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import gradio as gr |
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
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import torch |
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import phonemizer |
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import librosa |
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processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft") |
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model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft") |
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waveform, sample_rate = librosa.load('harvard.wav', sr=16000) |
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input_values = processor(waveform, sampling_rate=sample_rate, return_tensors="pt").input_values |
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with torch.no_grad(): |
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logits = model(input_values).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(predicted_ids) |
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def showTranscription(transcription): |
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return transcription |
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iface = gr.Interface(fn=showTranscription, inputs="text", outputs="text") |
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iface.launch() |