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import torch
import torch.nn as nn
import numpy as np
import random
from modules.audio2motion.cfm.icl_transformer import InContextTransformerAudio2Motion
from modules.audio2motion.cfm.cfm_wrapper import ConditionalFlowMatcherWrapper
from utils.commons.pitch_utils import f0_to_coarse
class InContextAudio2MotionModel(nn.Module):
def __init__(self, mode='icl_transformer', hparams=None):
super().__init__()
self.hparams = hparams
feat_dim = 256
self.hubert_encoder = nn.Sequential(*[
nn.Conv1d(1024, feat_dim , 3, 1, 1, bias=False),
nn.BatchNorm1d(feat_dim),
nn.GELU(),
nn.Conv1d(feat_dim, feat_dim, 3, 1, 1, bias=False)
])
dim_audio_in = feat_dim
if hparams.get("use_aux_features", False):
aux_feat_dim = 32
self.pitch_embed = nn.Embedding(300, aux_feat_dim, None)
self.pitch_encoder = nn.Sequential(*[
nn.Conv1d(aux_feat_dim, aux_feat_dim , 3, 1, 1, bias=False),
nn.BatchNorm1d(aux_feat_dim),
nn.GELU(),
nn.Conv1d(aux_feat_dim, aux_feat_dim, 3, 1, 1, bias=False)
])
self.blink_embed = nn.Embedding(2, aux_feat_dim)
self.null_blink_embed = nn.Parameter(torch.randn(aux_feat_dim))
self.mouth_amp_embed = nn.Parameter(torch.randn(aux_feat_dim))
self.null_mouth_amp_embed = nn.Parameter(torch.randn(aux_feat_dim))
dim_audio_in += 3 * aux_feat_dim
icl_transformer = InContextTransformerAudio2Motion(
dim_in=64 + 3 + 3, # exp and euler and trans
dim_audio_in=dim_audio_in,
dim=feat_dim,
depth=16,
dim_head=64,
heads=8,
frac_lengths_mask=(0.6, 1.),
)
self.mode = mode
if mode == 'icl_transformer':
self.backbone = icl_transformer
elif mode == 'icl_flow_matching':
flow_matching_model = ConditionalFlowMatcherWrapper(icl_transformer)
self.backbone = flow_matching_model
else:
raise NotImplementedError()
# used during inference
self.hubert_context = None
self.f0_context = None
self.motion_context = None
def num_params(self, model=None, print_out=True, model_name="model"):
if model is None:
model = self
parameters = filter(lambda p: p.requires_grad, model.parameters())
parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
if print_out:
print(f'| {model_name} Trainable Parameters: %.3fM' % parameters)
return parameters
def device(self):
return self.model.parameters().__next__().device
def forward(self, batch, ret, train=True, temperature=1., cond_scale=1.0, denoising_steps=10):
infer = not train
hparams = self.hparams
mask = batch['y_mask'].bool()
mel = batch['audio']
# f0 = batch['f0'] # [b,t]
if 'blink' not in batch:
batch['blink'] = torch.zeros([mel.shape[0], mel.shape[1]], dtype=torch.long, device=mel.device)
blink = batch['blink']
if 'mouth_amp' not in batch:
batch['mouth_amp'] = torch.ones([mel.shape[0], 1], device=mel.device)
mouth_amp = batch['mouth_amp']
cond_mask = None
cond = None
if infer and self.hubert_context is not None:
mel = torch.cat([self.hubert_context.to(mel.device), mel], dim=1)
# f0 = torch.cat([self.f0_context.to(mel.device), f0], dim=1)
blink = torch.cat([torch.zeros([mel.shape[0], self.hubert_context.shape[1],1],dtype=mel.dtype, device=mel.device), blink], dim=1)
mask = torch.ones([mel.shape[0], mel.shape[1]//2,], dtype=mel.dtype, device=mel.device).bool()
cond = torch.randn([mel.shape[0], mel.shape[1]//2, 64 + 6], dtype=mel.dtype, device=mel.device)
if hparams.get("zero_input_for_transformer", True) and self.mode == 'icl_transformer':
cond = cond * 0
cond[:, :self.motion_context.shape[1]] = self.motion_context.to(mel.device)
cond_mask = torch.ones([mel.shape[0], mel.shape[1]//2,], dtype=mel.dtype, device=mel.device) # 这个mask，1代表需要预测，0代表是reference
cond_mask[:, :self.motion_context.shape[1]] = 0. # 将reference部分设置为0
cond_mask = cond_mask.bool()
cond_feat = self.hubert_encoder(mel.transpose(1,2)).transpose(1,2)
cond_feats = [cond_feat]
if hparams.get("use_aux_features", False):
# use blink, f0, mouth_amp as auxiliary features in addtion to the hubert feature
if (self.training and random.random() < 0.5) or (batch.get("null_cond", False)):
use_null_aux_feats = True
else:
use_null_aux_feats = False
