inference.py

from utils.download_utils import download_files_from_repo
download_files_from_repo()

import os
import gc
import soundfile as sf
import shutil
import argparse
from omegaconf import OmegaConf
import random
import numpy as np
import librosa
import emage.mertic  # noqa: F401 # somehow this must be imported, even though it is not used directly
from decord import VideoReader
from PIL import Image
import cv2
import subprocess
import importlib
import torch
import torch.nn.functional as F
import smplx
import igraph
import fire

from utils.video_io import save_videos_from_pil
from utils.genextend_inference_utils import adjust_statistics_to_match_reference
from create_graph import path_visualization, graph_pruning, get_motion_reps_tensor, path_visualization_v2

SCRIPT_PATH = os.path.dirname(os.path.realpath(__file__))

def search_path_dp(graph, audio_low_np, audio_high_np, loop_penalty=0.1, top_k=1, search_mode="both", continue_penalty=0.1):
    T = audio_low_np.shape[0]  # Total time steps
    # N = len(graph.vs)  # Total number of nodes in the graph

    # Initialize DP tables
    min_cost = [
        {} for _ in range(T)
    ]  # min_cost[t][node_index] = list of tuples: (cost, prev_node_index, prev_tuple_index, non_continue_count, visited_nodes)

    # Initialize the first time step
    start_nodes = [v for v in graph.vs if v["previous"] is None or v["previous"] == -1]
    for node in start_nodes:
        node_index = node.index
        motion_low = node["motion_low"]  # Shape: [C]
        motion_high = node["motion_high"]  # Shape: [C]

        # Cost using cosine similarity
        if search_mode == "both":
            cost = 2 - (np.dot(audio_low_np[0], motion_low.T) + np.dot(audio_high_np[0], motion_high.T))
        elif search_mode == "high_level":
            cost = 1 - np.dot(audio_high_np[0], motion_high.T)
        elif search_mode == "low_level":
            cost = 1 - np.dot(audio_low_np[0], motion_low.T)

        visited_nodes = {node_index: 1}  # Initialize visit count as a dictionary

        min_cost[0][node_index] = [(cost, None, None, 0, visited_nodes)]  # Initialize with no predecessor and 0 non-continue count

    # DP over time steps
    for t in range(1, T):
        for node in graph.vs:
            node_index = node.index
            candidates = []

            # Incoming edges to the current node
            incoming_edges = graph.es.select(_to=node_index)
            for edge in incoming_edges:
                prev_node_index = edge.source
                edge_id = edge.index
                is_continue_edge = graph.es[edge_id]["is_continue"]
                # prev_node = graph.vs[prev_node_index]
                if prev_node_index in min_cost[t - 1]:
                    for tuple_index, (prev_cost, _, _, prev_non_continue_count, prev_visited) in enumerate(min_cost[t - 1][prev_node_index]):
                        # Loop punishment
                        if node_index in prev_visited:
                            loop_time = prev_visited[node_index]  # Get the count of previous visits
                            loop_cost = prev_cost + loop_penalty * np.exp(loop_time)  # Apply exponential penalty
                            new_visited = prev_visited.copy()
                            new_visited[node_index] = loop_time + 1  # Increment visit count
                        else:
                            loop_cost = prev_cost
                            new_visited = prev_visited.copy()
                            new_visited[node_index] = 1  # Initialize visit count for the new node

                        motion_low = node["motion_low"]  # Shape: [C]
                        motion_high = node["motion_high"]  # Shape: [C]

                        if search_mode == "both":
                            cost_increment = 2 - (np.dot(audio_low_np[t], motion_low.T) + np.dot(audio_high_np[t], motion_high.T))
                        elif search_mode == "high_level":
                            cost_increment = 1 - np.dot(audio_high_np[t], motion_high.T)
                        elif search_mode == "low_level":
                            cost_increment = 1 - np.dot(audio_low_np[t], motion_low.T)

                        # Check if the edge is "is_continue"
                        if not is_continue_edge:
                            non_continue_count = prev_non_continue_count + 1  # Increment the count of non-continue edges
                        else:
                            non_continue_count = prev_non_continue_count

