# ported from: https://github.com/neonbjb/tortoise-tts
import functools
import math
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import GPT2Config
from TTS.tts.layers.xtts.gpt_inference import GPT2InferenceModel
from TTS.tts.layers.xtts.latent_encoder import ConditioningEncoder
from TTS.tts.layers.xtts.perceiver_encoder import PerceiverResampler
def null_position_embeddings(range, dim):
return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
class LearnedPositionEmbeddings(nn.Module):
def __init__(self, seq_len, model_dim, init=0.02, relative=False):
super().__init__()
# nn.Embedding
self.emb = torch.nn.Embedding(seq_len, model_dim)
# Initializing this way is standard for GPT-2
self.emb.weight.data.normal_(mean=0.0, std=init)
self.relative = relative
self.seq_len = seq_len
def forward(self, x):
sl = x.shape[1]
if self.relative:
start = random.randint(sl, self.seq_len) - sl
return self.emb(torch.arange(start, start + sl, device=x.device))
else:
return self.emb(torch.arange(0, sl, device=x.device))
def get_fixed_embedding(self, ind, dev):
return self.emb(torch.tensor([ind], device=dev)).unsqueeze(0)
def build_hf_gpt_transformer(
layers,
model_dim,
heads,
max_mel_seq_len,
max_text_seq_len,
max_prompt_len,
checkpointing,
):
"""
GPT-2 implemented by the HuggingFace library.
"""
from transformers import GPT2Config, GPT2Model
gpt_config = GPT2Config(
vocab_size=256, # Unused.
n_positions=max_mel_seq_len + max_text_seq_len + max_prompt_len,
n_ctx=max_mel_seq_len + max_text_seq_len + max_prompt_len,
n_embd=model_dim,
n_layer=layers,
n_head=heads,
gradient_checkpointing=checkpointing,
use_cache=not checkpointing,
)
gpt = GPT2Model(gpt_config)
# Override the built in positional embeddings
del gpt.wpe
gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
# Built-in token embeddings are unused.
del gpt.wte
mel_pos_emb = (
LearnedPositionEmbeddings(max_mel_seq_len, model_dim)
if max_mel_seq_len != -1
else functools.partial(null_position_embeddings, dim=model_dim)
)
text_pos_emb = (
LearnedPositionEmbeddings(max_text_seq_len, model_dim)
if max_mel_seq_len != -1
else functools.partial(null_position_embeddings, dim=model_dim)
)
# gpt = torch.compile(gpt, mode="reduce-overhead", fullgraph=True)
return gpt, mel_pos_emb, text_pos_emb, None, None
class GPT(nn.Module):
def __init__(
self,
start_text_token=261,
stop_text_token=0,
layers=8,
model_dim=512,
heads=8,
max_text_tokens=120,
max_mel_tokens=250,
max_prompt_tokens=70,
max_conditioning_inputs=1,
code_stride_len=1024,
number_text_tokens=256,
num_audio_tokens=8194,
start_audio_token=8192,
stop_audio_token=8193,
train_solo_embeddings=False,
checkpointing=False,
average_conditioning_embeddings=False,
label_smoothing=0.0,
use_perceiver_resampler=False,
perceiver_cond_length_compression=256,
):
"""
Args:
"""
super().__init__()
self.label_smoothing = label_smoothing
self.number_text_tokens = number_text_tokens
self.start_text_token = start_text_token
self.stop_text_token = stop_text_token
self.num_audio_tokens = num_audio_tokens
self.start_audio_token = start_audio_token
self.stop_audio_token = stop_audio_token
self.start_prompt_token = start_audio_token
self.stop_prompt_token = stop_audio_token
self.layers = layers
self.heads = heads
self.model_dim = model_dim
self.max_conditioning_inputs = max_conditioning_inputs
self.max_gen_mel_tokens = max_mel_tokens - self.max_conditioning_inputs - 2
self.max_mel_tokens = -1 if max_mel_tokens == -1 else max_mel_tokens + 2 + self.max_conditioning_inputs
self.max_text_tokens = -1 if max_text_tokens == -1 else max_text_tokens + 2
self.max_prompt_tokens = max_prompt_tokens
self.code_stride_len = code_stride_len
self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
self.conditioning_dropout = nn.Dropout1d(0.1)
self.average_conditioning_embeddings = average_conditioning_embeddings
self.use_perceiver_resampler = use_perceiver_resampler
self.perceiver_cond_length_compression = perceiver_cond_length_compression
self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim)
self.mel_embedding = nn.Embedding(self.num_audio_tokens, model_dim)
(
self.gpt,
self.mel_pos_embedding,
self.text_pos_embedding,
self.mel_layer_pos_embedding,
self.text_layer_pos_embedding,
