import spaces
import os
# os.environ["XDG_RUNTIME_DIR"] = "/content"
# os.system("Xvfb :99 -ac &")
# os.environ["DISPLAY"] = ":99"
# os.environ["PYOPENGL_PLATFORM"] = "egl"
# os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1"
import gradio as gr
import gc
import soundfile as sf
import shutil
import argparse
from moviepy.tools import verbose_print
from omegaconf import OmegaConf
import random
import numpy as np
import json
import librosa
import emage.mertic
from datetime import datetime
from decord import VideoReader
from PIL import Image
import copy
import cv2
import importlib
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import AdamW
from torch.utils.data import DataLoader
from torch.nn.parallel import DistributedDataParallel as DDP
from tqdm import tqdm
import smplx
from moviepy.editor import VideoFileClip, AudioFileClip, ImageSequenceClip
import igraph
# import emage
import utils.rotation_conversions as rc
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
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)
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()
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
for path, is_continue in zip(path_list, is_continue_list):
if create_graph:
# 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")
checkpoint_path = './Wav2Lip/checkpoints/wav2lip_gan.pth' # Update this path to your Wav2Lip checkpoint
os.system(f'python ./Wav2Lip/inference.py --checkpoint_path {checkpoint_path} --face {video_temp_path} --audio {audio_temp_path} --outfile {lipsync_output_path} --nosmooth')
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")
checkpoint_path = './Wav2Lip/checkpoints/wav2lip_gan.pth' # Update this path to your Wav2Lip checkpoint
os.system(f'python ./Wav2Lip/inference.py --checkpoint_path {checkpoint_path} --face {video_temp_path} --audio {audio_temp_path} --outfile {lipsync_output_path} --nosmooth')
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):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def prepare_all(yaml_name):
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default=yaml_name)
parser.add_argument("--debug", action="store_true", help="Enable debugging mode")
parser.add_argument('overrides', nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.config.endswith(".yaml"):
config = OmegaConf.load(args.config)
config.exp_name = args.config.split("/")[-1][:-5]
else:
raise ValueError("Unsupported config file format. Only .yaml files are allowed.")
save_dir = os.path.join(config.output_dir, config.exp_name)
os.makedirs(save_dir, exist_ok=True)
return config
def save_first_10_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, 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()
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",
}
@spaces.GPU(duration=200)
def tango(audio_path, character_name, seed, create_graph=False, video_folder_path=None):
cfg = prepare_all("./configs/gradio.yaml")
cfg.seed = seed
seed_everything(cfg.seed)
experiment_ckpt_dir = experiment_log_dir = os.path.join(cfg.output_dir, cfg.exp_name)
saved_audio_path = "./saved_audio.wav"
sample_rate, audio_waveform = audio_path
sf.write(saved_audio_path, audio_waveform, sample_rate)
audio_waveform, sample_rate = librosa.load(saved_audio_path)
# print(audio_waveform.shape)
resampled_audio = librosa.resample(audio_waveform, orig_sr=sample_rate, target_sr=16000)
required_length = int(16000 * (128 / 30)) * 2
resampled_audio = resampled_audio[:required_length]
sf.write(saved_audio_path, resampled_audio, 16000)
audio_path = saved_audio_path
yaml_name = character_name_to_yaml.get(character_name.split("/")[-1], "./datasets/data_json/youtube_test/speaker1.json")
cfg.data.test_meta_paths = yaml_name
print(yaml_name, character_name.split("/")[-1])
if character_name.split("/")[-1] not in character_name_to_yaml.keys():
create_graph=True
# load video, and save it to "./save_video.mp4 for the first 20s of the video."
os.makedirs("./outputs/tmpvideo/", exist_ok=True)
save_first_10_seconds(character_name, "./outputs/tmpvideo/save_video.mp4")
if create_graph:
video_folder_path = "./outputs/tmpvideo/"
data_save_path = "./outputs/tmpdata/"
json_save_path = "./outputs/save_video.json"
graph_save_path = "./outputs/save_video.pkl"
os.system(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 ..")
print(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 ..")
