--- language: - pt license: mit datasets: - jwlang tags: - automatic-speech-recognition - speech - dataset viewer: true dataset_info: - config_name: de features: - name: client_id dtype: string - name: audio dtype: audio - name: sentence dtype: string - name: language dtype: string - name: split dtype: string splits: - name: train num_bytes: 44420148.0 num_examples: 949 - name: test num_bytes: 5730879.0 num_examples: 119 download_size: 50045852 dataset_size: 50151027.0 - config_name: pt features: - name: client_id dtype: string - name: audio dtype: audio - name: sentence dtype: string - name: language dtype: string - name: split dtype: string splits: - name: train num_bytes: 45540940.152 num_examples: 1004 - name: test num_bytes: 5906213.0 num_examples: 126 - name: val num_bytes: 5474968.0 num_examples: 125 download_size: 113555414 dataset_size: 56922121.152 configs: - config_name: de data_files: - split: train path: de/train-* - split: test path: de/test-* - config_name: pt data_files: - split: train path: pt/train-* - split: test path: pt/test-* - split: val path: pt/val-* --- # JWLang Corpus ## Dataset Summary The JWLang Corpus is a collection of audio and corresponding text data from JW Broadcasting videos available on the jw.org website. It is intended for training and fine-tuning automatic speech recognition (ASR) models, specifically OpenAI Whisper. ## Dataset Structure - Number of samples: 10,000 - Data format: Audio (WAV) and Text (SRT) - Size: 5 GB ## Splits | Split | Number of samples | |------------|-------------------| | Train | 8,000 | | Validation | 1,000 | | Test | 1,000 | ## Usage To load and use the dataset: ```python from datasets import load_dataset dataset = load_dataset("M2LabOrg/JWLang_Corpus") ``` ## Example Data Example text snippet from the dataset: ``` { "audio": "path/to/audio.wav", "text": "Example subtitle text." } ``` ## License ``` CC BY-SA 4.0 ``` ## Citation If you use this dataset, please cite: ``` @article{jwlang_corpus, title={JWLang Corpus for ASR Training}, author={Michel Mesquita}, journal={Unpublished}, year={2024}, } ``` ## Contact For any questions or issues, please contact [Michel Mesquita](mailto:mmeclimate@gmail.com).