parquet-converter
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Update parquet files
Browse files- README.md +0 -146
- clean/on/examples.zip → all/test2-clean.of.parquet +2 -2
- other/of/examples.zip → all/test2-clean.on.parquet +2 -2
- other/on/examples.zip → all/test2-other.of.parquet +2 -2
- clean/of/examples.zip → all/test2-other.on.parquet +2 -2
- clean/example.tsv +0 -3
- clean/keyword.tsv +0 -2
- clean/test2-of.parquet +3 -0
- clean/test2-on.parquet +3 -0
- other/example.tsv +0 -3
- other/keyword.tsv +0 -2
- other/test2-of.parquet +3 -0
- other/test2-on.parquet +3 -0
- test2.py +0 -224
README.md
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---
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annotations_creators:
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- expert-generated
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language:
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- pl
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license:
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- mit
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multilinguality:
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- monolingual
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dataset_info:
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- config_name: config
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features:
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- name: audio_id
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dtype: string
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- name: audio
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dtype:
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audio:
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sampling_rate: 16000
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- name: text
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dtype: string
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---
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# Dataset Card for [Dataset Name]
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:**
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- **Repository:**
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- **Paper:**
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- **Leaderboard:**
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- **Point of Contact:**
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### Dataset Summary
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[More Information Needed]
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### Supported Tasks and Leaderboards
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[More Information Needed]
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### Languages
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[More Information Needed]
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## Dataset Structure
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### Data Instances
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[More Information Needed]
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### Data Fields
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[More Information Needed]
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### Data Splits
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[More Information Needed]
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## Dataset Creation
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### Curation Rationale
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[More Information Needed]
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### Source Data
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#### Initial Data Collection and Normalization
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[More Information Needed]
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#### Who are the source language producers?
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[More Information Needed]
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### Annotations
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#### Annotation process
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[More Information Needed]
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#### Who are the annotators?
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[More Information Needed]
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### Personal and Sensitive Information
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[More Information Needed]
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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[More Information Needed]
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### Licensing Information
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[More Information Needed]
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### Citation Information
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[More Information Needed]
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### Contributions
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Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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clean/on/examples.zip → all/test2-clean.of.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:546c940affdd47eed8c868816c98afc7aa9a7045e9d61b1c5c6538922d52c928
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size 64850
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other/of/examples.zip → all/test2-clean.on.parquet
RENAMED
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:546c940affdd47eed8c868816c98afc7aa9a7045e9d61b1c5c6538922d52c928
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size 64850
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other/on/examples.zip → all/test2-other.of.parquet
RENAMED
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:546c940affdd47eed8c868816c98afc7aa9a7045e9d61b1c5c6538922d52c928
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size 64850
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clean/of/examples.zip → all/test2-other.on.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:546c940affdd47eed8c868816c98afc7aa9a7045e9d61b1c5c6538922d52c928
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size 64850
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clean/example.tsv
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audio_id ngram
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common_voice_pl_20547775.wav poślemy potest
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common_voice_pl_20547776.wav poślemy po wastest
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clean/keyword.tsv
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audio_id ngram
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common_voice_pl_20547774.wav poślemytrain
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clean/test2-of.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:546c940affdd47eed8c868816c98afc7aa9a7045e9d61b1c5c6538922d52c928
