Datasets:
less restrictive license
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README.md
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license: cc-by-
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task_categories:
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pretty_name: InfoRe Technology public dataset №1
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size_categories:
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dataset_info:
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features:
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- name: audio
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official announcement: https://www.facebook.com/groups/j2team.community/permalink/1010834009248719/
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25h,
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official download: `magnet:?xt=urn:btih:1cbe13fb14a390c852c016a924b4a5e879d85f41&dn=25hours.zip&tr=http%3A%2F%2Foffice.socials.vn%3A8725%2Fannounce`
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mirror: https://files.huylenguyen.com/25hours.zip
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unzip password: `BroughtToYouByInfoRe`
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license: cc-by-4.0
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task_categories:
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- automatic-speech-recognition
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- text-to-speech
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language:
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- vi
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pretty_name: InfoRe Technology public dataset №1
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size_categories:
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- 10K<n<100K
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dataset_info:
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features:
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- name: audio
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official announcement: https://www.facebook.com/groups/j2team.community/permalink/1010834009248719/
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25h, 14.9k samples, InfoRe paid a contractor to read text
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official download: `magnet:?xt=urn:btih:1cbe13fb14a390c852c016a924b4a5e879d85f41&dn=25hours.zip&tr=http%3A%2F%2Foffice.socials.vn%3A8725%2Fannounce`
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mirror: https://files.huylenguyen.com/25hours.zip
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unzip password: `BroughtToYouByInfoRe`
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pre-process: none
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need to do: check misspelling
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usage with HuggingFace:
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```python
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# pip install -q "datasets[audio]"
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from datasets import load_dataset
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from torch.utils.data import DataLoader
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dataset = load_dataset("doof-ferb/infore1_25hours", split="train", streaming=True)
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dataset.set_format(type="torch", columns=["audio", "transcription"])
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dataloader = DataLoader(dataset, batch_size=4)
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```
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