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| # Will be fixed soon, but meanwhile: | |
| import os | |
| if os.getenv('SPACES_ZERO_GPU') == "true": | |
| os.environ['SPACES_ZERO_GPU'] = "1" | |
| import gradio as gr | |
| import random | |
| import torch | |
| import os | |
| from torch import inference_mode | |
| from typing import Optional, List | |
| import numpy as np | |
| from models import load_model | |
| import utils | |
| import spaces | |
| import huggingface_hub | |
| from inversion_utils import inversion_forward_process, inversion_reverse_process | |
| LDM2 = "cvssp/audioldm2" | |
| MUSIC = "cvssp/audioldm2-music" | |
| LDM2_LARGE = "cvssp/audioldm2-large" | |
| STABLEAUD = "chaowenguo/stable-audio-open-1.0" | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| ldm2 = load_model(model_id=LDM2, device=device) | |
| ldm2_large = load_model(model_id=LDM2_LARGE, device=device) | |
| ldm2_music = load_model(model_id=MUSIC, device=device) | |
| ldm_stableaud = load_model(model_id=STABLEAUD, device=device, token=os.getenv('PRIV_TOKEN')) | |
| def randomize_seed_fn(seed, randomize_seed): | |
| if randomize_seed: | |
| seed = random.randint(0, np.iinfo(np.int32).max) | |
| torch.manual_seed(seed) | |
| return seed | |
| def invert(ldm_stable, x0, prompt_src, num_diffusion_steps, cfg_scale_src, duration, save_compute): | |
| # ldm_stable.model.scheduler.set_timesteps(num_diffusion_steps, device=device) | |
| with inference_mode(): | |
| w0 = ldm_stable.vae_encode(x0) | |
| # find Zs and wts - forward process | |
| _, zs, wts, extra_info = inversion_forward_process(ldm_stable, w0, etas=1, | |
| prompts=[prompt_src], | |
| cfg_scales=[cfg_scale_src], | |
| num_inference_steps=num_diffusion_steps, | |
| numerical_fix=True, | |
| duration=duration, | |
| save_compute=save_compute) | |
| return zs, wts, extra_info | |
| def sample(ldm_stable, zs, wts, extra_info, prompt_tar, tstart, cfg_scale_tar, duration, save_compute): | |
| # reverse process (via Zs and wT) | |
| tstart = torch.tensor(tstart, dtype=torch.int) | |
| w0, _ = inversion_reverse_process(ldm_stable, xT=wts, tstart=tstart, | |
| etas=1., prompts=[prompt_tar], | |
| neg_prompts=[""], cfg_scales=[cfg_scale_tar], | |
| zs=zs[:int(tstart)], | |
| duration=duration, | |
| extra_info=extra_info, | |
| save_compute=save_compute) | |
| # vae decode image | |
| with inference_mode(): | |
| x0_dec = ldm_stable.vae_decode(w0) | |
| if 'stable-audio' not in ldm_stable.model_id: | |
| if x0_dec.dim() < 4: | |
| x0_dec = x0_dec[None, :, :, :] | |
| with torch.no_grad(): | |
| audio = ldm_stable.decode_to_mel(x0_dec) | |
| else: | |
| audio = x0_dec.squeeze(0).T | |
| return (ldm_stable.get_sr(), audio.squeeze().cpu().numpy()) | |
| def get_duration(input_audio, | |
| model_id: str, | |
| do_inversion: bool, | |
| wts: Optional[torch.Tensor], zs: Optional[torch.Tensor], extra_info: Optional[List], | |
| saved_inv_model: str, | |
| source_prompt: str = "", | |
| target_prompt: str = "", | |
| steps: int = 200, | |
| cfg_scale_src: float = 3.5, | |
| cfg_scale_tar: float = 12, | |
| t_start: int = 45, | |
| randomize_seed: bool = True, | |
| save_compute: bool = True, | |
| oauth_token: Optional[gr.OAuthToken] = None): | |
| if model_id == LDM2: | |
| factor = 1 | |
| elif model_id == LDM2_LARGE: | |
| factor = 2.5 | |
| elif model_id == STABLEAUD: | |
| factor = 3.2 | |
| else: # MUSIC | |
| factor = 1 | |
| forwards = 0 | |
| if do_inversion or randomize_seed: | |
| forwards = steps if source_prompt == "" else steps * 2 # x2 when there is a prompt text | |
