Add files using upload-large-folder tool
Browse files- .gitattributes +3 -58
- README.md +71 -0
- data/test/LICENSE +395 -0
- data/test/README.md +166 -0
- data/test/metadata.csv +0 -0
- data/train/LICENSE +395 -0
- data/train/README.md +166 -0
- data/train/eight/01b4757a_nohash_0.wav +3 -0
- data/train/eight/042a8dde_nohash_0.wav +3 -0
- data/train/eight/07089da9_nohash_4.wav +3 -0
- data/train/eight/0819edb0_nohash_0.wav +3 -0
- data/train/eight/0b09edd3_nohash_0.wav +3 -0
- data/train/eight/0ba018fc_nohash_4.wav +3 -0
- data/train/eight/129c7d8d_nohash_0.wav +3 -0
- data/train/eight/14c7b073_nohash_3.wav +3 -0
- data/train/eight/1afd49bf_nohash_0.wav +3 -0
- data/train/eight/256c0a05_nohash_3.wav +3 -0
- data/train/eight/30802c5d_nohash_0.wav +3 -0
- data/train/eight/31583d30_nohash_2.wav +3 -0
- data/train/eight/3d53244b_nohash_0.wav +3 -0
- data/train/eight/435695e3_nohash_1.wav +3 -0
- data/train/eight/4e99c1b7_nohash_1.wav +3 -0
- data/train/eight/645ed69d_nohash_3.wav +3 -0
- data/train/eight/78030270_nohash_0.wav +3 -0
- data/train/eight/7ff4fc72_nohash_0.wav +3 -0
- data/train/eight/833d9f56_nohash_3.wav +3 -0
- data/train/eight/8b25410a_nohash_3.wav +3 -0
- data/train/eight/8eb4a1bf_nohash_0.wav +3 -0
- data/train/eight/9151f184_nohash_0.wav +3 -0
- data/train/eight/a996cf66_nohash_0.wav +3 -0
- data/train/eight/b0ae6326_nohash_0.wav +3 -0
- data/train/eight/b4ea0d9a_nohash_6.wav +3 -0
- data/train/eight/b76f6088_nohash_4.wav +3 -0
- data/train/eight/bb6d4301_nohash_0.wav +3 -0
- data/train/eight/bbf38549_nohash_0.wav +3 -0
- data/train/eight/c9b653a0_nohash_3.wav +3 -0
- data/train/eight/cc3b5b62_nohash_3.wav +3 -0
- data/train/eight/ccfd721c_nohash_1.wav +3 -0
- data/train/eight/db9cd41d_nohash_0.wav +3 -0
- data/train/eight/dbb7723a_nohash_0.wav +3 -0
- data/train/eight/ed032775_nohash_1.wav +3 -0
- data/train/eight/ef3367d9_nohash_3.wav +3 -0
- data/train/eight/efbc3952_nohash_2.wav +3 -0
- data/train/eight/fa57ab3b_nohash_4.wav +3 -0
- data/train/eight/fb7c9b3b_nohash_0.wav +3 -0
- data/train/eight/fcb25a78_nohash_0.wav +3 -0
- data/train/metadata.csv +0 -0
- data/validation/LICENSE +395 -0
- data/validation/README.md +166 -0
- data/validation/metadata.csv +0 -0
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README.md
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---
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| 2 |
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license: cc-by-4.0
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task_categories:
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- audio-classification
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| 5 |
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- automatic-speech-recognition
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tags:
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- speech-commands
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- keyword-spotting
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- audio
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train/**
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- split: validation
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path: data/validation/**
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- split: test
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path: data/test/**
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---
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# Speech Commands (v0.02)
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One-second WAV clips of spoken words and background noise, organised by
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split and label in the Hugging Face **audiofolder** convention.
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## Quick start
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```python
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from datasets import load_dataset
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ds = load_dataset("odunola/speech-commands")
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print(ds)
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```
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## Structure
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| 36 |
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```
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data/
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<split>/
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metadata.csv # file_name, label, is_unknown, speaker_id, utterance_id
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<label>/*.wav
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```
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## Labels (36)
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`_silence_`, `backward`, `bed`, `bird`, `cat`, `dog`, `down`, `eight`, `five`, `follow`, `forward`, `four`, `go`, `happy`, `house`, `learn`, `left`, `marvin`, `nine`, `no`, `off`, `on`, `one`, `right`, `seven`, `sheila`, `six`, `stop`, `three`, `tree`, `two`, `up`, `visual`, `wow`, `yes`, `zero`
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## Source
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| 49 |
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- Homepage: https://www.tensorflow.org/datasets/catalog/speech_commands
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- License: Creative Commons BY 4.0 License
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## Citation
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| 54 |
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```bibtex
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| 56 |
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@article{speechcommandsv2,
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author = {{Warden}, P.},
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title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
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journal = {ArXiv e-prints},
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archivePrefix = "arXiv",
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eprint = {1804.03209},
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primaryClass = "cs.CL",
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| 63 |
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year = 2018,
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month = apr,
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url = {https://arxiv.org/abs/1804.03209},
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}
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```
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## Description
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| 70 |
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A set of one-second .wav audio files, each containing a single spoken English word or background noise. These words are from a small set of commands, spoken by a variety of different speakers. Version 0.02 adds the words: backward, forward, follow, learn, visual.
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data/test/LICENSE
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|
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extent this Public License may be interpreted as a contract, You are
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consideration of benefits the Licensor receives from making the
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Section 1 -- Definitions.
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a. Adapted Material means material subject to Copyright and Similar
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Rights that is derived from or based upon the Licensed Material
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arranged, transformed, or otherwise modified in a manner requiring
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permission under the Copyright and Similar Rights held by the
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Material is a musical work, performance, or sound recording,
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Adapted Material is always produced where the Licensed Material is
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accordance with the terms and conditions of this Public License.
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closely related to copyright including, without limitation,
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performance, broadcast, sound recording, and Sui Generis Database
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categorized. For purposes of this Public License, the rights
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specified in Section 2(b)(1)-(2) are not Copyright and Similar
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Rights.
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d. Effective Technological Measures means those measures that, in the
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absence of proper authority, may not be circumvented under laws
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fulfilling obligations under Article 11 of the WIPO Copyright
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Treaty adopted on December 20, 1996, and/or similar international
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agreements.
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| 99 |
+
e. Exceptions and Limitations means fair use, fair dealing, and/or
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any other exception or limitation to Copyright and Similar Rights
|
| 101 |
+
that applies to Your use of the Licensed Material.
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| 102 |
+
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+
f. Licensed Material means the artistic or literary work, database,
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| 104 |
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or other material to which the Licensor applied this Public
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License.
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g. Licensed Rights means the rights granted to You subject to the
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terms and conditions of this Public License, which are limited to
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all Copyright and Similar Rights that apply to Your use of the
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Licensed Material and that the Licensor has authority to license.
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h. Licensor means the individual(s) or entity(ies) granting rights
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under this Public License.
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| 114 |
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i. Share means to provide material to the public by any means or
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process that requires permission under the Licensed Rights, such
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| 117 |
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as reproduction, public display, public performance, distribution,
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dissemination, communication, or importation, and to make material
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available to the public including in ways that members of the
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public may access the material from a place and at a time
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individually chosen by them.
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j. Sui Generis Database Rights means rights other than copyright
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resulting from Directive 96/9/EC of the European Parliament and of
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the Council of 11 March 1996 on the legal protection of databases,
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| 126 |
+
as amended and/or succeeded, as well as other essentially
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+
equivalent rights anywhere in the world.
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k. You means the individual or entity exercising the Licensed Rights
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under this Public License. Your has a corresponding meaning.
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Section 2 -- Scope.
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+
a. License grant.
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1. Subject to the terms and conditions of this Public License,
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the Licensor hereby grants You a worldwide, royalty-free,
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non-sublicensable, non-exclusive, irrevocable license to
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exercise the Licensed Rights in the Licensed Material to:
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a. reproduce and Share the Licensed Material, in whole or
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in part; and
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| 145 |
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b. produce, reproduce, and Share Adapted Material.
