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import torch
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from audiotools import AudioSignal
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from audiotools.ml import BaseModel
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from encodec import EncodecModel
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class Encodec(BaseModel):
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def __init__(self, sample_rate: int = 24000, bandwidth: float = 24.0):
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super().__init__()
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if sample_rate == 24000:
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self.model = EncodecModel.encodec_model_24khz()
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else:
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self.model = EncodecModel.encodec_model_48khz()
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self.model.set_target_bandwidth(bandwidth)
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self.sample_rate = 44100
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def forward(
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self,
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audio_data: torch.Tensor,
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sample_rate: int = 44100,
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n_quantizers: int = None,
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):
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signal = AudioSignal(audio_data, sample_rate)
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signal.resample(self.model.sample_rate)
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recons = self.model(signal.audio_data)
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recons = AudioSignal(recons, self.model.sample_rate)
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recons.resample(sample_rate)
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return {"audio": recons.audio_data}
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if __name__ == "__main__":
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import numpy as np
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from functools import partial
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model = Encodec()
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for n, m in model.named_modules():
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o = m.extra_repr()
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p = sum([np.prod(p.size()) for p in m.parameters()])
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fn = lambda o, p: o + f" {p/1e6:<.3f}M params."
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setattr(m, "extra_repr", partial(fn, o=o, p=p))
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print(model)
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print("Total # of params: ", sum([np.prod(p.size()) for p in model.parameters()]))
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length = 88200 * 2
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x = torch.randn(1, 1, length).to(model.device)
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x.requires_grad_(True)
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x.retain_grad()
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out = model(x)["audio"]
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print(x.shape, out.shape)
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