                        # Apply the penalty based on the square of the number of non-continuous edges
                        continue_penalty_cost = continue_penalty * non_continue_count

                        total_cost = loop_cost + cost_increment + continue_penalty_cost

                        candidates.append((total_cost, prev_node_index, tuple_index, non_continue_count, new_visited))

            # Keep the top k candidates
            if candidates:
                # Sort candidates by total_cost
                candidates.sort(key=lambda x: x[0])
                # Keep top k
                min_cost[t][node_index] = candidates[:top_k]
            else:
                # No candidates, do nothing
                pass

    # Collect all possible end paths at time T-1
    end_candidates = []
    for node_index, tuples in min_cost[T - 1].items():
        for tuple_index, (cost, _, _, _, _) in enumerate(tuples):
            end_candidates.append((cost, node_index, tuple_index))

    if not end_candidates:
        print("No valid path found.")
        return [], []

    # Sort end candidates by cost
    end_candidates.sort(key=lambda x: x[0])

    # Keep top k paths
    top_k_paths_info = end_candidates[:top_k]

    # Reconstruct the paths
    optimal_paths = []
    is_continue_lists = []
    for final_cost, node_index, tuple_index in top_k_paths_info:
        optimal_path_indices = []
        current_node_index = node_index
        current_tuple_index = tuple_index
        for t in range(T - 1, -1, -1):
            optimal_path_indices.append(current_node_index)
            tuple_data = min_cost[t][current_node_index][current_tuple_index]
            _, prev_node_index, prev_tuple_index, _, _ = tuple_data
            current_node_index = prev_node_index
            current_tuple_index = prev_tuple_index
            if current_node_index is None:
                break  # Reached the start node
        optimal_path_indices = optimal_path_indices[::-1]  # Reverse to get correct order
        optimal_path = [graph.vs[idx] for idx in optimal_path_indices]
        optimal_paths.append(optimal_path)

        # Extract continuity information
        is_continue = []
        for i in range(len(optimal_path) - 1):
            edge_id = graph.get_eid(optimal_path[i].index, optimal_path[i + 1].index)
            is_cont = graph.es[edge_id]["is_continue"]
            is_continue.append(is_cont)
        is_continue_lists.append(is_continue)

    print("Top {} Paths:".format(len(optimal_paths)))
    for i, path in enumerate(optimal_paths):
        path_indices = [node.index for node in path]
        print("Path {}: Cost: {}, Nodes: {}".format(i + 1, top_k_paths_info[i][0], path_indices))

    return optimal_paths, is_continue_lists


def test_fn(model, device, iteration, candidate_json_path, test_path, cfg, audio_path, **kwargs):
    create_graph = kwargs["create_graph"]
    torch.set_grad_enabled(False)
    pool_path = candidate_json_path.replace("data_json", "cached_graph").replace(".json", ".pkl")
    graph = igraph.Graph.Read_Pickle(fname=pool_path)
    # print(len(graph.vs))

    save_dir = os.path.join(test_path, f"retrieved_motions_{iteration}")
    os.makedirs(save_dir, exist_ok=True)

    actual_model = model.module if isinstance(model, torch.nn.parallel.DistributedDataParallel) else model
    actual_model.eval()

    # with open(candidate_json_path, 'r') as f:
    #     candidate_data = json.load(f)
    all_motions = {}
    for i, node in enumerate(graph.vs):
        if all_motions.get(node["name"]) is None:
            all_motions[node["name"]] = [node["axis_angle"].reshape(-1)]
        else:
            all_motions[node["name"]].append(node["axis_angle"].reshape(-1))
    for k, v in all_motions.items():
        all_motions[k] = np.stack(v)  # T, J*3
        # print(k, all_motions[k].shape)

    window_size = cfg.data.pose_length
    motion_high_all = []
    motion_low_all = []
    for k, v in all_motions.items():
        motion_tensor = torch.from_numpy(v).float().to(device).unsqueeze(0)
        _, t, _ = motion_tensor.shape

