Spaces:
Runtime error
Runtime error
File size: 8,891 Bytes
dbac20f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 |
import dataclasses
import logging
from pathlib import Path
from typing import Optional
import torch
from colorlog import ColoredFormatter
from torchvision.transforms import v2
from torio.io import StreamingMediaDecoder, StreamingMediaEncoder
from mmaudio.model.flow_matching import FlowMatching
from mmaudio.model.networks import MMAudio
from mmaudio.model.sequence_config import (CONFIG_16K, CONFIG_44K, SequenceConfig)
from mmaudio.model.utils.features_utils import FeaturesUtils
from mmaudio.utils.download_utils import download_model_if_needed
log = logging.getLogger()
@dataclasses.dataclass
class ModelConfig:
model_name: str
model_path: Path
vae_path: Path
bigvgan_16k_path: Optional[Path]
mode: str
synchformer_ckpt: Path = Path('./ext_weights/synchformer_state_dict.pth')
@property
def seq_cfg(self) -> SequenceConfig:
if self.mode == '16k':
return CONFIG_16K
elif self.mode == '44k':
return CONFIG_44K
def download_if_needed(self):
download_model_if_needed(self.model_path)
download_model_if_needed(self.vae_path)
if self.bigvgan_16k_path is not None:
download_model_if_needed(self.bigvgan_16k_path)
download_model_if_needed(self.synchformer_ckpt)
small_16k = ModelConfig(model_name='small_16k',
model_path=Path('./weights/mmaudio_small_16k.pth'),
vae_path=Path('./ext_weights/v1-16.pth'),
bigvgan_16k_path=Path('./ext_weights/best_netG.pt'),
mode='16k')
small_44k = ModelConfig(model_name='small_44k',
model_path=Path('./weights/mmaudio_small_44k.pth'),
vae_path=Path('./ext_weights/v1-44.pth'),
bigvgan_16k_path=None,
mode='44k')
medium_44k = ModelConfig(model_name='medium_44k',
model_path=Path('./weights/mmaudio_medium_44k.pth'),
vae_path=Path('./ext_weights/v1-44.pth'),
bigvgan_16k_path=None,
mode='44k')
large_44k = ModelConfig(model_name='large_44k',
model_path=Path('./weights/mmaudio_large_44k.pth'),
vae_path=Path('./ext_weights/v1-44.pth'),
bigvgan_16k_path=None,
mode='44k')
large_44k_v2 = ModelConfig(model_name='large_44k_v2',
model_path=Path('./weights/mmaudio_large_44k_v2.pth'),
vae_path=Path('./ext_weights/v1-44.pth'),
bigvgan_16k_path=None,
mode='44k')
all_model_cfg: dict[str, ModelConfig] = {
'small_16k': small_16k,
'small_44k': small_44k,
'medium_44k': medium_44k,
'large_44k': large_44k,
'large_44k_v2': large_44k_v2,
}
def generate(clip_video: Optional[torch.Tensor],
sync_video: Optional[torch.Tensor],
text: Optional[list[str]],
*,
negative_text: Optional[list[str]] = None,
feature_utils: FeaturesUtils,
net: MMAudio,
fm: FlowMatching,
rng: torch.Generator,
cfg_strength: float):
device = feature_utils.device
dtype = feature_utils.dtype
bs = len(text)
if clip_video is not None:
clip_video = clip_video.to(device, dtype, non_blocking=True)
clip_features = feature_utils.encode_video_with_clip(clip_video, batch_size=bs)
else:
clip_features = net.get_empty_clip_sequence(bs)
if sync_video is not None:
sync_video = sync_video.to(device, dtype, non_blocking=True)
sync_features = feature_utils.encode_video_with_sync(sync_video, batch_size=bs)
else:
sync_features = net.get_empty_sync_sequence(bs)
if text is not None:
text_features = feature_utils.encode_text(text)
else:
text_features = net.get_empty_string_sequence(bs)
if negative_text is not None:
assert len(negative_text) == bs
negative_text_features = feature_utils.encode_text(negative_text)
else:
negative_text_features = net.get_empty_string_sequence(bs)
x0 = torch.randn(bs,
net.latent_seq_len,
net.latent_dim,
device=device,
dtype=dtype,
generator=rng)
