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        "author": "Nik Vaessen",
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        "description": "# Word Error Rate for automatic speech recognition\n\nThis repository contains a simple python package to approximate the WER of a transcript. It computes the minimum-edit distance \nbetween the ground-truth sentence and the hypothesis sentence of a speech-to-text API. The minimum-edit distance is calculated\nusing the \n[Wagner-Fisher](https://en.wikipedia.org/wiki/Wagner%E2%80%93Fischer_algorithm) \nalgorithm. Because this algorithm computes the character-level minimum-edit distance, every word in a sentence is assigned a\nunique integer, and the edit-distance is computed over a string of integers. \n\n# Installation\n\nYou should be able to install this package using pip: \n\n```bash\n$ pip install jiwer\n```\n\n# Usage\n\nThe most simple use-case is computing the edit distance between two strings:\n\n```python\nfrom jiwer import wer\n\nground_truth = \"hello world\"\nhypothesis = \"hello duck\"\n\nerror = wer(ground_truth, hypothesis)\n```\n\nYou can also compute the WER over multiple sentences:\n\n```python\nfrom jiwer import wer\n\nground_truth = [\"hello world\", \"i like monthy python\"]\nhypothesis = [\"hello duck\", \"i like python\"]\n\nerror = wer(ground_truth, hypothesis)\n```\n\nWhen the amount of ground-truth sentences and hypothesis sentences differ, a minimum alignment is done over the merged sentence:\n\n```python\nground_truth = [\"hello world\", \"i like monthy python\", \"what do you mean, african or european swallow?\"]\nhypothesis = [\"hello\", \"i like\", \"python\", \"what you mean swallow\"]\n\n# is equivelent to\n\nground_truth = \"hello world i like monhty python what do you mean african or european swallow\"\nhypothesis = \"hello i like python what you mean swallow\"\n```\n\n# Additional preprocessing\n\nSome additional preprocessing can be done on the input. By default, whitespace is removed, everything is set to lower-case,\n`.` and `,` are removed, everything between `[]` and `<>` (common for Kaldi models) is removed and each word is tokenized by \nsplitting by one or more spaces. Additionally, common abbreviations, such as `won't`, `let's`,`n't` will be expanded if \n`standardize=True` is passed along the `wer` method.\n\n```python\nfrom jiwer import wer\n\nground_truth = \"he's my neminis\"\nhypothesis = \"he is my <unk> [laughter]\"\n\nwer(ground_truth, hypothesis, standardize=True)\n\n# is equivelent to \n\nground_truth = \"he is my neminis\"\nhypothesis = \"he is my\"\n\nwer(ground_truth, hypothesis)\n```\n\nAlso, there is an option give a list of words to remove from the \ntranscription, such as \"yhe\", or \"so\". \n\n```python\nfrom jiwer import wer\n\nground_truth = \"yhe about that bug\"\nhypothesis = \"yeah about that bug\"\n\nwer(ground_truth, hypothesis, words_to_filter=[\"yhe\", \"yeah\"])\n\n# is equivelent to \n\nground_truth = \"about that bug\"\nhypothesis = \"about that bug\"\n\nwer(ground_truth, hypothesis)\n\n```\n\n\n\n\n\n\n\n",
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