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import logging
from argparse import ArgumentParser
from pathlib import Path
import torch
import torchaudio
from mmaudio.eval_utils import (ModelConfig, all_model_cfg, generate, load_video, make_video,
setup_eval_logging)
from mmaudio.model.flow_matching import FlowMatching
from mmaudio.model.networks import MMAudio, get_my_mmaudio
from mmaudio.model.utils.features_utils import FeaturesUtils
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
log = logging.getLogger()
@torch.inference_mode()
def main():
setup_eval_logging()
parser = ArgumentParser()
parser.add_argument('--variant',
type=str,
default='large_44k_v2',
help='small_16k, small_44k, medium_44k, large_44k, large_44k_v2')
parser.add_argument('--video', type=Path, help='Path to the video file')
parser.add_argument('--prompt', type=str, help='Input prompt', default='')
parser.add_argument('--negative_prompt', type=str, help='Negative prompt', default='')
parser.add_argument('--duration', type=float, default=8.0)
parser.add_argument('--cfg_strength', type=float, default=4.5)
parser.add_argument('--num_steps', type=int, default=25)
parser.add_argument('--mask_away_clip', action='store_true')
parser.add_argument('--output', type=Path, help='Output directory', default='./output')
parser.add_argument('--seed', type=int, help='Random seed', default=42)
parser.add_argument('--skip_video_composite', action='store_true')
parser.add_argument('--full_precision', action='store_true')
args = parser.parse_args()
if args.variant not in all_model_cfg:
raise ValueError(f'Unknown model variant: {args.variant}')
model: ModelConfig = all_model_cfg[args.variant]
model.download_if_needed()
seq_cfg = model.seq_cfg
if args.video:
video_path: Path = Path(args.video).expanduser()
else:
video_path = None
prompt: str = args.prompt
negative_prompt: str = args.negative_prompt
output_dir: str = args.output.expanduser()
seed: int = args.seed
num_steps: int = args.num_steps
duration: float = args.duration
cfg_strength: float = args.cfg_strength
skip_video_composite: bool = args.skip_video_composite
mask_away_clip: bool = args.mask_away_clip
device = 'cuda'
dtype = torch.float32 if args.full_precision else torch.bfloat16
output_dir.mkdir(parents=True, exist_ok=True)
# load a pretrained model
net: MMAudio = get_my_mmaudio(model.model_name).to(device, dtype).eval()
net.load_weights(torch.load(model.model_path, map_location=device, weights_only=True))
log.info(f'Loaded weights from {model.model_path}')
# misc setup
rng = torch.Generator(device=device)
rng.manual_seed(seed)
fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps)
feature_utils = FeaturesUtils(tod_vae_ckpt=model.vae_path,
synchformer_ckpt=model.synchformer_ckpt,
enable_conditions=True,
mode=model.mode,
bigvgan_vocoder_ckpt=model.bigvgan_16k_path,
need_vae_encoder=False)
feature_utils = feature_utils.to(device, dtype).eval()
if video_path is not None:
log.info(f'Using video {video_path}')
video_info = load_video(video_path, duration)
clip_frames = video_info.clip_frames
sync_frames = video_info.sync_frames
duration = video_info.duration_sec
if mask_away_clip:
clip_frames = None
else:
clip_frames = clip_frames.unsqueeze(0)
sync_frames = sync_frames.unsqueeze(0)
else:
log.info('No video provided -- text-to-audio mode')
clip_frames = sync_frames = None
seq_cfg.duration = duration
net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len)
log.info(f'Prompt: {prompt}')
log.info(f'Negative prompt: {negative_prompt}')
audios = generate(clip_frames,
sync_frames, [prompt],
negative_text=[negative_prompt],
feature_utils=feature_utils,
net=net,
fm=fm,
rng=rng,
cfg_strength=cfg_strength)
audio = audios.float().cpu()[0]
if video_path is not None:
save_path = output_dir / f'{video_path.stem}.flac'
else:
safe_filename = prompt.replace(' ', '_').replace('/', '_').replace('.', '')
save_path = output_dir / f'{safe_filename}.flac'
torchaudio.save(save_path, audio, seq_cfg.sampling_rate)
log.info(f'Audio saved to {save_path}')
if video_path is not None and not skip_video_composite:
video_save_path = output_dir / f'{video_path.stem}.mp4'
make_video(video_info, video_save_path, audio, sampling_rate=seq_cfg.sampling_rate)
log.info(f'Video saved to {output_dir / video_save_path}')
log.info('Memory usage: %.2f GB', torch.cuda.max_memory_allocated() / (2**30))
if __name__ == '__main__':
main()
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