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import sys,os |
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) |
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import numpy as np |
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import argparse |
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import torch |
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from whisper.audio import load_audio |
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from hubert import hubert_model |
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def load_model(path, device): |
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model = hubert_model.hubert_soft(path) |
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model.eval() |
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model.to(device) |
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return model |
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def pred_vec(model, wavPath, vecPath, device): |
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feats = load_audio(wavPath) |
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feats = torch.from_numpy(feats).to(device) |
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feats = feats[None, None, :] |
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with torch.no_grad(): |
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vec = model.units(feats).squeeze().data.cpu().float().numpy() |
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np.save(vecPath, vec, allow_pickle=False) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.description = 'please enter embed parameter ...' |
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parser.add_argument("-w", "--wav", help="wav", dest="wav") |
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parser.add_argument("-v", "--vec", help="vec", dest="vec") |
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args = parser.parse_args() |
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print(args.wav) |
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print(args.vec) |
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wavPath = args.wav |
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vecPath = args.vec |
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device = "cpu" |
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hubert = load_model(os.path.join( |
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"hubert_pretrain", "hubert-soft-0d54a1f4.pt"), device) |
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pred_vec(hubert, wavPath, vecPath, device) |
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