os.system(f"python ./create_graph.py --json_save_path {json_save_path} --graph_save_path {graph_save_path}")
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
local_rank = 0
torch.cuda.set_device(local_rank)
device = torch.device("cuda", local_rank)
smplx_model = smplx_model.to(device).eval()
model = model.to(device)
model.smplx_model = model.smplx_model.to(device)
checkpoint_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, create_graph=create_graph)
gc.collect()
torch.cuda.empty_cache()
return result
examples_audio = [
["./datasets/cached_audio/example_male_voice_9_seconds.wav"],
["./datasets/cached_audio/example_female_voice_9_seconds.wav"],
]
examples_video = [
["./datasets/cached_audio/speaker8_jjRWaMCWs44_00-00-30.16_00-00-33.32.mp4"],
["./datasets/cached_audio/speaker7_iuYlGRnC7J8_00-00-0.00_00-00-3.25.mp4"],
["./datasets/cached_audio/speaker9_o7Ik1OB4TaE_00-00-38.15_00-00-42.33.mp4"],
["./datasets/cached_audio/1wrQ6Msp7wM_00-00-39.69_00-00-45.68.mp4"],
["./datasets/cached_audio/101099-00_18_09-00_18_19.mp4"],
]
combined_examples = [
["./datasets/cached_audio/example_male_voice_9_seconds.wav", "./datasets/cached_audio/101099-00_18_09-00_18_19.mp4", 2024],
["./datasets/cached_audio/example_female_voice_9_seconds.wav", "./datasets/cached_audio/101099-00_18_09-00_18_19.mp4", 2024],
]
def make_demo():
with gr.Blocks(analytics_enabled=False) as Interface:
gr.Markdown(
"""
TANGO
Generating full-body talking videos from audio and reference video
"""
)
# Create a gallery with 5 videos
with gr.Row():
video1 = gr.Video(value="./datasets/cached_audio/demo1.mp4", label="Demo 0")
video2 = gr.Video(value="./datasets/cached_audio/demo2.mp4", label="Demo 1")
video3 = gr.Video(value="./datasets/cached_audio/demo3.mp4", label="Demo 2")
video4 = gr.Video(value="./datasets/cached_audio/demo4.mp4", label="Demo 3")
video5 = gr.Video(value="./datasets/cached_audio/demo5.mp4", label="Demo 4")
with gr.Row():
video1 = gr.Video(value="./datasets/cached_audio/demo6.mp4", label="Demo 5")
video2 = gr.Video(value="./datasets/cached_audio/demo0.mp4", label="Demo 6")
video3 = gr.Video(value="./datasets/cached_audio/demo7.mp4", label="Demo 7")
video4 = gr.Video(value="./datasets/cached_audio/demo8.mp4", label="Demo 8")
video5 = gr.Video(value="./datasets/cached_audio/demo9.mp4", label="Demo 9")
with gr.Row():
gr.Markdown(
"""
This is an open-source project supported by Hugging Face's free ZeroGPU. Runtime is limited to 300s, so it operates in low-quality mode. Some generated results from high-quality mode are shown above.
"""
)
with gr.Row():
with gr.Column(scale=4):
video_output_1 = gr.Video(label="Generated video - 1",
interactive=False,
autoplay=False,
loop=False,
show_share_button=True)
with gr.Column(scale=4):
video_output_2 = gr.Video(label="Generated video - 2",
interactive=False,
autoplay=False,
loop=False,
show_share_button=True)
with gr.Column(scale=1):
file_output_1 = gr.File(label="Download 3D Motion and Visualize in Blender")
file_output_2 = gr.File(label="Download 3D Motion and Visualize in Blender")
gr.Markdown("""
Details of the low-quality mode:
0. for free users, hugging face zero-gpu has quota, if you see "over quota", please try it later, e.g., after 30 mins. for saving your quota, this project is estimated to run around 120~160s. by the following trade-off.
1. lower resolution, video resized as long-side 512 and keep aspect ratio.
2. subgraph instead of full-graph, causing noticeable "frame jumps".
3. only use the first 8s of your input audio.
4. only use the first 20s of your input video for custom character. if you custom character, it will only generate one video result without "smoothing" for saving time.
5. use open-source tools like SMPLerX-s-model, Wav2Lip, and FiLM for faster processing.
Feel free to open an issue on GitHub or contact the authors if this does not meet your needs.
""")
with gr.Row():
with gr.Column(scale=1):
audio_input = gr.Audio(label="Upload your audio")
seed_input = gr.Number(label="Seed", value=2024, interactive=True)
with gr.Column(scale=2):
gr.Examples(
examples=examples_audio,
inputs=[audio_input],
outputs=[video_output_1, video_output_2, file_output_1, file_output_2],
label="Select existing Audio examples",
cache_examples=False
)
with gr.Column(scale=1):
video_input = gr.Video(label="Your Character", elem_classes="video")
with gr.Column(scale=2):
gr.Examples(
examples=examples_video,
inputs=[video_input], # Correctly refer to video input
outputs=[video_output_1, video_output_2, file_output_1, file_output_2],
label="Character Examples",
cache_examples=False
)
# Fourth row: Generate video button
with gr.Row():
run_button = gr.Button("Generate Video")
# Define button click behavior
run_button.click(
fn=tango,
inputs=[audio_input, video_input, seed_input],
outputs=[video_output_1, video_output_2, file_output_1, file_output_2]
)
# with gr.Row():
# with gr.Column(scale=4):
# print(combined_examples)
# gr.Examples(
# examples=combined_examples,
# inputs=[audio_input, video_input, seed_input], # Both audio and video as inputs
# outputs=[video_output_1, video_output_2, file_output_1, file_output_2],
# fn=tango, # Function that processes both audio and video inputs
# label="Select Combined Audio and Video Examples (Cached)",
# cache_examples=True
# )
return Interface
if __name__ == "__main__":
os.environ["MASTER_ADDR"]='127.0.0.1'
os.environ["MASTER_PORT"]='8675'
demo = make_demo()
demo.launch(share=True)