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size 64850
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clean/test2-on.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:546c940affdd47eed8c868816c98afc7aa9a7045e9d61b1c5c6538922d52c928
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size 64850
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other/example.tsv
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audio_id ngram
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common_voice_pl_20547775.wav poślemy potest
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common_voice_pl_20547776.wav poślemy po wastest
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other/keyword.tsv
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audio_id ngram
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common_voice_pl_20547774.wav poślemytrain
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other/test2-of.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:546c940affdd47eed8c868816c98afc7aa9a7045e9d61b1c5c6538922d52c928
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size 64850
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other/test2-on.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:546c940affdd47eed8c868816c98afc7aa9a7045e9d61b1c5c6538922d52c928
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size 64850
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test2.py
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# coding=utf-8
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# Lint as: python3
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"""test set"""
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import csv
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import os
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import json
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import datasets
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from datasets.utils.py_utils import size_str
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from tqdm import tqdm
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import os
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import datasets
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_CITATION = """\
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@inproceedings{panayotov2015librispeech,
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title={Librispeech: an ASR corpus based on public domain audio books},
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author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
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booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
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pages={5206--5210},
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year={2015},
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organization={IEEE}
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}
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"""
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_DESCRIPTION = """\
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Lorem ipsum
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"""
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_URL = "https://huggingface.co/datasets/j-krzywdziak/test2"
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_AUDIO_URL = "https://huggingface.co/datasets/j-krzywdziak/test2/resolve/main"
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_DATA_URL = "https://huggingface.co/datasets/j-krzywdziak/test2/raw/main"
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_DL_URLS = {
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"clean": {
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"of": _AUDIO_URL + "/clean/of/examples.zip",
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"on": _AUDIO_URL + "/clean/on/examples.zip",
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"example": _DATA_URL + "/clean/example.tsv",
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"keyword": _DATA_URL + "/clean/keyword.tsv"
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},
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"other": {
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"of": _AUDIO_URL + "/other/of/examples.zip",
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"on": _AUDIO_URL + "/other/on/examples.zip",
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"example": _DATA_URL + "/other/example.tsv",
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"keyword": _DATA_URL + "/other/keyword.tsv"
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},
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"all": {
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"clean.of": _AUDIO_URL + "/clean/of/examples.zip",
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"clean.on": _AUDIO_URL + "/clean/on/examples.zip",
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"other.of": _AUDIO_URL + "/other/of/examples.zip",
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"other.on": _AUDIO_URL + "/other/on/examples.zip",
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"clean.example": _DATA_URL + "/clean/example.tsv",
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"clean.keyword": _DATA_URL + "/clean/keyword.tsv",
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"other.example": _DATA_URL + "/other/example.tsv",
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"other.keyword": _DATA_URL + "/other/keyword.tsv"
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},
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}
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class TestASR(datasets.GeneratorBasedBuilder):
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"""Lorem ipsum."""
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VERSION = "0.0.0"
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DEFAULT_CONFIG_NAME = "all"
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="clean", description="'Clean' speech."),
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datasets.BuilderConfig(name="other", description="'Other', more challenging, speech."),
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datasets.BuilderConfig(name="all", description="Combined clean and other dataset."),
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]
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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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"path": datasets.Value("string"),
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"audio": datasets.Audio(sampling_rate=16_000),
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"ngram": datasets.Value("string"),
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"type": datasets.Value("string")
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}
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),
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supervised_keys=("file", "text"),
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homepage=_URL,