        if t >= window_size:
            num_chunks = t // window_size
            motion_high_list = []
            motion_low_list = []

            for i in range(num_chunks):
                start_idx = i * window_size
                end_idx = start_idx + window_size
                motion_slice = motion_tensor[:, start_idx:end_idx, :]

                motion_features = actual_model.get_motion_features(motion_slice)

                motion_low = motion_features["motion_low"].cpu().numpy()
                motion_high = motion_features["motion_cls"].unsqueeze(0).repeat(1, motion_low.shape[1], 1).cpu().numpy()

                motion_high_list.append(motion_high[0])
                motion_low_list.append(motion_low[0])

            remain_length = t % window_size
            if remain_length > 0:
                start_idx = t - window_size
                motion_slice = motion_tensor[:, start_idx:, :]

                motion_features = actual_model.get_motion_features(motion_slice)
                # motion_high = motion_features["motion_high_weight"].cpu().numpy()
                motion_low = motion_features["motion_low"].cpu().numpy()
                motion_high = motion_features["motion_cls"].unsqueeze(0).repeat(1, motion_low.shape[1], 1).cpu().numpy()

                motion_high_list.append(motion_high[0][-remain_length:])
                motion_low_list.append(motion_low[0][-remain_length:])

            motion_high_all.append(np.concatenate(motion_high_list, axis=0))
            motion_low_all.append(np.concatenate(motion_low_list, axis=0))

        else:  # t < window_size:
            gap = window_size - t
            motion_slice = torch.cat(
                [motion_tensor, torch.zeros((motion_tensor.shape[0], gap, motion_tensor.shape[2])).to(motion_tensor.device)], 1
            )
            motion_features = actual_model.get_motion_features(motion_slice)
            # motion_high = motion_features["motion_high_weight"].cpu().numpy()
            motion_low = motion_features["motion_low"].cpu().numpy()
            motion_high = motion_features["motion_cls"].unsqueeze(0).repeat(1, motion_low.shape[1], 1).cpu().numpy()

            motion_high_all.append(motion_high[0][:t])
            motion_low_all.append(motion_low[0][:t])

    motion_high_all = np.concatenate(motion_high_all, axis=0)
    motion_low_all = np.concatenate(motion_low_all, axis=0)
    # print(motion_high_all.shape, motion_low_all.shape, len(graph.vs))
    motion_low_all = motion_low_all / np.linalg.norm(motion_low_all, axis=1, keepdims=True)
    motion_high_all = motion_high_all / np.linalg.norm(motion_high_all, axis=1, keepdims=True)
    assert motion_high_all.shape[0] == len(graph.vs)
    assert motion_low_all.shape[0] == len(graph.vs)

    for i, node in enumerate(graph.vs):
        node["motion_high"] = motion_high_all[i]
        node["motion_low"] = motion_low_all[i]

    graph = graph_pruning(graph)
    # for gradio, use a subgraph
    if len(graph.vs) > 1800:
        gap = len(graph.vs) - 1800
        start_d = random.randint(0, 1800)
        graph.delete_vertices(range(start_d, start_d + gap))
    ascc_2 = graph.clusters(mode="STRONG")
    graph = ascc_2.giant()

    # drop the id of gt
    idx = 0
    audio_waveform, sr = librosa.load(audio_path)
    audio_waveform = librosa.resample(audio_waveform, orig_sr=sr, target_sr=cfg.data.audio_sr)
    audio_tensor = torch.from_numpy(audio_waveform).float().to(device).unsqueeze(0)

    target_length = audio_tensor.shape[1] // cfg.data.audio_sr * 30
    window_size = int(cfg.data.audio_sr * (cfg.data.pose_length / 30))
    _, t = audio_tensor.shape
    audio_low_list = []
    audio_high_list = []

    if t >= window_size:
        num_chunks = t // window_size
        # print(num_chunks, t % window_size)
        for i in range(num_chunks):
            start_idx = i * window_size
            end_idx = start_idx + window_size
            # print(start_idx, end_idx, window_size)
            audio_slice = audio_tensor[:, start_idx:end_idx]

