File size: 8,520 Bytes
920fd91 | 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 | import math
import numpy as np
import torch
import torch.nn.functional as F
def get_sample_indices(original_fps, total_frames, target_fps, num_sample, fixed_start=None):
required_duration = num_sample / target_fps
required_origin_frames = int(np.ceil(required_duration * original_fps))
if required_duration > total_frames / original_fps:
raise ValueError("required_duration must be less than video length")
if not fixed_start is None and fixed_start >= 0:
start_frame = fixed_start
else:
max_start = total_frames - required_origin_frames
if max_start < 0:
raise ValueError("video length is too short")
start_frame = np.random.randint(0, max_start + 1)
start_time = start_frame / original_fps
end_time = start_time + required_duration
time_points = np.linspace(start_time, end_time, num_sample, endpoint=False)
frame_indices = np.round(np.array(time_points) * original_fps).astype(int)
frame_indices = np.clip(frame_indices, 0, total_frames - 1)
return frame_indices
def linear_interpolation(features, input_fps, output_fps, output_len=None):
"""
features: shape=[1, T, 512]
input_fps: fps for audio, f_a
output_fps: fps for video, f_m
output_len: video length
"""
features = features.transpose(1, 2)
seq_len = features.shape[2] / float(input_fps)
if output_len is None:
output_len = int(seq_len * output_fps)
output_features = F.interpolate(features, size=output_len, align_corners=True, mode='linear') # [1, 512, output_len]
return output_features.transpose(1, 2)
class WanS2VAudioEncoder(torch.nn.Module):
def __init__(self):
super().__init__()
from transformers import Wav2Vec2ForCTC, Wav2Vec2Config
config = {
"_name_or_path": "facebook/wav2vec2-large-xlsr-53",
"activation_dropout": 0.05,
"apply_spec_augment": True,
"architectures": ["Wav2Vec2ForCTC"],
"attention_dropout": 0.1,
"bos_token_id": 1,
"conv_bias": True,
"conv_dim": [512, 512, 512, 512, 512, 512, 512],
"conv_kernel": [10, 3, 3, 3, 3, 2, 2],
"conv_stride": [5, 2, 2, 2, 2, 2, 2],
"ctc_loss_reduction": "mean",
"ctc_zero_infinity": True,
"do_stable_layer_norm": True,
"eos_token_id": 2,
"feat_extract_activation": "gelu",
"feat_extract_dropout": 0.0,
"feat_extract_norm": "layer",
"feat_proj_dropout": 0.05,
"final_dropout": 0.0,
"hidden_act": "gelu",
"hidden_dropout": 0.05,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"layerdrop": 0.05,
"mask_channel_length": 10,
"mask_channel_min_space": 1,
"mask_channel_other": 0.0,
"mask_channel_prob": 0.0,
"mask_channel_selection": "static",
"mask_feature_length": 10,
"mask_feature_prob": 0.0,
"mask_time_length": 10,
"mask_time_min_space": 1,
"mask_time_other": 0.0,
"mask_time_prob": 0.05,
"mask_time_selection": "static",
"model_type": "wav2vec2",
"num_attention_heads": 16,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
"num_feat_extract_layers": 7,
"num_hidden_layers": 24,
"pad_token_id": 0,
"transformers_version": "4.7.0.dev0",
"vocab_size": 33
}
self.model = Wav2Vec2ForCTC(Wav2Vec2Config(**config))
self.video_rate = 30
def extract_audio_feat(self, input_audio, sample_rate, processor, return_all_layers=False, dtype=torch.float32, device='cpu'):
input_values = processor(input_audio, sampling_rate=sample_rate, return_tensors="pt").input_values.to(dtype=dtype, device=device)
# retrieve logits & take argmax
res = self.model(input_values, output_hidden_states=True)
if return_all_layers:
feat = torch.cat(res.hidden_states)
else:
