132 lines
6.2 KiB
Python
132 lines
6.2 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from models.gpt_voice.lucidrains_gpt import Transformer
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from models.gpt_voice.min_gpt import GPT, GPTConfig
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from models.tacotron2.taco_utils import get_mask_from_lengths
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from models.tacotron2.text import symbols
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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 GptTts(nn.Module):
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MAX_SYMBOLS_PER_PHRASE = 200
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NUMBER_SYMBOLS = len(symbols)
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NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS + MAX_SYMBOLS_PER_PHRASE + 2
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MEL_DICTIONARY_SIZE = 512+3
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MEL_START_TOKEN = MEL_DICTIONARY_SIZE-3
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MEL_STOP_TOKEN = MEL_DICTIONARY_SIZE-2
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def __init__(self, layers=8, model_dim=512, heads=8):
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super().__init__()
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max_mel_frames = 900 * 1 // 4 # 900 is the max number of MEL frames. The VQVAE outputs 1/8 of the input mel as tokens.
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self.model_dim = model_dim
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self.max_mel_frames = max_mel_frames
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self.text_embedding = nn.Embedding(self.NUMBER_TEXT_TOKENS, model_dim)
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self.mel_embedding = nn.Embedding(self.MEL_DICTIONARY_SIZE, model_dim)
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self.text_pos_embedding = nn.Embedding(self.MAX_SYMBOLS_PER_PHRASE, model_dim)
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self.mel_pos_embedding = nn.Embedding(max_mel_frames, model_dim)
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#self.gpt = GPT(GPTConfig(1+self.MAX_SYMBOLS_PER_PHRASE+max_mel_frames, n_layer=8, n_embd=model_dim, n_head=8), do_pos_emb=False)
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self.gpt = Transformer(dim=model_dim, depth=layers, seq_len=1+self.MAX_SYMBOLS_PER_PHRASE+max_mel_frames, heads=heads)
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self.final_norm = nn.LayerNorm(model_dim)
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self.text_head = nn.Linear(model_dim, self.NUMBER_TEXT_TOKENS)
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self.mel_head = nn.Linear(model_dim, self.MEL_DICTIONARY_SIZE)
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def forward(self, text_inputs, text_lengths, mel_targets, output_lengths):
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text_emb = self.text_embedding(text_inputs)
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text_emb = text_emb + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
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mel_emb = self.mel_embedding(mel_targets)
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mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_targets.shape[1], device=mel_targets.device))
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emb = torch.cat([text_emb, mel_emb], dim=1)
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enc = self.gpt(emb)
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# Compute logits for text and mel heads
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text_logits = self.final_norm(enc[:, :text_emb.shape[1]])
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mel_logits = self.final_norm(enc[:, text_emb.shape[1]:])
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text_logits = self.text_head(text_logits)
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mel_logits = self.mel_head(mel_logits)
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# Compute loss
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text_targets = text_inputs[:,1:]
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text_logits = text_logits.permute(0,2,1)[:,:,:-1] # The last element of the logits is unneeded because the input to the transformer contains a <EOS> token for both text and mel.
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loss_text = F.cross_entropy(text_logits, text_targets, reduction='none')
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mel_targets = mel_targets[:,1:]
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mel_logits = mel_logits.permute(0,2,1)[:,:,:-1]
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loss_mel = F.cross_entropy(mel_logits, mel_targets, reduction='none')
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# Fix up mel_logits so it can go into a VAE decoder as well.
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mel_codes = torch.argmax(F.softmax(mel_logits, dim=1), dim=1)
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mel_pad_mask = ~get_mask_from_lengths(output_lengths-1, mel_targets.shape[1])
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mel_codes = mel_codes * torch.ones_like(mel_codes).masked_fill_(mel_pad_mask, 0)
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mel_codes = mel_codes[:,:-1] # Strip off <EOS> token too (or padding). The important part is that the output sequence length is identical to the VAE input.
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extra_mask = mel_codes < self.MEL_DICTIONARY_SIZE-3 # The VAE doesn't know about START/STOP/PAD
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mel_codes = mel_codes * extra_mask
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# This class also returns the mel_targets for validation purposes. Format those.
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mel_targets = mel_targets[:,:-1]
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mel_targets = mel_targets * (mel_targets < self.MEL_DICTIONARY_SIZE-3)
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return loss_text.mean(), loss_mel.mean(), mel_codes, mel_targets
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def inference(self, text_inputs):
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text_emb = self.text_embedding(text_inputs)
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text_emb = text_emb + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
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mel_seq = torch.full((text_emb.shape[0],1), fill_value=self.MEL_START_TOKEN, device=text_emb.device)
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stop_encountered = torch.zeros((text_emb.shape[0],), device=text_emb.device)
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while not torch.all(stop_encountered) and len(mel_seq) < self.max_mel_frames:
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mel_emb = self.mel_embedding(mel_seq)
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mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
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emb = torch.cat([text_emb, mel_emb], dim=1)
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enc = self.gpt(emb)
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mel_logits = self.final_norm(enc[:, text_emb.shape[1]:])
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mel_logits = self.mel_head(mel_logits)
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mel_codes = torch.argmax(F.softmax(mel_logits, dim=-1), dim=-1)
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mel_seq = torch.cat([mel_seq, mel_codes[:, -1].unsqueeze(1)], dim=1)
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stop_encountered = torch.logical_or(stop_encountered, mel_seq[:,-1] == self.MEL_STOP_TOKEN)
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if len(mel_seq) >= self.max_mel_frames:
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print("Warning! Encountered frame limit before a stop token. Output is likely wrong.")
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# Format mel_seq so that the DVAE can actually use it (it is a two-tiered DVAE)
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cleaned = []
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for j in range(mel_seq.shape[0]):
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s = mel_seq[j][1:-1] # Strip out BOS and EOS tokens.
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gt = s >= 512
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l = (len(s)) // 3
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for i in reversed(range(l)):
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if gt[i]:
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l = i+1
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break
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top = s[:l]
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top = top + (top < 512) * 512
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bottom = s[l:l*3]
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bottom = bottom * (bottom < 512)
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combined = torch.cat([top,bottom], dim=0)
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assert not torch.any(combined < 0)
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combined = combined * (combined < 1024)
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cleaned.append(combined)
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return torch.stack(cleaned)
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@register_model
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def register_gpt_tts(opt_net, opt):
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return GptTts(**opt_get(opt_net, ['kwargs'], {})
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if __name__ == '__main__':
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gpt = GptTts()
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l1, l2, i = gpt(torch.randint(high=24, size=(2,60)),
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torch.tensor([55,58]),
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torch.randint(high=512, size=(2,310)),
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torch.tensor([300,305]))
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print(i.shape)
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#o = gpt.infer(torch.randint(high=24, size=(2,60)))
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#print(o.shape)
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