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() SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret') 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(secret_token: str, video: gr.Video, prompt: str, negative_prompt: str, seed: int, num_steps: int, cfg_strength: float, duration: float): if secret_token != SECRET_TOKEN: raise gr.Error( f'Invalid secret token. Please fork the original space if you want to use it for yourself.') rng = torch.Generator(device=device) rng.manual_seed(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 @spaces.GPU(duration=120) @torch.inference_mode() def text_to_audio(secret_token: str, prompt: str, negative_prompt: str, seed: int, num_steps: int, cfg_strength: float, duration: float): if secret_token != SECRET_TOKEN: raise gr.Error( f'Invalid secret token. Please fork the original space if you want to use it for yourself.') rng = torch.Generator(device=device) rng.manual_seed(seed) fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps) clip_frames = sync_frames = None 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] audio_save_path = tempfile.NamedTemporaryFile(delete=False, suffix='.flac').name torchaudio.save(audio_save_path, audio, seq_cfg.sampling_rate) log.info(f'Saved audio to {audio_save_path}') return audio_save_path video_to_audio_tab = gr.Interface( fn=video_to_audio, description=""" """, inputs=[ gr.Text(label='Secret token'), gr.Video(), gr.Text(label='Prompt'), gr.Text(label='Negative prompt', value='music'), gr.Number(label='Seed', value=0, precision=0, minimum=0), 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='MMAudio — Video-to-Audio Synthesis', show_api=True, api_name='video_to_audio', ) text_to_audio_tab = gr.Interface( fn=text_to_audio, inputs=[ gr.Text(label='Secret token'), gr.Text(label='Prompt'), gr.Text(label='Negative prompt'), gr.Number(label='Seed', value=0, precision=0, minimum=0), 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='audio', cache_examples=False, title='MMAudio — Text-to-Audio Synthesis', show_api=True, api_name='text_to_audio', ) if __name__ == "__main__": gr.HTML("""

This space is a headless component of the cloud rendering engine used by https://aitube.at (AiTube is looking for funding btw!).

It is not available for public use, but you can use the original space.

""") gr.TabbedInterface([video_to_audio_tab, text_to_audio_tab], ['Video-to-Audio', 'Text-to-Audio']).launch(allowed_paths=[output_dir])