159 lines
7.7 KiB
Python
159 lines
7.7 KiB
Python
import random
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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.gpt_asr_hf import GPT2InferenceModel
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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,
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checkpointing=True, mel_length_compression=1024, max_conditioning_length=44100//256):
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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_conditioning_inputs = max_conditioning_inputs
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self.mel_length_compression = mel_length_compression
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self.conditioning_encoder = AudioMiniEncoder(80, model_dim)
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self.text_embedding = nn.Embedding(self.NUMBER_TEXT_TOKENS, 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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self.max_conditioning_length = max_conditioning_length
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def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
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inp = F.pad(input, (1,0), value=start_token)
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tar = F.pad(input, (0,1), value=stop_token)
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return inp, tar
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def get_logits(self, text_inputs, cond_inputs, mel_inputs, get_attns=False):
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text_emb = self.text_embedding(text_inputs)
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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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mel_emb = self.gpt.get_input_embeddings()(mel_inputs)
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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[:, :text_emb.shape[1]])
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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[:, -mel_emb.shape[1]:])
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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, wav_lengths, 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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mel_lengths: long tensor, (b,)
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"""
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# Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
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mel_lengths = wav_lengths // self.mel_length_compression
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for b in range(len(mel_lengths)):
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if mel_lengths[b] < mel_targets.shape[-1]:
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mel_targets[b, mel_lengths[b]:] = self.STOP_MEL_TOKEN
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# Format conditioning inputs properly.
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if len(cond_inputs.shape) == 3:
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cond_inputs = cond_inputs.unsqueeze(1) # Format a single conditioning input as a set of {1}
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if cond_inputs.shape[-1] > self.max_conditioning_length:
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# Remember, that this doesn't necessarily mean that the conditioning inputs aren't mostly zero-padded, so
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# skew trimming towards the front end of the clip.
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rand_clip = random.randint(0, min(50, cond_inputs.shape[-1]-self.max_conditioning_length))
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cond_inputs = cond_inputs[:,:,:,rand_clip:rand_clip+self.max_conditioning_length]
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.START_TEXT_TOKEN, self.STOP_TEXT_TOKEN)
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mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_targets, self.START_MEL_TOKEN, self.STOP_MEL_TOKEN)
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text_logits, mel_logits = self.get_logits(text_inputs, cond_inputs, mel_inputs, get_attns=return_attentions)
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if return_attentions:
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return mel_logits
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loss_text = F.cross_entropy(text_logits, text_targets.long())
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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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def inference(self, text_inputs, cond_inputs, do_sample=False, temperature=1.0, num_beams=8, repetition_penalty=1):
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if not hasattr(self, 'inference_model'):
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self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, None, self.final_norm, self.mel_head)
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text_inputs = F.pad(text_inputs, (0, self.max_symbols_per_phrase - text_inputs.shape[1]), value=self.STOP_TEXT_TOKEN)
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.START_TEXT_TOKEN, self.STOP_TEXT_TOKEN)
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text_emb = self.text_embedding(text_inputs)
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# Format conditioning inputs properly.
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if len(cond_inputs.shape) == 3:
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cond_inputs = cond_inputs.unsqueeze(1) # Format a single conditioning input as a set of {1}
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if cond_inputs.shape[-1] > self.max_conditioning_length:
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cond_inputs = cond_inputs[:,:,:,:self.max_conditioning_length]
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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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emb = torch.cat([text_emb, conds], dim=1)
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self.inference_model.store_mel_emb(emb)
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fake_inputs = torch.full((emb.shape[0],emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=text_inputs.device)
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fake_inputs[:,-1] = self.START_MEL_TOKEN
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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,
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max_length=emb.shape[1]+self.max_mel_tokens, temperature=temperature, num_beams=num_beams, use_cache=True, repetition_penalty=repetition_penalty)
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return gen[:, fake_inputs.shape[1]:-1]
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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(model_dim=1024, heads=16)
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l = gpt(torch.randint(high=len(symbols), size=(2,200)),
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torch.randn(2,80,800),
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torch.randint(high=8192, size=(2,250)),
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torch.tensor([150*256,195*256]))
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