import functools
from math import log
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
def null_position_embeddings(range, dim):
return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
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 TopEncoder(nn.Module):
def __init__(self, layers, dim, heads, do_checkpointing=False, dim_reduction=16):
self.init = nn.Conv1d(dim, dim, kernel_size=1)
reduction_layers = []
for j in range(int(log(dim_reduction, 2))):
reduction_layers.append(AttentionBlock(dim, heads, do_checkpoint=do_checkpointing))
reduction_layers.append(nn.Conv1d(dim, dim, kernel_size=3, padding=1, stride=2))
self.reduction_layers = nn.Sequential(*reduction_layers)
actual_layers = [AttentionBlock(dim, heads, do_checkpoint=do_checkpointing) for _ in range(layers)]
self.actual_layers = nn.Sequential(*actual_layers)
def forward(self, x):
h = self.init(x)
h = self.reduction_layers(h)
h = self.actual_layers(h)
return h
class UnifiedGptVoice(nn.Module):
"""
Derived from GptTtsHf, but offers multiple modes of autoregressive operation:
- Text only
- Voice only
- Text conditioned on voice
- Voice conditioned on text
"""
def __init__(self, top_encoder_layers=4, top_layers=8, bottom_layers=8, top_dim_reduction=16, model_dim=512, heads=8,
max_symbols_per_phrase=120, max_mel_tokens=250, max_total_tokens=370, max_conditioning_inputs=3,
checkpointing=True, mel_length_compression=1024, max_conditioning_length=60, number_text_tokens=256,
start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192,
stop_mel_token=8193):
super().__init__()
self.number_text_tokens = number_text_tokens
self.start_text_token = start_text_token
self.stop_text_token = stop_text_token
self.number_mel_codes = number_mel_codes
self.start_mel_token = start_mel_token
self.stop_mel_token = stop_mel_token
self.max_mel_tokens = max_mel_tokens
self.max_symbols_per_phrase = max_symbols_per_phrase
self.max_total_tokens = max_total_tokens
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)
self.text_pos_solo_embedding = nn.Embedding(self.max_symbols_per_phrase + 1, model_dim)
self.text_pos_paired_embedding = nn.Embedding(self.max_symbols_per_phrase + 1, model_dim)
self.mel_pos_solo_embedding = nn.Embedding(self.max_mel_tokens + 1, model_dim)
self.mel_pos_paired_embedding = nn.Embedding(self.max_mel_tokens + 1, model_dim)
seq_length = 2+self.max_total_tokens+self.max_conditioning_inputs
self.top_encoder = TopEncoder(top_encoder_layers, model_dim, heads, do_checkpointing=checkpointing,
dim_reduction=top_dim_reduction)
self.top_gpt_config = GPT2Config(vocab_size=1,
n_positions=seq_length // top_dim_reduction,
n_ctx=seq_length // top_dim_reduction,
n_embd=model_dim,
n_layer=top_layers,
n_head=heads,
gradient_checkpointing=checkpointing,
use_cache=not checkpointing)
self.top_gpt = GPT2Model(self.top_gpt_config)
del self.top_gpt.wte
self.top_gpt_start_embedding = nn.Parameter(torch.randn(1,1,model_dim)*self.top_gpt_config.initializer_range,
requires_grad=True)
self.top_dim_reduction = top_dim_reduction
self.bottom_gpt_config = GPT2Config(vocab_size=self.number_mel_codes,
n_positions=seq_length,
n_ctx=seq_length,
n_embd=model_dim,
n_layer=bottom_layers,
n_head=heads,
gradient_checkpointing=checkpointing,
use_cache=not checkpointing)
self.bottom_gpt = GPT2Model(self.bottom_gpt_config)
# Override the built in positional embeddings
del self.bottom_gpt.wpe
self.bottom_gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
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
# Initialize the embeddings per the GPT-2 scheme
for module in [self.text_embedding, self.text_pos_solo_embedding, self.text_pos_paired_embedding,
self.mel_pos_solo_embedding, self.mel_pos_paired_embedding]:
module.weight.data.normal_(mean=0.0, std=self.bottom_gpt.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
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 set_mel_padding(self, mel_input_tokens, wav_lengths):
"""
Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required
preformatting to create a working TTS model.
"""
# Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
mel_lengths = wav_lengths // self.mel_length_compression
for b in range(len(mel_lengths)):
actual_end = mel_lengths[b] + 1 # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token.
if actual_end < mel_input_tokens.shape[-1]:
mel_input_tokens[b, actual_end:] = self.stop_mel_token
return mel_input_tokens
def randomly_permute_conditioning_input(self, speech_conditioning_input):
"""
Randomly permute the conditioning spectrogram, to destroy any structure present. Note that since the
conditioning input is derived from a discrete spectrogram, it does actually retain structure, but only a little
bit (actually: exactly how much we want; enough to discriminate different vocal qualities, but nothing about
what is being said).