| forwards += int(t_start / 100 * steps) * 2 | |
| duration = min(utils.get_duration(input_audio), utils.MAX_DURATION) | |
| time_for_maxlength = factor * forwards * 0.15 # 0.25 is the time per forward pass | |
| if model_id != STABLEAUD: | |
| time_for_maxlength = time_for_maxlength / utils.MAX_DURATION * duration | |
| print('expected time:', time_for_maxlength) | |
| spare_time = 5 | |
| return max(10, time_for_maxlength + spare_time) | |
| def verify_model_params(model_id: str, input_audio, src_prompt: str, tar_prompt: str, cfg_scale_src: float, | |
| oauth_token: gr.OAuthToken | None): | |
| if input_audio is None: | |
| raise gr.Error('Input audio missing!') | |
| if tar_prompt == "": | |
| raise gr.Error("Please provide a target prompt to edit the audio.") | |
| if src_prompt != "": | |
| if model_id == STABLEAUD and cfg_scale_src != 1: | |
| gr.Info("Consider using Source Guidance Scale=1 for Stable Audio Open 1.0.") | |
| elif model_id != STABLEAUD and cfg_scale_src != 3: | |
| gr.Info(f"Consider using Source Guidance Scale=3 for {model_id}.") | |
| if model_id == STABLEAUD: | |
| if oauth_token is None: | |
| raise gr.Error("You must be logged in to use Stable Audio Open 1.0. Please log in and try again.") | |
| try: | |
| huggingface_hub.get_hf_file_metadata(huggingface_hub.hf_hub_url(STABLEAUD, 'transformer/config.json'), | |
| token=oauth_token.token) | |
| print('Has Access') | |
| # except huggingface_hub.utils._errors.GatedRepoError: | |
| except huggingface_hub.errors.GatedRepoError: | |
| raise gr.Error("You need to accept the license agreement to use Stable Audio Open 1.0. " | |
| "Visit the <a href='https://huggingface.co/stabilityai/stable-audio-open-1.0'>" | |
| "model page</a> to get access.") | |
| def edit(input_audio, | |
| model_id: str, | |
| do_inversion: bool, | |
| wts: Optional[torch.Tensor], zs: Optional[torch.Tensor], extra_info: Optional[List], | |
| saved_inv_model: str, | |
| source_prompt: str = "", | |
| target_prompt: str = "", | |
| steps: int = 200, | |
| cfg_scale_src: float = 3.5, | |
| cfg_scale_tar: float = 12, | |
| t_start: int = 45, | |
| randomize_seed: bool = True, | |
| save_compute: bool = True, | |
| oauth_token: Optional[gr.OAuthToken] = None): | |
| print(model_id) | |
| if model_id == LDM2: | |
| ldm_stable = ldm2 | |
| elif model_id == LDM2_LARGE: | |
| ldm_stable = ldm2_large | |
| elif model_id == STABLEAUD: | |
| ldm_stable = ldm_stableaud | |
| else: # MUSIC | |
| ldm_stable = ldm2_music | |
| ldm_stable.model.scheduler.set_timesteps(steps, device=device) | |
| # If the inversion was done for a different model, we need to re-run the inversion | |
| if not do_inversion and (saved_inv_model is None or saved_inv_model != model_id): | |
| do_inversion = True | |
| if input_audio is None: | |
| raise gr.Error('Input audio missing!') | |
| x0, _, duration = utils.load_audio(input_audio, ldm_stable.get_fn_STFT(), device=device, | |
| stft=('stable-audio' not in ldm_stable.model_id), model_sr=ldm_stable.get_sr()) | |
| if wts is None or zs is None: | |
| do_inversion = True | |
| if do_inversion or randomize_seed: # always re-run inversion | |
| zs_tensor, wts_tensor, extra_info_list = invert(ldm_stable=ldm_stable, x0=x0, prompt_src=source_prompt, | |
| num_diffusion_steps=steps, | |
| cfg_scale_src=cfg_scale_src, | |
| duration=duration, | |
| save_compute=save_compute) | |
| wts = wts_tensor | |
| zs = zs_tensor | |
| extra_info = extra_info_list | |
| saved_inv_model = model_id | |
