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import gradio as gr |
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import torch |
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import torchaudio |
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from transformers import AutoFeatureExtractor, ASTForAudioClassification |
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model_name = "MIT/ast-finetuned-audioset-10-10-0.4593" |
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model = ASTForAudioClassification.from_pretrained(model_name) |
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) |
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device = torch.device("cpu") |
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model.to(device) |
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def classify_sound(file_path): |
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wv, sr = torchaudio.load(file_path) |
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original_shape = wv.shape |
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if wv.shape[0] > 1: |
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wv = wv.mean(dim=0, keepdim=True) |
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inputs = feature_extractor( |
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wv.squeeze().numpy(), sampling_rate=16000, return_tensors="pt" |
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) |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probs = torch.softmax(logits, dim=-1)[0] |
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top5 = torch.topk(probs, k=5) |
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top5_labels = [ |
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(model.config.id2label[idx.item()], round(prob.item(), 4)) |
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for idx, prob in zip(top5.indices, top5.values) |
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] |
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full_probs = { |
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model.config.id2label[i]: round(prob.item(), 4) |
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for i, prob in enumerate(probs) |
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} |
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return { |
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"Top 5 Predictions": dict(top5_labels), |
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"Sampling Rate": sr, |
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"Waveform Shape": str(original_shape), |
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"All Probabilities": full_probs |
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} |
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demo = gr.Interface( |
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fn=classify_sound, |
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inputs=gr.Audio(sources="upload", type="filepath"), |
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outputs=[ |
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gr.Label(label = "Top 5 Pred", num_top_classes=5), |
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gr.Textbox(label="Sample Rate"), |
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gr.Textbox(label="Waveform Shape"), |
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gr.JSON(label="All Class Probabilities") |
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], |
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title="Audio Classification with AST", |
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description="Upload an audio clip (speech, music, ambient sound, etc.). Model: MIT AST fine-tuned on AudioSet (10 classes).", |
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live=False, |
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) |
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demo.launch() |
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