) = build_hf_gpt_transformer(
layers,
model_dim,
heads,
self.max_mel_tokens,
self.max_text_tokens,
self.max_prompt_tokens,
checkpointing,
)
if train_solo_embeddings:
self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * 0.02, requires_grad=True)
self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * 0.02, requires_grad=True)
else:
self.mel_solo_embedding = 0
self.text_solo_embedding = 0
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.num_audio_tokens)
if self.use_perceiver_resampler:
# XTTS v2
self.conditioning_perceiver = PerceiverResampler(
dim=model_dim,
depth=2,
dim_context=model_dim,
num_latents=32,
dim_head=64,
heads=8,
ff_mult=4,
use_flash_attn=False,
)
else:
# XTTS v1
self.prompt_embedding = nn.Embedding(self.num_audio_tokens, model_dim)
self.prompt_pos_embedding = LearnedPositionEmbeddings(24 * 9, model_dim)
def get_grad_norm_parameter_groups(self):
return {
"conditioning_encoder": list(self.conditioning_encoder.parameters()),
"conditioning_perceiver": list(self.conditioning_perceiver.parameters())
if self.use_perceiver_resampler
else None,
"gpt": list(self.gpt.parameters()),
"heads": list(self.text_head.parameters()) + list(self.mel_head.parameters()),
}
def init_gpt_for_inference(self, kv_cache=True, use_deepspeed=False):
seq_length = self.max_prompt_tokens + self.max_mel_tokens + self.max_text_tokens + 1
gpt_config = GPT2Config(
vocab_size=self.max_mel_tokens,
n_positions=seq_length,
n_ctx=seq_length,
n_embd=self.model_dim,
n_layer=self.layers,
n_head=self.heads,
gradient_checkpointing=False,
use_cache=True,
)
self.gpt_inference = GPT2InferenceModel(
gpt_config,
self.gpt,
self.mel_pos_embedding,
self.mel_embedding,
self.final_norm,
self.mel_head,
kv_cache=kv_cache,
)
self.gpt.wte = self.mel_embedding
if use_deepspeed:
import deepspeed
self.ds_engine = deepspeed.init_inference(
model=self.gpt_inference.half(), # Transformers models
mp_size=1, # Number of GPU
dtype=torch.float32, # desired data type of output
replace_method="auto", # Lets DS autmatically identify the layer to replace
replace_with_kernel_inject=True, # replace the model with the kernel injector
)
self.gpt_inference = self.ds_engine.module.eval()
def set_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, code_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_audio_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>).
for b in range(len(code_lengths)):
actual_end = code_lengths[b]
if actual_end < mel_input_tokens.shape[-1]:
mel_input_tokens[b, actual_end:] = self.stop_audio_token
return mel_input_tokens
def get_logits(
self,
first_inputs,
first_head,
second_inputs=None,
second_head=None,
prompt=None,
get_attns=False,
return_latent=False,
attn_mask_cond=None,
attn_mask_text=None,
attn_mask_mel=None,
):
if prompt is not None:
offset = prompt.shape[1]
if second_inputs is not None:
emb = torch.cat([prompt, first_inputs, second_inputs], dim=1)
else:
emb = torch.cat([prompt, first_inputs], dim=1)
# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
attn_mask = None
if attn_mask_text is not None:
attn_mask = torch.cat([attn_mask_text, attn_mask_mel], dim=1)
if prompt is not None:
attn_mask_cond = torch.ones(prompt.shape[0], offset, dtype=torch.bool, device=emb.device)
attn_mask = torch.cat([attn_mask_cond, attn_mask], dim=1)
gpt_out = self.gpt(
inputs_embeds=emb,
return_dict=True,
output_attentions=get_attns,
attention_mask=attn_mask,
)
if get_attns:
return gpt_out.attentions
enc = gpt_out.last_hidden_state[:, offset:]
enc = self.final_norm(enc)
if return_latent:
return enc[:, : first_inputs.shape[1]], enc[:, -second_inputs.shape[1] :]
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 get_conditioning(self, speech_conditioning_input):
speech_conditioning_input = (
speech_conditioning_input.unsqueeze(1)
if len(speech_conditioning_input.shape) == 3
else speech_conditioning_input
)
conds = []
for j in range(speech_conditioning_input.shape[1]):
conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
conds = torch.stack(conds, dim=1)
conds = conds.mean(dim=1)
return conds
def get_prompts(self, prompt_codes):
"""
Create a prompt from the mel codes. This is used to condition the model on the mel codes.
Pad the prompt with start and stop mel tokens.
