| --- |
| 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} |
| } |
| ``` |
