take care of soundstream accepting audio without batch dimension

).cuda()

trainer.train()

# after a lot of training, you can test the autoencoding as so

audio = torch.randn(10080).cuda()

recons = soundstream(audio, return_recons_only = True) # (1, 10080) - 1 channel

```

Then three separate transformers (`SemanticTransformer`, `CoarseTransformer`, `FineTransformer`) need to be trained

import torchaudio.transforms as T

from einops import rearrange, reduce

from einops import rearrange, reduce, pack, unpack

from vector_quantize_pytorch import ResidualVQ

        assert path.exists()

        obj = torch.load(str(path))

        self.load_state_dict(obj['model'])

        exit()

    def non_discr_parameters(self):

        return [

        input_sample_hz = None,

        apply_grad_penalty = False

    ):

        x, ps = pack([x], '* n')

        if exists(input_sample_hz):

            x = resample(x, input_sample_hz, self.target_sample_hz)

        recon_x = self.decoder(x)

        if return_recons_only:

            recon_x, = unpack(recon_x, ps, '* c n')

            return recon_x

        # multi-scale discriminator loss

setup(

  name = 'audiolm-pytorch',

  packages = find_packages(exclude=[]),

  version = '0.15.1',

  version = '0.15.2',

  license='MIT',

  description = 'AudioLM - Language Modeling Approach to Audio Generation from Google Research - Pytorch',

  author = 'Phil Wang',