DL-Art-School/codes/models/audio/music/gpt_music.py
2022-06-04 22:23:08 -06:00

64 lines
2.8 KiB
Python

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