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import torchaudio |
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import soundfile as sf |
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import torch |
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from transformers import AutoProcessor, AutoModelForAudioClassification |
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from transformers import AutoFeatureExtractor |
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feature_extractor = AutoFeatureExtractor.from_pretrained("ThomasR/facebook_wav2vec2-large_October_03_2023_05h34PM") |
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model = AutoModelForAudioClassification.from_pretrained("ThomasR/facebook_wav2vec2-large_October_03_2023_05h34PM") |
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label2id={'fake':0, 'real':1} |
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id2label = {v:k for k,v in label2id.items()} |
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def predict(audio_path): |
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wavform, sample_rate = sf.read(audio_path) |
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inputs = feature_extractor( |
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wavform, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt", max_length=16000, truncation=True, padding=True |
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) |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probabilities = torch.sigmoid(logits[0]) |
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labels = (probabilities > 0.5).long() |
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pred_probs = list(probabilities.tolist()) |
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LABELS=list(id2label.values()) |
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pred_labels_and_probs = {LABELS[i]: round(float(pred_probs[i]),4) for i in range(len(LABELS))} |
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return pred_labels_and_probs |
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audio_path = 'fake_Elevenlabs_common_voice_en_36808626_Harry.wav' |
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predict(audio_path) |
