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Update README.md
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README.md
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---
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# Greek (el) version of the XLSR-Wav2Vec2 automatic speech recognition (ASR) model
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* language: el
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from datasets import load_dataset, load_metric
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import torch
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chars_to_ignore_regex = '[
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def remove_special_characters(batch):
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batch["text"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
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print(processor.decode(pred_ids[0]))
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# πού θέλεις να πάμε ρώτησε φοβισμένα ο βασιλιάς
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print("
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Reference:")
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print(common_voice_test_transcription["sentence"][example].lower())
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# πού θέλεις να πάμε; ρώτησε φοβισμένα ο βασιλιάς.
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model = Wav2Vec2ForCTC.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek")
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model.to("cuda")
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chars_to_ignore_regex = '[
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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### Acknowledgment
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Based on the tutorial of Patrick von Platen: https://huggingface.co/blog/fine-tune-xlsr-wav2vec2
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Original colab notebook here: https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb#scrollTo=V7YOT2mnUiea
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---
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# Greek (el) version of the XLSR-Wav2Vec2 automatic speech recognition (ASR) model
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### By the Hellenic Army Academy and the Technical University of Crete
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* language: el
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from datasets import load_dataset, load_metric
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import torch
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chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�]'
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def remove_special_characters(batch):
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batch["text"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
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print(processor.decode(pred_ids[0]))
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# πού θέλεις να πάμε ρώτησε φοβισμένα ο βασιλιάς
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print("\\\\
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Reference:")
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print(common_voice_test_transcription["sentence"][example].lower())
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# πού θέλεις να πάμε; ρώτησε φοβισμένα ο βασιλιάς.
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model = Wav2Vec2ForCTC.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek")
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model.to("cuda")
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chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�]'
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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### Acknowledgement
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The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)
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Based on the tutorial of Patrick von Platen: https://huggingface.co/blog/fine-tune-xlsr-wav2vec2
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Original colab notebook here: https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb#scrollTo=V7YOT2mnUiea
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