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update tfd11

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This commit is contained in:
James Betker 2022-06-11 17:53:27 -06:00
parent 5c6c8f6904
commit 11e70dde14
1 changed files with 23 additions and 6 deletions
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codes/models/audio/music/transformer_diffusion11.py
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@ -220,15 +220,17 @@ class TransformerDiffusion(nn.Module):
class TransformerDiffusionWithQuantizer(nn.Module):
def __init__(self, quantizer_dims=[1024], freeze_quantizer_until=20000, **kwargs):
def __init__(self, quantizer_dims=[1024], quantizer_codebook_size=256, quantizer_codebook_groups=2,
freeze_quantizer_until=20000, **kwargs):
super().__init__()
self.internal_step = 0
self.freeze_quantizer_until = freeze_quantizer_until
self.diff = TransformerDiffusion(**kwargs)
self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims,
codevector_dim=quantizer_dims[0], codebook_size=256,
codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
codevector_dim=quantizer_dims[0], codebook_size=quantizer_codebook_size,
codebook_groups=quantizer_codebook_groups, max_gumbel_temperature=4,
min_gumbel_temperature=.5)
self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
del self.quantizer.up
@ -277,7 +279,7 @@ class TransformerDiffusionWithQuantizer(nn.Module):
groups = {
'blk1_attention_layers': attn1,
'blk2_attention_layers': attn2,
'blk2_attention_layers': attn3,
'blk3_attention_layers': attn3,
'attention_layers': attn1 + attn2 + attn3,
'blk1_ff_layers': ff1,
'blk2_ff_layers': ff2,
@ -356,15 +358,30 @@ def test_quant_model():
clip = torch.randn(2, 256, 400)
cond = torch.randn(2, 256, 400)
ts = torch.LongTensor([600, 600])
"""
# For music:
model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=1024,
prenet_channels=1024, num_heads=4,
input_vec_dim=1024, num_layers=20, prenet_layers=6,
dropout=.1)
quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
model.quantizer.load_state_dict(quant_weights, strict=False)
torch.save(model.state_dict(), 'sample.pth')
"""
# For TTS:
model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=1024,
prenet_channels=1024, num_heads=4,
input_vec_dim=1024, num_layers=12, prenet_layers=10,
quantizer_dims=[1024,768,512], quantizer_codebook_size=64,
quantizer_codebook_groups=4,
dropout=.1)
quant_weights = torch.load('X:\\dlas\\experiments\\train_tts_quant_64\\models\\15500_generator.pth')
model.quantizer.load_state_dict(quant_weights, strict=False)
torch.save(model.state_dict(), 'sample.pth')
print_network(model)
o = model(clip, ts, clip, cond)
model.get_grad_norm_parameter_groups()