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--- |
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license: cc-by-4.0 |
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task_categories: |
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- automatic-speech-recognition |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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--- |
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### Original Dataset can be found in <https://www.openslr.org/145/> |
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- I made it for personal use, so the code might not be pefect. |
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### Citation Information |
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``` |
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@article{meister2023librispeechpc, |
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title={LibriSpeech-PC: Benchmark for Evaluation of Punctuation and Capitalization Capabilities of end-to-end ASR Models}, |
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author={A. Meister and M. Novikov and N. Karpov and E. Bakhturina and V. Lavrukhin and B. Ginsburg}, |
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journal={arXiv preprint arXiv:2310.02943}, |
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year={2023}, |
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} |
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``` |
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### How to Use |
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- Almost the same with [Librispeech](https://huggingface.co/datasets/openslr/librispeech_asr) dataset module since i refered to the [source code](https://huggingface.co/datasets/openslr/librispeech_asr/blob/main/librispeech_asr.py). |
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- three types of transcripts are given |
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- `text_normalized` : the trascript from Librispeech ASR |
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- `text`, `text_raw` : the trascripts from Librispeech-PC |
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```python |
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from datasets import load_dataset |
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from pprint import pprint |
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from IPython.display import display, Audio |
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import aiohttp |
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import os |
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# set huggingface cache directory for the extracted raw files and huggingface-cli token |
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os.environ['HF_HOME'] = "/data/to/download" |
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!export HF_HOME="/data/to/download" |
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# download dataset |
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# if already have librispeech_asr in the cache_dir it will use the same audio files. |
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libripc = load_dataset("yoom618/librispeech_pc", |
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"all", # all, clean, other |
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cache_dir="/data/to/download", |
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trust_remote_code=True, |
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# storage_options={'client_kwargs': {'timeout': aiohttp.ClientTimeout(total=7200)}}, # add if you need to increase the timeout |
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# check dataset info |
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print(libripc) |
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display(Audio(libripc['train.clean.100'][0]['audio']['array'], |
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rate=libripc['train.clean.100'][0]['audio']['sampling_rate'], |
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autoplay=False)) |
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pprint(libripc['train.clean.100'][0]['audio']) |
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``` |
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### The number of samples in Librispeech-PC |
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- train |
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- `train.clean.100` : 26,041 ( 2,498 out of 28,539 were dropped ) |
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- `train.clean.360` : 95,404 ( 8,610 out of 104,014 were dropped ) |
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- `train.other.500` : 134,679 ( 14,009 out of 148,688 were dropped ) |
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- dev |
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- `dev.clean` : 2,530 ( 173 out of 2,703 were dropped ) |
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- `dev.other` : 2,728 ( 136 out of 2,864 were dropped ) |
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- test |
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- `test.clean` : 2,417 ( 203 out of 2,620 were dropped ) |
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- `test.other` : 2,856 ( 83 out of 2,939 were dropped ) |
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