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import spaces
import logging
from datetime import datetime
from pathlib import Path
import gradio as gr
import torch
import torchaudio
import os
try:
import mmaudio
except ImportError:
os.system("pip install -e .")
import mmaudio
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.sequence_config import SequenceConfig
from mmaudio.model.utils.features_utils import FeaturesUtils
import tempfile
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
log = logging.getLogger()
device = 'cuda'
dtype = torch.bfloat16
model: ModelConfig = all_model_cfg['large_44k_v2']
model.download_if_needed()
output_dir = Path('./output/gradio')
setup_eval_logging()
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
net, feature_utils, seq_cfg = get_model()
@spaces.GPU(duration=120)
@torch.inference_mode()
def video_to_audio(video: gr.Video, prompt: str, negative_prompt: str):
num_steps = 25
cfg_strength = 4.5
duration = 8.0
rng = torch.Generator(device=device)
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
sync_frames = video_info.sync_frames
duration = video_info.duration_sec
clip_frames = clip_frames.unsqueeze(0)
sync_frames = sync_frames.unsqueeze(0)
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 = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4').name
# output_dir.mkdir(exist_ok=True, parents=True)
# video_save_path = output_dir / f'{current_time_string}.mp4'
make_video(video_info, video_save_path, audio, sampling_rate=seq_cfg.sampling_rate)
log.info(f'Saved video to {video_save_path}')
return video_save_path
video_to_audio_tab = gr.Interface(
fn=video_to_audio,
description="""
Video-to-Audio
NOTE: It takes longer to process high-resolution videos (>384 px on the shorter side).
Doing so does not improve results.
The model has been trained on 8-second videos. Using much longer or shorter videos will degrade performance. Around 5s~12s should be fine.
""",
inputs=[
gr.Video(),
gr.Text(label='Prompt'),
gr.Text(label='Negative prompt', value='music'),
# gr.Number(label='Seed (-1: random)', value=-1, precision=0, minimum=-1),
# gr.Number(label='Num steps', value=25, precision=0, minimum=1),
# gr.Number(label='Guidance Strength', value=4.5, minimum=1),
# gr.Number(label='Duration (sec)', value=8, minimum=1),
],
outputs='playable_video',
cache_examples=False,
title='Sonisphere — Sonic Branding through Multi-modal Audio Synthesis',
examples=[
])
if __name__ == "__main__":
gr.TabbedInterface([video_to_audio_tab],
['Video-to-Audio']).launch(allowed_paths=[output_dir])
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