preprocessed_conditions = net.preprocess_conditions(clip_features, sync_features, text_features)
empty_conditions = net.get_empty_conditions(
bs, negative_text_features=negative_text_features if negative_text is not None else None)
cfg_ode_wrapper = lambda t, x: net.ode_wrapper(t, x, preprocessed_conditions, empty_conditions,
cfg_strength)
x1 = fm.to_data(cfg_ode_wrapper, x0)
x1 = net.unnormalize(x1)
spec = feature_utils.decode(x1)
audio = feature_utils.vocode(spec)
return audio
LOGFORMAT = " %(log_color)s%(levelname)-8s%(reset)s | %(log_color)s%(message)s%(reset)s"
def setup_eval_logging(log_level: int = logging.INFO):
logging.root.setLevel(log_level)
formatter = ColoredFormatter(LOGFORMAT)
stream = logging.StreamHandler()
stream.setLevel(log_level)
stream.setFormatter(formatter)
log = logging.getLogger()
log.setLevel(log_level)
log.addHandler(stream)
def load_video(video_path: Path, duration_sec: float) -> tuple[torch.Tensor, torch.Tensor, float]:
_CLIP_SIZE = 384
_CLIP_FPS = 8.0
_SYNC_SIZE = 224
_SYNC_FPS = 25.0
clip_transform = v2.Compose([
v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
])
sync_transform = v2.Compose([
v2.Resize(_SYNC_SIZE, interpolation=v2.InterpolationMode.BICUBIC),
v2.CenterCrop(_SYNC_SIZE),
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
reader = StreamingMediaDecoder(video_path)
reader.add_basic_video_stream(
frames_per_chunk=int(_CLIP_FPS * duration_sec),
frame_rate=_CLIP_FPS,
format='rgb24',
)
reader.add_basic_video_stream(
frames_per_chunk=int(_SYNC_FPS * duration_sec),
frame_rate=_SYNC_FPS,
format='rgb24',
)
reader.fill_buffer()
data_chunk = reader.pop_chunks()
clip_chunk = data_chunk[0]
sync_chunk = data_chunk[1]
assert clip_chunk is not None
assert sync_chunk is not None
clip_frames = clip_transform(clip_chunk)
sync_frames = sync_transform(sync_chunk)
clip_length_sec = clip_frames.shape[0] / _CLIP_FPS
sync_length_sec = sync_frames.shape[0] / _SYNC_FPS
if clip_length_sec < duration_sec:
log.warning(f'Clip video is too short: {clip_length_sec:.2f} < {duration_sec:.2f}')
log.warning(f'Truncating to {clip_length_sec:.2f} sec')
duration_sec = clip_length_sec
if sync_length_sec < duration_sec:
log.warning(f'Sync video is too short: {sync_length_sec:.2f} < {duration_sec:.2f}')
log.warning(f'Truncating to {sync_length_sec:.2f} sec')
duration_sec = sync_length_sec
clip_frames = clip_frames[:int(_CLIP_FPS * duration_sec)]
sync_frames = sync_frames[:int(_SYNC_FPS * duration_sec)]
return clip_frames, sync_frames, duration_sec
def make_video(video_path: Path, output_path: Path, audio: torch.Tensor, sampling_rate: int,
duration_sec: float):
approx_max_length = int(duration_sec * 60)
reader = StreamingMediaDecoder(video_path)
reader.add_basic_video_stream(
frames_per_chunk=approx_max_length,
format='rgb24',
)
reader.fill_buffer()
video_chunk = reader.pop_chunks()[0]
assert video_chunk is not None
fps = int(reader.get_out_stream_info(0).frame_rate)
if fps > 60:
log.warning(f'This code supports only up to 60 fps, but the video has {fps} fps')
log.warning(f'Just change the *60 above me')
h, w = video_chunk.shape[-2:]
video_chunk = video_chunk[:int(fps * duration_sec)]
writer = StreamingMediaEncoder(output_path)
writer.add_audio_stream(
sample_rate=sampling_rate,
num_channels=audio.shape[0],
encoder='aac', # 'flac' does not work for some reason?
)
writer.add_video_stream(frame_rate=fps,
width=w,
height=h,
format='rgb24',
encoder='libx264',
encoder_format='yuv420p')
with writer.open():
writer.write_audio_chunk(0, audio.float().transpose(0, 1))
writer.write_video_chunk(1, video_chunk)
|