Spaces:
Running
Running
File size: 8,558 Bytes
1df74c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 |
import functools
import torch
import transformers
import peft
from transformers.trainer_pt_utils import LabelSmoother
from utils.dataset import AudioCollator
from utils.logger import MetricLogger
from utils.output import ansi, get_ansi_len, output_iter
IGNORE_TOKEN_ID = LabelSmoother.ignore_index
def train_gpt_lora(
chat,
dataset,
decoder_encoder,
dvae_encoder,
batch_size=16,
epochs=10,
train_text=True,
speaker_embeds=None,
lora_r=8,
lora_alpha=16,
):
if speaker_embeds is None:
speaker_embeds = {}
tokenizer = chat.pretrain_models["tokenizer"]
decoder_decoder = chat.pretrain_models["decoder"]
decoder_decoder.eval().requires_grad_(False)
decoder_encoder.to(device=dataset.device).eval().requires_grad_(False)
dvae_decoder = chat.pretrain_models["dvae"]
dvae_decoder.eval().requires_grad_(False)
dvae_encoder.to(device=dataset.device).eval().requires_grad_(False)
gpt = chat.pretrain_models["gpt"]
gpt.train().requires_grad_()
# Add LoRA to GPT model
lora_config = peft.LoraConfig(r=lora_r, lora_alpha=lora_alpha)
gpt.gpt = peft.get_peft_model(gpt.gpt, lora_config)
speaker_embeds = {
speaker: torch.randn(768, device=dataset.device, requires_grad=True)
for speaker in dataset.speakers
} | speaker_embeds
for speaker_embed in speaker_embeds.values():
std, mean = chat.pretrain_models["spk_stat"].chunk(2)
speaker_embed.data = speaker_embed.data * std + mean
SPEAKER_TOKEN_ID = tokenizer.convert_tokens_to_ids("[spk_emb]")
AUDIO_EOS_TOKEN_ID = 0
AUDIO_PAD_TOKEN_ID = AUDIO_EOS_TOKEN_ID
train_params = list(gpt.parameters()) + list(speaker_embeds.values())
optimizer = torch.optim.Adam(
gpt.parameters(), lr=1e-3, weight_decay=0, betas=[0.9, 0.95], eps=1e-5
)
optimizer.add_param_group({"params": speaker_embeds.values(), "lr": 1e-1})
loss_fn = torch.nn.CrossEntropyLoss()
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, epochs, 1e-7)
loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=True,
collate_fn=AudioCollator(text_pad=tokenizer.pad_token_id),
)
logger = MetricLogger()
logger.create_meters(loss=None, mse_loss=None, audio_loss=None, text_loss=None)
for _epoch in range(epochs):
_epoch += 1
logger.reset()
header = "{blue_light}{0}: {1}{reset}".format(
"Epoch", output_iter(_epoch, epochs), **ansi
)
header = header.ljust(max(len("Epoch"), 30) + get_ansi_len(header))
iterator = logger.log_every(loader, header=header, tqdm_header="Batch")
for batch in iterator:
speakers = batch["speaker"]
text_input_ids = batch["text_input_ids"]
text_attention_mask = batch["text_attention_mask"]
audio_mel_specs = batch["audio_mel_specs"]
audio_attention_mask = batch["audio_attention_mask"]
batch_size, text_len = text_attention_mask.size()
dvae_audio_latents = dvae_encoder(audio_mel_specs, audio_attention_mask)
_, dvae_audio_input_ids = quantize(
dvae_decoder.vq_layer.quantizer, dvae_audio_latents
)
dvae_audio_input_ids[~audio_attention_mask.bool()] = AUDIO_PAD_TOKEN_ID
extended_audio_attention_mask = torch.cat(
[
audio_attention_mask,
torch.zeros(
(batch_size, 1),
dtype=audio_attention_mask.dtype,
device=audio_attention_mask.device,
),
],
dim=1,
)
extended_audio_input_ids = torch.cat(
[
dvae_audio_input_ids,
AUDIO_PAD_TOKEN_ID
