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

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README.md ADDED
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1
+ ---
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+ annotations_creators:
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+ - expert-generated
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+ language_creators:
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+ - crowdsourced
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+ - expert-generated
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+ languages:
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+ - en
9
+ licenses:
10
+ - cc-by-4-0
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+ multilinguality:
12
+ - monolingual
13
+ size_categories:
14
+ - 100K<n<1M
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+ source_datasets:
16
+ - original
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+ task_categories:
18
+ - other
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+ task_ids:
20
+ - other-other-automatic speech recognition
21
+ ---
22
+
23
+ # Dataset Card for librispeech_asr
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+
25
+ ## Table of Contents
26
+ - [Dataset Description](#dataset-description)
27
+ - [Dataset Summary](#dataset-summary)
28
+ - [Supported Tasks](#supported-tasks-and-leaderboards)
29
+ - [Languages](#languages)
30
+ - [Dataset Structure](#dataset-structure)
31
+ - [Data Instances](#data-instances)
32
+ - [Data Fields](#data-instances)
33
+ - [Data Splits](#data-instances)
34
+ - [Dataset Creation](#dataset-creation)
35
+ - [Curation Rationale](#curation-rationale)
36
+ - [Source Data](#source-data)
37
+ - [Annotations](#annotations)
38
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
39
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
40
+ - [Social Impact of Dataset](#social-impact-of-dataset)
41
+ - [Discussion of Biases](#discussion-of-biases)
42
+ - [Other Known Limitations](#other-known-limitations)
43
+ - [Additional Information](#additional-information)
44
+ - [Dataset Curators](#dataset-curators)
45
+ - [Licensing Information](#licensing-information)
46
+ - [Citation Information](#citation-information)
47
+ - [Contributions](#contributions)
48
+
49
+ ## Dataset Description
50
+
51
+ - **Homepage:** [LibriSpeech ASR corpus](http://www.openslr.org/12)
52
+ - **Repository:** [Needs More Information]
53
+ - **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf)
54
+ - **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-other)
55
+ - **Point of Contact:** [Daniel Povey](mailto:[email protected])
56
+
57
+ ### Dataset Summary
58
+
59
+ LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.
60
+
61
+ ### Supported Tasks and Leaderboards
62
+
63
+ - `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean and ranks models based on their WER.
64
+
65
+ ### Languages
66
+
67
+ The audio is in English. There are two configurations: `clean` and `other`.
68
+ The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on
69
+ a different dataset, and were divided roughly in the middle,
70
+ with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other".
71
+
72
+ ## Dataset Structure
73
+
74
+ ### Data Instances
75
+
76
+ A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
77
+
78
+ ```
79
+ {'chapter_id': 141231,
80
+ 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
81
+ 'id': '1272-141231-0000',
82
+ 'speaker_id': 1272,
83
+ 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}
84
+ ```
85
+
86
+
87
+ ### Data Fields
88
+
89
+ - file: A path to the downloaded audio file in .flac format.
90
+
91
+ - text: the transcription of the audio file.
92
+
93
+ - id: unique id of the data sample.
94
+
95
+ - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
96
+
97
+ - chapter_id: id of the audiobook chapter which includes the transcription.
98
+
99
+ ### Data Splits
100
+
101
+ The size of the corpus makes it impractical, or at least inconvenient
102
+ for some users, to distribute it as a single large archive. Thus the
103
+ training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively.
104
+ A simple automatic
105
+ procedure was used to select the audio in the first two sets to be, on
106
+ average, of higher recording quality and with accents closer to US
107
+ English. An acoustic model was trained on WSJ’s si-84 data subset
108
+ and was used to recognize the audio in the corpus, using a bigram
109
+ LM estimated on the text of the respective books. We computed the
110
+ Word Error Rate (WER) of this automatic transcript relative to our
111
+ reference transcripts obtained from the book texts.
112
+ The speakers in the corpus were ranked according to the WER of
113
+ the WSJ model’s transcripts, and were divided roughly in the middle,
114
+ with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other".
115
+
116
+ For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360
117
+ respectively accounting for 100h and 360h of the training data.
118
+ For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.
119
+
120
+ | | Train.500 | Train.360 | Train.100 | Valid | Test |
121
+ | ----- | ------ | ----- | ---- | ---- | ---- |
122
+ | clean | - | 104014 | 28539 | 2703 | 2620|
123
+ | other | 148688 | - | - | 2864 | 2939 |
124
+
125
+
126
+
127
+ ## Dataset Creation
128
+
129
+ ### Curation Rationale
130
+
131
+ [Needs More Information]
132
+
133
+ ### Source Data
134
+
135
+ #### Initial Data Collection and Normalization
136
+
137
+ [Needs More Information]
138
+
139
+ #### Who are the source language producers?
140
+
141
+ [Needs More Information]
142
+
143
+ ### Annotations
144
+
145
+ #### Annotation process
146
+
147
+ [Needs More Information]
148
+
149
+ #### Who are the annotators?
