Datasets:
jimbozhang
commited on
Commit
•
dfe82cf
1
Parent(s):
050f24e
Add the loading script
Browse files- .gitignore +157 -0
- README.md +0 -7
- about.html +0 -9
- info.txt +0 -8
- speechocean762.py +96 -0
- test/dataviewer.tsv +0 -0
- train/dataviewer.tsv +0 -0
.gitignore
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# IDEs
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.vscode/
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.idea/
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README.md
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pretty_name: speechocean762
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size_categories:
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- 1K<n<10K
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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: "train/dataviewer.tsv"
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- split: test
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path: "test/dataviewer.tsv"
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---
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# speechocean762: A non-native English corpus for pronunciation scoring task
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pretty_name: speechocean762
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size_categories:
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- 1K<n<10K
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---
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# speechocean762: A non-native English corpus for pronunciation scoring task
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about.html
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This corpus aims to provide a free public dataset for the pronunciation scoring task.
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<p>
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This corpus consists of 5000 English sentences.
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All the speakers are non-native, and their mother tongue is Mandarin.
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Half of the speakers are Children, and the others are adults.
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The information of age and gender are provided. Five experts made the scores.
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To avoid subjective bias, each expert scores independently under the same metric.
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<p>
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For more details, please read the `README.md` in the corpus.
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info.txt
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name: speechocean762
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summary: Pronunciation scoring dataset, labeled independently by five human experts
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category: speech
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license: Apache License v.2.0
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file: WAVE.tgz The audio files of both the train-set and test-set
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file: train.tgz Information for the train-set
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file: test.tgz Information for the test-set
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file: resource.tgz Transcripts and some miscellaneous files
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speechocean762.py
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# Copyright 2023 Xiaomi Corporation (Author: Junbo Zhang) and the HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import datasets
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_CITATION = """\
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@inproceedings{speechocean762,
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title={speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment},
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booktitle={Proc. Interspeech 2021},
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year=2021,
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author={Junbo Zhang, Zhiwen Zhang, Yongqing Wang, Zhiyong Yan, Qiong Song, Yukai Huang, Ke Li, Daniel Povey, Yujun Wang}
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}
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"""
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_DESCRIPTION = """\
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A free public dataset for the pronunciation scoring task.
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This corpus consists of 5000 English sentences.
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All the speakers are non-native, and their mother tongue is Mandarin.
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Half of the speakers are Children, and the others are adults.
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The information of age and gender are provided. Five experts made the scores.
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To avoid subjective bias, each expert scores independently under the same metric.
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"""
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_HOMEPAGE = "https://github.com/jimbozhang/speechocean762"
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_LICENSE = "Attribution 4.0 International (CC BY 4.0)"
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class Speechocean762Dataset(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.3.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="train", version=VERSION),
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datasets.BuilderConfig(name="test", version=VERSION),
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]
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DEFAULT_CONFIG_NAME = "test"
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"file": datasets.Value("string"),
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"audio": datasets.Audio(sampling_rate=16_000),
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"text": datasets.Value("string"),
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"accuracy": datasets.Value("int16"),
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"fluency": datasets.Value("int16"),
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"prosodic": datasets.Value("int16"),
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"total": datasets.Value("int16"),
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}
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),
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self):
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"filepath": "train/all-info.json"},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"filepath": "test/all-info.json"},
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),
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]
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def _generate_examples(self, filepath):
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with open(filepath, encoding="utf-8") as f:
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for key, row in json.load(f).items():
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path = f"WAVE/SPEAKER{row['speaker']}/{key}.WAV"
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yield key, {
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"file": path,
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"audio": path,
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"text": row["text"],
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"accuracy": row["accuracy"],
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"fluency": row["fluency"],
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"prosodic": row["prosodic"],
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"total": row["total"],
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}
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test/dataviewer.tsv
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train/dataviewer.tsv
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