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Rework conditioning inputs provided

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This commit is contained in:
James Betker 2021-10-26 10:46:33 -06:00
parent 21b6daa0ed
commit 3a9d1c53ea
1 changed files with 5 additions and 7 deletions
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12
codes/models/gpt_voice/unet_diffusion_vocoder_with_ref.py
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@ -283,7 +283,7 @@ class DiffusionVocoderWithRef(nn.Module):
self.middle_block.apply(convert_module_to_f32) self.middle_block.apply(convert_module_to_f32)
self.output_blocks.apply(convert_module_to_f32) self.output_blocks.apply(convert_module_to_f32)
def forward(self, x, timesteps, spectrogram, conditioning_inputs=None, num_conditioning_signals=None): def forward(self, x, timesteps, spectrogram, conditioning_input=None):
""" """
Apply the model to an input batch. Apply the model to an input batch.
@ -294,14 +294,12 @@ class DiffusionVocoderWithRef(nn.Module):
""" """
assert x.shape[-1] % 4096 == 0 # This model operates at base//4096 at it's bottom levels, thus this requirement. assert x.shape[-1] % 4096 == 0 # This model operates at base//4096 at it's bottom levels, thus this requirement.
if self.conditioning_enabled: if self.conditioning_enabled:
assert conditioning_inputs is not None assert conditioning_input is not None
assert num_conditioning_signals is not None
hs = [] hs = []
emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels)) emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
if self.conditioning_enabled: if self.conditioning_enabled:
#emb2 = torch.stack([self.contextual_embedder(ci.squeeze(1)) for ci in list(torch.chunk(conditioning_inputs, conditioning_inputs.shape[1], dim=1))], dim=1) emb2 = self.contextual_embedder(conditioning_input)
emb2 = self.contextual_embedder(conditioning_inputs[:, 0])
emb = emb1 + emb2 emb = emb1 + emb2
else: else:
emb = emb1 emb = emb1
@ -331,7 +329,7 @@ if __name__ == '__main__':
clip = torch.randn(2, 1, 40960) clip = torch.randn(2, 1, 40960)
#spec = torch.randint(8192, (2, 40,)) #spec = torch.randint(8192, (2, 40,))
spec = torch.randn(2,512,160) spec = torch.randn(2,512,160)
cond = torch.randn(2, 3, 80, 173) cond = torch.randn(2, 80, 173)
ts = torch.LongTensor([555, 556]) ts = torch.LongTensor([555, 556])
model = DiffusionVocoderWithRef(32, conditioning_inputs_provided=True, time_embed_dim_multiplier=8) model = DiffusionVocoderWithRef(32, conditioning_inputs_provided=True, time_embed_dim_multiplier=8)
print(model(clip, ts, spec, cond, 3).shape) print(model(clip, ts, spec, cond).shape)