| import os |
| import argparse |
| import torch |
| from torchvision.io import write_video |
| import librosa |
| import time |
| import numpy as np |
| from tqdm import tqdm |
| from emage_utils.motion_io import beat_format_save |
| from emage_utils import fast_render |
| from models.disco_audio import DiscoAudioModel |
|
|
|
|
| def inference(model, audio_path, device, save_folder, sr, pose_fps, seed_frames): |
| audio, _ = librosa.load(audio_path, sr=sr) |
| audio = torch.from_numpy(audio).to(device).unsqueeze(0) |
| speaker_id = torch.zeros(1,1).long().to(device) |
| with torch.no_grad(): |
| motion_pred = model(audio, speaker_id, seed_frames=seed_frames, seed_motion=None)["motion_axis_angle"] |
| t = motion_pred.shape[1] |
| motion_pred = motion_pred.cpu().numpy().reshape(t, -1) |
| beat_format_save(os.path.join(save_folder, f"{os.path.splitext(os.path.basename(audio_path))[0]}_output.npz"), |
| motion_pred, upsample=30//pose_fps) |
| return t |
|
|
|
|
| def visualize_one(save_folder, audio_path, nopytorch3d=False): |
| npz_path = os.path.join(save_folder, f"{os.path.splitext(os.path.basename(audio_path))[0]}_output.npz") |
| motion_dict = np.load(npz_path, allow_pickle=True) |
| if not nopytorch3d: |
| from emage_utils.npz2pose import render2d |
| v2d_body = render2d(motion_dict, (720, 480), face_only=False, remove_global=True) |
| write_video(npz_path.replace(".npz", "_2dbody.mp4"), v2d_body.permute(0, 2, 3, 1), fps=30) |
| fast_render.add_audio_to_video(npz_path.replace(".npz", "_2dbody.mp4"), audio_path, npz_path.replace(".npz", "_2dbody_audio.mp4")) |
| fast_render.render_one_sequence_no_gt(npz_path, os.path.dirname(npz_path), audio_path, model_folder="./emage_evaltools/smplx_models/") |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--audio_folder", type=str, default="./examples/audio") |
| parser.add_argument("--save_folder", type=str, default="./examples/motion") |
| parser.add_argument("--visualization", action="store_true") |
| parser.add_argument("--nopytorch3d", action="store_true") |
| args = parser.parse_args() |
|
|
| os.makedirs(args.save_folder, exist_ok=True) |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
| model = DiscoAudioModel.from_pretrained("H-Liu1997/disco_audio").to(device) |
| model.eval() |
|
|
| audio_files = [os.path.join(args.audio_folder, f) for f in os.listdir(args.audio_folder) if f.endswith(".wav")] |
| sr, pose_fps, seed_frames = model.cfg.audio_sr, model.cfg.pose_fps, model.cfg.seed_frames |
| all_t = 0 |
| start_time = time.time() |
| for audio_path in tqdm(audio_files, desc="Inference"): |
| all_t += inference(model, audio_path, device, args.save_folder, sr, pose_fps, seed_frames) |
| print(f"generate total {all_t/pose_fps:.2f} seconds motion in {time.time()-start_time:.2f} seconds, saved in {args.save_folder}") |
| |
| start_time = time.time() |
| if args.visualization: |
| for audio_path in tqdm(audio_files, desc="Visualize"): |
| visualize_one(args.save_folder, audio_path, args.nopytorch3d) |
| print(f"render total {all_t/pose_fps:.2f} seconds motion in {time.time()-start_time:.2f} seconds, saved in {args.save_folder}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