| do_inversion = False | |
| else: | |
| wts_tensor = wts.to(device) | |
| zs_tensor = zs.to(device) | |
| extra_info_list = [e.to(device) for e in extra_info if e is not None] | |
| output = sample(ldm_stable, zs_tensor, wts_tensor, extra_info_list, prompt_tar=target_prompt, | |
| tstart=int(t_start / 100 * steps), cfg_scale_tar=cfg_scale_tar, duration=duration, | |
| save_compute=save_compute) | |
| return output, wts.cpu(), zs.cpu(), [e.cpu() for e in extra_info if e is not None], saved_inv_model, do_inversion | |
| # return output, wtszs_file, saved_inv_model, do_inversion | |
| def get_example(): | |
| case = [ | |
| ['Examples/Beethoven.mp3', | |
| '', | |
| 'A recording of an arcade game soundtrack.', | |
| 45, | |
| 'cvssp/audioldm2-music', | |
| '27s', | |
| 'Examples/Beethoven_arcade.mp3', | |
| ], | |
| ['Examples/Beethoven.mp3', | |
| 'A high quality recording of wind instruments and strings playing.', | |
| 'A high quality recording of a piano playing.', | |
| 45, | |
| 'cvssp/audioldm2-music', | |
| '27s', | |
| 'Examples/Beethoven_piano.mp3', | |
| ], | |
| ['Examples/Beethoven.mp3', | |
| '', | |
| 'Heavy Rock.', | |
| 40, | |
| 'stabilityai/stable-audio-open-1.0', | |
| '27s', | |
| 'Examples/Beethoven_rock.mp3', | |
| ], | |
| ['Examples/ModalJazz.mp3', | |
| 'Trumpets playing alongside a piano, bass and drums in an upbeat old-timey cool jazz song.', | |
| 'A banjo playing alongside a piano, bass and drums in an upbeat old-timey cool country song.', | |
| 45, | |
| 'cvssp/audioldm2-music', | |
| '106s', | |
| 'Examples/ModalJazz_banjo.mp3',], | |
| ['Examples/Shadows.mp3', | |
| '', | |
| '8-bit arcade game soundtrack.', | |
| 40, | |
| 'stabilityai/stable-audio-open-1.0', | |
| '34s', | |
| 'Examples/Shadows_arcade.mp3',], | |
| ['Examples/Cat.mp3', | |
| '', | |
| 'A dog barking.', | |
| 75, | |
| 'cvssp/audioldm2-large', | |
| '10s', | |
| 'Examples/Cat_dog.mp3',] | |
| ] | |
| return case | |
| intro = """ | |
| <h1 style="font-weight: 1000; text-align: center; margin: 0px;"> ZETA Editing 🎧 </h1> | |
| <h2 style="font-weight: 1000; text-align: center; margin: 0px;"> | |
| Zero-Shot Text-Based Audio Editing Using DDPM Inversion 🎛️ </h2> | |
| <h3 style="margin-top: 0px; margin-bottom: 10px; text-align: center;"> | |
| <a href="https://arxiv.org/abs/2402.10009">[Paper]</a> | | |
| <a href="https://hilamanor.github.io/AudioEditing/">[Project page]</a> | | |
| <a href="https://github.com/HilaManor/AudioEditingCode">[Code]</a> | |
| </h3> | |
| <p style="font-size: 1rem; line-height: 1.2em;"> | |
| For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. | |
| <a href="https://huggingface.co/spaces/hilamanor/audioEditing?duplicate=true"> | |
| <img style="margin-top: 0em; margin-bottom: 0em; display:inline" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" > | |
| </a> | |
| </p> | |
| <p style="margin: 0px;"> | |
| <b>NEW - 15.10.24:</b> You can now edit using <b>Stable Audio Open 1.0</b>. | |
| You must be <b>logged in</b> after accepting the | |
| <b><a href="https://huggingface.co/stabilityai/stable-audio-open-1.0">license agreement</a></b> to use it.</br> | |
| </p> | |
| <ul style="padding-left:40px; line-height:normal;"> | |
| <li style="margin: 0px;">Prompts behave differently - e.g., | |
| try "8-bit arcade" directly instead of "a recording of...". Check out the new examples below!</li> | |
| <li style="margin: 0px;">Try to play around <code>T-start=40%</code>.</li> | |
| <li style="margin: 0px;">Under "More Options": Use <code>Source Guidance Scale=1</code>, | |
