113 lines
5.3 KiB
Python
113 lines
5.3 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.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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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):
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super().__init__()
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model_dim = 512
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max_mel_frames = 900 * 3 // 8 # 900 is the max number of MEL frames. The VQVAE outputs 3/8 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+max_symbols_per_phrase+max_mel_frames, n_embd=model_dim, n_head=8), do_pos_emb=False)
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self.gpt = Transformer(dim=model_dim, depth=8, seq_len=1+self.MAX_SYMBOLS_PER_PHRASE+max_mel_frames, heads=8)
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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 = [self.MEL_START_TOKEN, 0]
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while mel_seq[-1] != self.MEL_STOP_TOKEN and len(mel_seq) < self.max_mel_frames:
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mel_seq.append(0)
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mel_emb = self.mel_embedding(torch.tensor(mel_seq, dtype=torch.long, device=text_inputs.device)).unsqueeze(0)
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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[-1] = mel_codes[-1]
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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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# Prevent sending invalid tokens to the VAE
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mel_seq = [s if s < 512 else 0 for s in mel_seq]
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return mel_seq[:-1]
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@register_model
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def register_gpt_tts(opt_net, opt):
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return GptTts()
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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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