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import os
import pdb
import math
import pickle
from types import SimpleNamespace
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from loguru import logger
from models.layers.layer import BasicBlock
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)
vocab_path = f"{data_path}weights/vocab.pkl"
if os.path.exists(vocab_path):
with open(vocab_path, 'rb') as f:
self.lang_model = pickle.load(f)
pre_trained_embedding = self.lang_model.word_embedding_weights
else:
logger.warning(f"vocab.pkl not found at {vocab_path}, using zeroed fallback embedding")
fallback_weights = np.zeros((2, 300), dtype=np.float32)
self.lang_model = SimpleNamespace(
PAD_token=0,
UNK_token=1,
word_embedding_weights=fallback_weights,
)
pre_trained_embedding = fallback_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)
joint_multiplier = 4 if use_exp else 3
self.context_dim = self.latent_dim * joint_multiplier
mix_input_dim = audio_dim * 3 if self.raw_audio else audio_dim * 2
self.mix_audio_text = nn.Linear(mix_input_dim, self.context_dim)
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_emb = self.text_pre_encoder_body(word)
text_feat = self.text_encoder_body(text_emb)
audio_len = audio_feat.shape[1]
text_len = text_feat.shape[1]
if audio_len != text_len:
target_len = text_len if text_len > 0 else audio_len
if target_len == 0:
logger.warning("Both audio and text sequences are empty; inserting single-frame zeros")
audio_feat = audio_feat.new_zeros(audio_feat.shape[0], 1, audio_feat.shape[2])
text_feat = text_feat.new_zeros(text_feat.shape[0], 1, text_feat.shape[2])
else:
if audio_len == 0:
audio_feat = audio_feat.new_zeros(text_feat.shape[0], target_len, audio_feat.shape[2])
else:
audio_feat = F.interpolate(
audio_feat.transpose(1, 2),
size=target_len,
mode="linear",
align_corners=False,
).transpose(1, 2)
if text_len == 0:
text_feat = text_feat.new_zeros(audio_feat.shape[0], target_len, text_feat.shape[2])
else:
text_feat = F.interpolate(
text_feat.transpose(1, 2),
size=target_len,
mode="nearest",
).transpose(1, 2)
logger.warning(
"Resampled modality features for length mismatch (audio=%d, text=%d -> %d)",
audio_len,
text_len,
target_len,
)
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)
device = next(self.audio_encoder.parameters()).device
wav_input_16khz = wav_input_16khz.to(device)
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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