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from bark.generation import load_codec_model, generate_text_semantic, grab_best_device |
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from bark import SAMPLE_RATE |
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from encodec.utils import convert_audio |
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from bark.hubert.hubert_manager import HuBERTManager |
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from bark.hubert.pre_kmeans_hubert import CustomHubert |
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from bark.hubert.customtokenizer import CustomTokenizer |
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from bark.api import semantic_to_waveform |
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from scipy.io.wavfile import write as write_wav |
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from util.helper import create_filename |
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from util.settings import Settings |
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import torchaudio |
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import torch |
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import os |
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import gradio |
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def swap_voice_from_audio(swap_audio_filename, selected_speaker, tokenizer_lang, seed, batchcount, progress=gradio.Progress(track_tqdm=True)): |
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use_gpu = not os.environ.get("BARK_FORCE_CPU", False) |
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progress(0, desc="Loading Codec") |
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hubert_manager = HuBERTManager() |
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hubert_manager.make_sure_hubert_installed() |
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hubert_manager.make_sure_tokenizer_installed(tokenizer_lang=tokenizer_lang) |
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device = grab_best_device(use_gpu) |
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hubert_model = CustomHubert(checkpoint_path='./models/hubert/hubert.pt').to(device) |
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model = load_codec_model(use_gpu=use_gpu) |
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tokenizer = CustomTokenizer.load_from_checkpoint(f'./models/hubert/{tokenizer_lang}_tokenizer.pth').to(device) |
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progress(0.25, desc="Converting WAV") |
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wav, sr = torchaudio.load(swap_audio_filename) |
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if wav.shape[0] == 2: |
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wav = wav.mean(0, keepdim=True) |
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wav = convert_audio(wav, sr, model.sample_rate, model.channels) |
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wav = wav.to(device) |
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semantic_vectors = hubert_model.forward(wav, input_sample_hz=model.sample_rate) |
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semantic_tokens = tokenizer.get_token(semantic_vectors) |
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audio = semantic_to_waveform( |
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semantic_tokens, |
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history_prompt=selected_speaker, |
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temp=0.7, |
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silent=False, |
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output_full=False) |
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settings = Settings('config.yaml') |
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result = create_filename(settings.output_folder_path, None, "swapvoice",".wav") |
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write_wav(result, SAMPLE_RATE, audio) |
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return result |
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