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| import gradio as gr | |
| import torch | |
| from diffusers import AudioLDM2Pipeline | |
| # make Space compatible with CPU duplicates | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| torch_dtype = torch.float16 | |
| else: | |
| device = "cpu" | |
| torch_dtype = torch.float32 | |
| # load the diffusers pipeline | |
| repo_id = "cvssp/audioldm2" | |
| pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch_dtype).to(device) | |
| # pipe.unet = torch.compile(pipe.unet) | |
| # set the generator for reproducibility | |
| generator = torch.Generator(device) | |
| def text2audio(text, negative_prompt, duration, guidance_scale, random_seed, n_candidates): | |
| if text is None: | |
| raise gr.Error("Please provide a text input.") | |
| # test values | |
| negative_prompt="Low quality." | |
| duration=10 | |
| guidance_scale=3.5 | |
| random_seed=45 | |
| n_candidates=3 | |
| waveforms = pipe( | |
| text, | |
| audio_length_in_s=duration, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=20, | |
| negative_prompt=negative_prompt, | |
| num_waveforms_per_prompt=n_candidates if n_candidates else 1, | |
| generator=generator.manual_seed(int(random_seed)), | |
| )["audios"] | |
| print(waveforms) | |
| return [gr.make_waveform((16000, waveforms[0])), gr.make_waveform((16000, waveforms[1])), gr.make_waveform((16000, waveforms[2]))] | |
| gradio_interface = gr.Interface( | |
| fn = text2audio, | |
| inputs = "text", | |
| outputs = ["audio", "audio", "audio"], | |
| ) | |
| gradio_interface.launch() | |