            model_out_candidates = actual_model.get_audio_features(audio_slice)
            audio_low = model_out_candidates["audio_low"]
            # audio_high = model_out_candidates["audio_high_weight"]
            audio_high = model_out_candidates["audio_cls"].unsqueeze(0).repeat(1, audio_low.shape[1], 1)
            # print(audio_low.shape, audio_high.shape)

            audio_low = F.normalize(audio_low, dim=2)[0].cpu().numpy()
            audio_high = F.normalize(audio_high, dim=2)[0].cpu().numpy()

            audio_low_list.append(audio_low)
            audio_high_list.append(audio_high)
            # print(audio_low.shape, audio_high.shape)

        remain_length = t % window_size
        if remain_length > 1:
            start_idx = t - window_size
            audio_slice = audio_tensor[:, start_idx:]

            model_out_candidates = actual_model.get_audio_features(audio_slice)
            audio_low = model_out_candidates["audio_low"]
            # audio_high = model_out_candidates["audio_high_weight"]
            audio_high = model_out_candidates["audio_cls"].unsqueeze(0).repeat(1, audio_low.shape[1], 1)

            gap = target_length - np.concatenate(audio_low_list, axis=0).shape[1]
            audio_low = F.normalize(audio_low, dim=2)[0][-gap:].cpu().numpy()
            audio_high = F.normalize(audio_high, dim=2)[0][-gap:].cpu().numpy()

            # print(audio_low.shape, audio_high.shape)
            audio_low_list.append(audio_low)
            audio_high_list.append(audio_high)
    else:
        gap = window_size - t
        audio_slice = audio_tensor
        model_out_candidates = actual_model.get_audio_features(audio_slice)
        audio_low = model_out_candidates["audio_low"]
        # audio_high = model_out_candidates["audio_high_weight"]
        audio_high = model_out_candidates["audio_cls"].unsqueeze(0).repeat(1, audio_low.shape[1], 1)
        audio_low = F.normalize(audio_low, dim=2)[0].cpu().numpy()
        audio_high = F.normalize(audio_high, dim=2)[0].cpu().numpy()
        audio_low_list.append(audio_low)
        audio_high_list.append(audio_high)

    audio_low_all = np.concatenate(audio_low_list, axis=0)
    audio_high_all = np.concatenate(audio_high_list, axis=0)
    path_list, is_continue_list = search_path_dp(graph, audio_low_all, audio_high_all, top_k=1, search_mode="both")

    res_motion = []
    counter = 0
    wav2lip_checkpoint_path = os.path.join(SCRIPT_PATH, "Wav2Lip/checkpoints/wav2lip_gan.pth")  # Update this path to your Wav2Lip checkpoint
    wav2lip_script_path = os.path.join(SCRIPT_PATH, "Wav2Lip/inference.py")
    for path, is_continue in zip(path_list, is_continue_list):
        if False:
            # time is limited if we create graph on hugging face, lets skip blending.
            res_motion_current = path_visualization(
                graph,
                path,
                is_continue,
                os.path.join(save_dir, f"audio_{idx}_retri_{counter}.mp4"),
                audio_path=audio_path,
                return_motion=True,
                verbose_continue=True,
            )
            video_temp_path = os.path.join(save_dir, f"audio_{idx}_retri_{counter}.mp4")
        else:
            res_motion_current = path_visualization_v2(
                graph,
                path,
                is_continue,
                os.path.join(save_dir, f"audio_{idx}_retri_{counter}.mp4"),
                audio_path=None,
                return_motion=True,
                verbose_continue=True,
            )
            video_temp_path = os.path.join(save_dir, f"audio_{idx}_retri_{counter}.mp4")
            video_reader = VideoReader(video_temp_path)
            video_np = []
            for i in range(len(video_reader)):
                if i == 0:
                    continue
                video_frame = video_reader[i].asnumpy()
                video_np.append(Image.fromarray(video_frame))
            adjusted_video_pil = adjust_statistics_to_match_reference([video_np])
            save_videos_from_pil(
                adjusted_video_pil[0], os.path.join(save_dir, f"audio_{idx}_retri_{counter}.mp4"), fps=graph.vs[0]["fps"], bitrate=2000000
            )

