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| import sys | |
| print(sys.path) | |
| sys.path.append('/home/user/audio_ai/diffusers_harp/venv/src') | |
| from pyharp import ModelCard, build_endpoint, save_and_return_filepath | |
| from audiotools import AudioSignal | |
| import scipy | |
| import torch | |
| import gradio as gr | |
| from diffusers import AudioLDM2Pipeline | |
| import subprocess as sp | |
| #harp_deps = [ | |
| #"descript-audiotools"] | |
| # | |
| #try: | |
| # from pyharp import ModelCard, build_endpoint, save_and_return_filepath | |
| #except ImportError: | |
| # print("Installing harp dependencies...") | |
| # sp.check_call(["pip", "install", *harp_deps]) | |
| # sp.check_call(["pip", "install", "-e git+https://github.com/audacitorch/pyharp.git#egg=pyharp"]) | |
| # sp.check_call(["pip", "install", "pydantic<2.0.0"]) | |
| # from pyharp import ModelCard, build_endpoint, save_and_return_filepath | |
| # Create a Model Card | |
| card = ModelCard( | |
| name='Diffusers AudioLDM2 Generation', | |
| description='AudioLDM2 text-to-audio generation, operates on region selected in track. Not conditioned on selected audio, simply replaces audio in source track with generation.', | |
| author='Team Audio', | |
| tags=['AudioLDM', 'Diffusers', 'Generation'] | |
| ) | |
| # Load the model | |
| repo_id = "cvssp/audioldm2" | |
| pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16) | |
| pipe = pipe.to("cuda") | |
| def process_fn(input_audio_path, prompt, negative_prompt, seed, num_inference_steps, audio_length_in_s, num_waveforms_per_prompt): | |
| """ | |
| This function defines the audio processing steps | |
| Args: | |
| input_audio_path (str): the audio filepath to be processed. | |
| <YOUR_KWARGS>: additional keyword arguments necessary for processing. | |
| NOTE: These should correspond to and match order of UI elements defined below. | |
| Returns: | |
| output_audio_path (str): the filepath of the processed audio. | |
| """ | |
| sig = AudioSignal(input_audio_path) | |
| outfile = "./output.wav" | |
| #prompt = "The sound of a hammer hitting a wooden surface." | |
| #negative_prompt = "Low quality." | |
| # set the seed for generator | |
| generator = torch.Generator("cuda").manual_seed(int(seed)) | |
| audio = pipe( | |
| prompt, | |
| negative_prompt=negative_prompt, | |
| num_inference_steps=int(num_inference_steps), | |
| audio_length_in_s=audio_length_in_s, | |
| num_waveforms_per_prompt=int(num_waveforms_per_prompt), | |
| generator=generator, | |
| ).audios | |
| scipy.io.wavfile.write(outfile, rate=16000, data=audio[0]) | |
| return outfile | |
| # Build the endpoint | |
| with gr.Blocks() as webapp: | |
| # Define your Gradio interface | |
| inputs = [ | |
| gr.Audio( | |
| label="Audio Input", | |
| type="filepath" | |
| ), | |
| gr.Text( | |
| label="Prompt", | |
| interactive=True | |
| ), | |
| gr.Text( | |
| label="Negative Prompt", | |
| interactive=True | |
| ), | |
| gr.Slider( | |
| label="seed", | |
| minimum="0", | |
| maximum="65535", | |
| value="0", | |
| step="1" | |
| ), | |
| gr.Slider( | |
| minimum=1, maximum=500, | |
| step=1, value=1, | |
| label="Inference Steps" | |
| ), | |
| gr.Slider( | |
| minimum=2.5, maximum=10.0, | |
| step=2.5, value=2.5, | |
| label="Duration" | |
| ), | |
| gr.Slider( | |
| minimum=1, maximum=10, | |
| step=1, value=1, | |
| label="Waveforms Per Prompt" | |
| ), | |
| ] | |
| # make an output audio widget | |
| output = gr.Audio(label="Audio Output", type="filepath") | |
| # Build the endpoint | |
| ctrls_data, ctrls_button, process_button, cancel_button = build_endpoint(inputs, output, process_fn, card) | |
| #webapp.queue() | |
| webapp.launch(share=True) | |