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| import torch | |
| from InferenceInterfaces.UtteranceCloner import UtteranceCloner | |
| if __name__ == '__main__': | |
| uc = UtteranceCloner(model_id="Meta", device="cuda" if torch.cuda.is_available() else "cpu") | |
| # What is said in path_to_reference_audio_for_intonation has to match the text in the reference_transcription exactly! | |
| uc.clone_utterance(path_to_reference_audio_for_intonation="audios/test.wav", | |
| path_to_reference_audio_for_voice="audios/test.wav", # the two reference audios can be the same, but don't have to be | |
| transcription_of_intonation_reference="Hello world, this is a test.", | |
| filename_of_result="audios/test_cloned.wav", | |
| lang="en") | |
| # Have multiple voices speak with the exact same intonation simultaneously | |
| uc.biblical_accurate_angel_mode(path_to_reference_audio_for_intonation="audios/test.wav", | |
| transcription_of_intonation_reference="Hello world, this is a test.", | |
| list_of_speaker_references_for_ensemble=["audios/speaker_references_for_testing/female_high_voice.wav", | |
| "audios/speaker_references_for_testing/female_mid_voice.wav", | |
| "audios/speaker_references_for_testing/male_low_voice.wav"], | |
| filename_of_result="audios/test_cloned_angelic.wav", | |
| lang="en") | |