asr-alignment

# 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:

```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```

The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:

```python
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](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:

```python
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.

# 语音识别对齐数据集（Speech Recognition Alignment Dataset）

本数据集为多款主流自动语音识别（Automatic Speech Recognition，ASR）数据集的衍生版本，涵盖Librispeech、MuST-C、TED-LIUM、VoxPopuli、Common Voice及GigaSpeech。其核心差异在于本数据集包含：
- 音频与文本间的精确对齐标注
- 经过标点规范化且区分大小写的文本标注
- 文本中的命名实体识别标注

## 使用方法

首先，安装最新版本的🤗 数据集（Datasets）库：
bash
pip install --upgrade pip
pip install --upgrade datasets[audio]

可通过[`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)函数实现数据集的下载与本地预处理：
python
from datasets import load_dataset

# 可选数据集名称：'libris','mustc','tedlium','voxpopuli','commonvoice','gigaspeech'
dataset = load_dataset("nguyenvulebinh/asr-alignment", "libris")

# 获取验证集的第一条样本
sample = dataset["train"][0]

也可通过数据集库的[流式加载模式](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet)直接从Hugging Face Hub流式读取数据。流式加载模式会单次加载单条数据样本，而非将完整数据集下载至本地磁盘：
python
from datasets import load_dataset

dataset = load_dataset("nguyenvulebinh/asr-alignment", "libris", streaming=True)
# 获取验证集的第一条样本
sample = next(iter(dataset["train"]))

## 引用方式

若您使用本数据集，请引用以下[ICASSP 2024论文：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={},
}

## 授权协议

本数据集的授权协议遵循其原始数据集的相关条款。