        audio_temp_path = audio_path
        lipsync_output_path = os.path.join(save_dir, f"audio_{idx}_retri_{counter}.mp4")
        cmd_wav2lip_1 = f"cd Wav2Lip; python {wav2lip_script_path} --checkpoint_path {wav2lip_checkpoint_path} --face {video_temp_path} --audio {audio_temp_path} --outfile {lipsync_output_path} --nosmooth --out_height 720"
        subprocess.run(cmd_wav2lip_1, shell=True)

        res_motion.append(res_motion_current)
        np.savez(os.path.join(save_dir, f"audio_{idx}_retri_{counter}.npz"), motion=res_motion_current)

        start_node = path[1].index
        end_node = start_node + 100

    if create_graph:
        # time is limited if create graph, let us skip the second video
        result = [
            os.path.join(save_dir, f"audio_{idx}_retri_0.mp4"),
            os.path.join(save_dir, f"audio_{idx}_retri_0.mp4"),
            os.path.join(save_dir, f"audio_{idx}_retri_0.npz"),
            os.path.join(save_dir, f"audio_{idx}_retri_0.npz"),
        ]
        return result

    print(f"delete gt-nodes {start_node}, {end_node}")
    nodes_to_delete = list(range(start_node, end_node))
    graph.delete_vertices(nodes_to_delete)
    graph = graph_pruning(graph)
    path_list, is_continue_list = search_path_dp(graph, audio_low_all, audio_high_all, top_k=1, search_mode="both")
    res_motion = []
    counter = 1
    for path, is_continue in zip(path_list, is_continue_list):
        res_motion_current = path_visualization_v2(
            graph,
            path,
            is_continue,
            os.path.join(save_dir, f"audio_{idx}_retri_{counter}.mp4"),
            audio_path=None,
            return_motion=True,
            verbose_continue=True,
        )
        video_temp_path = os.path.join(save_dir, f"audio_{idx}_retri_{counter}.mp4")
        video_reader = VideoReader(video_temp_path)
        video_np = []
        for i in range(len(video_reader)):
            if i == 0:
                continue
            video_frame = video_reader[i].asnumpy()
            video_np.append(Image.fromarray(video_frame))
        adjusted_video_pil = adjust_statistics_to_match_reference([video_np])
        save_videos_from_pil(
            adjusted_video_pil[0], os.path.join(save_dir, f"audio_{idx}_retri_{counter}.mp4"), fps=graph.vs[0]["fps"], bitrate=2000000
        )

        audio_temp_path = audio_path
        lipsync_output_path = os.path.join(save_dir, f"audio_{idx}_retri_{counter}.mp4")
        cmd_wav2lip_2 = f"cd Wav2Lip; python {wav2lip_script_path} --checkpoint_path {wav2lip_checkpoint_path} --face {video_temp_path} --audio {audio_temp_path} --outfile {lipsync_output_path} --nosmooth --out_height 720"
        subprocess.run(cmd_wav2lip_2, shell=True)
        res_motion.append(res_motion_current)
        np.savez(os.path.join(save_dir, f"audio_{idx}_retri_{counter}.npz"), motion=res_motion_current)

    result = [
        os.path.join(save_dir, f"audio_{idx}_retri_0.mp4"),
        os.path.join(save_dir, f"audio_{idx}_retri_1.mp4"),
        os.path.join(save_dir, f"audio_{idx}_retri_0.npz"),
        os.path.join(save_dir, f"audio_{idx}_retri_1.npz"),
    ]
    return result


def init_class(module_name, class_name, config, **kwargs):
    module = importlib.import_module(module_name)
    model_class = getattr(module, class_name)
    instance = model_class(config, **kwargs)
    return instance


