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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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import PIL |
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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 isinstance(audio_file, str): |
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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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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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gr.Interface( |
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fn=predict, |
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inputs=[ |
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gr.Audio(sources=["upload", "microphone"], type="filepath", label="Upload or Record audio (WAV or MP3)"), |
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], |
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outputs=gr.components.Label(num_top_classes=3), |
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live=True |
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).launch() |