import json
import torch
import torch.nn as nn
import torch.nn.functional as F
from x_transformers import Encoder, XTransformer, TransformerWrapper
from models.gpt_voice.unet_diffusion_tts6 import CheckpointedLayer
from models.gpt_voice.unified_voice2 import ConditioningEncoder
from models.tacotron2.text.cleaners import english_cleaners
from trainer.networks import register_model
from utils.util import opt_get
def clustered_mask(probability, shape, dev, lateral_expansion_radius_max=3):
"""
Produces a masking vector of the specified shape where each element has probability to be zero.
lateral_expansion_radius_max neighbors of any element that is zero also have a 50% chance to be zero.
Effectively, this produces clusters of masks tending to be lateral_expansion_radius_max wide.
Note: This means the algorithm has a far higher output probability for zeros then <probability>.
"""
mask = torch.rand(shape, device=dev)
mask = (mask < probability).float()
kernel = torch.tensor([.5 for _ in range(lateral_expansion_radius_max)] + [1] + [.5 for _ in range(lateral_expansion_radius_max)], device=dev)
mask = F.conv1d(mask.unsqueeze(1), kernel.view(1,1,2*lateral_expansion_radius_max+1), padding=lateral_expansion_radius_max).squeeze(1)
return torch.bernoulli(torch.clamp(mask, 0, 1)) == 0 # ==0 logically inverts the mask.
class CheckpointedTransformerWrapper(nn.Module):
"""
Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
to channels-last that XTransformer expects.
"""
def __init__(self, **xtransformer_kwargs):
super().__init__()
self.transformer = TransformerWrapper(**xtransformer_kwargs)
for i in range(len(self.transformer.transformer.attn_layers.layers)):
n, b, r = self.transformer.transformer.attn_layers.layers[i]
self.transformer.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
def forward(self, *args, **kwargs):
return self.transformer(*args, **kwargs)
class CtcCodeGenerator(nn.Module):
def __init__(self, model_dim=512, layers=10, num_heads=8, dropout=.1, ctc_codes=36, max_pad=121, max_repeat=30, mask_probability=.1):
super().__init__()
self.max_pad = max_pad
self.max_repeat = max_repeat
self.mask_probability = mask_probability
self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=num_heads, mean=True)
self.initial_embedding = nn.Embedding(ctc_codes, model_dim)
self.combiner = nn.Linear(model_dim*2, model_dim)
self.transformer = TransformerWrapper(
num_tokens=max_pad*max_repeat+1,
max_seq_len=-1, # Unneeded for rotary embeddings.
attn_layers=Encoder(
dim=model_dim,
depth=layers,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True
)
)
self.transformer.token_emb = nn.Identity() # This class handles the initial embeddings.
self.transformer.to_logits = nn.Identity()
self.ctc_head = nn.Linear(model_dim, max_pad*max_repeat+1)
self.inp_head = nn.Linear(model_dim, ctc_codes)
def forward(self, conditioning_input, codes, separators, repeats, unpadded_lengths):
max_len = unpadded_lengths.max()
codes = codes[:, :max_len]
loss_mask = torch.ones_like(codes)
for i, l in enumerate(unpadded_lengths):
loss_mask[i, l:] = 0
if self.training:
codes = clustered_mask(self.mask_probability, codes.shape, codes.device) * codes
if separators.max() > self.max_pad:
print(f"Got unexpectedly long separators. Max: {separators.max()}, {separators}")
separators = torch.clip(separators, 0, self.max_pad)
separators = separators[:, :max_len]
if repeats.max() > self.max_repeat:
print(f"Got unexpectedly long repeats. Max: {repeats.max()}, {repeats}")
repeats = torch.clip(repeats, 1, self.max_repeat)
repeats = repeats[:, :max_len]
repeats = repeats - 1 # min(repeats) is 1; make it 0 to avoid wasting a prediction slot.
