README.md

---
license: apache-2.0
dataset_info:
- config_name: commonvoice
  features:
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- config_name: gigaspeech
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- config_name: libris
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- config_name: mustc
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- config_name: tedlium
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- config_name: voxpopuli
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configs:
- config_name: commonvoice
  data_files:
  - split: train
    path: commonvoice/train-*
  - split: valid
    path: commonvoice/valid-*
- config_name: gigaspeech
  data_files:
  - split: train
    path: gigaspeech/train-*
  - split: valid
    path: gigaspeech/valid-*
- config_name: libris
  data_files:
  - split: train
    path: libris/train-*
  - split: valid
    path: libris/valid-*
- config_name: mustc
  data_files:
  - split: train
    path: mustc/train-*
  - split: valid
    path: mustc/valid-*
- config_name: tedlium
  data_files:
  - split: train
    path: tedlium/train-*
  - split: valid
    path: tedlium/valid-*
- config_name: voxpopuli
  data_files:
  - split: train
    path: voxpopuli/train-*
  - split: valid
    path: voxpopuli/valid-*
language:
- en
pretty_name: Speech Recognition Alignment Dataset
size_categories:
- 10M<n<100M
---

# 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.