"""
prompt = prompt_codes
if self.training:
lengths = []
# Compute the real prompt length based on the first encounter with the token 83 used for padding
for i in range(prompt_codes.shape[0]):
length = 0
for j in range(prompt_codes.shape[1]):
if prompt_codes[i, j] == 83:
break
else:
length += 1
lengths.append(length)
# prompt_len = random.randint(1, 9) # in secs
prompt_len = 3
prompt_len = prompt_len * 24 # in frames
if prompt_codes.shape[-1] >= prompt_len:
for i in range(prompt_codes.shape[0]):
if lengths[i] < prompt_len:
start = 0
else:
start = random.randint(0, lengths[i] - prompt_len)
prompt = prompt_codes[:, start : start + prompt_len]
# add start and stop tokens
prompt = F.pad(prompt, (1, 0), value=self.start_prompt_token)
prompt = F.pad(prompt, (0, 1), value=self.stop_prompt_token)
return prompt
def get_style_emb(self, cond_input, return_latent=False):
"""
cond_input: (b, 80, s) or (b, 1, 80, s)
conds: (b, 1024, s)
"""
conds = None
if not return_latent:
if cond_input.ndim == 4:
cond_input = cond_input.squeeze(1)
conds = self.conditioning_encoder(cond_input) # (b, d, s)
if self.use_perceiver_resampler:
conds = self.conditioning_perceiver(conds.permute(0, 2, 1)).transpose(1, 2) # (b, d, 32)
else:
# already computed
conds = cond_input.unsqueeze(1)
return conds
def forward(
self,
text_inputs,
text_lengths,
audio_codes,
wav_lengths,
cond_mels=None,
cond_idxs=None,
cond_lens=None,
cond_latents=None,
return_attentions=False,
return_latent=False,
):
"""
Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
(actuated by `text_first`).
text_inputs: long tensor, (b,t)
text_lengths: long tensor, (b,)
mel_inputs: long tensor, (b,m)
wav_lengths: long tensor, (b,)
cond_mels: MEL float tensor, (b, 1, 80,s)
cond_idxs: cond start and end indexs, (b, 2)
If return_attentions is specified, only logits are returned.
If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
"""
# ❗ FIXIT
if self.max_conditioning_inputs == 0:
assert cond_mels is None, " ❗ cond_mels is not None, but max_conditioning_inputs == 0"
max_text_len = text_lengths.max()
code_lengths = torch.ceil(wav_lengths / self.code_stride_len).long() + 3
if cond_lens is not None:
if self.use_perceiver_resampler:
cond_lens = cond_lens // self.perceiver_cond_length_compression
else:
cond_lens = cond_lens // self.code_stride_len
if cond_idxs is not None:
# recompute cond idxs for mel lengths
for idx in range(cond_idxs.size(0)):
if self.use_perceiver_resampler:
cond_idxs[idx] = cond_idxs[idx] // self.perceiver_cond_length_compression
else:
cond_idxs[idx] = cond_idxs[idx] // self.code_stride_len
# ensure that the cond_mel does not have padding
# if cond_lens is not None and cond_idxs is None:
# min_cond_len = torch.min(cond_lens)
# cond_mels = cond_mels[:, :, :, :min_cond_len]
# If len(codes) + 3 is larger than maxiumum allowed length, we truncate the codes.
max_mel_len = code_lengths.max()
if max_mel_len > audio_codes.shape[-1]:
audio_codes = F.pad(audio_codes, (0, max_mel_len - audio_codes.shape[-1]))
# 💖 Lovely assertions
assert (
max_mel_len <= audio_codes.shape[-1]
), f" ❗ max_mel_len ({max_mel_len}) > audio_codes.shape[-1] ({audio_codes.shape[-1]})"
assert (
max_text_len <= text_inputs.shape[-1]
), f" ❗ max_text_len ({max_text_len}) > text_inputs.shape[-1] ({text_inputs.shape[-1]})"
# Append stop token to text inputs
text_inputs = F.pad(text_inputs[:, :max_text_len], (0, 1), value=self.stop_text_token)
# Append silence token to mel codes
audio_codes = F.pad(audio_codes[:, :max_mel_len], (0, 1), value=self.stop_audio_token)
# Pad mel codes with stop_audio_token
audio_codes = self.set_mel_padding(
audio_codes, code_lengths - 3
) # -3 to get the real code lengths without consider start and stop tokens that was not added yet
# Build input and target tensors
# Prepend start token to inputs and append stop token to targets
text_inputs, text_targets = self.set_inputs_and_targets(
text_inputs, self.start_text_token, self.stop_text_token
)
audio_codes, mel_targets = self.set_inputs_and_targets(
audio_codes, self.start_audio_token, self.stop_audio_token
)
# Set attn_mask
attn_mask_cond = None
