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import torch |
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import soundfile |
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from openvoice import utils |
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from openvoice import commons |
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import os |
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import librosa |
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from openvoice.mel_processing import spectrogram_torch |
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from openvoice.models import SynthesizerTrn |
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class OpenVoiceBaseClass(object): |
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def __init__(self, config_path, device="cuda:0"): |
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if "cuda" in device: |
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assert torch.cuda.is_available() |
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hps = utils.get_hparams_from_file(config_path) |
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model = SynthesizerTrn( |
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len(getattr(hps, "symbols", [])), |
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hps.data.filter_length // 2 + 1, |
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n_speakers=hps.data.n_speakers, |
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**hps.model, |
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).to(device) |
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model.eval() |
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self.model = model |
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self.hps = hps |
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self.device = device |
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def load_ckpt(self, ckpt_path): |
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checkpoint_dict = torch.load(ckpt_path, map_location=torch.device(self.device)) |
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a, b = self.model.load_state_dict(checkpoint_dict["model"], strict=False) |
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print("Loaded checkpoint '{}'".format(ckpt_path)) |
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print("missing/unexpected keys:", a, b) |
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class ToneColorConverter(OpenVoiceBaseClass): |
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def __init__(self, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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self.version = getattr(self.hps, "_version_", "v1") |
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def extract_se(self, ref_wav_list, se_save_path=None): |
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device = self.device |
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hps = self.hps |
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gs = [] |
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for fname in ref_wav_list: |
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audio_ref, sr = librosa.load(fname, sr=hps.data.sampling_rate) |
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y = torch.FloatTensor(audio_ref) |
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y = y.to(device) |
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y = y.unsqueeze(0) |
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y = spectrogram_torch( |
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y, |
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hps.data.filter_length, |
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hps.data.sampling_rate, |
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hps.data.hop_length, |
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hps.data.win_length, |
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center=False, |
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).to(device) |
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with torch.no_grad(): |
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g = self.model.ref_enc(y.transpose(1, 2)).unsqueeze(-1) |
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gs.append(g.detach()) |
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gs = torch.stack(gs).mean(0) |
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if se_save_path is not None: |
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os.makedirs(os.path.dirname(se_save_path), exist_ok=True) |
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torch.save(gs.cpu(), se_save_path) |
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return gs |
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def convert( |
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self, |
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audio_src_path, |
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src_se, |
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tgt_se, |
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output_path=None, |
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tau=0.3, |
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): |
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hps = self.hps |
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audio, sample_rate = librosa.load(audio_src_path, sr=hps.data.sampling_rate) |
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audio = torch.tensor(audio).float() |
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with torch.no_grad(): |
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y = torch.FloatTensor(audio).to(self.device) |
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y = y.unsqueeze(0) |
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spec = spectrogram_torch( |
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y, |
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hps.data.filter_length, |
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hps.data.sampling_rate, |
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hps.data.hop_length, |
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hps.data.win_length, |
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center=False, |
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).to(self.device) |
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spec_lengths = torch.LongTensor([spec.size(-1)]).to(self.device) |
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audio = ( |
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self.model.voice_conversion( |
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spec, spec_lengths, sid_src=src_se, sid_tgt=tgt_se, tau=tau |
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)[0][0, 0] |
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.data.cpu() |
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.float() |
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.numpy() |
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) |
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if output_path is None: |
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return audio |
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else: |
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soundfile.write(output_path, audio, hps.data.sampling_rate) |
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