import torch import torch.nn as nn import torch.nn.functional as F from transformers import GPT2Model, GPT2Config from models.arch_util import AttentionBlock from models.gpt_voice.gpt_asr_hf import GPT2InferenceModel from models.tacotron2.text import symbols from trainer.networks import register_model from utils.util import opt_get class ConditioningEncoder(nn.Module): def __init__(self, spec_dim, embedding_dim, attn_blocks=6, num_attn_heads=4, do_checkpointing=False): super().__init__() attn = [] self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1) for a in range(attn_blocks): attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing)) self.attn = nn.Sequential(*attn) self.dim = embedding_dim self.do_checkpointing = do_checkpointing def forward(self, x): h = self.init(x) h = self.attn(h) return h[:, :, 0] class GptTtsHf(nn.Module): NUMBER_TEXT_TOKENS = 256 # The number of tokens produced by our bespoke BPE tokenizer. START_TEXT_TOKEN = 255 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=80, max_mel_tokens=250, max_conditioning_inputs=3, checkpointing=True, mel_length_compression=1024, max_conditioning_length=60): 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_conditioning_inputs = max_conditioning_inputs self.mel_length_compression = mel_length_compression self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads) self.text_embedding = nn.Embedding(self.NUMBER_TEXT_TOKENS, model_dim) seq_length = 2+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) self.max_conditioning_length = max_conditioning_length def build_aligned_inputs_and_targets(self, input, start_token, stop_token): inp = F.pad(input, (1,0), value=start_token) tar = F.pad(input, (0,1), value=stop_token) return inp, tar def get_logits(self, text_inputs, cond_input, mel_inputs, get_attns=False): text_emb = self.text_embedding(text_inputs) cond = self.conditioning_encoder(cond_input).unsqueeze(1) mel_emb = self.gpt.get_input_embeddings()(mel_inputs) emb = torch.cat([text_emb, cond, 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[:, :text_emb.shape[1]]) text_logits = self.text_head(text_logits) text_logits = text_logits.permute(0,2,1) mel_logits = self.final_norm(enc[:, -mel_emb.shape[1]:]) 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_input, 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 # Randomly permute the conditioning spectrogram, to destroy any structure present. cond_input = cond_input[:,:,torch.randperm(cond_input.shape[-1])] if cond_input.shape[-1] > self.max_conditioning_length: cond_input = cond_input[:,:,:self.max_conditioning_length] text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.START_TEXT_TOKEN, self.STOP_TEXT_TOKEN) mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_targets, self.START_MEL_TOKEN, self.STOP_MEL_TOKEN) text_logits, mel_logits = self.get_logits(text_inputs, cond_input, mel_inputs, get_attns=return_attentions) if return_attentions: return mel_logits loss_text = F.cross_entropy(text_logits, text_targets.long()) 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_input, **hf_generate_kwargs): if not hasattr(self, 'inference_model'): self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, None, self.final_norm, self.mel_head) text_inputs = F.pad(text_inputs, (0, self.max_symbols_per_phrase - text_inputs.shape[1]), value=self.STOP_TEXT_TOKEN) text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.START_TEXT_TOKEN, self.STOP_TEXT_TOKEN) text_emb = self.text_embedding(text_inputs) # Randomly permute the conditioning spectrogram, to destroy any structure present. cond_input = cond_input[:,:,torch.randperm(cond_input.shape[-1])] if cond_input.shape[-1] > self.max_conditioning_length: cond_input = cond_input[:,:,:self.max_conditioning_length] cond = self.conditioning_encoder(cond_input).unsqueeze(1) emb = torch.cat([text_emb, cond], dim=1) self.inference_model.store_mel_emb(emb) fake_inputs = torch.full((emb.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, 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, **hf_generate_kwargs) return gen[:, fake_inputs.shape[1]:] @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.arange(0, 80, 1, dtype=torch.float).view(1,80,1).repeat(2,1,800), torch.randint(high=8192, size=(2,250)), torch.tensor([150*256,195*256]))