Update README.md

README.md

@@ -224,4 +224,69 @@ configs:
     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.