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README.md
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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torchaudio.save(new_path, waveform, sample_rate=SAMPLING_RATE)
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```
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### Supported Tasks and Leaderboards
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- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition.
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [How to use?](#how-to-use)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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torchaudio.save(new_path, waveform, sample_rate=SAMPLING_RATE)
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```
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### How to use?
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There are two configurations for the data: one to download the original data and the other to download the preprocessed data as described above.
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1. First, to download the original dataset with HuggingFace's [Dataset](https://huggingface.co/docs/datasets/) API:
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```python
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from datasets import load_dataset
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dataset = load_dataset("spktsagar/openslr-nepali-asr-cleaned", name="original", split='train')
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```
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2. To download the preprocessed dataset:
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```python
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from datasets import load_dataset
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dataset = load_dataset("spktsagar/openslr-nepali-asr-cleaned", name="cleaned", split='train')
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```
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### Supported Tasks and Leaderboards
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- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition.
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