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