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2. Exceptions and Limitations. For the avoidance of doubt, where
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Exceptions and Limitations apply to Your use, this Public
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its terms and conditions.
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3. Term. The term of this Public License is specified in Section
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6(a).
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4. Media and formats; technical modifications allowed. The
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Licensor authorizes You to exercise the Licensed Rights in
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all media and formats whether now known or hereafter created,
|
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and to make technical modifications necessary to do so. The
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Licensor waives and/or agrees not to assert any right or
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+
authority to forbid You from making technical modifications
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necessary to exercise the Licensed Rights, including
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technical modifications necessary to circumvent Effective
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Technological Measures. For purposes of this Public License,
|
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simply making modifications authorized by this Section 2(a)
|
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(4) never produces Adapted Material.
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5. Downstream recipients.
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a. Offer from the Licensor -- Licensed Material. Every
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recipient of the Licensed Material automatically
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receives an offer from the Licensor to exercise the
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Licensed Rights under the terms and conditions of this
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Public License.
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b. No downstream restrictions. You may not offer or impose
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any additional or different terms or conditions on, or
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apply any Effective Technological Measures to, the
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Licensed Material if doing so restricts exercise of the
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Licensed Rights by any recipient of the Licensed
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Material.
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6. No endorsement. Nothing in this Public License constitutes or
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+
may be construed as permission to assert or imply that You
|
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+
are, or that Your use of the Licensed Material is, connected
|
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with, or sponsored, endorsed, or granted official status by,
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the Licensor or others designated to receive attribution as
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provided in Section 3(a)(1)(A)(i).
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b. Other rights.
|
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1. Moral rights, such as the right of integrity, are not
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the extent possible, the Licensor waives and/or agrees not to
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assert any such rights held by the Licensor to the limited
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extent necessary to allow You to exercise the Licensed
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2. Patent and trademark rights are not licensed under this
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Public License.
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+
3. To the extent possible, the Licensor waives any right to
|
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collect royalties from You for the exercise of the Licensed
|
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|
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under any voluntary or waivable statutory or compulsory
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licensing scheme. In all other cases the Licensor expressly
|
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+
reserves any right to collect such royalties.
|
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+
Section 3 -- License Conditions.
|
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+
|
| 212 |
+
Your exercise of the Licensed Rights is expressly made subject to the
|
| 213 |
+
following conditions.
|
| 214 |
+
|
| 215 |
+
a. Attribution.
|
| 216 |
+
|
| 217 |
+
1. If You Share the Licensed Material (including in modified
|
| 218 |
+
form), You must:
|
| 219 |
+
|
| 220 |
+
a. retain the following if it is supplied by the Licensor
|
| 221 |
+
with the Licensed Material:
|
| 222 |
+
|
| 223 |
+
i. identification of the creator(s) of the Licensed
|
| 224 |
+
Material and any others designated to receive
|
| 225 |
+
attribution, in any reasonable manner requested by
|
| 226 |
+
the Licensor (including by pseudonym if
|
| 227 |
+
designated);
|
| 228 |
+
|
| 229 |
+
ii. a copyright notice;
|
| 230 |
+
|
| 231 |
+
iii. a notice that refers to this Public License;
|
| 232 |
+
|
| 233 |
+
iv. a notice that refers to the disclaimer of
|
| 234 |
+
warranties;
|
| 235 |
+
|
| 236 |
+
v. a URI or hyperlink to the Licensed Material to the
|
| 237 |
+
extent reasonably practicable;
|
| 238 |
+
|
| 239 |
+
b. indicate if You modified the Licensed Material and
|
| 240 |
+
retain an indication of any previous modifications; and
|
| 241 |
+
|
| 242 |
+
c. indicate the Licensed Material is licensed under this
|
| 243 |
+
Public License, and include the text of, or the URI or
|
| 244 |
+
hyperlink to, this Public License.
|
| 245 |
+
|
| 246 |
+
2. You may satisfy the conditions in Section 3(a)(1) in any
|
| 247 |
+
reasonable manner based on the medium, means, and context in
|
| 248 |
+
which You Share the Licensed Material. For example, it may be
|
| 249 |
+
reasonable to satisfy the conditions by providing a URI or
|
| 250 |
+
hyperlink to a resource that includes the required
|
| 251 |
+
information.
|
| 252 |
+
|
| 253 |
+
3. If requested by the Licensor, You must remove any of the
|
| 254 |
+
information required by Section 3(a)(1)(A) to the extent
|
| 255 |
+
reasonably practicable.
|
| 256 |
+
|
| 257 |
+
4. If You Share Adapted Material You produce, the Adapter's
|
| 258 |
+
License You apply must not prevent recipients of the Adapted
|
| 259 |
+
Material from complying with this Public License.
|
| 260 |
+
|
| 261 |
+
|
| 262 |
+
Section 4 -- Sui Generis Database Rights.
|
| 263 |
+
|
| 264 |
+
Where the Licensed Rights include Sui Generis Database Rights that
|
| 265 |
+
apply to Your use of the Licensed Material:
|
| 266 |
+
|
| 267 |
+
a. for the avoidance of doubt, Section 2(a)(1) grants You the right
|
| 268 |
+
to extract, reuse, reproduce, and Share all or a substantial
|
| 269 |
+
portion of the contents of the database;
|
| 270 |
+
|
| 271 |
+
b. if You include all or a substantial portion of the database
|
| 272 |
+
contents in a database in which You have Sui Generis Database
|
| 273 |
+
Rights, then the database in which You have Sui Generis Database
|
| 274 |
+
Rights (but not its individual contents) is Adapted Material; and
|
| 275 |
+
|
| 276 |
+
c. You must comply with the conditions in Section 3(a) if You Share
|
| 277 |
+
all or a substantial portion of the contents of the database.
|
| 278 |
+
|
| 279 |
+
For the avoidance of doubt, this Section 4 supplements and does not
|
| 280 |
+
replace Your obligations under this Public License where the Licensed
|
| 281 |
+
Rights include other Copyright and Similar Rights.
|
| 282 |
+
|
| 283 |
+
|
| 284 |
+
Section 5 -- Disclaimer of Warranties and Limitation of Liability.
|
| 285 |
+
|
| 286 |
+
a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
|
| 287 |
+
EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
|
| 288 |
+
AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF
|
| 289 |
+
ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS,
|
| 290 |
+
IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION,
|
| 291 |
+
WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR
|
| 292 |
+
PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS,
|
| 293 |
+
ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT
|
| 294 |
+
KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT
|
| 295 |
+
ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
|
| 296 |
+
|
| 297 |
+
b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE
|
| 298 |
+
TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION,
|
| 299 |
+
NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT,
|
| 300 |
+
INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES,
|
| 301 |
+
COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR
|
| 302 |
+
USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN
|
| 303 |
+
ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR
|
| 304 |
+
DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR
|
| 305 |
+
IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
|
| 306 |
+
|
| 307 |
+
c. The disclaimer of warranties and limitation of liability provided
|
| 308 |
+
above shall be interpreted in a manner that, to the extent
|
| 309 |
+
possible, most closely approximates an absolute disclaimer and
|
| 310 |
+
waiver of all liability.
|
| 311 |
+
|
| 312 |
+
|
| 313 |
+
Section 6 -- Term and Termination.
|
| 314 |
+
|
| 315 |
+
a. This Public License applies for the term of the Copyright and
|
| 316 |
+
Similar Rights licensed here. However, if You fail to comply with
|
| 317 |
+
this Public License, then Your rights under this Public License
|
| 318 |
+
terminate automatically.
|
| 319 |
+
|
| 320 |
+
b. Where Your right to use the Licensed Material has terminated under
|
| 321 |
+
Section 6(a), it reinstates:
|
| 322 |
+
|
| 323 |
+
1. automatically as of the date the violation is cured, provided
|
| 324 |
+
it is cured within 30 days of Your discovery of the
|
| 325 |
+
violation; or
|
| 326 |
+
|
| 327 |
+
2. upon express reinstatement by the Licensor.
