159 lines
7.4 KiB
Python
159 lines
7.4 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 munch import munchify
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from tqdm import tqdm
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from models.arch_util import ConvGnSilu
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from models.gpt_voice.pixelshuffle_1d import PixelUnshuffle1D, PixelShuffle1D
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from models.tacotron2 import hparams
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from models.tacotron2.taco_utils import get_mask_from_lengths
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from models.tacotron2.tacotron2 import Postnet
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from models.tacotron2.text import symbols
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from models.gpt_voice.min_gpt import GPT, GPTConfig
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from trainer.networks import register_model
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class GptTts(nn.Module):
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NUMBER_SYMBOLS = len(symbols)+3
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TEXT_START_TOKEN = NUMBER_SYMBOLS-3
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TEXT_STOP_TOKEN = NUMBER_SYMBOLS-2
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TEXT_PAD_TOKEN = NUMBER_SYMBOLS-1
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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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MEL_PAD_TOKEN = MEL_DICTIONARY_SIZE-1
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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_symbols_per_phrase = 200
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max_mel_frames = 900 * 3 // 8 # The VQVAE outputs 3/8 of the input mel as tokens.
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mel_dim=80
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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_SYMBOLS, model_dim)
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self.mel_embedding = nn.Embedding(self.MEL_DICTIONARY_SIZE, model_dim)
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# *_tags are additively applied to
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self.text_pos_embedding = nn.Embedding(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.final_norm = nn.LayerNorm(model_dim)
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self.text_head = nn.Linear(model_dim, self.NUMBER_SYMBOLS)
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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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output_lengths = output_lengths * 3 // 8 # The data we are dealing with has been compressed by the vqvae.
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# Add the stop tokens to the end of the texts and mels. Theoretically this would be better done at the dataloader level.
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batch_range = torch.arange(0, text_inputs.shape[0])
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text_inputs = F.pad(text_inputs, (0,1))
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text_inputs.index_put_((batch_range, text_lengths), torch.tensor([self.TEXT_STOP_TOKEN], dtype=torch.long, device=text_inputs.device))
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text_lengths = text_lengths + 1
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mel_targets = F.pad(mel_targets, (0,1))
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mel_targets.index_put_((batch_range, output_lengths), torch.tensor([self.MEL_STOP_TOKEN], dtype=torch.long, device=text_inputs.device))
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output_lengths = output_lengths + 1
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# Add the start tokens to the beginnings of the texts and mels.
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text_inputs = F.pad(text_inputs, (1,0), value=self.TEXT_START_TOKEN)
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text_lengths = text_lengths + 1
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mel_targets = F.pad(mel_targets, (1,0), value=self.MEL_START_TOKEN)
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output_lengths = output_lengths + 1
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# Add padding as well. This also should realistically be done at the dataloader level.
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text_pad_mask = ~get_mask_from_lengths(text_lengths, text_inputs.shape[1])
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text_inputs.data.masked_fill_(text_pad_mask, self.TEXT_PAD_TOKEN)
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mel_pad_mask = ~get_mask_from_lengths(output_lengths, mel_targets.shape[1])
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mel_targets.data.masked_fill_(mel_pad_mask, self.MEL_PAD_TOKEN)
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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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text_logits = self.text_head(text_logits)
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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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# Compute loss
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loss_text = F.cross_entropy(text_logits.permute(0,2,1)[:,:,1:], text_inputs[:,1:], reduction='none')
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loss_mel = F.cross_entropy(mel_logits.permute(0,2,1)[:,:,1:], mel_targets[:,1:], reduction='none')
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# Apply a reduction factor across MEL_PAD and TEXT_PAD tokens.
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pad_loss_reduction_factor = .01
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loss_text = loss_text * torch.ones_like(loss_text).masked_fill_(text_pad_mask[:,1:], pad_loss_reduction_factor)
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loss_mel = loss_mel * torch.ones_like(loss_mel).masked_fill_(mel_pad_mask[:,1:], pad_loss_reduction_factor)
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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_codes = mel_codes[:,1:]
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mel_codes = mel_codes * torch.ones_like(mel_codes).masked_fill_(mel_pad_mask[:,1:], 0)
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mel_codes = mel_codes[:,:-1]
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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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return loss_text.mean(), loss_mel.mean(), mel_codes
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def inference(self, text_inputs, mel_guide):
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MEL_HEAD_EXPANSION = 2
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GATE_THRESHOLD = .95
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text_emb = self.text_embedding(text_inputs)
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text_emb = self.text_preprocess_xformer(text_emb, text_emb.shape[1])
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text_emb = text_emb + self.text_tags
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b,s,c = text_emb.shape
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emb = torch.cat([text_emb,
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self.separator.repeat(text_emb.shape[0],1,1),], dim=1)
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#self.test_guide(mel_guide)], dim=1)
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completed = torch.zeros((b,), device=text_inputs.device, dtype=torch.bool)
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output = None
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for i in tqdm(range(self.max_mel_frames)):
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enc = self.gpt(emb, text_emb.shape[1])
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inferred = enc[:,s:,:].permute(0,2,1)
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# Create output frames.
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inferred_mel_frame = self.mel_head(inferred)[:,:,-MEL_HEAD_EXPANSION:]
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inferred_mel_frame = inferred_mel_frame * (~completed).float().view(b,1,1)
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if output is None:
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output = inferred_mel_frame
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else:
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output = torch.cat([output, inferred_mel_frame], dim=2)
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# Test termination condition
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gate = F.sigmoid(self.gate_head(inferred)).max(dim=-1).values # TODO: accept single-frame terminations.
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completed = completed.logical_or((gate > GATE_THRESHOLD).squeeze(1)) # This comprises a latch - but that may not be wise.
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if torch.all(completed):
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break
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# Apply inferred mel_frames to emb for next pass.
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mel_emb = self.mel_encoder(output).permute(0,2,1)
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mel_emb = mel_emb + self.audio_tags
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emb = torch.cat([text_emb,
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self.separator.repeat(text_emb.shape[0],1,1),
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mel_emb], dim=1)
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if i == self.max_mel_frames//2:
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print("Warning! Inference hit mel frame cap without encountering a stop token.")
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break
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return output
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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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