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import warnings |
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import torch |
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import gradio as gr |
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import librosa |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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warnings.filterwarnings("ignore") |
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processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") |
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model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") |
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def transcribe_audio(audio_path): |
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try: |
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audio, sr = librosa.load(audio_path, sr=16000) |
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input_values = processor(audio, return_tensors='pt', sampling_rate=sr).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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transcriptions = processor.batch_decode(predicted_ids)[0] |
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return transcriptions |
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except Exception as e: |
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return str(e) |
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demo = gr.Interface( |
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fn=transcribe_audio, |
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inputs=gr.Audio(type='filepath'), |
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outputs='text', |
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title="Subtitle Generator", |
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description="This tool transcribes audio files into text" |
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) |
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demo.launch() |
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