|
| 328 |
+
|
| 329 |
+
For the avoidance of doubt, this Section 6(b) does not affect any
|
| 330 |
+
right the Licensor may have to seek remedies for Your violations
|
| 331 |
+
of this Public License.
|
| 332 |
+
|
| 333 |
+
c. For the avoidance of doubt, the Licensor may also offer the
|
| 334 |
+
Licensed Material under separate terms or conditions or stop
|
| 335 |
+
distributing the Licensed Material at any time; however, doing so
|
| 336 |
+
will not terminate this Public License.
|
| 337 |
+
|
| 338 |
+
d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
|
| 339 |
+
License.
|
| 340 |
+
|
| 341 |
+
|
| 342 |
+
Section 7 -- Other Terms and Conditions.
|
| 343 |
+
|
| 344 |
+
a. The Licensor shall not be bound by any additional or different
|
| 345 |
+
terms or conditions communicated by You unless expressly agreed.
|
| 346 |
+
|
| 347 |
+
b. Any arrangements, understandings, or agreements regarding the
|
| 348 |
+
Licensed Material not stated herein are separate from and
|
| 349 |
+
independent of the terms and conditions of this Public License.
|
| 350 |
+
|
| 351 |
+
|
| 352 |
+
Section 8 -- Interpretation.
|
| 353 |
+
|
| 354 |
+
a. For the avoidance of doubt, this Public License does not, and
|
| 355 |
+
shall not be interpreted to, reduce, limit, restrict, or impose
|
| 356 |
+
conditions on any use of the Licensed Material that could lawfully
|
| 357 |
+
be made without permission under this Public License.
|
| 358 |
+
|
| 359 |
+
b. To the extent possible, if any provision of this Public License is
|
| 360 |
+
deemed unenforceable, it shall be automatically reformed to the
|
| 361 |
+
minimum extent necessary to make it enforceable. If the provision
|
| 362 |
+
cannot be reformed, it shall be severed from this Public License
|
| 363 |
+
without affecting the enforceability of the remaining terms and
|
| 364 |
+
conditions.
|
| 365 |
+
|
| 366 |
+
c. No term or condition of this Public License will be waived and no
|
| 367 |
+
failure to comply consented to unless expressly agreed to by the
|
| 368 |
+
Licensor.
|
| 369 |
+
|
| 370 |
+
d. Nothing in this Public License constitutes or may be interpreted
|
| 371 |
+
as a limitation upon, or waiver of, any privileges and immunities
|
| 372 |
+
that apply to the Licensor or You, including from the legal
|
| 373 |
+
processes of any jurisdiction or authority.
|
| 374 |
+
|
| 375 |
+
|
| 376 |
+
=======================================================================
|
| 377 |
+
|
| 378 |
+
Creative Commons is not a party to its public
|
| 379 |
+
licenses. Notwithstanding, Creative Commons may elect to apply one of
|
| 380 |
+
its public licenses to material it publishes and in those instances
|
| 381 |
+
will be considered the “Licensor.” The text of the Creative Commons
|
| 382 |
+
public licenses is dedicated to the public domain under the CC0 Public
|
| 383 |
+
Domain Dedication. Except for the limited purpose of indicating that
|
| 384 |
+
material is shared under a Creative Commons public license or as
|
| 385 |
+
otherwise permitted by the Creative Commons policies published at
|
| 386 |
+
creativecommons.org/policies, Creative Commons does not authorize the
|
| 387 |
+
use of the trademark "Creative Commons" or any other trademark or logo
|
| 388 |
+
of Creative Commons without its prior written consent including,
|
| 389 |
+
without limitation, in connection with any unauthorized modifications
|
| 390 |
+
to any of its public licenses or any other arrangements,
|
| 391 |
+
understandings, or agreements concerning use of licensed material. For
|
| 392 |
+
the avoidance of doubt, this paragraph does not form part of the
|
| 393 |
+
public licenses.
|
| 394 |
+
|
| 395 |
+
Creative Commons may be contacted at creativecommons.org.
|
data/test/README.md
ADDED
|
@@ -0,0 +1,166 @@
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|
|
| 1 |
+
# Speech Commands Data Set v0.02
|
| 2 |
+
|
| 3 |
+
This is a set of one-second .wav audio files, each containing a single spoken
|
| 4 |
+
English word. These words are from a small set of commands, and are spoken by a
|
| 5 |
+
variety of different speakers. The audio files are organized into folders based
|
| 6 |
+
on the word they contain, and this data set is designed to help train simple
|
| 7 |
+
machine learning models. This dataset is covered in more detail at [https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
|
| 8 |
+
|
| 9 |
+
It's licensed under the [Creative Commons BY 4.0
|
| 10 |
+
license](https://creativecommons.org/licenses/by/4.0/). See the LICENSE
|
| 11 |
+
file in this folder for full details. Its original location was at
|
| 12 |
+
[http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz](http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz).
|
| 13 |
+
|
| 14 |
+
## History
|
| 15 |
+
|
| 16 |
+
Version 0.01 of the data set was released on August 3rd 2017 and contained
|
| 17 |
+
64,727 audio files.
|
| 18 |
+
|
| 19 |
+
This is version 0.02 of the data set containing 105,829 audio files, released on
|
| 20 |
+
April 11th 2018.
|
| 21 |
+
|
| 22 |
+
## Collection
|
| 23 |
+
|
| 24 |
+
The audio files were collected using crowdsourcing, see
|
| 25 |
+
[aiyprojects.withgoogle.com/open_speech_recording](https://github.com/petewarden/extract_loudest_section)
|
| 26 |
+
for some of the open source audio collection code we used (and please consider
|
| 27 |
+
contributing to enlarge this data set). The goal was to gather examples of
|
| 28 |
+
people speaking single-word commands, rather than conversational sentences, so
|
| 29 |
+
they were prompted for individual words over the course of a five minute
|
| 30 |
+
session. Twenty core command words were recorded, with most speakers saying each
|
| 31 |
+
of them five times. The core words are "Yes", "No", "Up", "Down", "Left",
|
| 32 |
+
"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four",
|
| 33 |
+
"Five", "Six", "Seven", "Eight", and "Nine". To help distinguish unrecognized
|
| 34 |
+
words, there are also ten auxiliary words, which most speakers only said once.
|
| 35 |
+
These include "Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila",
|
| 36 |
+
"Tree", and "Wow".
|
| 37 |
+
|
| 38 |
+
## Organization
|
| 39 |
+
|
| 40 |
+
The files are organized into folders, with each directory name labelling the
|
| 41 |
+
word that is spoken in all the contained audio files. No details were kept of
|
| 42 |
+
any of the participants age, gender, or location, and random ids were assigned
|
| 43 |
+
to each individual. These ids are stable though, and encoded in each file name
|
| 44 |
+
as the first part before the underscore. If a participant contributed multiple
|
| 45 |
+
utterances of the same word, these are distinguished by the number at the end of
|
| 46 |
+
the file name. For example, the file path `happy/3cfc6b3a_nohash_2.wav`
|
| 47 |
+
indicates that the word spoken was "happy", the speaker's id was "3cfc6b3a", and
|
| 48 |
+
this is the third utterance of that word by this speaker in the data set. The
|
| 49 |
+
'nohash' section is to ensure that all the utterances by a single speaker are
|
| 50 |
+
sorted into the same training partition, to keep very similar repetitions from
|
| 51 |
+
giving unrealistically optimistic evaluation scores.
|
| 52 |
+
|
| 53 |
+
## Partitioning
|
| 54 |
+
|
| 55 |
+
The audio clips haven't been separated into training, test, and validation sets
|
| 56 |
+
explicitly, but by convention a hashing function is used to stably assign each
|
| 57 |
+
file to a set. Here's some Python code demonstrating how a complete file path
|
| 58 |
+
and the desired validation and test set sizes (usually both 10%) are used to
|
| 59 |
+
assign a set:
|
| 60 |
+
|
| 61 |
+
```python
|
| 62 |
+
MAX_NUM_WAVS_PER_CLASS = 2**27 - 1 # ~134M
|
| 63 |
+
|
| 64 |
+
def which_set(filename, validation_percentage, testing_percentage):
|
| 65 |
+
"""Determines which data partition the file should belong to.