* torch.ones(
(batch_size, 1, gpt.num_vq),
dtype=dvae_audio_input_ids.dtype,
device=dvae_audio_input_ids.device,
),
],
dim=1,
)
indices = audio_attention_mask.int().sum(dim=1)
for i in range(batch_size):
extended_audio_attention_mask[i, indices[i]] = 1
extended_audio_input_ids[i, indices[i]] = AUDIO_EOS_TOKEN_ID
input_ids = torch.cat(
[
text_input_ids.unsqueeze(-1).repeat(1, 1, gpt.num_vq),
extended_audio_input_ids,
],
dim=1,
)
attention_mask = torch.cat(
[text_attention_mask, extended_audio_attention_mask], dim=1
)
text_mask = torch.cat(
[
torch.ones_like(text_attention_mask, dtype=bool),
torch.zeros_like(extended_audio_attention_mask, dtype=bool),
],
dim=1,
)
labels = input_ids.clone()
labels[~attention_mask.bool()] = IGNORE_TOKEN_ID
inputs_embeds = gpt.get_emb(input_ids=input_ids, text_mask=text_mask)
indices = torch.all(input_ids == SPEAKER_TOKEN_ID, dim=-1)
for i, speaker in enumerate(speakers):
inputs_embeds[i, indices[i]] = torch.nn.functional.normalize(
speaker_embeds[speaker].to(dtype=inputs_embeds.dtype),
p=2.0,
dim=-1,
eps=1e-12,
).unsqueeze(0)
outputs = gpt.gpt.forward(
inputs_embeds=inputs_embeds, attention_mask=attention_mask
)
hidden_states = outputs.last_hidden_state
text_hidden_states = hidden_states[:, : text_len - 1]
audio_hidden_states = hidden_states[:, text_len - 1 : -1]
audio_logits = torch.stack(
[gpt.head_code[i](audio_hidden_states) for i in range(gpt.num_vq)],
dim=2,
)
audio_loss = loss_fn(
audio_logits.flatten(0, 2), labels[:, text_len:].flatten(0, 2)
)
loss = audio_loss
if train_text:
text_logits = gpt.head_text(text_hidden_states)
text_loss = loss_fn(
text_logits.flatten(0, 1), labels[:, 1:text_len, 0].flatten(0, 1)
)
loss += text_loss
logger.meters["text_loss"].update(text_loss.item(), n=batch_size)
gpt_gen_mel_specs = decoder_decoder(
audio_hidden_states[:, :-1].transpose(1, 2)
).transpose(1, 2)
mse_loss = torch.nn.functional.mse_loss(gpt_gen_mel_specs, audio_mel_specs)
loss += 0.01 * mse_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(train_params, 1.0)
optimizer.step()
logger.meters["loss"].update(loss.item(), n=batch_size)
logger.meters["mse_loss"].update(mse_loss.item(), n=batch_size)
logger.meters["audio_loss"].update(audio_loss.item(), n=batch_size)
lr_scheduler.step()
optimizer.zero_grad()
return speaker_embeds
# Example usage
def main():
# Load necessary models and data paths
chat = ChatTTS.Chat()
chat.load_models()
dataset = XzListTar(
root="data/all.list",
tokenizer=chat.pretrain_models["tokenizer"],
vocos_model=chat.pretrain_models["vocos"],
tar_path="data/Xz.tar",
tar_in_memory=True,
process_ahead=True,
)
decoder_encoder = DVAEEncoder(
**get_encoder_config(chat.pretrain_models["decoder"].decoder)
)
dvae_encoder = DVAEEncoder(
**get_encoder_config(chat.pretrain_models["dvae"].decoder)
)
# Train GPT with LoRA
speaker_embeds = train_gpt_lora(
chat=chat,
dataset=dataset,
decoder_encoder=decoder_encoder,
dvae_encoder=dvae_encoder,
batch_size=32,
epochs=10,
train_text=True,
lora_r=8,
lora_alpha=16,
)
# Save LoRA parameters and embeddings
lora_save_path = "./saved_models/gpt_lora.pth"
peft.save_pretrained(gpt.gpt, lora_save_path)
np.savez(
"./saved_models/speaker_embeds.npz",
**{k: v.cpu().numpy() for k, v in speaker_embeds.items()}
)
if __name__ == "__main__":
main()
|