import gradio as gr import torchaudio from audiocraft.models import AudioGen from audiocraft.data.audio import audio_write model = AudioGen.get_pretrained('facebook/audiogen-medium') model.set_generation_params(duration=5) # generate 8 seconds. wav = model.generate_unconditional(4) # generates 4 unconditional audio samples descriptions = ['dog barking', 'sirenes of an emergency vehicule', 'footsteps in a corridor'] wav = model.generate(descriptions) # generates 3 samples. for idx, one_wav in enumerate(wav): # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True) iface = gr.Interface(fn=greet, inputs="text", outputs="text") iface.launch() def greet(name): return "Hello " + name + "!!"