|
| 66 |
+
|
| 67 |
+
We want to keep files in the same training, validation, or testing sets even
|
| 68 |
+
if new ones are added over time. This makes it less likely that testing
|
| 69 |
+
samples will accidentally be reused in training when long runs are restarted
|
| 70 |
+
for example. To keep this stability, a hash of the filename is taken and used
|
| 71 |
+
to determine which set it should belong to. This determination only depends on
|
| 72 |
+
the name and the set proportions, so it won't change as other files are added.
|
| 73 |
+
|
| 74 |
+
It's also useful to associate particular files as related (for example words
|
| 75 |
+
spoken by the same person), so anything after '_nohash_' in a filename is
|
| 76 |
+
ignored for set determination. This ensures that 'bobby_nohash_0.wav' and
|
| 77 |
+
'bobby_nohash_1.wav' are always in the same set, for example.
|
| 78 |
+
|
| 79 |
+
Args:
|
| 80 |
+
filename: File path of the data sample.
|
| 81 |
+
validation_percentage: How much of the data set to use for validation.
|
| 82 |
+
testing_percentage: How much of the data set to use for testing.
|
| 83 |
+
|
| 84 |
+
Returns:
|
| 85 |
+
String, one of 'training', 'validation', or 'testing'.
|
| 86 |
+
"""
|
| 87 |
+
base_name = os.path.basename(filename)
|
| 88 |
+
# We want to ignore anything after '_nohash_' in the file name when
|
| 89 |
+
# deciding which set to put a wav in, so the data set creator has a way of
|
| 90 |
+
# grouping wavs that are close variations of each other.
|
| 91 |
+
hash_name = re.sub(r'_nohash_.*$', '', base_name)
|
| 92 |
+
# This looks a bit magical, but we need to decide whether this file should
|
| 93 |
+
# go into the training, testing, or validation sets, and we want to keep
|
| 94 |
+
# existing files in the same set even if more files are subsequently
|
| 95 |
+
# added.
|
| 96 |
+
# To do that, we need a stable way of deciding based on just the file name
|
| 97 |
+
# itself, so we do a hash of that and then use that to generate a
|
| 98 |
+
# probability value that we use to assign it.
|
| 99 |
+
hash_name_hashed = hashlib.sha1(hash_name).hexdigest()
|
| 100 |
+
percentage_hash = ((int(hash_name_hashed, 16) %
|
| 101 |
+
(MAX_NUM_WAVS_PER_CLASS + 1)) *
|
| 102 |
+
(100.0 / MAX_NUM_WAVS_PER_CLASS))
|
| 103 |
+
if percentage_hash < validation_percentage:
|
| 104 |
+
result = 'validation'
|
| 105 |
+
elif percentage_hash < (testing_percentage + validation_percentage):
|
| 106 |
+
result = 'testing'
|
| 107 |
+
else:
|
| 108 |
+
result = 'training'
|
| 109 |
+
return result
|
| 110 |
+
```
|
| 111 |
+
|
| 112 |
+
The results of running this over the current set are included in this archive as
|
| 113 |
+
validation_list.txt and testing_list.txt. These text files contain the paths to
|
| 114 |
+
all the files in each set, with each path on a new line. Any files that aren't
|
| 115 |
+
in either of these lists can be considered to be part of the training set.
|
| 116 |
+
|
| 117 |
+
## Processing
|
| 118 |
+
|
| 119 |
+
The original audio files were collected in uncontrolled locations by people
|
| 120 |
+
around the world. We requested that they do the recording in a closed room for
|
| 121 |
+
privacy reasons, but didn't stipulate any quality requirements. This was by
|
| 122 |
+
design, since we wanted examples of the sort of speech data that we're likely to
|
| 123 |
+
encounter in consumer and robotics applications, where we don't have much
|
| 124 |
+
control over the recording equipment or environment. The data was captured in a
|
| 125 |
+
variety of formats, for example Ogg Vorbis encoding for the web app, and then
|
| 126 |
+
converted to a 16-bit little-endian PCM-encoded WAVE file at a 16000 sample
|
| 127 |
+
rate. The audio was then trimmed to a one second length to align most
|
| 128 |
+
utterances, using the
|
| 129 |
+
[extract_loudest_section](https://github.com/petewarden/extract_loudest_section)
|
| 130 |
+
tool. The audio files were then screened for silence or incorrect words, and
|
| 131 |
+
arranged into folders by label.
|
| 132 |
+
|
| 133 |
+
## Background Noise
|
| 134 |
+
|
| 135 |
+
To help train networks to cope with noisy environments, it can be helpful to mix
|
| 136 |
+
in realistic background audio. The `_silence_` folder contains a set of
|
| 137 |
+
longer audio clips that are either recordings or mathematical simulations of
|
| 138 |
+
noise.
|
| 139 |
+
|
| 140 |
+
## Citations
|
| 141 |
+
|
| 142 |
+
If you use the Speech Commands dataset in your work, please cite it as:
|
| 143 |
+
|
| 144 |
+
```
|
| 145 |
+
@article{speechcommandsv2,
|
| 146 |
+
author = {{Warden}, P.},
|
| 147 |
+
title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
|
| 148 |
+
journal = {ArXiv e-prints},
|
| 149 |
+
archivePrefix = "arXiv",
|
| 150 |
+
eprint = {1804.03209},
|
| 151 |
+
primaryClass = "cs.CL",
|
| 152 |
+
keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
|
| 153 |
+
year = 2018,
|
| 154 |
+
month = apr,
|
| 155 |
+
url = {https://arxiv.org/abs/1804.03209},
|
| 156 |
+
}
|
| 157 |
+
```
|
| 158 |
+
|
| 159 |
+
## Credits
|
| 160 |
+
|
| 161 |
+
Massive thanks are due to everyone who donated recordings to this data set, I'm
|
| 162 |
+
very grateful. I also couldn't have put this together without the help and
|
| 163 |
+
support of Billy Rutledge, Rajat Monga, Raziel Alvarez, Brad Krueger, Barbara
|
| 164 |
+
Petit, Gursheesh Kour, and all the AIY and TensorFlow teams.
|
| 165 |
+
|
| 166 |
+
Pete Warden, petewarden@google.com
|
data/test/metadata.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/train/LICENSE
ADDED
|
@@ -0,0 +1,395 @@
|
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Material is a musical work, performance, or sound recording,
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Adapted Material is always produced where the Licensed Material is
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closely related to copyright including, without limitation,
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categorized. For purposes of this Public License, the rights
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Rights.
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d. Effective Technological Measures means those measures that, in the
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fulfilling obligations under Article 11 of the WIPO Copyright
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agreements.
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e. Exceptions and Limitations means fair use, fair dealing, and/or
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any other exception or limitation to Copyright and Similar Rights
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that applies to Your use of the Licensed Material.
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f. Licensed Material means the artistic or literary work, database,
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License.
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all Copyright and Similar Rights that apply to Your use of the
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h. Licensor means the individual(s) or entity(ies) granting rights
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under this Public License.
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i. Share means to provide material to the public by any means or
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process that requires permission under the Licensed Rights, such
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dissemination, communication, or importation, and to make material
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j. Sui Generis Database Rights means rights other than copyright
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resulting from Directive 96/9/EC of the European Parliament and of
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equivalent rights anywhere in the world.
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Section 2 -- Scope.
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a. License grant.
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the Licensor hereby grants You a worldwide, royalty-free,
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non-sublicensable, non-exclusive, irrevocable license to
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in part; and
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3. To the extent possible, the Licensor waives any right to
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reserves any right to collect such royalties.