150
+
151
+ [Needs More Information]
152
+
153
+ ### Personal and Sensitive Information
154
+
155
+ [Needs More Information]
156
+
157
+ ## Considerations for Using the Data
158
+
159
+ ### Social Impact of Dataset
160
+
161
+ [More Information Needed]
162
+
163
+ ### Discussion of Biases
164
+
165
+ [More Information Needed]
166
+
167
+ ### Other Known Limitations
168
+
169
+ [Needs More Information]
170
+
171
+ ## Additional Information
172
+
173
+ ### Dataset Curators
174
+
175
+ The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.
176
+
177
+ ### Licensing Information
178
+
179
+ CC BY 4.0
180
+
181
+ ### Citation Information
182
+
183
+ [Needs More Information]
184
+
185
+ ### Contributions
186
+
187
+ Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
dataset_infos.json ADDED
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+ {"clean": {"description": "LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,\nprepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read\naudiobooks from the LibriVox project, and has been carefully segmented and aligned.87\n\nNote that in order to limit the required storage for preparing this dataset, the audio\nis stored in the .flac 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", "citation": "@inproceedings{panayotov2015librispeech,\n title={Librispeech: an ASR corpus based on public domain audio books},\n author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},\n booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},\n pages={5206--5210},\n year={2015},\n organization={IEEE}\n}\n", "homepage": "http://www.openslr.org/12", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "chapter_id": {"dtype": "int64", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "speech", "output": "text"}, "builder_name": "librispeech_asr", "config_name": "clean", "version": {"version_str": "2.1.0", "description": "", "major": 2, "minor": 1, "patch": 0}, "splits": {"train.100": {"name": "train.100", "num_bytes": 11823891, "num_examples": 28539, "dataset_name": "librispeech_asr"}, "train.360": {"name": "train.360", "num_bytes": 43049490, "num_examples": 104014, "dataset_name": "librispeech_asr"}, "validation": {"name": "validation", "num_bytes": 894510, "num_examples": 2703, "dataset_name": "librispeech_asr"}, "test": {"name": "test", "num_bytes": 868614, "num_examples": 2620, "dataset_name": "librispeech_asr"}}, "download_checksums": {"http://www.openslr.org/resources/12/dev-clean.tar.gz": {"num_bytes": 337926286, "checksum": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3"}, "http://www.openslr.org/resources/12/test-clean.tar.gz": {"num_bytes": 346663984, "checksum": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23"}, "http://www.openslr.org/resources/12/train-clean-100.tar.gz": {"num_bytes": 6387309499, "checksum": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2"}, "http://www.openslr.org/resources/12/train-clean-360.tar.gz": {"num_bytes": 23049477885, "checksum": "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf"}}, "download_size": 30121377654, "post_processing_size": null, "dataset_size": 56636505, "size_in_bytes": 30178014159}, "other": {"description": "LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,\nprepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read\naudiobooks from the LibriVox project, and has been carefully segmented and aligned.87\n\nNote that in order to limit the required storage for preparing this dataset, the audio\nis stored in the .flac 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", "citation": "@inproceedings{panayotov2015librispeech,\n title={Librispeech: an ASR corpus based on public domain audio books},\n author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},\n booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},\n pages={5206--5210},\n year={2015},\n organization={IEEE}\n}\n", "homepage": "http://www.openslr.org/12", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "chapter_id": {"dtype": "int64", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "speech", "output": "text"}, "builder_name": "librispeech_asr", "config_name": "other", "version": {"version_str": "2.1.0", "description": "", "major": 2, "minor": 1, "patch": 0}, "splits": {"train.500": {"name": "train.500", "num_bytes": 59561081, "num_examples": 148688, "dataset_name": "librispeech_asr"}, "validation": {"name": "validation", "num_bytes": 907644, "num_examples": 2864, "dataset_name": "librispeech_asr"}, "test": {"name": "test", "num_bytes": 934838, "num_examples": 2939, "dataset_name": "librispeech_asr"}}, "download_checksums": {"http://www.openslr.org/resources/12/test-other.tar.gz": {"num_bytes": 328757843, "checksum": "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29"}, "http://www.openslr.org/resources/12/dev-other.tar.gz": {"num_bytes": 314305928, "checksum": "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365"}, "http://www.openslr.org/resources/12/train-other-500.tar.gz": {"num_bytes": 30593501606, "checksum": "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2"}}, "download_size": 31236565377, "post_processing_size": null, "dataset_size": 61403563, "size_in_bytes": 31297968940}}
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librispeech_asr.py ADDED
@@ -0,0 +1,154 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ # Lint as: python3
17
+ """Librispeech automatic speech recognition dataset."""
