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--- |
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license: mit |
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language: |
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- vi |
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metrics: |
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- wer |
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base_model: |
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- facebook/wav2vec2-xls-r-300m |
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pipeline_tag: automatic-speech-recognition |
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--- |
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Tôi đã fine-tune với 15Gb dữ liệu audio với kết quả Wer: 24.46 |
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## Cách sử dụng |
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``` |
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import torch |
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
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import torchaudio |
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mydevice = 'cuda' |
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processor = Wav2Vec2Processor.from_pretrained("hataphu/wav2vec2-vi-300m") |
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model = Wav2Vec2ForCTC.from_pretrained("hataphu/wav2vec2-vi-300m") |
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model.to(mydevice) |
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model.eval() |
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audio_input, sampling_rate = torchaudio.load('audio-path-file') |
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input_values = processor( |
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audio_input.squeeze().numpy(), sampling_rate=sampling_rate |
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).input_values[0] |
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logits = model(torch.tensor(input_values).unsqueeze(0).to(mydevice)).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.decode(predicted_ids[0]) |
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print(transcription) |
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``` |
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