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End of preview. Expand
in Dataset Viewer.
Speech Recognition Alignment Dataset
This dataset is a variation of several widely-used ASR datasets, encompassing Librispeech, MuST-C, TED-LIUM, VoxPopuli, Common Voice, and GigaSpeech. The difference is this dataset includes:
- Precise alignment between audio and text.
- Text that has been punctuated and made case-sensitive.
- Identification of named entities in the text.
Usage
First, install the latest version of the 🤗 Datasets package:
pip install --upgrade pip
pip install --upgrade datasets[audio]
The dataset can be downloaded and pre-processed on disk using the load_dataset
function:
from datasets import load_dataset
# Available dataset: 'libris','mustc','tedlium','voxpopuli','commonvoice','gigaspeech'
dataset = load_dataset("nguyenvulebinh/asr-alignment", "libris")
# take the first sample of the validation set
sample = dataset["train"][0]
It can also be streamed directly from the Hub using Datasets' streaming mode. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk:
from datasets import load_dataset
dataset = load_dataset("nguyenvulebinh/asr-alignment", "libris", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["train"]))
Citation
If you use this data, please consider citing the ICASSP 2024 Paper: SYNTHETIC CONVERSATIONS IMPROVE MULTI-TALKER ASR:
@INPROCEEDINGS{synthetic-multi-asr-nguyen,
author={Nguyen, Thai-Binh and Waibel, Alexander},
booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={SYNTHETIC CONVERSATIONS IMPROVE MULTI-TALKER ASR},
year={2024},
volume={},
number={},
}
License
This dataset is licensed in accordance with the terms of the original dataset.
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