Update files from the datasets library (from 1.3.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.3.0

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- other-public-domain
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-other-automatic-speech-recognition
+- other-other-text-to-speech
+---
+
+# Dataset Card for lj_speech
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [The LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/)
+- **Repository:** [N/A]
+- **Paper:** [N/A]
+- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech)
+- **Point of Contact:** [Keith Ito](mailto:kito@kito.us)
+
+### Dataset Summary
+
+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.
+
+The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.
+
+### Supported Tasks and Leaderboards
+
+The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS). 
+- `other:automatic-speech-recognition`: An ASR model is presented with an audio file and asked to transcribe the audio file to written text. 
+The most common ASR evaluation metric is the word error rate (WER). 
+- `other:text-to-speech`: A TTS model is given a written text in natural language and asked to generate a speech audio file. 
+A reasonable evaluation metric is the mean opinion score (MOS) of audio quality. 
+The dataset has an active leaderboard which can be found at https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech
+
+### Languages
+
+The transcriptions and audio are in English. 
+
+## Dataset Structure
+
+### Data Instances
+
+A data point comprises the path to the audio file, called `file` and its transcription, called `text`. 
+A normalized version of the text is also provided.
+
+```
+{
+    'id': 'LJ002-0026', 
+    'file': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav', 
+    'text': 'in the three years between 1813 and 1816,' 
+    'normalized_text': 'in the three years between eighteen thirteen and eighteen sixteen,',
+}
+```
+
+Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz.
+
+### Data Fields
+
+- id: unique id of the data sample.
+
+- file: a path to the downloaded audio file in .wav format.
+
+- text: the transcription of the audio file.
+
+- normalized_text: the transcription with numbers, ordinals, and monetary units expanded into full words.
+
+### Data Splits
+
+The dataset is not pre-split. Some statistics:
+
+- Total Clips: 13,100
+- Total Words: 225,715
+- Total Characters: 1,308,678
+- Total Duration: 23:55:17
+- Mean Clip Duration: 6.57 sec
+- Min Clip Duration: 1.11 sec
+- Max Clip Duration: 10.10 sec
+- Mean Words per Clip: 17.23
+- Distinct Words: 13,821
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+This dataset consists of excerpts from the following works:
+
+- Morris, William, et al. Arts and Crafts Essays. 1893.
+- Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
+- Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42.
+- Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
+- Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910.
+- Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
+- President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964.
+
+Some details about normalization:
+- The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8)
+- 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être").
+- The following abbreviations appear in the text. They may be expanded as follows:
+
+| Abbreviation | Expansion |
+|--------------|-----------|
+| Mr. | Mister |
+| Mrs. | Misess (*) |
+| Dr. | Doctor |
+| No. | Number |
+| St. | Saint |
+| Co. | Company |
+| Jr. | Junior |
+| Maj. | Major |
+| Gen. | General |
+| Drs. | Doctors |
+| Rev. | Reverend |
+| Lt. | Lieutenant |
+| Hon. | Honorable |
+| Sgt. | Sergeant |
+| Capt. | Captain |
+| Esq. | Esquire |
+| Ltd. | Limited |
+| Col. | Colonel |
+| Ft. | Fort |
+(*) there's no standard expansion for "Mrs."
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+- The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always.
+- The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio.
+
+#### Who are the annotators?
+
+Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito. 
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+- The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding.
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was initially created by Keith Ito and Linda Johnson.
+
+### Licensing Information
+
+Public Domain ([LibriVox](https://librivox.org/pages/public-domain/))
+
+### Citation Information
+
+```
+@misc{ljspeech17,
+  author       = {Keith Ito and Linda Johnson},
+  title        = {The LJ Speech Dataset},
+  howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}},
+  year         = 2017
+}
+```
+
+### Contributions
+
+Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset.

dataset_infos.json

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+{"main": {"description": "This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading \npassages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length \nfrom 1 to 10 seconds and have a total length of approximately 24 hours.\n\nNote that in order to limit the required storage for preparing this dataset, the audio\nis stored in the .wav format and is not converted to a float32 array. To convert the audio\nfile to a float32 array, please make use of the `.map()` function as follows:\n\n\n```python\nimport soundfile as sf\n\ndef map_to_array(batch):\n    speech_array, _ = sf.read(batch[\"file\"])\n    batch[\"speech\"] = speech_array\n    return batch\n\ndataset = dataset.map(map_to_array, remove_columns=[\"file\"])\n```\n", "citation": "@misc{ljspeech17,\n  author       = {Keith Ito and Linda Johnson},\n  title        = {The LJ Speech Dataset},\n  howpublished = {\\url{https://keithito.com/LJ-Speech-Dataset/}},\n  year         = 2017\n}\n", "homepage": "https://keithito.com/LJ-Speech-Dataset/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "file": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "normalized_text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "file", "output": "text"}, "builder_name": "lj_speech", "config_name": "main", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4667022, "num_examples": 13100, "dataset_name": "lj_speech"}}, "download_checksums": {"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2": {"num_bytes": 2748572632, "checksum": "be1a30453f28eb8dd26af4101ae40cbf2c50413b1bb21936cbcdc6fae3de8aa5"}}, "download_size": 2748572632, "post_processing_size": null, "dataset_size": 4667022, "size_in_bytes": 2753239654}}

dummy/main/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fbc98fcaf43b89df9c4c4e218613298edc79211af75c3fa516ec31ef35020db6
+size 74086

lj_speech.py

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+# 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."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_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"),
+                    "file": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "normalized_text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("file", "text"),
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    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),
+                    "text": text,
+                    "normalized_text": norm_text,
+                }
+                yield uid, example