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