d9936df363
- Adds a script which preprocesses quantized mels given a DVAE - Adds a dataset which can consume preprocessed qmels - Reworks GPT TTS to consume the outputs of that dataset (removes logic to add padding and start/end tokens) - Adds inference to gpt_tts
121 lines
5.3 KiB
Python
121 lines
5.3 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from munch import munchify
|
|
from torch import LongTensor
|
|
from tqdm import tqdm
|
|
|
|
from models.arch_util import ConvGnSilu
|
|
from models.gpt_voice.pixelshuffle_1d import PixelUnshuffle1D, PixelShuffle1D
|
|
from models.tacotron2 import hparams
|
|
from models.tacotron2.taco_utils import get_mask_from_lengths
|
|
from models.tacotron2.tacotron2 import Postnet
|
|
from models.tacotron2.text import symbols
|
|
from models.gpt_voice.min_gpt import GPT, GPTConfig
|
|
from trainer.networks import register_model
|
|
|
|
|
|
class GptTts(nn.Module):
|
|
NUMBER_SYMBOLS = len(symbols)+3
|
|
TEXT_START_TOKEN = NUMBER_SYMBOLS-3
|
|
TEXT_STOP_TOKEN = NUMBER_SYMBOLS-2
|
|
TEXT_PAD_TOKEN = NUMBER_SYMBOLS-1
|
|
MEL_DICTIONARY_SIZE = 512+3
|
|
MEL_START_TOKEN = MEL_DICTIONARY_SIZE-3
|
|
MEL_STOP_TOKEN = MEL_DICTIONARY_SIZE-2
|
|
MEL_PAD_TOKEN = MEL_DICTIONARY_SIZE-1
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
model_dim = 512
|
|
max_symbols_per_phrase = 200
|
|
max_mel_frames = 900 * 3 // 8 # The VQVAE outputs 3/8 of the input mel as tokens.
|
|
mel_dim=80
|
|
|
|
self.model_dim = model_dim
|
|
self.max_mel_frames = max_mel_frames
|
|
self.text_embedding = nn.Embedding(self.NUMBER_SYMBOLS, model_dim)
|
|
self.mel_embedding = nn.Embedding(self.MEL_DICTIONARY_SIZE, model_dim)
|
|
# *_tags are additively applied to
|
|
self.text_pos_embedding = nn.Embedding(max_symbols_per_phrase, model_dim)
|
|
self.mel_pos_embedding = nn.Embedding(max_mel_frames, model_dim)
|
|
self.gpt = GPT(GPTConfig(1+max_symbols_per_phrase+max_mel_frames, n_embd=model_dim, n_head=8), do_pos_emb=False)
|
|
|
|
self.final_norm = nn.LayerNorm(model_dim)
|
|
self.text_head = nn.Linear(model_dim, self.NUMBER_SYMBOLS)
|
|
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]])
|
|
text_logits = self.text_head(text_logits)
|
|
mel_logits = self.final_norm(enc[:, text_emb.shape[1]:])
|
|
mel_logits = self.mel_head(mel_logits)
|
|
|
|
# Compute loss
|
|
loss_text = F.cross_entropy(text_logits.permute(0,2,1)[:,:,1:], text_inputs[:,1:], reduction='none')
|
|
loss_mel = F.cross_entropy(mel_logits.permute(0,2,1)[:,:,1:], mel_targets[:,1:], reduction='none')
|
|
|
|
# Apply a reduction factor across MEL_PAD and TEXT_PAD tokens.
|
|
pad_loss_reduction_factor = .01
|
|
text_pad_mask = ~get_mask_from_lengths(text_lengths, text_inputs.shape[1])
|
|
mel_pad_mask = ~get_mask_from_lengths(output_lengths, mel_targets.shape[1])
|
|
loss_text = loss_text * torch.ones_like(loss_text).masked_fill_(text_pad_mask[:,1:], pad_loss_reduction_factor)
|
|
loss_mel = loss_mel * torch.ones_like(loss_mel).masked_fill_(mel_pad_mask[:,1:], pad_loss_reduction_factor)
|
|
|
|
# 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_codes = mel_codes[:,1:-1] # Strip off first and last tokens (START+STOP were added by the dataloader)
|
|
mel_codes = mel_codes * torch.ones_like(mel_codes).masked_fill_(mel_pad_mask[:,1:-1], 0)
|
|
extra_mask = mel_codes < self.MEL_DICTIONARY_SIZE-3 # The VAE doesn't know about START/STOP/PAD
|
|
mel_codes = mel_codes * extra_mask
|
|
|
|
return loss_text.mean(), loss_mel.mean(), mel_codes
|
|
|
|
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 = [self.MEL_START_TOKEN, 0]
|
|
while mel_seq[-1] != self.MEL_STOP_TOKEN and len(mel_seq) < self.max_mel_frames:
|
|
mel_emb = self.mel_embedding(LongTensor(mel_seq, device=text_inputs.device))
|
|
mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_seq.shape[1], device=mel_seq.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[-1] = mel_codes[-1]
|
|
mel_seq.append(0)
|
|
|
|
if len(mel_seq) >= self.max_mel_frames:
|
|
print("Warning! Encountered frame limit before a stop token. Output is likely wrong.")
|
|
|
|
return mel_seq[:-1]
|
|
|
|
|
|
@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)
|
|
|
|
|