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Create app.py
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app.py
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import gradio as gr
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from fastai.vision.all import *
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import librosa
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import numpy as np
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import matplotlib.pyplot as plt
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from pydub import AudioSegment
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import tempfile
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learn = load_learner('model.pkl')
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labels = learn.dls.vocab
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def audio_to_spectrogram(audio_file):
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if audio_file.endswith('.mp3'):
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with tempfile.NamedTemporaryFile(suffix='.wav') as temp_wav:
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audio = AudioSegment.from_mp3(audio_file)
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audio.export(temp_wav.name, format='wav')
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y, sr = librosa.load(temp_wav.name, sr=None)
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else:
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y, sr = librosa.load(audio_file, sr=None)
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S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128, fmax=8000)
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S_dB = librosa.power_to_db(S, ref=np.max)
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fig, ax = plt.subplots()
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img = librosa.display.specshow(S_dB, x_axis='time', y_axis='mel', sr=sr, fmax=8000, ax=ax)
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fig.colorbar(img, ax=ax, format='%+2.0f dB')
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ax.set(title='Mel-frequency spectrogram')
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spectrogram_file = "spectrogram.png"
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plt.savefig(spectrogram_file)
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plt.close()
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return spectrogram_file
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def predict(audio):
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spectrogram_file = audio_to_spectrogram(audio)
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img = PILImage.create(spectrogram_file)
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img = img.resize((512, 512))
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pred, pred_idx, probs = learn.predict(img)
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return {labels[i]: float(probs[i]) for i in range(len(labels))}
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examples = ['example_audio.mp3']
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gr.Interface(
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fn=predict,
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inputs=gr.Audio(sources="upload", type="filepath", label="Upload audio (WAV or MP3)"),
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outputs=gr.components.Label(num_top_classes=3),
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examples=examples,
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).launch(
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