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app.py
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import whisper
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model = whisper.load_model("large")
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import gradio as gr
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import time
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def transcribe(audio):
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#time.sleep(3)
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# load audio and pad/trim it to fit 30 seconds
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audio = whisper.load_audio(audio)
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audio = whisper.pad_or_trim(audio)
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# make log-Mel spectrogram and move to the same device as the model
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mel = whisper.log_mel_spectrogram(audio).to(model.device)
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# detect the spoken language
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_, probs = model.detect_language(mel)
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print(f"Detected language: {max(probs, key=probs.get)}")
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# decode the audio
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options = whisper.DecodingOptions()
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result = whisper.decode(model, mel, options)
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return result.text
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