Spaces:
Runtime error
Runtime error
File size: 5,000 Bytes
73ed896 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
import gc
import logging
from datetime import datetime
from fractions import Fraction
from pathlib import Path
import gradio as gr
import torch
import torchaudio
from mmaudio.eval_utils import (ModelConfig, VideoInfo, all_model_cfg, generate, load_image,
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.sequence_config import SequenceConfig
from mmaudio.model.utils.features_utils import FeaturesUtils
# Setup logging
setup_eval_logging()
log = logging.getLogger()
# Configure device and dtype
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if device == 'cpu':
log.warning('CUDA is not available, running on CPU')
dtype = torch.bfloat16
# Configure model and paths
model: ModelConfig = all_model_cfg['large_44k_v2']
model.download_if_needed()
output_dir = Path('./output/gradio')
output_dir.mkdir(exist_ok=True, parents=True)
def get_model() -> tuple[MMAudio, FeaturesUtils, SequenceConfig]:
seq_cfg = model.seq_cfg
net: MMAudio = get_my_mmaudio(model.model_name).to(device, dtype).eval()
net.load_weights(torch.load(model.model_path, map_location=device, weights_only=True))
log.info(f'Loaded weights from {model.model_path}')
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()
return net, feature_utils, seq_cfg
# Load model once at startup
net, feature_utils, seq_cfg = get_model()
@torch.inference_mode()
def video_to_audio(video: gr.Video, prompt: str, negative_prompt: str, seed: int, num_steps: int,
cfg_strength: float, duration: float):
try:
rng = torch.Generator(device=device)
if seed >= 0:
rng.manual_seed(seed)
else:
rng.seed()
fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps)
video_info = load_video(video, duration)
clip_frames = video_info.clip_frames.unsqueeze(0)
sync_frames = video_info.sync_frames.unsqueeze(0)
duration = video_info.duration_sec
seq_cfg.duration = duration
net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len)
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]
current_time_string = datetime.now().strftime('%Y%m%d_%H%M%S')
video_save_path = output_dir / f'{current_time_string}.mp4'
make_video(video_info, video_save_path, audio, sampling_rate=seq_cfg.sampling_rate)
gc.collect()
torch.cuda.empty_cache()
return video_save_path
except Exception as e:
log.error(f"Error in video_to_audio: {str(e)}")
raise gr.Error(f"An error occurred: {str(e)}")
# Create the Gradio interface
demo = gr.Interface(
fn=video_to_audio,
title="MMAudio — Video-to-Audio Synthesis",
description="""
Generate realistic audio for your videos using MMAudio!
Project page: [MMAudio](https://hkchengrex.com/MMAudio/)
Code: [GitHub](https://github.com/hkchengrex/MMAudio)
Note: Processing high-resolution videos (>384px on shorter side) takes longer and doesn't improve results.
""",
inputs=[
gr.Video(label="Upload Video"),
gr.Text(label="Prompt", placeholder="Describe the audio you want to generate..."),
gr.Text(label="Negative prompt", value="music", placeholder="What you don't want in the audio..."),
gr.Number(label="Seed (-1: random)", value=-1, precision=0, minimum=-1),
gr.Number(label="Number of steps", value=25, precision=0, minimum=1),
gr.Slider(label="Guidance Strength", value=4.5, minimum=1, maximum=10, step=0.5),
gr.Slider(label="Duration (seconds)", value=8, minimum=1, maximum=30, step=1),
],
outputs=gr.Video(label="Generated Result"),
examples=[
["https://huggingface.co/hkchengrex/MMAudio/resolve/main/examples/sora_beach.mp4",
"waves, seagulls", "", 0, 25, 4.5, 10],
["https://huggingface.co/hkchengrex/MMAudio/resolve/main/examples/sora_serpent.mp4",
"", "music", 0, 25, 4.5, 10],
],
cache_examples=True,
)
# Launch the app
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860)
|