from time import time import torch import torch.nn as nn import torch.nn.functional as F from transformers import GPT2Model, GPT2Config, GPT2LMHeadModel, GPT2PreTrainedModel from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions from transformers.utils.model_parallel_utils import get_device_map, assert_device_map from models.gpt_voice.gpt_asr_hf import GPT2InferenceModel from models.gpt_voice.mini_encoder import AudioMiniEncoder from models.tacotron2.text import symbols from trainer.networks import register_model from utils.util import opt_get class GptTtsHf(nn.Module): NUMBER_TEXT_TOKENS = len(symbols)+1 START_TEXT_TOKEN = len(symbols) STOP_TEXT_TOKEN = 0 NUMBER_MEL_CODES = 8194 START_MEL_TOKEN = 8192 STOP_MEL_TOKEN = 8193 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, mel_length_compression=256): super().__init__() self.max_mel_tokens = max_mel_tokens self.max_symbols_per_phrase = max_symbols_per_phrase self.model_dim = model_dim self.max_mel_tokens = max_mel_tokens self.max_conditioning_inputs = max_conditioning_inputs self.mel_length_compression = mel_length_compression self.conditioning_encoder = AudioMiniEncoder(80, model_dim) self.text_embedding = nn.Embedding(self.NUMBER_TEXT_TOKENS, model_dim) self.text_pos_embedding = nn.Embedding(self.max_symbols_per_phrase + 2, model_dim) self.conditioning_embedding = nn.Parameter(torch.randn(1,model_dim), requires_grad=True) self.mel_pos_embedding = nn.Embedding(self.max_mel_tokens + 2, model_dim) seq_length = 4+self.max_symbols_per_phrase+self.max_conditioning_inputs+self.max_mel_tokens self.gpt_config = GPT2Config(vocab_size=self.NUMBER_MEL_CODES, n_positions=seq_length, n_ctx=seq_length, n_embd=model_dim, n_layer=layers, n_head=heads, gradient_checkpointing=checkpointing, use_cache=not checkpointing) self.gpt = GPT2Model(self.gpt_config) 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.NUMBER_MEL_CODES) def get_logits(self, text_inputs, cond_inputs, mel_targets, get_attns=False): assert text_inputs.shape[1] <= self.max_symbols_per_phrase assert cond_inputs.shape[1] <= self.max_conditioning_inputs assert mel_targets.shape[1] <= self.max_mel_tokens text_targets = F.pad(text_inputs, (1,0), value=self.START_TEXT_TOKEN) text_targets = F.pad(text_targets, (0,1), value=self.STOP_TEXT_TOKEN) text_emb = self.text_embedding(text_targets) text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device)) conds = [] for k in range(cond_inputs.shape[1]): conds.append(self.conditioning_encoder(cond_inputs[:, k])) while len(conds) < self.max_conditioning_inputs: conds.append(conds[-1]) conds = torch.stack(conds, dim=1) conds = conds + self.conditioning_embedding mel_targets = F.pad(mel_targets, (1,0), value=self.START_MEL_TOKEN) mel_targets = F.pad(mel_targets, (0,1), value=self.STOP_MEL_TOKEN) mel_emb = self.gpt.get_input_embeddings()(mel_targets) mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_targets.device)) emb = torch.cat([text_emb, conds, mel_emb], dim=1) gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns) if get_attns: return gpt_out.attentions enc = gpt_out.last_hidden_state text_logits = self.final_norm(enc[:, :self.max_symbols_per_phrase]) text_logits = self.text_head(text_logits) text_logits = text_logits.permute(0,2,1) mel_logits = self.final_norm(enc[:, -self.max_mel_tokens:]) mel_logits = self.mel_head(mel_logits) mel_logits = mel_logits.permute(0,2,1) return text_logits, mel_logits def forward(self, text_inputs, cond_inputs, mel_targets, wav_lengths, return_attentions=False): """ Forward pass text_inputs: long tensor, (b,t) cond_inputs: MEL float tensor, (b,c,80,s) mel_targets: long tensor, (b,m) mel_lengths: long tensor, (b,) """ # Set padding areas within MEL (currently it is coded with the MEL code for ) mel_lengths = wav_lengths // self.mel_length_compression for b in range(len(mel_lengths)): if mel_lengths[b] < mel_targets.shape[-1]: mel_targets[b, mel_lengths[b]:] = self.STOP_MEL_TOKEN text_logits, mel_logits = self.get_logits(text_inputs, cond_inputs, mel_targets, get_attns=return_attentions) if return_attentions: return mel_logits text_targets = F.pad(text_inputs, (0,self.max_symbols_per_phrase-text_inputs.shape[1]), value=self.STOP_TEXT_TOKEN) loss_text = F.cross_entropy(text_logits, text_targets.long()) mel_targets = F.pad(mel_targets, (0,self.max_mel_tokens-mel_targets.shape[1]), value=self.STOP_MEL_TOKEN) loss_mel = F.cross_entropy(mel_logits, mel_targets.long()) return loss_text.mean(), loss_mel.mean(), mel_logits def inference(self, text_inputs, cond_inputs, do_sample=False, temperature=1.0, num_beams=8): if not hasattr(self, 'inference_model'): self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, self.text_pos_embedding, self.final_norm, self.text_head) text_targets = F.pad(text_inputs, (1,0), value=self.START_TEXT_TOKEN) text_targets = F.pad(text_targets, (0,1), value=self.STOP_TEXT_TOKEN) text_emb = self.text_embedding(text_targets) text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device)) conds = [] for k in range(cond_inputs.shape[1]): conds.append(self.conditioning_encoder(cond_inputs[:, k])) while len(conds) < self.max_conditioning_inputs: conds.append(conds[-1]) conds = torch.stack(conds, dim=1) conds = conds + self.conditioning_embedding(torch.arange(conds.shape[1], device=conds.device)) emb = torch.cat([text_emb, conds], dim=1) self.inference_model.store_mel_emb(emb) fake_inputs = torch.full((text_inputs.shape[0],emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=text_inputs.device) fake_inputs[:,-1] = self.START_MEL_TOKEN gen = self.inference_model.generate(fake_inputs, do_sample=do_sample, bos_token_id=self.START_MEL_TOKEN, pad_token_id=self.STOP_MEL_TOKEN, eos_token_id=self.STOP_MEL_TOKEN, max_length=emb.shape[1]+self.max_mel_tokens, temperature=temperature, num_beams=num_beams, use_cache=True) return gen[:, self.max_mel_frames:] @register_model def register_gpt_tts_hf(opt_net, opt): return GptTtsHf(**opt_get(opt_net, ['kwargs'], {})) if __name__ == '__main__': gpt = GptTtsHf(model_dim=1024, heads=16) l = gpt(torch.randint(high=len(symbols), size=(2,200)), torch.randn(2,2,80,800), torch.randint(high=8192, size=(2,250)), torch.tensor([150*256,195*256]))