update README
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README.md
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@@ -77,8 +77,6 @@ The model can be evaluated as follows on the Chinese (Hong Kong) test data of Co
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```python
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!mkdir cer
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!wget -O cer/cer.py https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese/raw/main/cer.py
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!pip install jiwer
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import torch
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import torchaudio
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@@ -93,7 +91,7 @@ model_id = "ctl/wav2vec2-large-xlsr-cantonese"
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:"\“\%\‘\”\�\.\⋯\!\-\:\–\。\》\,\)\,\?\;\~\~\…\︰\,\(\」\‧\《\﹔\、\—\/\,\「\﹖\·\']'
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test_dataset = load_dataset("common_voice", f"{lang_id}", split="test")
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cer = load_metric("
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processor = Wav2Vec2Processor.from_pretrained(f"{model_id}")
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model = Wav2Vec2ForCTC.from_pretrained(f"{model_id}")
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```python
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!pip install jiwer
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import torch
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import torchaudio
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:"\“\%\‘\”\�\.\⋯\!\-\:\–\。\》\,\)\,\?\;\~\~\…\︰\,\(\」\‧\《\﹔\、\—\/\,\「\﹖\·\']'
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test_dataset = load_dataset("common_voice", f"{lang_id}", split="test")
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cer = load_metric("cer")
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processor = Wav2Vec2Processor.from_pretrained(f"{model_id}")
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model = Wav2Vec2ForCTC.from_pretrained(f"{model_id}")
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