18
+
19
+ from __future__ import absolute_import, division, print_function
20
+
21
+ import glob
22
+ import os
23
+
24
+ import datasets
25
+
26
+
27
+ _CITATION = """\
28
+ @inproceedings{panayotov2015librispeech,
29
+ title={Librispeech: an ASR corpus based on public domain audio books},
30
+ author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
31
+ booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
32
+ pages={5206--5210},
33
+ year={2015},
34
+ organization={IEEE}
35
+ }
36
+ """
37
+
38
+ _DESCRIPTION = """\
39
+ LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
40
+ prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
41
+ audiobooks from the LibriVox project, and has been carefully segmented and aligned.87
42
+
43
+ Note that in order to limit the required storage for preparing this dataset, the audio
44
+ is stored in the .flac format and is not converted to a float32 array. To convert, the audio
45
+ file to a float32 array, please make use of the `.map()` function as follows:
46
+
47
+
48
+ ```python
49
+ import soundfile as sf
50
+
51
+ def map_to_array(batch):
52
+ speech_array, _ = sf.read(batch["file"])
53
+ batch["speech"] = speech_array
54
+ return batch
55
+
56
+ dataset = dataset.map(map_to_array, remove_columns=["file"])
57
+ ```
58
+ """
59
+
60
+ _URL = "http://www.openslr.org/12"
61
+ _DL_URL = "http://www.openslr.org/resources/12/"
62
+
63
+ _DL_URLS = {
64
+ "clean": {
65
+ "dev": _DL_URL + "dev-clean.tar.gz",
66
+ "test": _DL_URL + "test-clean.tar.gz",
67
+ "train.100": _DL_URL + "train-clean-100.tar.gz",
68
+ "train.360": _DL_URL + "train-clean-360.tar.gz",
69
+ },
70
+ "other": {
71
+ "test": _DL_URL + "test-other.tar.gz",
72
+ "dev": _DL_URL + "dev-other.tar.gz",
73
+ "train.500": _DL_URL + "train-other-500.tar.gz",
74
+ },
75
+ }
76
+
77
+
78
+ class LibrispeechASRConfig(datasets.BuilderConfig):
79
+ """BuilderConfig for LibriSpeechASR."""
80
+
81
+ def __init__(self, **kwargs):
82
+ """
83
+ Args:
84
+ data_dir: `string`, the path to the folder containing the files in the
85
+ downloaded .tar
86
+ citation: `string`, citation for the data set
87
+ url: `string`, url for information about the data set
88
+ **kwargs: keyword arguments forwarded to super.
89
+ """
90
+ super(LibrispeechASRConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
91
+
92
+
93
+ class LibrispeechASR(datasets.GeneratorBasedBuilder):
94
+ """Librispeech dataset."""
95
+
96
+ BUILDER_CONFIGS = [
97
+ LibrispeechASRConfig(name="clean", description="'Clean' speech."),
98
+ LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."),
99
+ ]
100
+
101
+ def _info(self):
102
+ return datasets.DatasetInfo(
103
+ description=_DESCRIPTION,
104
+ features=datasets.Features(
105
+ {
106
+ "file": datasets.Value("string"),
107
+ "text": datasets.Value("string"),
108
+ "speaker_id": datasets.Value("int64"),
109
+ "chapter_id": datasets.Value("int64"),
110
+ "id": datasets.Value("string"),
111
+ }
112
+ ),
113
+ supervised_keys=("file", "text"),
114
+ homepage=_URL,
115
+ citation=_CITATION,
116
+ )
117
+
118
+ def _split_generators(self, dl_manager):
119
+ archive_path = dl_manager.download_and_extract(_DL_URLS[self.config.name])
120
+
121
+ if self.config.name == "clean":
122
+ train_splits = [
123
+ datasets.SplitGenerator(name="train.100", gen_kwargs={"archive_path": archive_path["train.100"]}),
124
+ datasets.SplitGenerator(name="train.360", gen_kwargs={"archive_path": archive_path["train.360"]}),
125
+ ]
126
+ elif self.config.name == "other":
127
+ train_splits = [
128
+ datasets.SplitGenerator(name="train.500", gen_kwargs={"archive_path": archive_path["train.500"]}),
129
+ ]
130
+
131
+ return train_splits + [
132
+ datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"archive_path": archive_path["dev"]}),
133
+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path["test"]}),
134
+ ]
135
+
136
+ def _generate_examples(self, archive_path):
137
+ """Generate examples from a Librispeech archive_path."""
138
+ transcripts_glob = os.path.join(archive_path, "LibriSpeech", "*/*/*/*.txt")
139
+ for transcript_file in sorted(glob.glob(transcripts_glob)):
140
+ path = os.path.dirname(transcript_file)
141
+ with open(os.path.join(path, transcript_file), "r", encoding="utf-8") as f:
142
+ for line in f:
143
+ line = line.strip()
144
+ key, transcript = line.split(" ", 1)
145
+ audio_file = f"{key}.flac"
146
+ speaker_id, chapter_id = [int(el) for el in key.split("-")[:2]]
147
+ example = {
148
+ "id": key,
149
+ "speaker_id": speaker_id,
150
+ "chapter_id": chapter_id,
151
+ "file": os.path.join(path, audio_file),
152
+ "text": transcript,
153
+ }
154
+ yield key, example