| and you can try fewer timesteps (even 20!).</li> | |
| <li style="margin: 0px;">Stable Audio Open is a general-audio model. | |
| For better music editing, duplicate the space and change to a | |
| <a href="https://huggingface.co/models?other=base_model:finetune:stabilityai/stable-audio-open-1.0"> | |
| fine-tuned model for music</a>.</li> | |
| </ul> | |
| <p> | |
| <b>NEW - 15.10.24:</b> Parallel editing is enabled by default. | |
| To disable, uncheck <code>Efficient editing</code> under "More Options". | |
| Saves a bit of time. | |
| </p> | |
| """ | |
| help = """ | |
| <div style="font-size:medium"> | |
| <b>Instructions:</b><br> | |
| <ul style="line-height: normal"> | |
| <li>You must provide an input audio and a target prompt to edit the audio. </li> | |
| <li>T<sub>start</sub> is used to control the tradeoff between fidelity to the original signal and text-adhearance. | |
| Lower value -> favor fidelity. Higher value -> apply a stronger edit.</li> | |
| <li>Make sure that you use a model version that is suitable for your input audio. | |
| For example, use AudioLDM2-music for music while AudioLDM2-large for general audio. | |
| </li> | |
| <li>You can additionally provide a source prompt to guide even further the editing process.</li> | |
| <li>Longer input will take more time.</li> | |
| <li><strong>Unlimited length</strong>: This space automatically trims input audio to a maximum length of 30 seconds. | |
| For unlimited length, duplicated the space, and change the | |
| <code style="display:inline; background-color: lightgrey;">MAX_DURATION</code> parameter | |
| inside <code style="display:inline; background-color: lightgrey;">utils.py</code> | |
| to <code style="display:inline; background-color: lightgrey;">None</code>. | |
| </li> | |
| </ul> | |
| </div> | |
| """ | |
| css = '.gradio-container {max-width: 1000px !important; padding-top: 1.5rem !important;}' \ | |
| '.audio-upload .wrap {min-height: 0px;}' | |
| # with gr.Blocks(css='style.css') as demo: | |
| with gr.Blocks(css=css) as demo: | |
| def reset_do_inversion(do_inversion_user, do_inversion): | |
| # do_inversion = gr.State(value=True) | |
| do_inversion = True | |
| do_inversion_user = True | |
| return do_inversion_user, do_inversion | |
| # handle the case where the user clicked the button but the inversion was not done | |
| def clear_do_inversion_user(do_inversion_user): | |
| do_inversion_user = False | |
| return do_inversion_user | |
| def post_match_do_inversion(do_inversion_user, do_inversion): | |
| if do_inversion_user: | |
| do_inversion = True | |
| do_inversion_user = False | |
| return do_inversion_user, do_inversion | |
| gr.HTML(intro) | |
| wts = gr.State() | |
| zs = gr.State() | |
| extra_info = gr.State() | |
| saved_inv_model = gr.State() | |
| do_inversion = gr.State(value=True) # To save some runtime when editing the same thing over and over | |
| do_inversion_user = gr.State(value=False) | |
| with gr.Group(): | |
| gr.Markdown("💡 **note**: input longer than **30 sec** is automatically trimmed " | |
| "(for unlimited input, see the Help section below)") | |
| with gr.Row(equal_height=True): | |
| input_audio = gr.Audio(sources=["upload", "microphone"], type="filepath", | |
| editable=True, label="Input Audio", interactive=True, scale=1, format='wav', | |
| elem_classes=['audio-upload']) | |
| output_audio = gr.Audio(label="Edited Audio", interactive=False, scale=1, format='wav') | |
| with gr.Row(): | |