def seed_everything(seed):
    torch.cuda.manual_seed_all(seed)


def prepare_all(yaml_name):
    if yaml_name.endswith(".yaml"):
        config = OmegaConf.load(yaml_name)
        config.exp_name = os.path.basename(yaml_name)[:-5]
    else:
        raise ValueError("Unsupported config file format. Only .yaml files are allowed.")
    save_dir = os.path.join(OUTPUT_DIR, config.exp_name)
    os.makedirs(save_dir, exist_ok=True)
    return config


def save_first_20_seconds(video_path, output_path="./save_video.mp4", max_length=512):
    if os.path.exists(output_path):
        os.remove(output_path)

    cap = cv2.VideoCapture(video_path)

    if not cap.isOpened():
        return

    fps = int(cap.get(cv2.CAP_PROP_FPS))
    original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    # Calculate the aspect ratio and resize dimensions
    if original_width >= original_height:
        new_width = max_length
        new_height = int(original_height * (max_length / original_width))
    else:
        new_height = max_length
        new_width = int(original_width * (max_length / original_height))

    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
    out = cv2.VideoWriter(output_path.replace(".mp4", "_fps.mp4"), fourcc, fps, (new_width, new_height))

    frames_to_save = fps * 20
    frame_count = 0

    while cap.isOpened() and frame_count < frames_to_save:
        ret, frame = cap.read()
        if not ret:
            break
        # Resize the frame while keeping the aspect ratio
        resized_frame = cv2.resize(frame, (new_width, new_height))
        # resized_frame = frame
        out.write(resized_frame)
        frame_count += 1

    cap.release()
    out.release()
    command = [
        "ffmpeg",
        "-i",
        output_path.replace(".mp4", "_fps.mp4"),
        "-vf",
        "minterpolate=fps=30:mi_mode=mci:mc_mode=aobmc:vsbmc=1",
        output_path,
    ]
    subprocess.run(command)
    os.remove(output_path.replace(".mp4", "_fps.mp4"))


character_name_to_yaml = {
    # "speaker8_jjRWaMCWs44_00-00-30.16_00-00-33.32.mp4": "./datasets/data_json/youtube_test/speaker8.json",
    # "speaker7_iuYlGRnC7J8_00-00-0.00_00-00-3.25.mp4": "./datasets/data_json/youtube_test/speaker7.json",
    "speaker9_o7Ik1OB4TaE_00-00-38.15_00-00-42.33.mp4": "./datasets/data_json/youtube_test/speaker9.json",
    # "1wrQ6Msp7wM_00-00-39.69_00-00-45.68.mp4": "./datasets/data_json/youtube_test/speaker1.json",
    # "101099-00_18_09-00_18_19.mp4": "./datasets/data_json/show_oliver_test/Stupid_Watergate_-_Last_Week_Tonight_with_John_Oliver_HBO-FVFdsl29s_Q.mkv.json",
}


TARGET_SR = 16000
OUTPUT_DIR = os.path.join(SCRIPT_PATH, "outputs/")


# @spaces.GPU(duration=200)
def tango(audio_path, character_name, seed=2024, create_graph=False, video_folder_path=None):
    shutil.rmtree(OUTPUT_DIR, ignore_errors=True)
    os.makedirs(OUTPUT_DIR, exist_ok=True)
    cfg_file = os.path.join(SCRIPT_PATH, "configs/gradio.yaml")
    cfg = prepare_all(cfg_file)
    cfg.seed = seed
    seed_everything(cfg.seed)
    experiment_ckpt_dir = os.path.join(OUTPUT_DIR, cfg.exp_name)

    if isinstance(audio_path, tuple):
        # From Gradio: (sample_rate, audio_waveform)
        sample_rate, audio_waveform = audio_path
    elif isinstance(audio_path, str) and os.path.exists(audio_path):
        # From CLI: file path
        audio_waveform, sample_rate = librosa.load(audio_path, sr=None)  # Load with original SR
    else:
        raise TypeError(f"Invalid audio_path provided. It must be a tuple from Gradio or a valid file path from CLI. Got: {audio_path}")

    saved_audio_path = os.path.join(OUTPUT_DIR, "saved_audio.wav")

