| import random | |
| import vampnet | |
| import audiotools as at | |
| # load the default vampnet model | |
| interface = vampnet.interface.Interface.default() | |
| # list available finetuned models | |
| finetuned_model_choices = interface.available_models() | |
| print(f"available finetuned models: {finetuned_model_choices}") | |
| # pick a random finetuned model | |
| model_choice = random.choice(finetuned_model_choices) | |
| print(f"choosing model: {model_choice}") | |
| # load a finetuned model | |
| interface.load_finetuned(model_choice) | |
| # load an example audio file | |
| signal = at.AudioSignal("assets/example.wav") | |
| # get the tokens for the audio | |
| codes = interface.encode(signal) | |
| # build a mask for the audio | |
| mask = interface.build_mask( | |
| codes, signal, | |
| periodic_prompt=7, | |
| upper_codebook_mask=3, | |
| ) | |
| # generate the output tokens | |
| output_tokens = interface.vamp( | |
| codes, mask, return_mask=False, | |
| temperature=1.0, | |
| typical_filtering=True, | |
| ) | |
| # convert them to a signal | |
| output_signal = interface.decode(output_tokens) | |
| # save the output signal | |
| output_signal.write("scratch/output.wav") |