# 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