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import logging |
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from datetime import datetime |
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from pathlib import Path |
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import gradio as gr |
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import torch |
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import torchaudio |
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from mmaudio.eval_utils import (ModelConfig, all_model_cfg, generate, load_video, make_video, |
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setup_eval_logging) |
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from mmaudio.model.flow_matching import FlowMatching |
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from mmaudio.model.networks import MMAudio, get_my_mmaudio |
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from mmaudio.model.sequence_config import SequenceConfig |
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from mmaudio.model.utils.features_utils import FeaturesUtils |
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torch.backends.cuda.matmul.allow_tf32 = True |
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torch.backends.cudnn.allow_tf32 = True |
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log = logging.getLogger() |
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device = 'cuda' |
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dtype = torch.bfloat16 |
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model: ModelConfig = all_model_cfg['large_44k_v2'] |
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model.download_if_needed() |
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output_dir = Path('./output/gradio') |
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setup_eval_logging() |
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def get_model() -> tuple[MMAudio, FeaturesUtils, SequenceConfig]: |
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seq_cfg = model.seq_cfg |
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net: MMAudio = get_my_mmaudio(model.model_name).to(device, dtype).eval() |
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net.load_weights(torch.load(model.model_path, map_location=device, weights_only=True)) |
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log.info(f'Loaded weights from {model.model_path}') |
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feature_utils = FeaturesUtils(tod_vae_ckpt=model.vae_path, |
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synchformer_ckpt=model.synchformer_ckpt, |
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enable_conditions=True, |
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mode=model.mode, |
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bigvgan_vocoder_ckpt=model.bigvgan_16k_path) |
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feature_utils = feature_utils.to(device, dtype).eval() |
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return net, feature_utils, seq_cfg |
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net, feature_utils, seq_cfg = get_model() |
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@torch.inference_mode() |
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def video_to_audio(video: gr.Video, prompt: str, negative_prompt: str, seed: int, num_steps: int, |
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cfg_strength: float, duration: float): |
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rng = torch.Generator(device=device) |
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rng.manual_seed(seed) |
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fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps) |
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clip_frames, sync_frames, duration = load_video(video, duration) |
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clip_frames = clip_frames.unsqueeze(0) |
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sync_frames = sync_frames.unsqueeze(0) |
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seq_cfg.duration = duration |
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net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len) |
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audios = generate(clip_frames, |
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sync_frames, [prompt], |
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negative_text=[negative_prompt], |
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feature_utils=feature_utils, |
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net=net, |
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fm=fm, |
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rng=rng, |
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cfg_strength=cfg_strength) |
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audio = audios.float().cpu()[0] |
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current_time_string = datetime.now().strftime('%Y%m%d_%H%M%S') |
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output_dir.mkdir(exist_ok=True, parents=True) |
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video_save_path = output_dir / f'{current_time_string}.mp4' |
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make_video(video, |
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video_save_path, |
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audio, |
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sampling_rate=seq_cfg.sampling_rate, |
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duration_sec=seq_cfg.duration) |
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return video_save_path |
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@torch.inference_mode() |
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def text_to_audio(prompt: str, negative_prompt: str, seed: int, num_steps: int, cfg_strength: float, |
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duration: float): |
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rng = torch.Generator(device=device) |
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rng.manual_seed(seed) |
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fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps) |
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clip_frames = sync_frames = None |
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seq_cfg.duration = duration |
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net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len) |
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audios = generate(clip_frames, |
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sync_frames, [prompt], |
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negative_text=[negative_prompt], |
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feature_utils=feature_utils, |
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net=net, |
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fm=fm, |
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rng=rng, |
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cfg_strength=cfg_strength) |
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audio = audios.float().cpu()[0] |
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current_time_string = datetime.now().strftime('%Y%m%d_%H%M%S') |
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output_dir.mkdir(exist_ok=True, parents=True) |
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audio_save_path = output_dir / f'{current_time_string}.flac' |
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torchaudio.save(audio_save_path, audio, seq_cfg.sampling_rate) |
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return audio_save_path |
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video_to_audio_tab = gr.Interface( |
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fn=video_to_audio, |
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inputs=[ |
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gr.Video(), |
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gr.Text(label='Prompt'), |
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gr.Text(label='Negative prompt', value='music'), |
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gr.Number(label='Seed', value=0, precision=0, minimum=0), |
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gr.Number(label='Num steps', value=25, precision=0, minimum=1), |
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gr.Number(label='Guidance Strength', value=4.5, minimum=1), |
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gr.Number(label='Duration (sec)', value=8, minimum=1), |
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], |
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outputs='playable_video', |
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cache_examples=False, |
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title='MMAudio — Video-to-Audio Synthesis', |
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) |
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text_to_audio_tab = gr.Interface( |
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fn=text_to_audio, |
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inputs=[ |
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gr.Text(label='Prompt'), |
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gr.Text(label='Negative prompt'), |
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gr.Number(label='Seed', value=0, precision=0, minimum=0), |
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gr.Number(label='Num steps', value=25, precision=0, minimum=1), |
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gr.Number(label='Guidance Strength', value=4.5, minimum=1), |
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gr.Number(label='Duration (sec)', value=8, minimum=1), |
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], |
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outputs='audio', |
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cache_examples=False, |
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title='MMAudio — Text-to-Audio Synthesis', |
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) |
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if __name__ == "__main__": |
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gr.TabbedInterface([video_to_audio_tab, text_to_audio_tab], |
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['Video-to-Audio', 'Text-to-Audio']).launch(server_port=17888, |
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allowed_paths=[output_dir]) |
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