"""
cond_input = speech_conditioning_input[:,:,torch.randperm(speech_conditioning_input.shape[-1])]
if cond_input.shape[-1] > self.max_conditioning_length:
cond_input = cond_input[:,:,:self.max_conditioning_length]
return cond_input
def get_top_embeddings(self, embedded_input):
true_embeddings = self.top_encoder(embedded_input)
inputs = torch.cat([self.top_gpt_start_embedding, true_embeddings[:,:-1]], dim=1)
top_pred = self.top_gpt(inputs_embeds=inputs, return_dict=True)
return top_pred.last_hidden_state, true_embeddings
def inject_top_embeddings(self, embedded_input, probability_of_true_top_embedding=.5):
pred, true = self.get_top_embeddings(embedded_input)
rand = torch.bernoulli(torch.full((1,embedded_input.shape[1]),
fill_value=probability_of_true_top_embedding)).to(embedded_input.device)
mix = pred * rand + true * (not rand)
embs = torch.chunk(embedded_input, self.top_dim_reduction, dim=1)
assert len(embs) == mix.shape[1]
rejoin = []
for i, emb in enumerate(embs):
rejoin.append(torch.cat([mix[i], emb]), dim=1)
return torch.cat(rejoin, dim=1)
def get_logits(self, speech_conditioning_input, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False):
if second_inputs is not None:
emb = torch.cat([speech_conditioning_input, first_inputs, second_inputs], dim=1)
else:
emb = torch.cat([speech_conditioning_input, first_inputs], dim=1)
gpt_out = self.bottom_gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
if get_attns:
return gpt_out.attentions
enc = gpt_out.last_hidden_state[:, 1:] # The first logit is tied to the speech_conditioning_input
enc = self.final_norm(enc)
first_logits = enc[:, :first_inputs.shape[1]]
first_logits = first_head(first_logits)
first_logits = first_logits.permute(0,2,1)
if second_inputs is not None:
second_logits = enc[:, -second_inputs.shape[1]:]
second_logits = second_head(second_logits)
second_logits = second_logits.permute(0,2,1)
return first_logits, second_logits
else:
return first_logits
def forward(self, speech_conditioning_input, text_inputs, mel_inputs, wav_lengths, text_first=True, return_attentions=False):
"""
Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
(actuated by `text_first`).
speech_conditioning_input: MEL float tensor, (b,80,s)
text_inputs: long tensor, (b,t)
mel_inputs: long tensor, (b,m)
wav_lengths: long tensor, (b,)
"""
assert self.max_mel_tokens >= mel_inputs.shape[1], f'{mel_inputs.shape[1]}'
assert self.max_symbols_per_phrase >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
assert self.max_total_tokens >= mel_inputs.shape[1] + text_inputs.shape[1], f'{mel_inputs.shape[1]}, {text_inputs.shape[1]}'
mel_inputs = self.set_mel_padding(mel_inputs, wav_lengths)
speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
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) + self.text_pos_paired_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_inputs, self.start_mel_token, self.stop_mel_token)
mel_emb = self.bottom_gpt.get_input_embeddings()(mel_inputs)
mel_emb = mel_emb + self.mel_pos_paired_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
if text_first:
text_logits, mel_logits = self.get_logits(speech_conditioning_input, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions)
else:
mel_logits, text_logits = self.get_logits(speech_conditioning_input, mel_emb, self.mel_head, text_emb, self.text_head, 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 text_forward(self, speech_conditioning_input, text_inputs):
"""
Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the
model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided).
"""
assert self.max_symbols_per_phrase >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
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) + self.text_pos_solo_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
text_logits = self.get_logits(speech_conditioning_input, text_emb, self.text_head)
loss_text = F.cross_entropy(text_logits, text_targets.long())
return loss_text.mean()
def speech_forward(self, speech_conditioning_input, mel_inputs, wav_lengths):
"""
Performs autoregressive modeling on only speech data.
"""
assert self.max_mel_tokens >= mel_inputs.shape[1], f'{mel_inputs.shape[1]}'
mel_inputs = self.set_mel_padding(mel_inputs, wav_lengths)
speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_inputs, self.start_mel_token, self.stop_mel_token)
mel_emb = self.bottom_gpt.get_input_embeddings()(mel_inputs)
mel_emb = mel_emb + self.mel_pos_solo_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
mel_logits = self.get_logits(speech_conditioning_input, mel_emb, self.mel_head)
loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
return loss_mel.mean()
def inference_speech(self, speech_conditioning_input, text_inputs, **hf_generate_kwargs):
if not hasattr(self, 'inference_model'):
self.inference_model = GPT2InferenceModel(self.bottom_gpt_config, self.bottom_gpt, self.mel_pos_paired_embedding, self.final_norm, self.mel_head)
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) + self.text_pos_paired_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
# Randomly permute the conditioning spectrogram, to destroy any structure present.
speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
cond = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
emb = torch.cat([cond, text_emb], 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=self.bottom_gpt_config.n_positions, **hf_generate_kwargs)
return gen[:, fake_inputs.shape[1]:]
@register_model
def register_unified_gpt_voice_bilevel(opt_net, opt):
return UnifiedGptVoice(**opt_get(opt_net, ['kwargs'], {}))
if __name__ == '__main__':
gpt = UnifiedGptVoice(model_dim=256, heads=4)
l = gpt(torch.randn(2, 80, 800),
torch.randint(high=len(symbols), size=(2,80)),
torch.randint(high=8192, size=(2,250)),
torch.tensor([150*256,195*256]))