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Your exercise of the Licensed Rights is expressly made subject to the
|
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following conditions.
|
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|
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a. Attribution.
|
| 216 |
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|
| 217 |
+
1. If You Share the Licensed Material (including in modified
|
| 218 |
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form), You must:
|
| 219 |
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|
| 220 |
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a. retain the following if it is supplied by the Licensor
|
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with the Licensed Material:
|
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|
| 223 |
+
i. identification of the creator(s) of the Licensed
|
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Material and any others designated to receive
|
| 225 |
+
attribution, in any reasonable manner requested by
|
| 226 |
+
the Licensor (including by pseudonym if
|
| 227 |
+
designated);
|
| 228 |
+
|
| 229 |
+
ii. a copyright notice;
|
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|
| 231 |
+
iii. a notice that refers to this Public License;
|
| 232 |
+
|
| 233 |
+
iv. a notice that refers to the disclaimer of
|
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+
warranties;
|
| 235 |
+
|
| 236 |
+
v. a URI or hyperlink to the Licensed Material to the
|
| 237 |
+
extent reasonably practicable;
|
| 238 |
+
|
| 239 |
+
b. indicate if You modified the Licensed Material and
|
| 240 |
+
retain an indication of any previous modifications; and
|
| 241 |
+
|
| 242 |
+
c. indicate the Licensed Material is licensed under this
|
| 243 |
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Public License, and include the text of, or the URI or
|
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hyperlink to, this Public License.
|
| 245 |
+
|
| 246 |
+
2. You may satisfy the conditions in Section 3(a)(1) in any
|
| 247 |
+
reasonable manner based on the medium, means, and context in
|
| 248 |
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|
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reasonable to satisfy the conditions by providing a URI or
|
| 250 |
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hyperlink to a resource that includes the required
|
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information.
|
| 252 |
+
|
| 253 |
+
3. If requested by the Licensor, You must remove any of the
|
| 254 |
+
information required by Section 3(a)(1)(A) to the extent
|
| 255 |
+
reasonably practicable.
|
| 256 |
+
|
| 257 |
+
4. If You Share Adapted Material You produce, the Adapter's
|
| 258 |
+
License You apply must not prevent recipients of the Adapted
|
| 259 |
+
Material from complying with this Public License.
|
| 260 |
+
|
| 261 |
+
|
| 262 |
+
Section 4 -- Sui Generis Database Rights.
|
| 263 |
+
|
| 264 |
+
Where the Licensed Rights include Sui Generis Database Rights that
|
| 265 |
+
apply to Your use of the Licensed Material:
|
| 266 |
+
|
| 267 |
+
a. for the avoidance of doubt, Section 2(a)(1) grants You the right
|
| 268 |
+
to extract, reuse, reproduce, and Share all or a substantial
|
| 269 |
+
portion of the contents of the database;
|
| 270 |
+
|
| 271 |
+
b. if You include all or a substantial portion of the database
|
| 272 |
+
contents in a database in which You have Sui Generis Database
|
| 273 |
+
Rights, then the database in which You have Sui Generis Database
|
| 274 |
+
Rights (but not its individual contents) is Adapted Material; and
|
| 275 |
+
|
| 276 |
+
c. You must comply with the conditions in Section 3(a) if You Share
|
| 277 |
+
all or a substantial portion of the contents of the database.
|
| 278 |
+
|
| 279 |
+
For the avoidance of doubt, this Section 4 supplements and does not
|
| 280 |
+
replace Your obligations under this Public License where the Licensed
|
| 281 |
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Rights include other Copyright and Similar Rights.
|
| 282 |
+
|
| 283 |
+
|
| 284 |
+
Section 5 -- Disclaimer of Warranties and Limitation of Liability.
|
| 285 |
+
|
| 286 |
+
a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
|
| 287 |
+
EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
|
| 288 |
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1. automatically as of the date the violation is cured, provided
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| 324 |
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| 330 |
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| 335 |
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d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
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License.
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a. For the avoidance of doubt, this Public License does not, and
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+
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|
| 378 |
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Creative Commons is not a party to its public
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| 379 |
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licenses. Notwithstanding, Creative Commons may elect to apply one of
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| 380 |
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will be considered the “Licensor.” The text of the Creative Commons
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public licenses is dedicated to the public domain under the CC0 Public
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public licenses.
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+
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+
Creative Commons may be contacted at creativecommons.org.
|
data/train/README.md
ADDED
|
@@ -0,0 +1,166 @@
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|
| 1 |
+
# Speech Commands Data Set v0.02
|
| 2 |
+
|
| 3 |
+
This is a set of one-second .wav audio files, each containing a single spoken
|
| 4 |
+
English word. These words are from a small set of commands, and are spoken by a
|
| 5 |
+
variety of different speakers. The audio files are organized into folders based
|
| 6 |
+
on the word they contain, and this data set is designed to help train simple
|
| 7 |
+
machine learning models. This dataset is covered in more detail at [https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
|
| 8 |
+
|
| 9 |
+
It's licensed under the [Creative Commons BY 4.0
|
| 10 |
+
license](https://creativecommons.org/licenses/by/4.0/). See the LICENSE
|
| 11 |
+
file in this folder for full details. Its original location was at
|
| 12 |
+
[http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz](http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz).
|
| 13 |
+
|
| 14 |
+
## History
|
| 15 |
+
|
| 16 |
+
Version 0.01 of the data set was released on August 3rd 2017 and contained
|
| 17 |
+
64,727 audio files.
|
| 18 |
+
|
| 19 |
+
This is version 0.02 of the data set containing 105,829 audio files, released on
|
| 20 |
+
April 11th 2018.
|
| 21 |
+
|
| 22 |
+
## Collection
|
| 23 |
+
|
| 24 |
+
The audio files were collected using crowdsourcing, see
|
| 25 |
+
[aiyprojects.withgoogle.com/open_speech_recording](https://github.com/petewarden/extract_loudest_section)
|
| 26 |
+
for some of the open source audio collection code we used (and please consider
|
| 27 |
+
contributing to enlarge this data set). The goal was to gather examples of
|
| 28 |
+
people speaking single-word commands, rather than conversational sentences, so
|
| 29 |
+
they were prompted for individual words over the course of a five minute
|
| 30 |
+
session. Twenty core command words were recorded, with most speakers saying each
|
| 31 |
+
of them five times. The core words are "Yes", "No", "Up", "Down", "Left",
|
| 32 |
+
"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four",
|
| 33 |
+
"Five", "Six", "Seven", "Eight", and "Nine". To help distinguish unrecognized
|
| 34 |
+
words, there are also ten auxiliary words, which most speakers only said once.
|
| 35 |
+
These include "Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila",
|
| 36 |
+
"Tree", and "Wow".
|
| 37 |
+
|
| 38 |
+
## Organization
|
| 39 |
+
|
| 40 |
+
The files are organized into folders, with each directory name labelling the
|
| 41 |
+
word that is spoken in all the contained audio files. No details were kept of
|
| 42 |
+
any of the participants age, gender, or location, and random ids were assigned
|
| 43 |
+
to each individual. These ids are stable though, and encoded in each file name
|
| 44 |
+
as the first part before the underscore. If a participant contributed multiple
|
| 45 |
+
utterances of the same word, these are distinguished by the number at the end of
|
| 46 |
+
the file name. For example, the file path `happy/3cfc6b3a_nohash_2.wav`
|
| 47 |
+
indicates that the word spoken was "happy", the speaker's id was "3cfc6b3a", and
|
| 48 |
+
this is the third utterance of that word by this speaker in the data set. The
|
| 49 |
+
'nohash' section is to ensure that all the utterances by a single speaker are
|
| 50 |
+
sorted into the same training partition, to keep very similar repetitions from
|
| 51 |
+
giving unrealistically optimistic evaluation scores.
