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Update README.md

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  1. README.md +12 -5
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@@ -45,12 +45,17 @@ test_dataset = load_dataset("common_voice", "br", split="test[:2%]")
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  processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
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  model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
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- resampler = torchaudio.transforms.Resample(48_000, 16_000)
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  # Preprocessing the datasets.
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  # We need to read the aduio files as arrays
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  def speech_file_to_array_fn(batch):
 
 
 
 
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  speech_array, sampling_rate = torchaudio.load(batch["path"])
 
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  batch["speech"] = resampler(speech_array).squeeze().numpy()
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  return batch
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@@ -85,15 +90,17 @@ processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-breton"
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  model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
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  model.to("cuda")
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- chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'
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-
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- resampler = torchaudio.transforms.Resample(48_000, 16_000)
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  # Preprocessing the datasets.
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  # We need to read the aduio files as arrays
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  def speech_file_to_array_fn(batch):
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- batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
 
 
 
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  speech_array, sampling_rate = torchaudio.load(batch["path"])
 
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  batch["speech"] = resampler(speech_array).squeeze().numpy()
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  return batch
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  processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
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  model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
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+ chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
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  # Preprocessing the datasets.
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  # We need to read the aduio files as arrays
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  def speech_file_to_array_fn(batch):
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+ batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
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+ batch["sentence"] = batch["sentence"].replace("ʼ", "'")
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+ batch["sentence"] = batch["sentence"].replace("’", "'")
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+ batch["sentence"] = batch["sentence"].replace('‘', "'")
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  speech_array, sampling_rate = torchaudio.load(batch["path"])
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+ resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
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  batch["speech"] = resampler(speech_array).squeeze().numpy()
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  return batch
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  model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
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  model.to("cuda")
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+ chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
 
 
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  # Preprocessing the datasets.
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  # We need to read the aduio files as arrays
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  def speech_file_to_array_fn(batch):
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+ batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
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+ batch["sentence"] = batch["sentence"].replace("ʼ", "'")
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+ batch["sentence"] = batch["sentence"].replace("’", "'")
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+ batch["sentence"] = batch["sentence"].replace('‘', "'")
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  speech_array, sampling_rate = torchaudio.load(batch["path"])
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+ resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
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  batch["speech"] = resampler(speech_array).squeeze().numpy()
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  return batch
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