Decrease resolution requirements to 2048
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@ -297,7 +297,7 @@ class DiffusionTts(nn.Module):
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:return: an [N x C x ...] Tensor of outputs.
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"""
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orig_x_shape = x.shape[-1]
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cm = ceil_multiple(x.shape[-1], 4096)
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cm = ceil_multiple(x.shape[-1], 2048)
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if cm != 0:
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pc = (cm-x.shape[-1])/x.shape[-1]
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x = F.pad(x, (0,cm-x.shape[-1]))
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@ -310,7 +310,7 @@ class DiffusionVocoderWithRef(nn.Module):
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:param y: an [N] Tensor of labels, if class-conditional.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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assert x.shape[-1] % 4096 == 0 # This model operates at base//4096 at it's bottom levels, thus this requirement.
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assert x.shape[-1] % 2048 == 0 # This model operates at base//2048 at it's bottom levels, thus this requirement.
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if self.conditioning_enabled:
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assert conditioning_input is not None
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@ -65,9 +65,9 @@ def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, c
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if plt_spec:
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plot_spectrogram(mel[0].cpu())
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# Pad MEL to multiples of 4096//spectrogram_compression_factor
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# Pad MEL to multiples of 2048//spectrogram_compression_factor
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msl = mel.shape[-1]
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dsl = 4096 // spectrogram_compression_factor
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dsl = 2048 // spectrogram_compression_factor
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gap = dsl - (msl % dsl)
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if gap > 0:
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mel = torch.nn.functional.pad(mel, (0, gap))
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