import gradio as gr import numpy as np from audioldm import text_to_audio, seed_everything, build_model audioldm = build_model() def text2audio(text, duration, guidance_scale, random_seed): # print(text, length, guidance_scale) waveform = text_to_audio(audioldm, text, random_seed, duration=duration, guidance_scale=guidance_scale, n_candidate_gen_per_text=1) # [bs, 1, samples] waveform = [(16000, wave[0]) for wave in waveform] # waveform = [(16000, np.random.randn(16000)), (16000, np.random.randn(16000))] return waveform iface = gr.Interface(fn=text2audio, inputs=[ gr.Textbox(value="A man is speaking in a huge room", max_lines=1), gr.Slider(2.5, 10, value=5, step=2.5), gr.Slider(0, 5, value=2.5, step=0.5), gr.Number(value=42) ], outputs=[gr.Audio(label="Output", type="numpy"), gr.Audio(label="Output", type="numpy")] ) iface.launch(share=True) # block = gr.Blocks() # with block: # gr.HTML( # """ #