    # Resample and truncate audio, then save to a canonical path
    resampled_audio = librosa.resample(audio_waveform, orig_sr=sample_rate, target_sr=TARGET_SR)
    required_length = int(TARGET_SR * (128 / 30)) * 2
    resampled_audio = resampled_audio[:required_length]
    sf.write(saved_audio_path, resampled_audio, TARGET_SR)
    audio_path_for_model = saved_audio_path

    video_folder_path = os.path.join(OUTPUT_DIR, "tmpvideo")
    char_basename = os.path.basename(character_name)

    if char_basename in character_name_to_yaml:
        yaml_name = os.path.join(SCRIPT_PATH, character_name_to_yaml[char_basename])
    else:
        create_graph = True
        # load video, and save it to "./save_video.mp4 for the first 20s of the video."
        os.makedirs(video_folder_path, exist_ok=True)
        save_first_20_seconds(character_name, os.path.join(video_folder_path, "save_video.mp4"))

    cfg.data.test_meta_paths = yaml_name
    print(f"Using character config: {yaml_name}")

    if create_graph:
        print("warning: make sure you are in the conda environment with python 3.9 and installed mmcv and mmpose")
        data_save_path = os.path.join(OUTPUT_DIR, "tmpdata")
        json_save_path = os.path.join(OUTPUT_DIR, "save_video.json")
        graph_save_path = os.path.join(OUTPUT_DIR, "save_video.pkl")
        cmd_smplx = f"cd ./SMPLer-X/ && python app.py --video_folder_path {video_folder_path} --data_save_path {data_save_path} --json_save_path {json_save_path} && cd .."
        subprocess.run(cmd_smplx, shell=True)
        print("cmd_smplx: ", cmd_smplx)
        cmd_graph = f"python ./create_graph.py --json_save_path {json_save_path} --graph_save_path {graph_save_path}"
        subprocess.run(cmd_graph, shell=True)
        print("cmd_graph: ", cmd_graph)
        cfg.data.test_meta_paths = json_save_path
        gc.collect()
        torch.cuda.empty_cache()

    smplx_model = smplx.create(
        "./emage/smplx_models/",
        model_type="smplx",
        gender="NEUTRAL_2020",
        use_face_contour=False,
        num_betas=300,
        num_expression_coeffs=100,
        ext="npz",
        use_pca=False,
    )
    model = init_class(cfg.model.name_pyfile, cfg.model.class_name, cfg)
    for param in model.parameters():
        param.requires_grad = False
    model.smplx_model = smplx_model
    model.get_motion_reps = get_motion_reps_tensor
    assert torch.cuda.is_available(), "CUDA is not available"
    device = torch.device("cuda:0")
    smplx_model = smplx_model.to(device).eval()
    model = model.to(device)
    model.smplx_model = model.smplx_model.to(device)

    checkpoint_path = os.path.join(SCRIPT_PATH, "datasets/cached_ckpts/ckpt.pth")
    checkpoint = torch.load(checkpoint_path)
    state_dict = checkpoint["model_state_dict"]
    new_state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
    model.load_state_dict(new_state_dict, strict=False)

    test_path = os.path.join(experiment_ckpt_dir, f"test_{0}")
    os.makedirs(test_path, exist_ok=True)
    result = test_fn(model, device, 0, cfg.data.test_meta_paths, test_path, cfg, audio_path_for_model, create_graph=create_graph)
    gc.collect()
    torch.cuda.empty_cache()
    return result


if __name__ == '__main__':
    os.environ["MASTER_ADDR"] = "127.0.0.1"
    os.environ["MASTER_PORT"] = "8675"
    fire.Fire(tango)
    # python inference.py --audio_path ./datasets/cached_audio/example_male_voice_9_seconds.wav --character_name ./datasets/cached_audio/speaker9_o7Ik1OB4TaE_00-00-38.15_00-00-42.33.mp4