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citation=_CITATION
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)
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def _split_generators(self, dl_manager):
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archive_path = dl_manager.download(_DL_URLS[self.config.name])
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# (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
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local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
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if self.config.name == "clean":
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of_split = [
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datasets.SplitGenerator(
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name="of",
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gen_kwargs={
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"local_extracted_archive": local_extracted_archive.get("of"),
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"files": dl_manager.iter_archive(archive_path["of"]),
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"examples": archive_path["example"],
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"keywords": archive_path["keyword"]
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},
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)
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]
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on_split = [
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datasets.SplitGenerator(
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name="on",
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gen_kwargs={
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"local_extracted_archive": local_extracted_archive.get("on"),
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"files": dl_manager.iter_archive(archive_path["on"]),
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"examples": archive_path["example"],
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"keywords": archive_path["keyword"]
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},
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)
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]
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elif self.config.name == "other":
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of_split = [
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datasets.SplitGenerator(
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name="of",
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gen_kwargs={
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"local_extracted_archive": local_extracted_archive.get("of"),
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"files": dl_manager.iter_archive(archive_path["of"]),
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"examples": archive_path["example"],
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"keywords": archive_path["keyword"]
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},
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)
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]
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on_split = [
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datasets.SplitGenerator(
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name="on",
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gen_kwargs={
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"local_extracted_archive": local_extracted_archive.get("on"),
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"files": dl_manager.iter_archive(archive_path["on"]),
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"examples": archive_path["example"],
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"keywords": archive_path["keyword"]
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},
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)
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139 |
-
]
|
140 |
-
elif self.config.name == "all":
|
141 |
-
of_split = [
|
142 |
-
datasets.SplitGenerator(
|
143 |
-
name="clean.of",
|
144 |
-
gen_kwargs={
|
145 |
-
"local_extracted_archive": local_extracted_archive.get("clean.of"),
|
146 |
-
"files": dl_manager.iter_archive(archive_path["clean.of"]),
|
147 |
-
"examples": archive_path["clean.example"],
|
148 |
-
"keywords": archive_path["clean.keyword"]
|
149 |
-
},
|
150 |
-
),
|
151 |
-
datasets.SplitGenerator(
|
152 |
-
name="other.of",
|
153 |
-
gen_kwargs={
|
154 |
-
"local_extracted_archive": local_extracted_archive.get("other.of"),
|
155 |
-
"files": dl_manager.iter_archive(archive_path["other.of"]),
|
156 |
-
"examples": archive_path["other.example"],
|
157 |
-
"keywords": archive_path["other.keyword"]
|
158 |
-
}
|
159 |
-
)
|
160 |
-
]
|
161 |
-
on_split = [
|
162 |
-
datasets.SplitGenerator(
|
163 |
-
name="clean.on",
|
164 |
-
gen_kwargs={
|
165 |
-
"local_extracted_archive": local_extracted_archive.get("clean.on"),
|
166 |
-
"files": dl_manager.iter_archive(archive_path["clean.on"]),
|
167 |
-
"examples": archive_path["clean.example"],
|
168 |
-
"keywords": archive_path["clean.keyword"]
|
169 |
-
},
|
170 |
-
),
|
171 |
-
datasets.SplitGenerator(
|
172 |
-
name="other.on",
|
173 |
-
gen_kwargs={
|
174 |
-
"local_extracted_archive": local_extracted_archive.get("other.on"),
|
175 |
-
"files": dl_manager.iter_archive(archive_path["other.on"]),
|
176 |
-
"examples": archive_path["other.example"],
|
177 |
-
"keywords": archive_path["other.keyword"]
|
178 |
-
}
|
179 |
-
)
|
180 |
-
]
|
181 |
-
return on_split + of_split
|
182 |
-
|
183 |
-
def _generate_examples(self, files, local_extracted_archive, examples, keywords):
|
184 |
-
"""Lorem ipsum."""
|
185 |
-
audio_data = {}
|
186 |
-
transcripts = []
|
187 |
-
key = 0
|
188 |
-
print(examples, keywords)
|
189 |
-
print(local_extracted_archive)
|
190 |
-
for path, f in files:
|
191 |
-
audio_data[path] = f.read()
|
192 |
-
with open(keywords, encoding="utf-8") as f:
|
193 |
-
next(f)
|
194 |
-
for row in f:
|
195 |
-
r = row.split("\t")
|
196 |
-
print(r)
|
197 |
-
path = 'examples/'+r[0]
|
198 |
-
ngram = r[1]
|
199 |
-
transcripts.append({
|
200 |
-
"path": path,
|
201 |
-
"ngram": ngram,
|
202 |
-
"type": "keyword"
|
203 |
-
})
|
204 |
-
with open(examples, encoding="utf-8") as f2:
|
205 |
-
next(f2)
|
206 |
-
for row in f2:
|
207 |
-
r = row.split("\t")
|
208 |
-
print(r)
|
209 |
-
path = 'examples/'+r[0]
|
210 |
-
ngram = r[1]
|
211 |
-
transcripts.append({
|
212 |
-
"path": path,
|
213 |
-
"ngram": ngram,
|
214 |
-
"type": "example"
|
215 |
-
})
|
216 |
-
print("AUDIO DATA: ", audio_data)
|
217 |
-
print("TRANSCRIPT: ", transcripts)
|
218 |
-
if audio_data and len(audio_data) == len(transcripts):
|
219 |
-
for transcript in transcripts:
|
220 |
-
audio = {"path": transcript["path"], "bytes": audio_data[transcript["path"]]}
|
221 |
-
yield key, {"audio": audio, **transcript}
|
222 |
-
key += 1
|
223 |
-
audio_data = {}
|
224 |
-
transcripts = []
|
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