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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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