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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
from datetime import datetime
import traceback
import numpy as np
import os
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
log = logging.getLogger()
####CUDA_VISIBLE_DEVICES=0 python demo.py --output ./output
####CUDA_VISIBLE_DEVICES=4 nohup python demo.py --output ./output_v2c_neg --start 0 --end 1500 &
@torch.inference_mode()
def main():
setup_eval_logging()
parser = ArgumentParser()
parser.add_argument('--variant',
type=str,
default='large_44k',
#default='small_16k',
#default='medium_44k',
#default='small_44k',
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('--start', type=int, default=0)
parser.add_argument('--end', type=int, default=99999999)
parser.add_argument('--scp', type=str, help='video list', default='/ailab-train/speech/zhanghaomin/datas/v2cdata/tmp.scp')
parser.add_argument('--calc_energy', type=int, default=0)
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()
video_path = args.video
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 = 'cpu'
if torch.cuda.is_available():
device = 'cuda'
elif torch.backends.mps.is_available():
device = 'mps'
else:
log.warning('CUDA/MPS are not available, running on CPU')
print("full_precision", args.full_precision)
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()
####model.model_path = "/ailab-train/speech/zhanghaomin/codes3/MMAudio-main/output/exp_1/exp_1_shadow.pth"
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()
####test_scp = "/ailab-train/speech/zhanghaomin/animation_dataset_v2a/test.scp"
#test_scp = "/ailab-train/speech/zhanghaomin/datas/v2cdata/tmp.scp"
#test_scp = "/ailab-train/speech/zhanghaomin/datas/v2cdata/test.scp"
test_scp = args.scp
if video_path is None:
lines = []
with open(test_scp, "r") as fr:
lines += fr.readlines()
#with open(test_scp2, "r") as fr:
# lines += fr.readlines()
tests = []
for line in lines[args.start: args.end]:
####video_path, prompt = line.strip().split("\t")
####prompt = "the sound of " + prompt
####negative_prompt = ""
video_path, _, audio_path = line.strip().split("\t")
####video_path = "/ailab-train/speech/zhanghaomin/datas/v2cdata/DragonII/DragonII_videos/Gobber/0725.mp4"
prompt = ""
#negative_prompt = "speech, voice, talking, speaking"
negative_prompt = ""
tests.append([video_path, prompt, negative_prompt, audio_path])
else:
tests = [[video_path, prompt, negative_prompt, ""]]
print(datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3], "start")
for video_path, prompt, negative_prompt, audio_path in tests:
if video_path is not None:
video_path = Path(video_path).expanduser()
log.info(f'Using video {video_path}')
try:
video_info = load_video(video_path, args.duration)
except:
print("Error load_video", video_path)
traceback.print_exc()
continue
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'
####save_path = str(output_dir) + "/" + str(video_path).replace("/", "__").strip(".") + ".flac"
save_path = str(output_dir) + "/__" + os.path.basename(video_path).strip(".") + ".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)
#### calculate energy
if args.calc_energy:
waveform_v2a, sr_v2a = torchaudio.load(save_path)
duration_v2a = waveform_v2a.shape[-1] / sr_v2a
if os.path.exists(audio_path):
waveform, sr = torchaudio.load(audio_path)
duration = waveform.shape[-1] / sr
if duration_v2a >= duration:
waveform_v2a = waveform_v2a[:, :int(sr_v2a*duration)]
else:
waveform_v2a = torch.cat([waveform_v2a, torch.zeros([waveform_v2a.shape[0], int(sr_v2a*duration)-waveform_v2a.shape[1]])], dim=1)
duration_v2a = duration
energy_v2a = []
for i in range(int(duration_v2a/(256/24000))):
energy_v2a.append(waveform_v2a[0,int(i*sr_v2a*(256/24000)):int((i+1)*sr_v2a*(256/24000))].abs().mean())
energy_v2a = np.array(energy_v2a)
energy_v2a = energy_v2a / max(energy_v2a)
#print(len(energy_v2a), max(energy_v2a), min(energy_v2a), energy_v2a.mean())
np.savez(save_path+".npz", energy_v2a)
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'
####video_save_path = str(output_dir) + "/" + str(video_path).replace("/", "__").strip(".") + ".mp4"
video_save_path = str(output_dir) + "/__" + os.path.basename(video_path).strip(".") + ".mp4"
make_video(video_info, video_save_path, audio, sampling_rate=seq_cfg.sampling_rate)
log.info(f'Video saved to {video_save_path}')
log.info('Memory usage: %.2f GB', torch.cuda.max_memory_allocated() / (2**30))
print(datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3], "end")
if __name__ == '__main__':
main()
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