labels = separators + repeats * self.max_pad
# Perform conditioning encoder in FP32, with the transformer in FP16
cond = self.conditioning_encoder(conditioning_input).unsqueeze(1).repeat(1,codes.shape[1],1)
h = torch.cat([cond, self.initial_embedding(codes)], dim=-1)
h = self.combiner(h)
with torch.autocast(codes.device.type):
logits = self.transformer(h)
ctc_pred = self.ctc_head(logits)
code_pred = self.inp_head(logits)
ctcloss = F.cross_entropy(ctc_pred.float().permute(0,2,1), labels, reduction='none')
ctcloss = torch.mean(ctcloss * loss_mask)
codeloss = F.cross_entropy(code_pred.float().permute(0,2,1), codes, reduction='none')
codeloss = torch.mean(codeloss * loss_mask)
return ctcloss, codeloss
def generate(self, speech_conditioning_input, texts):
codes = []
max_seq = 50
for text in texts:
# First, generate CTC codes from the given texts.
vocab = json.loads('{" ": 4, "E": 5, "T": 6, "A": 7, "O": 8, "N": 9, "I": 10, "H": 11, "S": 12, "R": 13, "D": 14, "L": 15, "U": 16, "M": 17, "W": 18, "C": 19, "F": 20, "G": 21, "Y": 22, "P": 23, "B": 24, "V": 25, "K": 26, "\'": 27, "X": 28, "J": 29, "Q": 30, "Z": 31}')
text = english_cleaners(text)
text = text.strip().upper()
cd = []
for c in text:
if c not in vocab.keys():
continue
cd.append(vocab[c])
codes.append(torch.tensor(cd, device=speech_conditioning_input.device))
max_seq = max(max_seq, codes[-1].shape[-1])
# Collate
for i in range(len(codes)):
if codes[i].shape[-1] < max_seq:
codes[i] = F.pad(codes[i], (0, max_seq-codes[i].shape[-1]))
codes = torch.stack(codes, dim=0)
cond = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1).repeat(1,codes.shape[1],1)
h = torch.cat([cond, self.initial_embedding(codes)], dim=-1)
h = self.combiner(h)
with torch.autocast(codes.device.type):
logits = self.transformer(h)
ctc_pred = self.ctc_head(logits)
generate = torch.argmax(ctc_pred, dim=-1)
# De-compress the codes from the generated output
pads = generate % self.max_pad
repeats = (generate // self.max_pad) + 1
ctc_batch = []
max_seq = 0
for bc, bp, br in zip(codes, pads, repeats):
ctc = []
for c, p, r in zip(bc, bp, br):
for _ in range(p):
ctc.append(0)
for _ in range(r):
ctc.append(c.item())
ctc_batch.append(torch.tensor(ctc, device=speech_conditioning_input.device))
max_seq = max(max_seq, ctc_batch[-1].shape[-1])
# Collate the batch
for i in range(len(ctc_batch)):
if ctc_batch[i].shape[-1] < max_seq:
ctc_batch[i] = F.pad(ctc_batch[i], (0, max_seq-ctc_batch[i].shape[-1]))
return torch.stack(ctc_batch, dim=0)
@register_model
def register_ctc_code_generator(opt_net, opt):
return CtcCodeGenerator(**opt_get(opt_net, ['kwargs'], {}))
def inf():
sd = torch.load('D:\\dlas\\experiments\\train_encoder_build_ctc_alignments_medium\\models\\24000_generator.pth', map_location='cpu')
model = CtcCodeGenerator(model_dim=1024,layers=32).eval()
model.load_state_dict(sd)
with torch.no_grad():
from data.audio.unsupervised_audio_dataset import load_audio
from scripts.audio.gen.speech_synthesis_utils import wav_to_mel
ref_mel = torch.cat([wav_to_mel(load_audio("D:\\tortoise-tts\\voices\\atkins\\1.wav", 22050))[:,:,:450],
wav_to_mel(load_audio("D:\\tortoise-tts\\voices\\kennard\\1.wav", 22050))[:,:,:450],
wav_to_mel(load_audio("D:\\tortoise-tts\\voices\\grace\\1.wav", 22050))[:,:,:450],
wav_to_mel(load_audio("D:\\tortoise-tts\\voices\\atkins\\1.wav", 22050))[:,:,:450]], dim=0)
ctc = model.generate(ref_mel, (["i suppose though it's too early for them"] * 3) + ["i suppose though it's too early for them, dear"])
print("Break")
if __name__ == '__main__':
#inf()
mask = clustered_mask(.1, (4,100), 'cpu')
model = CtcCodeGenerator()
inps = torch.randint(0,36, (4, 300))
pads = torch.randint(0,100, (4,300))
repeats = torch.randint(1,20, (4,300))
conds = torch.randn(4,80,600)
loss1, loss2 = model(conds, inps, pads, repeats, torch.tensor([250, 300, 280, 30]))
print(loss1.shape, loss2.shape)