
-
-
-_CITATION = """\
-@misc{ljspeech17,
-  author       = {Keith Ito and Linda Johnson},
-  title        = {The LJ Speech Dataset},
-  howpublished = {\\url{https://keithito.com/LJ-Speech-Dataset/}},
-  year         = 2017
-}
-"""
-
-_DESCRIPTION = """\
-This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading
-passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length
-from 1 to 10 seconds and have a total length of approximately 24 hours.
-
-Note that in order to limit the required storage for preparing this dataset, the audio
-is stored in the .wav 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 = "https://keithito.com/LJ-Speech-Dataset/"
-_DL_URL = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"
-
-
-class LJSpeech(datasets.GeneratorBasedBuilder):
-    """LJ Speech dataset."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="main", version=VERSION, description="The full LJ Speech dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "audio": datasets.Audio(sampling_rate=22050),
-                    "file": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                    "normalized_text": 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):
-        root_path = dl_manager.download_and_extract(_DL_URL)
-        root_path = os.path.join(root_path, "LJSpeech-1.1")
-        wav_path = os.path.join(root_path, "wavs")
-        csv_path = os.path.join(root_path, "metadata.csv")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"wav_path": wav_path, "csv_path": csv_path}
-            ),
-        ]
-
-    def _generate_examples(self, wav_path, csv_path):
-        """Generate examples from an LJ Speech archive_path."""
-
-        with open(csv_path, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(csv_file, delimiter="|", quotechar=None, skipinitialspace=True)
-            for row in csv_reader:
-                uid, text, norm_text = row
-                filename = f"{uid}.wav"
-                example = {
-                    "id": uid,
-                    "file": os.path.join(wav_path, filename),
-                    "audio": os.path.join(wav_path, filename),
-                    "text": text,
-                    "normalized_text": norm_text,
-                }
-                yield uid, example