diff --git a/codes/models/audio/tts/autoregressive_codegen.py b/codes/models/audio/tts/autoregressive_codegen.py index b35cc764..b3c32ee4 100644 --- a/codes/models/audio/tts/autoregressive_codegen.py +++ b/codes/models/audio/tts/autoregressive_codegen.py @@ -3,10 +3,120 @@ import functools import torch import torch.nn as nn import torch.nn.functional as F -from x_transformers import XTransformer, TransformerWrapper, Encoder, Decoder +from transformers import GPT2PreTrainedModel, GPT2Config +from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions +from x_transformers import TransformerWrapper, Encoder, Decoder +from data.audio.voice_tokenizer import VoiceBpeTokenizer from models.arch_util import AttentionBlock +from scripts.audio.gen.speech_synthesis_utils import wav_to_mel from trainer.networks import register_model +from utils.util import load_audio + + +class InferenceModel(GPT2PreTrainedModel): + """ + Implementation of GPT2PreTrainedModel from transformers, which allows us to use their generation library with + this transformer. + """ + def __init__(self, model): + super().__init__(GPT2Config()) + self.transformer = model + self.context = None + + def parallelize(self, device_map=None): + # Not implemented. + pass + + def deparallelize(self): + # Not implemented. + pass + + def get_output_embeddings(self): + assert False, "Unsupported operation." + + def set_output_embeddings(self, new_embeddings): + assert False, "Unsupported operation." + + def store_context(self, context): + self.context = context + + def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): + token_type_ids = kwargs.get("token_type_ids", None) + # only last token for inputs_ids if past is defined in kwargs + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + if token_type_ids is not None: + token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past: + position_ids = position_ids[:, -1].unsqueeze(-1) + else: + position_ids = None + return { + "input_ids": input_ids, + "past_key_values": past, + "use_cache": kwargs.get("use_cache"), + "position_ids": position_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + } + + def forward( + self, + input_ids=None, + past_key_values=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + assert self.context is not None + assert inputs_embeds is None # Not supported by this inference model. + assert labels is None # Training not supported by this inference model. + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + hidden_states = self.transformer.decoder(input_ids, context=self.context, return_embeddings=True) + logits = self.transformer.decoder.transformer.to_logits(hidden_states) + + if not return_dict: + return (logits, ) + + return CausalLMOutputWithCrossAttentions( + loss=None, + logits=logits, + past_key_values=None, + hidden_states=hidden_states, + attentions=None, + cross_attentions=None, + ) + + @staticmethod + def _reorder_cache(past, beam_idx): + """ + This function is used to re-order the :obj:`past_key_values` cache if + :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is + called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step. + """ + return tuple( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) + for layer_past in past + ) class ResBlock(nn.Module): @@ -92,6 +202,7 @@ class AutoregressiveCodegen(nn.Module): self.START_TOKEN=8192 self.STOP_TOKEN=8193 + self.max_mel_tokens = max_mel_tokens self.mel_embedding = ConditioningEncoder(80, model_dim, do_checkpointing=False) self.encoder = CheckpointedXTransformerWrapper( num_tokens=num_text_tokens, @@ -139,6 +250,7 @@ class AutoregressiveCodegen(nn.Module): mel_codes[b, mel_lengths[b]:] = self.STOP_TOKEN mel_codes = F.pad(mel_codes, (0, 1), value=self.STOP_TOKEN) + # Build the context if len(conditioning_signal.shape) != 4: conditioning_signal = conditioning_signal.unsqueeze(1) cond_embs = [] @@ -147,6 +259,8 @@ class AutoregressiveCodegen(nn.Module): cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True) enc_text = self.encoder(text_codes, return_embeddings=True) context = torch.cat([cond_emb, enc_text], dim=1) + + # Execute the decoder dec_inputs = F.pad(mel_codes, (1,0), value=self.START_TOKEN)[:, :-1] dec = self.decoder(dec_inputs, context=context) if not return_loss: @@ -154,6 +268,25 @@ class AutoregressiveCodegen(nn.Module): loss_mel = F.cross_entropy(dec.permute(0,2,1), mel_codes) return loss_mel + def generate(self, conditioning_signal, text_codes, **hf_generate_kwargs): + if not hasattr(self, 'inference_model'): + self.inference_model = InferenceModel(self) + + if len(conditioning_signal.shape) != 4: + conditioning_signal = conditioning_signal.unsqueeze(1) + cond_embs = [] + for i in range(conditioning_signal.shape[1]): + cond_embs.append(self.mel_embedding(conditioning_signal[:, i])) + cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True) + enc_text = self.encoder(text_codes, return_embeddings=True) + context = torch.cat([cond_emb, enc_text], dim=1) + self.inference_model.store_context(context) + + gen = self.inference_model.generate(bos_token_id=self.START_TOKEN, pad_token_id=self.STOP_TOKEN, eos_token_id=self.STOP_TOKEN, + max_length=self.max_mel_tokens, output_attentions=False, return_dict_in_generate=True, + **hf_generate_kwargs) + return gen + @register_model def register_autoregressive_codegen(opt_net, opt): @@ -161,8 +294,9 @@ def register_autoregressive_codegen(opt_net, opt): if __name__ == '__main__': - codegen = AutoregressiveCodegen(1024, 20) + codegen = AutoregressiveCodegen(512, 20) torch.save(codegen.state_dict(), 'sample.pth') + codegen.generate(torch.randn((1,80,120)), torch.randint(0,256,(1,200))) codegen(torch.randint(0,256, (2,200)), torch.randn(2,80,120), torch.randint(0,8192, (2,350)),