Retrieval-based-Voice-Conversion-WebUI/infer/lib/jit/get_synthesizer.py

import torch


def get_synthesizer(pth_path, device=torch.device("cpu")):
    from infer.lib.infer_pack.models import (
        SynthesizerTrnMs256NSFsid,
        SynthesizerTrnMs256NSFsid_nono,
        SynthesizerTrnMs768NSFsid,
        SynthesizerTrnMs768NSFsid_nono,
    )

    cpt = torch.load(pth_path, map_location=torch.device("cpu"))
    # tgt_sr = cpt["config"][-1]
    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
    if_f0 = cpt.get("f0", 1)
    version = cpt.get("version", "v1")
    if version == "v1":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=False)
        else:
            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
    elif version == "v2":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=False)
        else:
            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
    del net_g.enc_q
    # net_g.forward = net_g.infer
    # ckpt = {}
    # ckpt["config"] = cpt["config"]
    # ckpt["f0"] = if_f0
    # ckpt["version"] = version
    # ckpt["info"] = cpt.get("info", "0epoch")
    net_g.load_state_dict(cpt["weight"], strict=False)
    net_g = net_g.float()
    net_g.eval().to(device)
    net_g.remove_weight_norm()
    return net_g, cpt