Convert dataset to Parquet (#3)

- Convert dataset to Parquet (bd63f52367e86436214030eeaca764a6b1e4b554)
- Delete loading script (7611c363b2e66bbd7ab3d1dce7afa6d1883f834d)

README.md

@@ -9,8 +9,6 @@ license:
 - unlicense
 multilinguality:
 - monolingual
-paperswithcode_id: ljspeech
-pretty_name: LJ Speech
 size_categories:
 - 10K<n<100K
 source_datasets:
@@ -20,21 +18,10 @@ task_categories:
 - text-to-speech
 - text-to-audio
 task_ids: []
-train-eval-index:
-- config: main
-  task: automatic-speech-recognition
-  task_id: speech_recognition
-  splits:
-    train_split: train
-  col_mapping:
-    file: path
-    text: text
-  metrics:
-  - type: wer
-    name: WER
-  - type: cer
-    name: CER
+paperswithcode_id: ljspeech
+pretty_name: LJ Speech
 dataset_info:
+  config_name: main
   features:
   - name: id
     dtype: string
@@ -48,13 +35,32 @@ dataset_info:
     dtype: string
   - name: normalized_text
     dtype: string
-  config_name: main
   splits:
   - name: train
-    num_bytes: 4667022
+    num_bytes: 3860187268.0
     num_examples: 13100
-  download_size: 2748572632
-  dataset_size: 4667022
+  download_size: 3786217548
+  dataset_size: 3860187268.0
+configs:
+- config_name: main
+  data_files:
+  - split: train
+    path: main/train-*
+  default: true
+train-eval-index:
+- config: main
+  task: automatic-speech-recognition
+  task_id: speech_recognition
+  splits:
+    train_split: train
+  col_mapping:
+    file: path
+    text: text
+  metrics:
+  - type: wer
+    name: WER
+  - type: cer
+    name: CER
 ---
 
 # Dataset Card for lj_speech

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

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