attn_mask_text = None
attn_mask_mel = None
if not return_latent:
attn_mask_cond = torch.ones(
cond_mels.shape[0],
cond_mels.shape[-1],
dtype=torch.bool,
device=text_inputs.device,
)
attn_mask_text = torch.ones(
text_inputs.shape[0],
text_inputs.shape[1],
dtype=torch.bool,
device=text_inputs.device,
)
attn_mask_mel = torch.ones(
audio_codes.shape[0],
audio_codes.shape[1],
dtype=torch.bool,
device=audio_codes.device,
)
if cond_idxs is not None:
# use masking approach
for idx, r in enumerate(cond_idxs):
l = r[1] - r[0]
attn_mask_cond[idx, l:] = 0.0
elif cond_lens is not None:
for idx, l in enumerate(cond_lens):
attn_mask_cond[idx, l:] = 0.0
for idx, l in enumerate(text_lengths):
attn_mask_text[idx, l + 1 :] = 0.0
for idx, l in enumerate(code_lengths):
attn_mask_mel[idx, l + 1 :] = 0.0
# Compute text embeddings + positional embeddings
text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
# Compute mel embeddings + positional embeddings
mel_emb = self.mel_embedding(audio_codes) + self.mel_pos_embedding(audio_codes)
# Compute speech conditioning input
if cond_latents is None:
cond_latents = self.get_style_emb(cond_mels).transpose(1, 2)
# Get logits
sub = -5 # don't ask me why 😄
if self.training:
sub = -1
text_logits, mel_logits = self.get_logits(
text_emb,
self.text_head,
mel_emb,
self.mel_head,
prompt=cond_latents,
get_attns=return_attentions,
return_latent=return_latent,
attn_mask_cond=attn_mask_cond,
attn_mask_text=attn_mask_text,
attn_mask_mel=attn_mask_mel,
)
if return_latent:
return mel_logits[:, :sub] # sub to prevent bla.
if return_attentions:
return mel_logits
# Set paddings to -1 to ignore them in loss
for idx, l in enumerate(text_lengths):
text_targets[idx, l + 1 :] = -1
for idx, l in enumerate(code_lengths):
mel_targets[idx, l + 1 :] = -1
# check if stoptoken is in every row of mel_targets
assert (mel_targets == self.stop_audio_token).sum() >= mel_targets.shape[
0
], f" ❗ mel_targets does not contain stop token ({self.stop_audio_token}) in every row."
# ignore the loss for the segment used for conditioning
# coin flip for the segment to be ignored
if cond_idxs is not None:
cond_start = cond_idxs[idx, 0]
cond_end = cond_idxs[idx, 1]
mel_targets[idx, cond_start:cond_end] = -1
# Compute losses
loss_text = F.cross_entropy(
text_logits, text_targets.long(), ignore_index=-1, label_smoothing=self.label_smoothing
)
loss_mel = F.cross_entropy(
mel_logits, mel_targets.long(), ignore_index=-1, label_smoothing=self.label_smoothing
)
return loss_text.mean(), loss_mel.mean(), mel_logits
def inference(self, cond_latents, text_inputs, **hf_generate_kwargs):
self.compute_embeddings(cond_latents, text_inputs)
return self.generate(cond_latents, text_inputs, **hf_generate_kwargs)
def compute_embeddings(
self,
cond_latents,
text_inputs,
):
text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token)
text_inputs = F.pad(text_inputs, (1, 0), value=self.start_text_token)
emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
emb = torch.cat([cond_latents, emb], dim=1)
self.gpt_inference.store_prefix_emb(emb)
gpt_inputs = torch.full(
(
emb.shape[0],
emb.shape[1] + 1, # +1 for the start_audio_token
),
fill_value=1,
dtype=torch.long,
device=text_inputs.device,
)
gpt_inputs[:, -1] = self.start_audio_token
return gpt_inputs
def generate(
self,
cond_latents,
text_inputs,
**hf_generate_kwargs,
):
gpt_inputs = self.compute_embeddings(cond_latents, text_inputs)
gen = self.gpt_inference.generate(
gpt_inputs,
bos_token_id=self.start_audio_token,
pad_token_id=self.stop_audio_token,
eos_token_id=self.stop_audio_token,
max_length=self.max_gen_mel_tokens + gpt_inputs.shape[-1],
**hf_generate_kwargs,
)
if "return_dict_in_generate" in hf_generate_kwargs:
return gen.sequences[:, gpt_inputs.shape[1] :], gen
return gen[:, gpt_inputs.shape[1] :]
def get_generator(self, fake_inputs, **hf_generate_kwargs):
return self.gpt_inference.generate_stream(
fake_inputs,
bos_token_id=self.start_audio_token,
pad_token_id=self.stop_audio_token,
eos_token_id=self.stop_audio_token,
max_length=self.max_gen_mel_tokens + fake_inputs.shape[-1],
do_stream=True,
**hf_generate_kwargs,
)