blackteay/GPT-SoVITS
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KakaruHayate 2025-05-12 21:03:06 +08:00
parent 8158a97909
commit 88a1f88891
2 changed files with 34 additions and 8 deletions
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18
GPT_SoVITS/f5_tts/model/backbones/dit.py
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@ -143,7 +143,9 @@ class DiT(nn.Module):
drop_audio_cond=False, # cfg for cond audio drop_audio_cond=False, # cfg for cond audio
drop_text=False, # cfg for text drop_text=False, # cfg for text
# mask: bool["b n"] | None = None, # noqa: F722 # mask: bool["b n"] | None = None, # noqa: F722
infer=False, # bool
text_cache=None, # torch tensor as text_embed
dt_cache=None, # torch tensor as dt
): ):
x=x0.transpose(2,1) x=x0.transpose(2,1)
@ -157,9 +159,16 @@ class DiT(nn.Module):
# t: conditioning time, c: context (text + masked cond audio), x: noised input audio # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
t = self.time_embed(time) t = self.time_embed(time)
if infer and dt_cache is not None:
dt = dt_cache
else:
dt = self.d_embed(dt_base_bootstrap) dt = self.d_embed(dt_base_bootstrap)
t+=dt t += dt
text_embed = self.text_embed(text, seq_len, drop_text=drop_text)###need to change
if infer and text_cache is not None:
text_embed = text_cache
else:
text_embed = self.text_embed(text, seq_len, drop_text=drop_text) ###need to change
x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond) x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
rope = self.rotary_embed.forward_from_seq_len(seq_len) rope = self.rotary_embed.forward_from_seq_len(seq_len)
@ -179,4 +188,7 @@ class DiT(nn.Module):
x = self.norm_out(x, t) x = self.norm_out(x, t)
output = self.proj_out(x) output = self.proj_out(x)
if infer:
return output, text_embed, dt
else:
return output return output

20
GPT_SoVITS/module/models.py
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@ -1059,6 +1059,7 @@ class SynthesizerTrn(nn.Module):
ssl = self.ssl_proj(x) ssl = self.ssl_proj(x)
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl) quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
return codes.transpose(0, 1) return codes.transpose(0, 1)
class CFM(torch.nn.Module): class CFM(torch.nn.Module):
def __init__( def __init__(
self, self,
@ -1073,6 +1074,8 @@ class CFM(torch.nn.Module):
self.criterion = torch.nn.MSELoss() self.criterion = torch.nn.MSELoss()
self.use_conditioner_cache = True
@torch.inference_mode() @torch.inference_mode()
def inference(self, mu, x_lens, prompt, n_timesteps, temperature=1.0, inference_cfg_rate=0): def inference(self, mu, x_lens, prompt, n_timesteps, temperature=1.0, inference_cfg_rate=0):
"""Forward diffusion""" """Forward diffusion"""
@ -1085,13 +1088,24 @@ class CFM(torch.nn.Module):
mu=mu.transpose(2,1) mu=mu.transpose(2,1)
t = 0 t = 0
d = 1 / n_timesteps d = 1 / n_timesteps
text_cache = None
text_cfg_cache = None
dt_cache = None
d_tensor = torch.ones(x.shape[0], device=x.device, dtype=mu.dtype) * d
for j in range(n_timesteps): for j in range(n_timesteps):
t_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * t t_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * t
d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d # d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
# v_pred = model(x, t_tensor, d_tensor, **extra_args) # v_pred = model(x, t_tensor, d_tensor, **extra_args)
v_pred = self.estimator(x, prompt_x, x_lens, t_tensor,d_tensor, mu, use_grad_ckpt=False,drop_audio_cond=False,drop_text=False).transpose(2, 1) v_pred, text_emb, dt = self.estimator(x, prompt_x, x_lens, t_tensor,d_tensor, mu, use_grad_ckpt=False,drop_audio_cond=False,drop_text=False, infer=True, text_cache=text_cache, dt_cache=dt_cache)
v_pred = v_pred.transpose(2, 1)
if self.use_conditioner_cache:
text_cache = text_emb
dt_cache = dt
if inference_cfg_rate>1e-5: if inference_cfg_rate>1e-5:
neg = self.estimator(x, prompt_x, x_lens, t_tensor, d_tensor, mu, use_grad_ckpt=False, drop_audio_cond=True, drop_text=True).transpose(2, 1) neg, text_cfg_emb, _ = self.estimator(x, prompt_x, x_lens, t_tensor, d_tensor, mu, use_grad_ckpt=False, drop_audio_cond=True, drop_text=True, infer=True, text_cache=text_cfg_cache, dt_cache=dt_cache)
neg = neg.transpose(2, 1)
if self.use_conditioner_cache:
text_cfg_cache = text_cfg_emb
v_pred=v_pred+(v_pred-neg)*inference_cfg_rate v_pred=v_pred+(v_pred-neg)*inference_cfg_rate
x = x + d * v_pred x = x + d * v_pred
t = t + d t = t + d