|
| 52 |
+
|
| 53 |
+
## Partitioning
|
| 54 |
+
|
| 55 |
+
The audio clips haven't been separated into training, test, and validation sets
|
| 56 |
+
explicitly, but by convention a hashing function is used to stably assign each
|
| 57 |
+
file to a set. Here's some Python code demonstrating how a complete file path
|
| 58 |
+
and the desired validation and test set sizes (usually both 10%) are used to
|
| 59 |
+
assign a set:
|
| 60 |
+
|
| 61 |
+
```python
|
| 62 |
+
MAX_NUM_WAVS_PER_CLASS = 2**27 - 1 # ~134M
|
| 63 |
+
|
| 64 |
+
def which_set(filename, validation_percentage, testing_percentage):
|
| 65 |
+
"""Determines which data partition the file should belong to.
|
| 66 |
+
|
| 67 |
+
We want to keep files in the same training, validation, or testing sets even
|
| 68 |
+
if new ones are added over time. This makes it less likely that testing
|
| 69 |
+
samples will accidentally be reused in training when long runs are restarted
|
| 70 |
+
for example. To keep this stability, a hash of the filename is taken and used
|
| 71 |
+
to determine which set it should belong to. This determination only depends on
|
| 72 |
+
the name and the set proportions, so it won't change as other files are added.
|
| 73 |
+
|
| 74 |
+
It's also useful to associate particular files as related (for example words
|
| 75 |
+
spoken by the same person), so anything after '_nohash_' in a filename is
|
| 76 |
+
ignored for set determination. This ensures that 'bobby_nohash_0.wav' and
|
| 77 |
+
'bobby_nohash_1.wav' are always in the same set, for example.
|
| 78 |
+
|
| 79 |
+
Args:
|
| 80 |
+
filename: File path of the data sample.
|
| 81 |
+
validation_percentage: How much of the data set to use for validation.
|
| 82 |
+
testing_percentage: How much of the data set to use for testing.
|
| 83 |
+
|
| 84 |
+
Returns:
|
| 85 |
+
String, one of 'training', 'validation', or 'testing'.
|
| 86 |
+
"""
|
| 87 |
+
base_name = os.path.basename(filename)
|
| 88 |
+
# We want to ignore anything after '_nohash_' in the file name when
|
| 89 |
+
# deciding which set to put a wav in, so the data set creator has a way of
|
| 90 |
+
# grouping wavs that are close variations of each other.
|
| 91 |
+
hash_name = re.sub(r'_nohash_.*$', '', base_name)
|
| 92 |
+
# This looks a bit magical, but we need to decide whether this file should
|
| 93 |
+
# go into the training, testing, or validation sets, and we want to keep
|
| 94 |
+
# existing files in the same set even if more files are subsequently
|
| 95 |
+
# added.
|
| 96 |
+
# To do that, we need a stable way of deciding based on just the file name
|
| 97 |
+
# itself, so we do a hash of that and then use that to generate a
|
| 98 |
+
# probability value that we use to assign it.
|
| 99 |
+
hash_name_hashed = hashlib.sha1(hash_name).hexdigest()
|
| 100 |
+
percentage_hash = ((int(hash_name_hashed, 16) %
|
| 101 |
+
(MAX_NUM_WAVS_PER_CLASS + 1)) *
|
| 102 |
+
(100.0 / MAX_NUM_WAVS_PER_CLASS))
|
| 103 |
+
if percentage_hash < validation_percentage:
|
| 104 |
+
result = 'validation'
|
| 105 |
+
elif percentage_hash < (testing_percentage + validation_percentage):
|
| 106 |
+
result = 'testing'
|
| 107 |
+
else:
|
| 108 |
+
result = 'training'
|
| 109 |
+
return result
|
| 110 |
+
```
|
| 111 |
+
|
| 112 |
+
The results of running this over the current set are included in this archive as
|
| 113 |
+
validation_list.txt and testing_list.txt. These text files contain the paths to
|
| 114 |
+
all the files in each set, with each path on a new line. Any files that aren't
|
| 115 |
+
in either of these lists can be considered to be part of the training set.
|
| 116 |
+
|
| 117 |
+
## Processing
|
| 118 |
+
|
| 119 |
+
The original audio files were collected in uncontrolled locations by people
|
| 120 |
+
around the world. We requested that they do the recording in a closed room for
|
| 121 |
+
privacy reasons, but didn't stipulate any quality requirements. This was by
|
| 122 |
+
design, since we wanted examples of the sort of speech data that we're likely to
|
| 123 |
+
encounter in consumer and robotics applications, where we don't have much
|
| 124 |
+
control over the recording equipment or environment. The data was captured in a
|
| 125 |
+
variety of formats, for example Ogg Vorbis encoding for the web app, and then
|
| 126 |
+
converted to a 16-bit little-endian PCM-encoded WAVE file at a 16000 sample
|
| 127 |
+
rate. The audio was then trimmed to a one second length to align most
|
| 128 |
+
utterances, using the
|
| 129 |
+
[extract_loudest_section](https://github.com/petewarden/extract_loudest_section)
|
| 130 |
+
tool. The audio files were then screened for silence or incorrect words, and
|
| 131 |
+
arranged into folders by label.
|
| 132 |
+
|
| 133 |
+
## Background Noise
|
| 134 |
+
|
| 135 |
+
To help train networks to cope with noisy environments, it can be helpful to mix
|
| 136 |
+
in realistic background audio. The `_silence_` folder contains a set of
|
| 137 |
+
longer audio clips that are either recordings or mathematical simulations of
|
| 138 |
+
noise.
|
| 139 |
+
|
| 140 |
+
## Citations
|
| 141 |
+
|
| 142 |
+
If you use the Speech Commands dataset in your work, please cite it as:
|
| 143 |
+
|
| 144 |
+
```
|
| 145 |
+
@article{speechcommandsv2,
|
| 146 |
+
author = {{Warden}, P.},
|
| 147 |
+
title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
|
| 148 |
+
journal = {ArXiv e-prints},
|
| 149 |
+
archivePrefix = "arXiv",
|
| 150 |
+
eprint = {1804.03209},
|
| 151 |
+
primaryClass = "cs.CL",
|
| 152 |
+
keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
|
| 153 |
+
year = 2018,
|
| 154 |
+
month = apr,
|
| 155 |
+
url = {https://arxiv.org/abs/1804.03209},
|
| 156 |
+
}
|
| 157 |
+
```
|
| 158 |
+
|
| 159 |
+
## Credits
|
| 160 |
+
|
| 161 |
+
Massive thanks are due to everyone who donated recordings to this data set, I'm
|
| 162 |
+
very grateful. I also couldn't have put this together without the help and
|
| 163 |
+
support of Billy Rutledge, Rajat Monga, Raziel Alvarez, Brad Krueger, Barbara
|
| 164 |
+
Petit, Gursheesh Kour, and all the AIY and TensorFlow teams.
|
| 165 |
+
|
| 166 |
+
Pete Warden, petewarden@google.com
|
data/train/eight/01b4757a_nohash_0.wav
ADDED
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9d9a439ab833ed60261ccbce6b034cb160d6ec2b3e0589589b7c4c5cb56e7fbb
|
| 3 |
+
size 32044
|
data/train/eight/042a8dde_nohash_0.wav
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
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arranged, transformed, or otherwise modified in a manner requiring
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permission under the Copyright and Similar Rights held by the
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Material is a musical work, performance, or sound recording,
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Adapted Material is always produced where the Licensed Material is
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closely related to copyright including, without limitation,
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categorized. For purposes of this Public License, the rights
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Rights.
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d. Effective Technological Measures means those measures that, in the
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absence of proper authority, may not be circumvented under laws
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fulfilling obligations under Article 11 of the WIPO Copyright
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agreements.
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e. Exceptions and Limitations means fair use, fair dealing, and/or
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any other exception or limitation to Copyright and Similar Rights
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that applies to Your use of the Licensed Material.
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| 102 |
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f. Licensed Material means the artistic or literary work, database,
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License.
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g. Licensed Rights means the rights granted to You subject to the
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all Copyright and Similar Rights that apply to Your use of the
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Licensed Material and that the Licensor has authority to license.
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h. Licensor means the individual(s) or entity(ies) granting rights
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under this Public License.
