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| # import gradio as gr | |
| # from audioldm import text_to_audio, build_model | |
| # model_id="haoheliu/AudioLDM-S-Full" | |
| # audioldm = None | |
| # current_model_name = None | |
| # def text2audio(text, duration, guidance_scale, random_seed, n_candidates, model_name="audioldm-m-text-ft"): | |
| # global audioldm, current_model_name | |
| # if audioldm is None or model_name != current_model_name: | |
| # audioldm=build_model(model_name=model_name) | |
| # current_model_name = model_name | |
| # # print(text, length, guidance_scale) | |
| # waveform = text_to_audio( | |
| # latent_diffusion=audioldm, | |
| # text=text, | |
| # seed=random_seed, | |
| # duration=duration, | |
| # guidance_scale=guidance_scale, | |
| # n_candidate_gen_per_text=int(n_candidates), | |
| # ) # [bs, 1, samples] | |
| # waveform = [ | |
| # gr.make_waveform((16000, wave[0]), bg_image="bg.png") for wave in waveform | |
| # ] | |
| # # waveform = [(16000, np.random.randn(16000)), (16000, np.random.randn(16000))] | |
| # if(len(waveform) == 1): | |
| # waveform = waveform[0] | |
| # return waveform |