Revert Convert dataset to Parquet (#6)

- Revert "Convert dataset to Parquet (#3)" (dd561d612217712e3e032309995fb72415cce0a4)

README.md

@@ -9,6 +9,8 @@ license:
 - unlicense
 multilinguality:
 - monolingual
+paperswithcode_id: ljspeech
+pretty_name: LJ Speech
 size_categories:
 - 10K<n<100K
 source_datasets:
@@ -18,10 +20,21 @@ task_categories:
 - text-to-speech
 - text-to-audio
 task_ids: []
-paperswithcode_id: ljspeech
-pretty_name: LJ Speech
+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_info:
-  config_name: main
   features:
   - name: id
     dtype: string
@@ -35,32 +48,13 @@ dataset_info:
     dtype: string
   - name: normalized_text
     dtype: string
+  config_name: main
   splits:
   - name: train
-    num_bytes: 3860187268.0
+    num_bytes: 4667022
     num_examples: 13100
-  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
+  download_size: 2748572632
+  dataset_size: 4667022
 ---
 
 # Dataset Card for lj_speech

lj_speech.py

@@ -0,0 +1,116 @@
+# 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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@@ -1,3 +0,0 @@
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