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i. Share means to provide material to the public by any means or
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process that requires permission under the Licensed Rights, such
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as reproduction, public display, public performance, distribution,
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dissemination, communication, or importation, and to make material
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j. Sui Generis Database Rights means rights other than copyright
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resulting from Directive 96/9/EC of the European Parliament and of
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as amended and/or succeeded, as well as other essentially
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equivalent rights anywhere in the world.
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under this Public License. Your has a corresponding meaning.
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Section 2 -- Scope.
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a. License grant.
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the Licensor hereby grants You a worldwide, royalty-free,
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non-sublicensable, non-exclusive, irrevocable license to
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exercise the Licensed Rights in the Licensed Material to:
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a. reproduce and Share the Licensed Material, in whole or
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in part; and
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3. To the extent possible, the Licensor waives any right to
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under any voluntary or waivable statutory or compulsory
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reserves any right to collect such royalties.
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Section 3 -- License Conditions.
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Your exercise of the Licensed Rights is expressly made subject to the
|
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following conditions.
|
| 214 |
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|
| 215 |
+
a. Attribution.
|
| 216 |
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|
| 217 |
+
1. If You Share the Licensed Material (including in modified
|
| 218 |
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form), You must:
|
| 219 |
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|
| 220 |
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a. retain the following if it is supplied by the Licensor
|
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with the Licensed Material:
|
| 222 |
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|
| 223 |
+
i. identification of the creator(s) of the Licensed
|
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Material and any others designated to receive
|
| 225 |
+
attribution, in any reasonable manner requested by
|
| 226 |
+
the Licensor (including by pseudonym if
|
| 227 |
+
designated);
|
| 228 |
+
|
| 229 |
+
ii. a copyright notice;
|
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|
| 231 |
+
iii. a notice that refers to this Public License;
|
| 232 |
+
|
| 233 |
+
iv. a notice that refers to the disclaimer of
|
| 234 |
+
warranties;
|
| 235 |
+
|
| 236 |
+
v. a URI or hyperlink to the Licensed Material to the
|
| 237 |
+
extent reasonably practicable;
|
| 238 |
+
|
| 239 |
+
b. indicate if You modified the Licensed Material and
|
| 240 |
+
retain an indication of any previous modifications; and
|
| 241 |
+
|
| 242 |
+
c. indicate the Licensed Material is licensed under this
|
| 243 |
+
Public License, and include the text of, or the URI or
|
| 244 |
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hyperlink to, this Public License.
|
| 245 |
+
|
| 246 |
+
2. You may satisfy the conditions in Section 3(a)(1) in any
|
| 247 |
+
reasonable manner based on the medium, means, and context in
|
| 248 |
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which You Share the Licensed Material. For example, it may be
|
| 249 |
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reasonable to satisfy the conditions by providing a URI or
|
| 250 |
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hyperlink to a resource that includes the required
|
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information.
|
| 252 |
+
|
| 253 |
+
3. If requested by the Licensor, You must remove any of the
|
| 254 |
+
information required by Section 3(a)(1)(A) to the extent
|
| 255 |
+
reasonably practicable.
|
| 256 |
+
|
| 257 |
+
4. If You Share Adapted Material You produce, the Adapter's
|
| 258 |
+
License You apply must not prevent recipients of the Adapted
|
| 259 |
+
Material from complying with this Public License.
|
| 260 |
+
|
| 261 |
+
|
| 262 |
+
Section 4 -- Sui Generis Database Rights.
|
| 263 |
+
|
| 264 |
+
Where the Licensed Rights include Sui Generis Database Rights that
|
| 265 |
+
apply to Your use of the Licensed Material:
|
| 266 |
+
|
| 267 |
+
a. for the avoidance of doubt, Section 2(a)(1) grants You the right
|
| 268 |
+
to extract, reuse, reproduce, and Share all or a substantial
|
| 269 |
+
portion of the contents of the database;
|
| 270 |
+
|
| 271 |
+
b. if You include all or a substantial portion of the database
|
| 272 |
+
contents in a database in which You have Sui Generis Database
|
| 273 |
+
Rights, then the database in which You have Sui Generis Database
|
| 274 |
+
Rights (but not its individual contents) is Adapted Material; and
|
| 275 |
+
|
| 276 |
+
c. You must comply with the conditions in Section 3(a) if You Share
|
| 277 |
+
all or a substantial portion of the contents of the database.
|
| 278 |
+
|
| 279 |
+
For the avoidance of doubt, this Section 4 supplements and does not
|
| 280 |
+
replace Your obligations under this Public License where the Licensed
|
| 281 |
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Rights include other Copyright and Similar Rights.
|
| 282 |
+
|
| 283 |
+
|
| 284 |
+
Section 5 -- Disclaimer of Warranties and Limitation of Liability.
|
| 285 |
+
|
| 286 |
+
a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
|
| 287 |
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EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
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1. automatically as of the date the violation is cured, provided
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| 324 |
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2. upon express reinstatement by the Licensor.
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d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
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License.
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Section 8 -- Interpretation.
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a. For the avoidance of doubt, this Public License does not, and
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| 370 |
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| 371 |
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| 373 |
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| 375 |
+
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| 376 |
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| 377 |
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|
| 378 |
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Creative Commons is not a party to its public
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| 379 |
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licenses. Notwithstanding, Creative Commons may elect to apply one of
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| 380 |
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will be considered the “Licensor.” The text of the Creative Commons
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public licenses is dedicated to the public domain under the CC0 Public
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|
| 395 |
+
Creative Commons may be contacted at creativecommons.org.
|
data/validation/README.md
ADDED
|
@@ -0,0 +1,166 @@
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|
| 1 |
+
# Speech Commands Data Set v0.02
|
| 2 |
+
|
| 3 |
+
This is a set of one-second .wav audio files, each containing a single spoken
|
| 4 |
+
English word. These words are from a small set of commands, and are spoken by a
|
| 5 |
+
variety of different speakers. The audio files are organized into folders based
|
| 6 |
+
on the word they contain, and this data set is designed to help train simple
|
| 7 |
+
machine learning models. This dataset is covered in more detail at [https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
|
| 8 |
+
|
| 9 |
+
It's licensed under the [Creative Commons BY 4.0
|
| 10 |
+
license](https://creativecommons.org/licenses/by/4.0/). See the LICENSE
|
| 11 |
+
file in this folder for full details. Its original location was at
|
| 12 |
+
[http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz](http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz).
|
| 13 |
+
|
| 14 |
+
## History
|
| 15 |
+
|
| 16 |
+
Version 0.01 of the data set was released on August 3rd 2017 and contained
|
| 17 |
+
64,727 audio files.
|
| 18 |
+
|
| 19 |
+
This is version 0.02 of the data set containing 105,829 audio files, released on
|
| 20 |
+
April 11th 2018.
|
| 21 |
+
|
| 22 |
+
## Collection
|
| 23 |
+
|
| 24 |
+
The audio files were collected using crowdsourcing, see
|
| 25 |
+
[aiyprojects.withgoogle.com/open_speech_recording](https://github.com/petewarden/extract_loudest_section)
|
| 26 |
+
for some of the open source audio collection code we used (and please consider
|
| 27 |
+
contributing to enlarge this data set). The goal was to gather examples of
|
| 28 |
+
people speaking single-word commands, rather than conversational sentences, so
|
| 29 |
+
they were prompted for individual words over the course of a five minute
|
| 30 |
+
session. Twenty core command words were recorded, with most speakers saying each
|
| 31 |
+
of them five times. The core words are "Yes", "No", "Up", "Down", "Left",
|
| 32 |
+
"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four",
|
| 33 |
+
"Five", "Six", "Seven", "Eight", and "Nine". To help distinguish unrecognized
|
| 34 |
+
words, there are also ten auxiliary words, which most speakers only said once.
|
| 35 |
+
These include "Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila",
|
| 36 |
+
"Tree", and "Wow".
