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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: transcription |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 3125353264.6964455 |
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num_examples: 5778 |
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- name: test |
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num_bytes: 1004055850.0756147 |
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num_examples: 1683 |
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download_size: 3490774262 |
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dataset_size: 4129409114.7720604 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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task_categories: |
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- automatic-speech-recognition |
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- text-to-speech |
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language: |
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- km |
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tags: |
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- openslr42 |
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- fleurs |
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- asr |
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--- |
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__NOTE:__ If your colab crashes, please use `pip install --upgrade --quiet datasets[audio]==3.6.0` to install `datasets[audio]` version `3.6.0`. |
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This dataset combined [google/fleurs](https://huggingface.co/datasets/google/fleurs), [openslr/openslr42](https://huggingface.co/datasets/openslr/openslr), and cleaned [seanghay/khmer_mpwt_speech](https://huggingface.co/datasets/seanghay/khmer_mpwt_speech). |
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Severals processes are executed: |
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1. clean up [seanghay/khmer_mpwt_speech](https://huggingface.co/datasets/seanghay/khmer_mpwt_speech): manually correct wrong transcriptions over 2058 rows |
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2. normalize transcription: remove invisible white space; process `ៗ`, numbers, currencies, date into khmer text; and separate each word by space |
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3. filter out texts whose number of token ids are more than 448: use tokenizer of Whisper-Small to encode text and filter out sequences longer than 448 |
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4. filter out audio with length longer than 30 seconds |
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5. resample audio to 16000kHz |
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__Disclaimer__ I do not own any of these datasets. |
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