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--- |
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dataset_info: |
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features: |
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- name: audio |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: text |
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dtype: string |
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- name: cleaned_text |
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dtype: string |
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- name: speaker_age |
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dtype: string |
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- name: speaker_gender |
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dtype: string |
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- name: speaker_dialect |
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dtype: string |
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- name: input_features |
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sequence: |
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sequence: float32 |
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- name: input_length |
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dtype: float64 |
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- name: labels |
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sequence: int64 |
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- name: cleaned_labels |
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sequence: int64 |
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splits: |
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- name: validation |
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num_bytes: 5862458096.364273 |
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num_examples: 5024 |
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download_size: 2002683497 |
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dataset_size: 5862458096.364273 |
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configs: |
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- config_name: default |
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data_files: |
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- split: validation |
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path: data/validation-* |
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license: apache-2.0 |
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task_categories: |
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- automatic-speech-recognition |
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language: |
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- ar |
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tags: |
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- WhisperTiny |
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- WhisperSmall |
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- WhisperBase |
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- WhisperMedium |
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- OpenAI |
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- ASR |
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- Arabic |
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- Preprocessed |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Details |
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This is the SADA 2022 dataset with the input_features whish are log mels and the cleaned_labels which is the tokenized version of the cleaned_text. You can directly use this as the validation dataset when training Whisper Tiny, Small, Base & Medium models, as they all use the same tokenizer. Please double check this as well from the original model repo. |
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In addtition, the following filters were applied to this data: |
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- All audios are less than 30 seconds and greater than 0 seconds. |
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- All cleaned_text have token lengths less than 448 and greater than 0. |
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- All rows with 'nan' in cleaned_text or cleaned_text only having whitespace or being empty were dropped. |
