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--- |
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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: FPT Open Speech Dataset (FOSD) |
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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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dtype: audio |
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- name: transcription |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 684961355.008 |
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num_examples: 25917 |
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download_size: 819140462 |
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dataset_size: 684961355.008 |
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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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--- |
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# unofficial mirror of FPT Open Speech Dataset (FOSD) |
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released publicly in 2018 by FPT Corporation |
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100h, 25.9k samples |
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official link (dead): https://fpt.ai/fpt-open-speech-data/ |
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mirror: https://data.mendeley.com/datasets/k9sxg2twv4/4 |
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DOI: `10.17632/k9sxg2twv4.4` |
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pre-process: |
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- remove non-sense strings: `-N` `\r\n` |
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- remove 4 files because missing transcription: |
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- `Set001_V0.1_008210.mp3` |
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- `Set001_V0.1_010753.mp3` |
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- `Set001_V0.1_011477.mp3` |
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- `Set001_V0.1_011841.mp3` |
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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/fpt_fosd", 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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``` |