# f0_coarse = f0_to_coarse(f0)
# pitch_emb = self.pitch_embed(f0_coarse)
# pitch_feat = self.pitch_encoder(pitch_emb.transpose(1,2)).transpose(1,2)
if use_null_aux_feats:
mouth_amp_feat = self.null_mouth_amp_embed.unsqueeze(0).repeat([mel.shape[0], cond_feat.shape[1],1])
blink_feat = self.null_blink_embed.unsqueeze(0).repeat([mel.shape[0], cond_feat.shape[1],1])
else:
blink_feat = self.blink_embed(blink.squeeze(2).long())
mouth_amp_feat = mouth_amp.unsqueeze(1) * self.mouth_amp_embed.unsqueeze(0)
mouth_amp_feat = mouth_amp_feat.repeat([1,cond_feat.shape[1],1])
cond_feats.append(pitch_feat)
cond_feats.append(blink_feat)
cond_feats.append(mouth_amp_feat)
cond_feat = torch.cat(cond_feats, dim=-1)
if not infer:
# Train
exp = batch['y']
if self.mode == 'icl_transformer':
x = torch.randn_like(exp)
times_tensor = torch.zeros((x.shape[0],), dtype=x.dtype, device=x.device)
if hparams.get("zero_input_for_transformer", True):
mse_loss = self.backbone(x=x*0, times=times_tensor, cond_audio=cond_feat, self_attn_mask=mask, cond_drop_prob=0., target=exp, cond=exp, cond_mask=None, ret=ret)
else:
mse_loss = self.backbone(x=x, times=times_tensor, cond_audio=cond_feat, self_attn_mask=mask, cond_drop_prob=0., target=exp, cond=exp, cond_mask=None, ret=ret)
elif self.mode == 'icl_flow_matching':
mse_loss = self.backbone(x1=exp, cond_audio=cond_feat, mask=mask, cond=exp, cond_mask=None, ret=ret)
ret['pred'] = ret['pred']
ret['loss_mask'] = ret['loss_mask']
ret['mse'] = mse_loss
return mse_loss
else:
# Infer
# todo: 在infer的时候能够使用上context，即提供cond_mask
if cond is None:
target_x_len = mask.shape[1]
cond = torch.randn([cond_feat.shape[0], target_x_len, 64 + 6], dtype=cond_feat.dtype, device=cond_feat.device)
if hparams.get("zero_input_for_transformer", True):
cond = cond * 0
if self.mode == 'icl_transformer':
times_tensor = torch.zeros((x.shape[0],), dtype=x.dtype, device=x.device)
x_recon = self.backbone(x=cond, times=times_tensor, cond_audio=cond_feat, self_attn_mask=mask, cond_drop_prob=0., cond=cond, cond_mask=cond_mask, ret=ret)
elif self.mode == 'icl_flow_matching':
# default of voicebox is steps=3, elapsed time 0.56s; as for our steps=1000, elapsed time 0.66s
x_recon = self.backbone.sample(cond_audio=cond_feat, self_attn_mask=mask, cond=cond, cond_mask=cond_mask, temperature=temperature, steps=denoising_steps, cond_scale=cond_scale)
# x_recon = self.backbone.sample(cond_audio=cond_feat, self_attn_mask=mask, cond=cond, cond_mask=cond_mask, temperature=temperature, steps=5, )
x_recon = x_recon * mask.unsqueeze(-1)
ret['pred'] = x_recon
ret['mask'] = mask
if self.motion_context is not None:
len_reference = self.motion_context.shape[1]
ret['pred'] = x_recon[:,len_reference:]
ret['mask'] = mask[:,len_reference:]
return x_recon
def add_sample_to_context(self, motion, hubert=None, f0=None):
# B, T, C, audio should 2X length of motion
assert motion is not None
if self.motion_context is None:
self.motion_context = motion
else:
self.motion_context = torch.cat([self.motion_context, motion], dim=1)
if self.hubert_context is None:
if hubert is None:
self.hubert_context = torch.zeros([motion.shape[0], motion.shape[1]*2, 1024], dtype=motion.dtype, device=motion.device)
self.f0_context = torch.zeros([motion.shape[0], motion.shape[1]*2], dtype=motion.dtype, device=motion.device)
else:
self.hubert_context = hubert
self.f0_context = f0.reshape([hubert.shape[0],hubert.shape[1]])
else:
if hubert is None:
self.hubert_context = torch.cat([self.hubert_context, torch.zeros([motion.shape[0], motion.shape[1]*2, 1024], dtype=motion.dtype, device=motion.device)], dim=1)
self.f0_context = torch.cat([self.f0_context, torch.zeros([motion.shape[0], motion.shape[1]*2], dtype=motion.dtype, device=motion.device)], dim=1)
else:
self.hubert_context = torch.cat([self.hubert_context, hubert], dim=1)
self.f0_context = torch.cat([self.f0_context, f0], dim=1)
return 0
def empty_context(self):
self.hubert_context = None
self.f0_context = None
self.motion_context = None
#
if __name__ == '__main__':
model = InContextAudio2MotionModel()
model.num_params() |