|
| 37 |
+
|
| 38 |
+
## Organization
|
| 39 |
+
|
| 40 |
+
The files are organized into folders, with each directory name labelling the
|
| 41 |
+
word that is spoken in all the contained audio files. No details were kept of
|
| 42 |
+
any of the participants age, gender, or location, and random ids were assigned
|
| 43 |
+
to each individual. These ids are stable though, and encoded in each file name
|
| 44 |
+
as the first part before the underscore. If a participant contributed multiple
|
| 45 |
+
utterances of the same word, these are distinguished by the number at the end of
|
| 46 |
+
the file name. For example, the file path `happy/3cfc6b3a_nohash_2.wav`
|
| 47 |
+
indicates that the word spoken was "happy", the speaker's id was "3cfc6b3a", and
|
| 48 |
+
this is the third utterance of that word by this speaker in the data set. The
|
| 49 |
+
'nohash' section is to ensure that all the utterances by a single speaker are
|
| 50 |
+
sorted into the same training partition, to keep very similar repetitions from
|
| 51 |
+
giving unrealistically optimistic evaluation scores.
|
| 52 |
+
|
| 53 |
+
## Partitioning
|
| 54 |
+
|
| 55 |
+
The audio clips haven't been separated into training, test, and validation sets
|
| 56 |
+
explicitly, but by convention a hashing function is used to stably assign each
|
| 57 |
+
file to a set. Here's some Python code demonstrating how a complete file path
|
| 58 |
+
and the desired validation and test set sizes (usually both 10%) are used to
|
| 59 |
+
assign a set:
|
| 60 |
+
|
| 61 |
+
```python
|
| 62 |
+
MAX_NUM_WAVS_PER_CLASS = 2**27 - 1 # ~134M
|
| 63 |
+
|
| 64 |
+
def which_set(filename, validation_percentage, testing_percentage):
|
| 65 |
+
"""Determines which data partition the file should belong to.
|
| 66 |
+
|
| 67 |
+
We want to keep files in the same training, validation, or testing sets even
|
| 68 |
+
if new ones are added over time. This makes it less likely that testing
|
| 69 |
+
samples will accidentally be reused in training when long runs are restarted
|
| 70 |
+
for example. To keep this stability, a hash of the filename is taken and used
|
| 71 |
+
to determine which set it should belong to. This determination only depends on
|
| 72 |
+
the name and the set proportions, so it won't change as other files are added.
|
| 73 |
+
|
| 74 |
+
It's also useful to associate particular files as related (for example words
|
| 75 |
+
spoken by the same person), so anything after '_nohash_' in a filename is
|
| 76 |
+
ignored for set determination. This ensures that 'bobby_nohash_0.wav' and
|
| 77 |
+
'bobby_nohash_1.wav' are always in the same set, for example.
|
| 78 |
+
|
| 79 |
+
Args:
|
| 80 |
+
filename: File path of the data sample.
|
| 81 |
+
validation_percentage: How much of the data set to use for validation.
|
| 82 |
+
testing_percentage: How much of the data set to use for testing.
|
| 83 |
+
|
| 84 |
+
Returns:
|
| 85 |
+
String, one of 'training', 'validation', or 'testing'.
|
| 86 |
+
"""
|
| 87 |
+
base_name = os.path.basename(filename)
|
| 88 |
+
# We want to ignore anything after '_nohash_' in the file name when
|
| 89 |
+
# deciding which set to put a wav in, so the data set creator has a way of
|
| 90 |
+
# grouping wavs that are close variations of each other.
|
| 91 |
+
hash_name = re.sub(r'_nohash_.*$', '', base_name)
|
| 92 |
+
# This looks a bit magical, but we need to decide whether this file should
|
| 93 |
+
# go into the training, testing, or validation sets, and we want to keep
|
| 94 |
+
# existing files in the same set even if more files are subsequently
|
| 95 |
+
# added.
|
| 96 |
+
# To do that, we need a stable way of deciding based on just the file name
|
| 97 |
+
# itself, so we do a hash of that and then use that to generate a
|
| 98 |
+
# probability value that we use to assign it.
|
| 99 |
+
hash_name_hashed = hashlib.sha1(hash_name).hexdigest()
|
| 100 |
+
percentage_hash = ((int(hash_name_hashed, 16) %
|
| 101 |
+
(MAX_NUM_WAVS_PER_CLASS + 1)) *
|
| 102 |
+
(100.0 / MAX_NUM_WAVS_PER_CLASS))
|
| 103 |
+
if percentage_hash < validation_percentage:
|
| 104 |
+
result = 'validation'
|
| 105 |
+
elif percentage_hash < (testing_percentage + validation_percentage):
|
| 106 |
+
result = 'testing'
|
| 107 |
+
else:
|
| 108 |
+
result = 'training'
|
| 109 |
+
return result
|
| 110 |
+
```
|
| 111 |
+
|
| 112 |
+
The results of running this over the current set are included in this archive as
|
| 113 |
+
validation_list.txt and testing_list.txt. These text files contain the paths to
|
| 114 |
+
all the files in each set, with each path on a new line. Any files that aren't
|
| 115 |
+
in either of these lists can be considered to be part of the training set.
|
| 116 |
+
|
| 117 |
+
## Processing
|
| 118 |
+
|
| 119 |
+
The original audio files were collected in uncontrolled locations by people
|
| 120 |
+
around the world. We requested that they do the recording in a closed room for
|
| 121 |
+
privacy reasons, but didn't stipulate any quality requirements. This was by
|
| 122 |
+
design, since we wanted examples of the sort of speech data that we're likely to
|
| 123 |
+
encounter in consumer and robotics applications, where we don't have much
|
| 124 |
+
control over the recording equipment or environment. The data was captured in a
|
| 125 |
+
variety of formats, for example Ogg Vorbis encoding for the web app, and then
|
| 126 |
+
converted to a 16-bit little-endian PCM-encoded WAVE file at a 16000 sample
|
| 127 |
+
rate. The audio was then trimmed to a one second length to align most
|
| 128 |
+
utterances, using the
|
| 129 |
+
[extract_loudest_section](https://github.com/petewarden/extract_loudest_section)
|
| 130 |
+
tool. The audio files were then screened for silence or incorrect words, and
|
| 131 |
+
arranged into folders by label.
|
| 132 |
+
|
| 133 |
+
## Background Noise
|
| 134 |
+
|
| 135 |
+
To help train networks to cope with noisy environments, it can be helpful to mix
|
| 136 |
+
in realistic background audio. The `_silence_` folder contains a set of
|
| 137 |
+
longer audio clips that are either recordings or mathematical simulations of
|
| 138 |
+
noise.
|
| 139 |
+
|
| 140 |
+
## Citations
|
| 141 |
+
|
| 142 |
+
If you use the Speech Commands dataset in your work, please cite it as:
|
| 143 |
+
|
| 144 |
+
```
|
| 145 |
+
@article{speechcommandsv2,
|
| 146 |
+
author = {{Warden}, P.},
|
| 147 |
+
title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
|
| 148 |
+
journal = {ArXiv e-prints},
|
| 149 |
+
archivePrefix = "arXiv",
|
| 150 |
+
eprint = {1804.03209},
|
| 151 |
+
primaryClass = "cs.CL",
|
| 152 |
+
keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
|
| 153 |
+
year = 2018,
|
| 154 |
+
month = apr,
|
| 155 |
+
url = {https://arxiv.org/abs/1804.03209},
|
| 156 |
+
}
|
| 157 |
+
```
|
| 158 |
+
|
| 159 |
+
## Credits
|
| 160 |
+
|
| 161 |
+
Massive thanks are due to everyone who donated recordings to this data set, I'm
|
| 162 |
+
very grateful. I also couldn't have put this together without the help and
|
| 163 |
+
support of Billy Rutledge, Rajat Monga, Raziel Alvarez, Brad Krueger, Barbara
|
| 164 |
+
Petit, Gursheesh Kour, and all the AIY and TensorFlow teams.
|
| 165 |
+
|
| 166 |
+
Pete Warden, petewarden@google.com
|
data/validation/metadata.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|