Ilyes Jeronymous commited on
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Update README.md (#1)

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- Update README.md (c86d5adc3bccf8dcca8c00e8757675c0742e3ca5)


Co-authored-by: Jérôme Louradour <Jeronymous@users.noreply.huggingface.co>

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  1. README.md +3 -5
README.md CHANGED
@@ -39,15 +39,13 @@ import re
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  model_name = "Ilyes/wav2vec2-large-xlsr-53-french"
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-
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- model = Wav2Vec2ForCTC.from_pretrained(model_name).to('cuda')
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  processor = Wav2Vec2Processor.from_pretrained(model_name)
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  ds = load_dataset("common_voice", "fr", split="test", cache_dir="./data/fr")
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-
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-
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  chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\!\ǃ\?\«\‹\»\›“\”\\Ê¿\ʾ\„\∞\\|\.\,\;\:\*\—\–\─\―\_\/\:\ː\;\,\=\«\»\→]'
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  def map_to_array(batch):
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  speech, _ = torchaudio.load(batch["path"])
@@ -55,10 +53,10 @@ def map_to_array(batch):
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  batch["sampling_rate"] = resampler.new_freq
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  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
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  return batch
 
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  ds = ds.map(map_to_array)
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- resampler = torchaudio.transforms.Resample(48_000, 16_000)
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  def map_to_pred(batch):
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  features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
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  input_values = features.input_values.to(device)
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  model_name = "Ilyes/wav2vec2-large-xlsr-53-french"
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+ device = "cpu" # "cuda"
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+ model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
 
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  processor = Wav2Vec2Processor.from_pretrained(model_name)
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  ds = load_dataset("common_voice", "fr", split="test", cache_dir="./data/fr")
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  chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\!\ǃ\?\«\‹\»\›“\”\\Ê¿\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["sampling_rate"] = resampler.new_freq
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  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
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  return batch
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+ resampler = torchaudio.transforms.Resample(48_000, 16_000)
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  ds = ds.map(map_to_array)
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  def map_to_pred(batch):
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  features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
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  input_values = features.input_values.to(device)