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979b6aa | 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 | import pdb
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.layers.layer import BasicBlock
from einops import rearrange
import pickle
import math
from models.wavlm.WavLM import WavLM, WavLMConfig
class ExactLengthAdjuster(nn.Module):
"""
Layer that ensures the output has exactly the target length along the time dimension.
It either adds or removes frames as needed.
"""
def __init__(self, target_length=196):
super(ExactLengthAdjuster, self).__init__()
self.target_length = target_length
def forward(self, x):
# x is expected to be [batch, channels, time]
current_length = x.shape[2]
if current_length == self.target_length:
return x
elif current_length < self.target_length:
# Need to add frames
frames_to_add = self.target_length - current_length
# Duplicate the last frame as many times as needed
last_frame = x[:, :, -1:]
extra_frames = last_frame.repeat(1, 1, frames_to_add)
return torch.cat([x, extra_frames], dim=2)
else:
# Need to remove frames
# Just truncate to the target length
return x[:, :, :self.target_length]
class WavEncoder(nn.Module):
def __init__(self, out_dim, audio_in=2, target_length=256):
super().__init__()
self.out_dim = out_dim
self.feat_extractor = nn.Sequential(
BasicBlock(audio_in, out_dim//4, 15, 5, first_dilation=1700, downsample=True),
BasicBlock(out_dim//4, out_dim//4, 15, 6, first_dilation=0, downsample=True),
BasicBlock(out_dim//4, out_dim//4, 15, 1, first_dilation=7, ),
BasicBlock(out_dim//4, out_dim//2, 15, 6, first_dilation=0, downsample=True),
BasicBlock(out_dim//2, out_dim//2, 15, 1, first_dilation=7),
BasicBlock(out_dim//2, out_dim, 15, 3, first_dilation=0,downsample=True),
)
self.length_adjuster = ExactLengthAdjuster(target_length=target_length)
def forward(self, wav_data):
if wav_data.dim() == 2:
wav_data = wav_data.unsqueeze(1)
else:
wav_data = wav_data.transpose(1, 2)
out = self.feat_extractor(wav_data)
out = self.length_adjuster(out)
return out.transpose(1, 2)
class ModalityEncoder(nn.Module):
def __init__(self,
data_path,
t_fix_pre,
audio_dim,
audio_in=2,
raw_audio=False,
latent_dim=256,
audio_fps=30,
use_exp=False,
target_length=256,
spatial_temporal=False
):
super().__init__()
self.raw_audio = raw_audio
self.latent_dim = latent_dim
self.audio_fps = audio_fps
self.WavEncoder = WavEncoder(audio_dim, audio_in=audio_in, target_length=target_length)
self.text_encoder_body = nn.Linear(300, audio_dim)
with open(f"{data_path}weights/vocab.pkl", 'rb') as f:
self.lang_model = pickle.load(f)
pre_trained_embedding = self.lang_model.word_embedding_weights
self.text_pre_encoder_body = nn.Embedding.from_pretrained(torch.FloatTensor(pre_trained_embedding),freeze=t_fix_pre)
word_dim = pre_trained_embedding.shape[1]
if self.raw_audio:
# load the pre-trained wavlm model
# self.load_and_freeze_wavlm()
self.audio_projection = nn.Linear(1024, audio_dim)
if self.raw_audio:
if use_exp:
self.mix_audio_text = nn.Linear(audio_dim*3, self.latent_dim * (4 if spatial_temporal else 1))
else:
self.mix_audio_text = nn.Linear(audio_dim*3, self.latent_dim * (3 if spatial_temporal else 1))
else:
if use_exp:
self.mix_audio_text = nn.Linear(audio_dim*2, self.latent_dim * (4 if spatial_temporal else 1))
else:
self.mix_audio_text = nn.Linear(audio_dim*2, self.latent_dim * (3 if spatial_temporal else 1))
def forward(self, audio, word, raw_audio=None, squeeze_scale=4):
# Initial features extraction - single transpose each
# [B, T, D] -> [T, B, D]
audio_feat = self.WavEncoder(audio)
text_feat = self.text_encoder_body(self.text_pre_encoder_body(word))
if raw_audio is not None and self.raw_audio:
# Keep the same transpose pattern for consistency
# raw_feat = self.extract_wavlm_feats(raw_audio)
raw_feat = self.audio_projection(raw_audio)
at_feat = torch.cat([audio_feat, raw_feat, text_feat], dim=2)
else:
at_feat = torch.cat([audio_feat, text_feat], dim=2) # [B, T, D]
at_feat = self.mix_audio_text(at_feat) # [B, T, D']
at_feat = F.avg_pool1d(at_feat.transpose(1, 2), squeeze_scale)
at_feat = at_feat.transpose(1, 2) # [B, T/scale, D']
return at_feat
@torch.no_grad()
def load_and_freeze_wavlm(self, wavlm_path='./dataloaders/wavlm/WavLM-Base+.pt'):
checkpoint = torch.load(wavlm_path)
self.wavlm_cfg = WavLMConfig(checkpoint['cfg'])
self.audio_encoder = WavLM(self.wavlm_cfg)
self.audio_encoder.load_state_dict(checkpoint['model'])
self.audio_encoder.eval()
for param in self.audio_encoder.parameters():
param.requires_grad = False
def extract_wavlm_feats(self, wav_input_16khz):
assert self.audio_encoder is not None, "Please load the wavlm model first"
# check the input type
if isinstance(wav_input_16khz, np.ndarray):
wav_input_16khz = torch.from_numpy(wav_input_16khz)
if wav_input_16khz.dim() == 1:
wav_input_16khz = wav_input_16khz.unsqueeze(0)
wav_input_16khz = wav_input_16khz.cuda()
if self.wavlm_cfg.normalize:
wav_input_16khz = F.layer_norm(wav_input_16khz, wav_input_16khz.shape)
wavlm_feats = self.audio_encoder.extract_features(wav_input_16khz)[0]
wavlm_feats = wavlm_feats.detach() # (bs, seq_len, dim)
target_size = math.ceil(wavlm_feats.shape[1] / 50 * self.audio_fps)
wavlm_feats = F.interpolate(
wavlm_feats.transpose(1, 2),
size=target_size,
align_corners=True,
mode='linear'
).transpose(1, 2)
return wavlm_feats
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