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| import gradio as gr | |
| from diffusers import AudioLDMControlNetPipeline, ControlNetModel | |
| import os | |
| from pretty_midi import PrettyMIDI | |
| from tempfile import _TemporaryFileWrapper | |
| import torch | |
| import torchaudio | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| torch_dtype = torch.float16 | |
| else: | |
| device = "cpu" | |
| torch_dtype = torch.float32 | |
| controlnet = ControlNetModel.from_pretrained( | |
| "lauraibnz/midi-audioldm", torch_dtype=torch_dtype) | |
| pipe = AudioLDMControlNetPipeline.from_pretrained( | |
| "cvssp/audioldm-m-full", controlnet=controlnet, torch_dtype=torch_dtype) | |
| pipe = pipe.to(device) | |
| generator = torch.Generator(device) | |
| def predict(midi_file=None, prompt="", negative_prompt="", audio_length_in_s=5, random_seed=0, controlnet_conditioning_scale=1, num_inference_steps=20, guess_mode=False): | |
| if isinstance(midi_file, _TemporaryFileWrapper): | |
| midi_file = midi_file.name | |
| midi = PrettyMIDI(midi_file) | |
| audio = pipe( | |
| prompt, | |
| negative_prompt=negative_prompt, | |
| midi=midi, | |
| audio_length_in_s=audio_length_in_s, | |
| num_inference_steps=num_inference_steps, | |
| controlnet_conditioning_scale=float(controlnet_conditioning_scale), | |
| guess_mode=guess_mode, | |
| generator=generator.manual_seed(int(random_seed)), | |
| ) | |
| return (16000, audio.audios.T) | |
| with gr.Blocks(title="🎹 MIDI-AudioLDM", theme=gr.themes.Base(text_size=gr.themes.sizes.text_md, font=[gr.themes.GoogleFont("Nunito Sans")])) as demo: | |
| gr.HTML( | |
| """ | |
| <h1 align="center";font size="16">🎹 MIDI-AudioLDM </h1>) | |
| """) | |
| gr.Markdown( | |
| """ | |
| MIDI-AudioLDM is a MIDI-conditioned text-to-audio model based on the project [AudioLDM](https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation). The model has been conditioned using the ControlNet architecture and has been developed within Hugging Face’s [🧨 Diffusers](https://huggingface.co/docs/diffusers/) framework. Once trained, MIDI-AudioLDM accepts a MIDI file and a text prompt as inputs and returns an audio file, which is an interpretation of the MIDI based on the given text description. This enables detailed control over different musical aspects such as notes, mood and timbre. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(variant='panel'): | |
| midi = gr.File(label="midi file", file_types=[".mid"]) | |
| prompt = gr.Textbox(label="prompt") | |
| with gr.Column(variant='panel'): | |
| audio = gr.Audio(label="audio") | |
| with gr.Accordion("Advanced Settings", open=False): | |
| neg_prompt = gr.Textbox(label="negative prompt") | |
| duration = gr.Slider(0, 30, value=5, step=5, label="duration (seconds)") | |
| seed = gr.Number(value=42, label="seed") | |
| cond = gr.Slider(0.0, 1.0, value=1.0, step=0.1, label="conditioning scale") | |
| inf = gr.Slider(0, 50, value=20, step=0.1, label="inference steps") | |
| guess = gr.Checkbox(label="guess mode") | |
| btn = gr.Button("Generate") | |
| btn.click(predict, inputs=[midi, prompt, neg_prompt, duration, seed, cond, inf, guess], outputs=[audio]) | |
| gr.Examples(examples=[["S00.mid", "piano", "", 10, 25, 1.0, 20, False]], inputs=[midi, prompt, neg_prompt, duration, seed, cond, inf, guess], fn=predict, outputs=audio, cache_examples=True) | |
| # demo = gr.Interface( | |
| # fn=predict, inputs=[ | |
| # gr.File(label="midi file", file_types=[".mid"]), | |
| # "text", | |
| # gr.Textbox(label="negative prompt"), | |
| # gr.Slider(0, 30, value=5, step=5, label="duration (seconds)"), | |
| # gr.Number(value=42, label="seed"), | |
| # gr.Slider(0.0, 1.0, value=1.0, step=0.1, label="conditioning scale"), | |
| # gr.Slider(0, 50, value=20, step=0.1, label="inference steps"), | |
| # gr.Checkbox(label="guess mode") | |
| # ], | |
| # outputs="audio", | |
| # examples=[["S00.mid", "piano", "", 10, 25, 1.0, 20, False]], | |
| # cache_examples=True, | |
| # title="🎹 MIDI-AudioLDM", | |
| # description="MIDI-AudioLDM is a MIDI-conditioned text-to-audio model based on the project [AudioLDM](https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation). The model has been conditioned using the ControlNet architecture and has been developed within Hugging Face’s [🧨 Diffusers](https://huggingface.co/docs/diffusers/) framework. Once trained, MIDI-AudioLDM accepts a MIDI file and a text prompt as inputs and returns an audio file, which is an interpretation of the MIDI based on the given text description. This enables detailed control over different musical aspects such as notes, mood and timbre.", | |
| # theme=gr.themes.Base(text_size=gr.themes.sizes.text_md, font=[gr.themes.GoogleFont("Nunito Sans")]) | |
| # ) | |
| demo.launch() |