64 lines
2.8 KiB
Python
64 lines
2.8 KiB
Python
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import torch
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from torch import nn
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import torch.nn.functional as F
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from transformers import GPT2Config, GPT2Model
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from models.audio.music.music_quantizer import MusicQuantizer
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from models.audio.music.music_quantizer2 import MusicQuantizer2
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from trainer.networks import register_model
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from utils.util import opt_get
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class GptMusic(nn.Module):
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def __init__(self, dim, layers, num_target_vectors=512, num_target_groups=2, cv_dim=1024, num_upper_vectors=64, num_upper_groups=4):
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super().__init__()
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self.num_groups = num_target_groups
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self.config = GPT2Config(vocab_size=1, n_positions=8192, n_embd=dim, n_layer=layers, n_head=dim//64,
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n_inner=dim*2)
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self.target_quantizer = MusicQuantizer(inp_channels=256, inner_dim=[1024,1024,512], codevector_dim=cv_dim, codebook_size=num_target_vectors, codebook_groups=num_target_groups)
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del self.target_quantizer.decoder
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del self.target_quantizer.up
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self.upper_quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024,896,768,640,512,384], codevector_dim=cv_dim, codebook_size=num_upper_vectors, codebook_groups=num_upper_groups)
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del self.upper_quantizer.up
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self.gpt = GPT2Model(self.config)
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del self.gpt.wte # Unused, we'll do our own embeddings.
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self.embeddings = nn.ModuleList([nn.Embedding(num_target_vectors, dim // num_target_groups) for _ in range(num_target_groups)])
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self.upper_proj = nn.Conv1d(cv_dim, dim, kernel_size=1)
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self.heads = nn.ModuleList([nn.Linear(dim, num_target_vectors) for _ in range(num_target_groups)])
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def forward(self, mel):
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with torch.no_grad():
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self.target_quantizer.eval()
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codes = self.target_quantizer.get_codes(mel)
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upper_vector, upper_diversity = self.upper_quantizer(mel, return_decoder_latent=True)
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upper_vector = self.upper_proj(upper_vector)
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upper_vector = F.interpolate(upper_vector, size=codes.shape[1], mode='linear')
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upper_vector = upper_vector.permute(0,2,1)
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inputs = codes[:, :-1]
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upper_vector = upper_vector[:, :-1]
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targets = codes[:, 1:]
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h = [embedding(inputs[:, :, i]) for i, embedding in enumerate(self.embeddings)]
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h = torch.cat(h, dim=-1) + upper_vector
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h = self.gpt(inputs_embeds=h, return_dict=True).last_hidden_state
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losses = 0
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for i, head in enumerate(self.heads):
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logits = head(h).permute(0,2,1)
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loss = F.cross_entropy(logits, targets[:,:,i])
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losses = losses + loss
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return losses / self.num_groups
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@register_model
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def register_music_gpt(opt_net, opt):
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return GptMusic(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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model = GptMusic(512, 12)
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mel = torch.randn(2,256,400)
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model(mel)
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