| tar_prompt = gr.Textbox(label="Prompt", info="Describe your desired edited output", | |
| placeholder="a recording of a happy upbeat arcade game soundtrack", | |
| lines=2, interactive=True) | |
| with gr.Row(): | |
| t_start = gr.Slider(minimum=15, maximum=85, value=45, step=1, label="T-start (%)", interactive=True, scale=3, | |
| info="Lower T-start -> closer to original audio. Higher T-start -> stronger edit.") | |
| model_id = gr.Dropdown(label="Model Version", | |
| choices=[LDM2, | |
| LDM2_LARGE, | |
| MUSIC, | |
| STABLEAUD], | |
| info="Choose a checkpoint suitable for your audio and edit", | |
| value="cvssp/audioldm2-music", interactive=True, type="value", scale=2) | |
| with gr.Row(): | |
| submit = gr.Button("Edit", variant="primary", scale=3) | |
| gr.LoginButton(value="Login to HF (For Stable Audio)", scale=1) | |
| with gr.Accordion("More Options", open=False): | |
| with gr.Row(): | |
| src_prompt = gr.Textbox(label="Source Prompt", lines=2, interactive=True, | |
| info="Optional: Describe the original audio input", | |
| placeholder="A recording of a happy upbeat classical music piece",) | |
| with gr.Row(equal_height=True): | |
| cfg_scale_src = gr.Number(value=3, minimum=0.5, maximum=25, precision=None, | |
| label="Source Guidance Scale", interactive=True, scale=1) | |
| cfg_scale_tar = gr.Number(value=12, minimum=0.5, maximum=25, precision=None, | |
| label="Target Guidance Scale", interactive=True, scale=1) | |
| steps = gr.Number(value=50, step=1, minimum=10, maximum=300, | |
| info="Higher values (e.g. 200) yield higher-quality generation.", | |
| label="Num Diffusion Steps", interactive=True, scale=2) | |
| with gr.Row(equal_height=True): | |
| seed = gr.Number(value=0, precision=0, label="Seed", interactive=True) | |
| randomize_seed = gr.Checkbox(label='Randomize seed', value=False) | |
| save_compute = gr.Checkbox(label='Efficient editing', value=True) | |
| length = gr.Number(label="Length", interactive=False, visible=False) | |
| with gr.Accordion("Help💡", open=False): | |
| gr.HTML(help) | |
| submit.click( | |
| fn=verify_model_params, | |
| inputs=[model_id, input_audio, src_prompt, tar_prompt, cfg_scale_src], | |
| outputs=[] | |
| ).success( | |
| fn=randomize_seed_fn, inputs=[seed, randomize_seed], outputs=[seed], queue=False | |
| ).then( | |
| fn=clear_do_inversion_user, inputs=[do_inversion_user], outputs=[do_inversion_user] | |
| ).then( | |
| fn=edit, | |
| inputs=[input_audio, | |
| model_id, | |
| do_inversion, | |
| wts, zs, extra_info, | |
| saved_inv_model, | |
| src_prompt, | |
| tar_prompt, | |
| steps, | |
| cfg_scale_src, | |
| cfg_scale_tar, | |
| t_start, | |
| randomize_seed, | |
| save_compute, | |
| ], | |
| outputs=[output_audio, wts, zs, extra_info, saved_inv_model, do_inversion] | |
| ).success( | |
| fn=post_match_do_inversion, | |
| inputs=[do_inversion_user, do_inversion], | |
| outputs=[do_inversion_user, do_inversion] | |
| ) | |
| # If sources changed we have to rerun inversion | |
| gr.on( | |
| triggers=[input_audio.change, src_prompt.change, model_id.change, cfg_scale_src.change, | |
| steps.change, save_compute.change], | |
| fn=reset_do_inversion, | |
| inputs=[do_inversion_user, do_inversion], | |
| outputs=[do_inversion_user, do_inversion] | |
| ) | |
| gr.Examples( | |
| label="Examples", | |
| examples=get_example(), | |
| inputs=[input_audio, src_prompt, tar_prompt, t_start, model_id, length, output_audio], | |
| outputs=[output_audio] | |
| ) | |
| demo.queue() | |
| demo.launch(state_session_capacity=15) | |