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Update README.md

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@@ -45,7 +45,7 @@ ds = load_dataset("common_voice", "fr", split="test", cache_dir="./data/fr")
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- chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\‘\\’\\’\\’\\‘\\…\\·\\!\\ǃ\\?\\«\\‹\\»\\›“\\”\\\\ʿ\\ʾ\\„\\∞\\\\|\\.\\,\\;\\:\\*\\—\\–\\─\\―\\_\\/\\:\\ː\\;\\,\\=\\«\\»\\→]'
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  def map_to_array(batch):
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  speech, _ = torchaudio.load(batch["path"])
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  batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
@@ -74,7 +74,7 @@ print(wer.compute(predictions=result["predicted"], references=result["target"]))
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  ## Training
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- 6% of the Common Voice `train`, `validation` datasets were used for training.
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  ## Testing
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+ chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“\\\\%\\\\‘\\\\”\\\\�\\\\‘\\\\’\\\\’\\\\’\\\\‘\\\\…\\\\·\\\\!\\\\ǃ\\\\?\\\\«\\\\‹\\\\»\\\\›“\\\\”\\\\\\\\ʿ\\\\ʾ\\\\„\\\\∞\\\\\\\\|\\\\.\\\\,\\\\;\\\\:\\\\*\\\\—\\\\–\\\\─\\\\―\\\\_\\\\/\\\\:\\\\ː\\\\;\\\\,\\\\=\\\\«\\\\»\\\\→]'
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  def map_to_array(batch):
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  speech, _ = torchaudio.load(batch["path"])
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  batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
 
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  ## Training
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+ 6% of the Common Voice `train`, `validation` datasets (20K files) were used for training.
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  ## Testing
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