feat = res.hidden_states[-1]
feat = linear_interpolation(feat, input_fps=50, output_fps=self.video_rate)
return feat
def get_audio_embed_bucket(self, audio_embed, stride=2, batch_frames=12, m=2):
num_layers, audio_frame_num, audio_dim = audio_embed.shape
if num_layers > 1:
return_all_layers = True
else:
return_all_layers = False
min_batch_num = int(audio_frame_num / (batch_frames * stride)) + 1
bucket_num = min_batch_num * batch_frames
batch_idx = [stride * i for i in range(bucket_num)]
batch_audio_eb = []
for bi in batch_idx:
if bi < audio_frame_num:
audio_sample_stride = 2
chosen_idx = list(range(bi - m * audio_sample_stride, bi + (m + 1) * audio_sample_stride, audio_sample_stride))
chosen_idx = [0 if c < 0 else c for c in chosen_idx]
chosen_idx = [audio_frame_num - 1 if c >= audio_frame_num else c for c in chosen_idx]
if return_all_layers:
frame_audio_embed = audio_embed[:, chosen_idx].flatten(start_dim=-2, end_dim=-1)
else:
frame_audio_embed = audio_embed[0][chosen_idx].flatten()
else:
frame_audio_embed = \
torch.zeros([audio_dim * (2 * m + 1)], device=audio_embed.device) if not return_all_layers \
else torch.zeros([num_layers, audio_dim * (2 * m + 1)], device=audio_embed.device)
batch_audio_eb.append(frame_audio_embed)
batch_audio_eb = torch.cat([c.unsqueeze(0) for c in batch_audio_eb], dim=0)
return batch_audio_eb, min_batch_num
def get_audio_embed_bucket_fps(self, audio_embed, fps=16, batch_frames=81, m=0):
num_layers, audio_frame_num, audio_dim = audio_embed.shape
if num_layers > 1:
return_all_layers = True
else:
return_all_layers = False
scale = self.video_rate / fps
min_batch_num = int(audio_frame_num / (batch_frames * scale)) + 1
bucket_num = min_batch_num * batch_frames
padd_audio_num = math.ceil(min_batch_num * batch_frames / fps * self.video_rate) - audio_frame_num
batch_idx = get_sample_indices(
original_fps=self.video_rate, total_frames=audio_frame_num + padd_audio_num, target_fps=fps, num_sample=bucket_num, fixed_start=0
)
batch_audio_eb = []
audio_sample_stride = int(self.video_rate / fps)
for bi in batch_idx:
if bi < audio_frame_num:
chosen_idx = list(range(bi - m * audio_sample_stride, bi + (m + 1) * audio_sample_stride, audio_sample_stride))
chosen_idx = [0 if c < 0 else c for c in chosen_idx]
chosen_idx = [audio_frame_num - 1 if c >= audio_frame_num else c for c in chosen_idx]
if return_all_layers:
frame_audio_embed = audio_embed[:, chosen_idx].flatten(start_dim=-2, end_dim=-1)
else:
frame_audio_embed = audio_embed[0][chosen_idx].flatten()
else:
frame_audio_embed = \
torch.zeros([audio_dim * (2 * m + 1)], device=audio_embed.device) if not return_all_layers \
else torch.zeros([num_layers, audio_dim * (2 * m + 1)], device=audio_embed.device)
batch_audio_eb.append(frame_audio_embed)
batch_audio_eb = torch.cat([c.unsqueeze(0) for c in batch_audio_eb], dim=0)
return batch_audio_eb, min_batch_num
def get_audio_feats_per_inference(self, input_audio, sample_rate, processor, fps=16, batch_frames=80, m=0, dtype=torch.float32, device='cpu'):
audio_feat = self.extract_audio_feat(input_audio, sample_rate, processor, return_all_layers=True, dtype=dtype, device=device)
audio_embed_bucket, min_batch_num = self.get_audio_embed_bucket_fps(audio_feat, fps=fps, batch_frames=batch_frames, m=m)
audio_embed_bucket = audio_embed_bucket.unsqueeze(0).permute(0, 2, 3, 1).to(device, dtype)
audio_embeds = [audio_embed_bucket[..., i * batch_frames:(i + 1) * batch_frames] for i in range(min_batch_num)]
return audio_embeds
|