117 lines
5.4 KiB
Python
117 lines
5.4 KiB
Python
from time import time
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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 transformers import GPT2Model, GPT2Config, GPT2LMHeadModel, GPT2PreTrainedModel
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from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
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from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
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from models.gpt_voice.mini_encoder import AudioMiniEncoder
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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 GptTtsHf(nn.Module):
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NUMBER_TEXT_TOKENS = len(symbols)+1
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START_TEXT_TOKEN = len(symbols)
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STOP_TEXT_TOKEN = 0
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NUMBER_MEL_CODES = 8194
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START_MEL_TOKEN = 8192
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STOP_MEL_TOKEN = 8193
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def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=200, max_mel_tokens=250, max_conditioning_inputs=3, checkpointing=True):
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super().__init__()
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self.max_mel_tokens = max_mel_tokens
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self.max_symbols_per_phrase = max_symbols_per_phrase
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self.model_dim = model_dim
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self.max_mel_tokens = max_mel_tokens
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self.max_conditioning_inputs = max_conditioning_inputs
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self.conditioning_encoder = AudioMiniEncoder(80, model_dim)
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self.text_pos_embedding = nn.Embedding(self.max_symbols_per_phrase + 1, model_dim)
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self.conditioning_embedding = nn.Embedding(self.max_conditioning_inputs, model_dim)
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self.mel_pos_embedding = nn.Embedding(self.max_mel_tokens + 2, model_dim)
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seq_length = 2+self.max_symbols_per_phrase+self.max_conditioning_inputs+self.max_mel_tokens
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self.gpt_config = GPT2Config(vocab_size=self.NUMBER_MEL_CODES,
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n_positions=seq_length,
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n_ctx=seq_length,
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n_embd=model_dim,
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n_layer=layers,
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n_head=heads,
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gradient_checkpointing=checkpointing,
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use_cache=not checkpointing)
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self.gpt = GPT2Model(self.gpt_config)
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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.NUMBER_MEL_CODES)
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def get_logits(self, text_inputs, cond_inputs, mel_targets, get_attns=False):
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assert text_inputs.shape[1] <= self.max_symbols_per_phrase
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assert cond_inputs.shape[1] <= self.max_conditioning_inputs
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assert mel_targets.shape[1] <= self.max_mel_tokens
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mel_targets = F.pad(mel_targets, (1,0), value=self.START_MEL_TOKEN)
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mel_targets = F.pad(mel_targets, (0, self.max_mel_tokens - mel_targets.shape[1]), value=self.STOP_MEL_TOKEN)
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mel_emb = self.gpt.get_input_embeddings()(mel_targets)
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mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_targets.device))
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text_targets = F.pad(text_inputs, (1,0), value=self.START_TEXT_TOKEN)
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text_targets = F.pad(text_inputs, (0, self.max_symbols_per_phrase - text_targets.shape[1]), value=self.STOP_TEXT_TOKEN)
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text_emb = self.gpt.get_input_embeddings()(text_targets)
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text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device))
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conds = []
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for k in range(cond_inputs.shape[1]):
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conds.append(self.conditioning_encoder(cond_inputs[:, k]))
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while len(conds) < self.max_conditioning_inputs:
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conds.append(conds[-1])
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conds = torch.stack(conds, dim=1)
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conds = conds + self.conditioning_embedding(torch.arange(conds.shape[1], device=conds.device))
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emb = torch.cat([text_emb, conds, mel_emb], dim=1)
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gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
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if get_attns:
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return gpt_out.attentions
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enc = gpt_out.last_hidden_state
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text_logits = self.final_norm(enc[:, :self.max_symbols_per_phrase])
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text_logits = self.text_head(text_logits)
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text_logits = text_logits.permute(0,2,1)
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mel_logits = self.final_norm(enc[:, -self.max_mel_tokens:])
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mel_logits = self.mel_head(mel_logits)
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mel_logits = mel_logits.permute(0,2,1)
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return text_logits, mel_logits
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def forward(self, text_inputs, cond_inputs, mel_targets, return_attentions=False):
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"""
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Forward pass
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text_inputs: long tensor, (b,t)
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cond_inputs: MEL float tensor, (b,c,80,s)
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mel_targets: long tensor, (b,m)
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"""
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text_logits, mel_logits = self.get_logits(text_inputs, cond_inputs, mel_targets, get_attns=return_attentions)
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if return_attentions:
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return mel_logits
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text_targets = F.pad(text_inputs, (0,self.max_symbols_per_phrase-text_inputs.shape[1]), value=self.STOP_TEXT_TOKEN)
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loss_text = F.cross_entropy(text_logits, text_targets.long())
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mel_targets = F.pad(mel_targets, (0,self.max_mel_tokens-mel_targets.shape[1]), value=self.STOP_MEL_TOKEN)
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loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
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return loss_text.mean(), loss_mel.mean(), mel_logits
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@register_model
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def register_gpt_tts_hf(opt_net, opt):
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return GptTtsHf(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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gpt = GptTtsHf()
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l = gpt(torch.randint(high=len(symbols), size=(2,100)),
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torch.randn(2,2,80,800),
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torch.randint(high=8192, size=(2,200)))
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