import torch import torchaudio import spaces from typing import List import soundfile as sf device = torch.device("cuda" if torch.cuda.is_available() else "cpu") knn_vc = torch.hub.load('bshall/knn-vc', 'knn_vc', prematched=True, trust_repo=True, pretrained=True, device=device) def convert_voice(src_wav_path:str, ref_wav_paths, top_k:int): query_seq = knn_vc.get_features(src_wav_path) matching_set = knn_vc.get_matching_set([ref_wav_paths]) out_wav = knn_vc.match(query_seq, matching_set, topk=int(top_k)) with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as converted_file: sf.write(converted_file.name, out_wav, 16000, "PCM_24") return converted_file.name title = """

KNN Voice Conversion

""" description = """ Voice Conversion With Just k-Nearest Neighbors. The source and reference utterance(s) are encoded into self-supervised features using WavLM. Each source feature is assigned to the mean of the k closest features from the reference. The resulting feature sequence is then vocoded with HiFi-GAN to arrive at the converted waveform output. """ article = """ If the model contributes to your research please cite the following work: Baas, M., van Niekerk, B., & Kamper, H. (2023). Voice conversion with just nearest neighbors. arXiv preprint arXiv:2305.18975. demo contributed by [@wetdog](https://github.com/wetdog) """ demo = gr.Blocks() with demo: gr.Markdown(title) gr.Markdown(description) gr.Interface( fn=convert_voice, inputs=[ gr.Audio(type='filepath'), gr.Audio(type='filepath'), gr.Slider( 3, 10, value=4, step=1, label="Top-k", info=f"These default settings provide pretty good results, but feel free to modify the kNN topk", )], outputs=[gr.Audio(type='filepath')], allow_flagging=False,) gr.Markdown(article) demo.queue(max_size=10) demo.launch(show_api=False, server_name="0.0.0.0", server_port=7860)