| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| - split: dev | |
| path: data/dev-* | |
| - split: test | |
| path: data/test-* | |
| dataset_info: | |
| features: | |
| - name: audio | |
| dtype: | |
| audio: | |
| sampling_rate: 16000 | |
| - name: transcription | |
| dtype: string | |
| - name: translation | |
| dtype: string | |
| - name: file | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 6392221146.655 | |
| num_examples: 17779 | |
| - name: dev | |
| num_bytes: 786905707.92 | |
| num_examples: 2997 | |
| - name: test | |
| num_bytes: 4054213966.96 | |
| num_examples: 14916 | |
| download_size: 8220600841 | |
| dataset_size: 11233340821.535 | |
| license: mit | |
| # Dataset Card for "ML2021_ASR_ST" | |
| This dataset contains the audio recordings, the transcriptions, and the English translation of the transcriptions of the Machine Learning Course in 2021 at National Taiwan Univeristy. | |
| This can be used for domain-specific and code-switching ASR/Speech-to-text translation. | |
| If you find this dataset useful, please consider to cite the following paper: | |
| ``` | |
| @inproceedings{yang2024investigating, | |
| title={Investigating zero-shot generalizability on mandarin-english code-switched asr and speech-to-text translation of recent foundation models with self-supervision and weak supervision}, | |
| author={Yang, Chih-Kai and Huang, Kuan-Po and Lu, Ke-Han and Kuan, Chun-Yi and Hsiao, Chi-Yuan and Lee, Hung-yi}, | |
| booktitle={2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)}, | |
| pages={540--544}, | |
| year={2024}, | |
| organization={IEEE} | |
| } | |
| ``` |