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import gradio |
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import torchaudio |
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from fastai.vision.all import * |
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from fastai.learner import load_learner |
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from torchvision.utils import save_image |
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from huggingface_hub import hf_hub_download |
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model = load_learner( |
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hf_hub_download("kurianbenoy/music_genre_classification_baseline", "model.pkl") |
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) |
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EXAMPLES_PATH = Path("./examples") |
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labels = model.dls.vocab |
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with open("article.md") as f: |
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article = f.read() |
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interface_options = { |
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"title": "Music Genre Classification", |
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"description": "A simple baseline model for classifying music genres with fast.ai on [Kaggle competition data](https://www.kaggle.com/competitions/kaggle-pog-series-s01e02/data)", |
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"article": article, |
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"interpretation": "default", |
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"layout": "horizontal", |
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"examples": ["000003.ogg", "000032.ogg", "000038.ogg", "000050.ogg", "000103.ogg"], |
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"allow_flagging": "never" |
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} |
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N_FFT = 2048 |
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HOP_LEN = 1024 |
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def create_spectrogram(filename): |
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audio, sr = torchaudio.load(filename) |
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specgram = torchaudio.transforms.MelSpectrogram( |
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sample_rate=sr, |
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n_fft=N_FFT, |
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win_length=N_FFT, |
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hop_length=HOP_LEN, |
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center=True, |
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pad_mode="reflect", |
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power=2.0, |
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norm="slaney", |
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onesided=True, |
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n_mels=224, |
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mel_scale="htk", |
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)(audio).mean(axis=0) |
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specgram = torchaudio.transforms.AmplitudeToDB()(specgram) |
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specgram = specgram - specgram.min() |
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specgram = specgram / specgram.max() |
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return specgram |
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def create_image(filename): |
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specgram = create_spectrogram(filename) |
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dest = Path("temp.png") |
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save_image(specgram, "temp.png") |
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def predict(img): |
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img = PILImage.create(img) |
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_pred, _pred_w_idx, probs = model.predict(img) |
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labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)} |
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return labels_probs |
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def end2endpipeline(filename): |
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create_image(filename) |
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return predict("temp.png") |
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demo = gradio.Interface( |
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fn=end2endpipeline, |
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inputs=gradio.inputs.Audio(source="upload", type="filepath"), |
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outputs=gradio.outputs.Label(num_top_classes=5), |
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**interface_options, |
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) |
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launch_options = { |
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"enable_queue": True, |
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"share": False, |
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"cache_examples": True, |
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} |
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demo.launch(**launch_options) |
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