diff --git a/rvc/lib/jit/__init__.py b/rvc/lib/jit/__init__.py
new file mode 100644
index 0000000..03950d3
--- /dev/null
+++ b/rvc/lib/jit/__init__.py
@@ -0,0 +1,164 @@
+import pickle
+import time
+from collections import OrderedDict
+from io import BytesIO
+
+import torch
+from tqdm import tqdm
+
+
+def load_inputs(path, device, is_half=False):
+    parm = torch.load(path, map_location=torch.device("cpu"))
+    for key in parm.keys():
+        parm[key] = parm[key].to(device)
+        if is_half and parm[key].dtype == torch.float32:
+            parm[key] = parm[key].half()
+        elif not is_half and parm[key].dtype == torch.float16:
+            parm[key] = parm[key].float()
+    return parm
+
+
+def benchmark(
+    model, inputs_path, device=torch.device("cpu"), epoch=1000, is_half=False
+):
+    parm = load_inputs(inputs_path, device, is_half)
+    total_ts = 0.0
+    bar = tqdm(range(epoch))
+    for i in bar:
+        start_time = time.perf_counter()
+        o = model(**parm)
+        total_ts += time.perf_counter() - start_time
+    print(f"num_epoch: {epoch} | avg time(ms): {(total_ts*1000)/epoch}")
+
+
+def jit_warm_up(model, inputs_path, device=torch.device("cpu"), epoch=5, is_half=False):
+    benchmark(model, inputs_path, device, epoch=epoch, is_half=is_half)
+
+
+def to_jit_model(
+    model_path,
+    model_type: str,
+    mode: str = "trace",
+    inputs_path: str = None,
+    device=torch.device("cpu"),
+    is_half=False,
+):
+    model = None
+    if model_type.lower() == "synthesizer":
+        from .get_synthesizer import get_synthesizer
+
+        model, _ = get_synthesizer(model_path, device)
+        model.forward = model.infer
+    elif model_type.lower() == "rmvpe":
+        from .get_rmvpe import get_rmvpe
+
+        model = get_rmvpe(model_path, device)
+    elif model_type.lower() == "hubert":
+        from .get_hubert import get_hubert_model
+
+        model = get_hubert_model(model_path, device)
+        model.forward = model.infer
+    else:
+        raise ValueError(f"No model type named {model_type}")
+    model = model.eval()
+    model = model.half() if is_half else model.float()
+    if mode == "trace":
+        assert not inputs_path
+        inputs = load_inputs(inputs_path, device, is_half)
+        model_jit = torch.jit.trace(model, example_kwarg_inputs=inputs)
+    elif mode == "script":
+        model_jit = torch.jit.script(model)
+    model_jit.to(device)
+    model_jit = model_jit.half() if is_half else model_jit.float()
+    # model = model.half() if is_half else model.float()
+    return (model, model_jit)
+
+
+def export(
+    model: torch.nn.Module,
+    mode: str = "trace",
+    inputs: dict = None,
+    device=torch.device("cpu"),
+    is_half: bool = False,
+) -> dict:
+    model = model.half() if is_half else model.float()
+    model.eval()
+    if mode == "trace":
+        assert inputs is not None
+        model_jit = torch.jit.trace(model, example_kwarg_inputs=inputs)
+    elif mode == "script":
+        model_jit = torch.jit.script(model)
+    model_jit.to(device)
+    model_jit = model_jit.half() if is_half else model_jit.float()
+    buffer = BytesIO()
+    # model_jit=model_jit.cpu()
+    torch.jit.save(model_jit, buffer)
+    del model_jit
+    cpt = OrderedDict()
+    cpt["model"] = buffer.getvalue()
+    cpt["is_half"] = is_half
+    return cpt
+
+
+def load(path: str):
+    with open(path, "rb") as f:
+        return pickle.load(f)
+
+
+def save(ckpt: dict, save_path: str):
+    with open(save_path, "wb") as f:
+        pickle.dump(ckpt, f)
+
+
+def rmvpe_jit_export(
+    model_path: str,
+    mode: str = "script",
+    inputs_path: str = None,
+    save_path: str = None,
+    device=torch.device("cpu"),
+    is_half=False,
+):
+    if not save_path:
+        save_path = model_path.rstrip(".pth")
+        save_path += ".half.jit" if is_half else ".jit"
+    if "cuda" in str(device) and ":" not in str(device):
+        device = torch.device("cuda:0")
+    from .get_rmvpe import get_rmvpe
+
+    model = get_rmvpe(model_path, device)
+    inputs = None
+    if mode == "trace":
+        inputs = load_inputs(inputs_path, device, is_half)
+    ckpt = export(model, mode, inputs, device, is_half)
+    ckpt["device"] = str(device)
+    save(ckpt, save_path)
+    return ckpt
+
+
+def synthesizer_jit_export(
+    model_path: str,
+    mode: str = "script",
+    inputs_path: str = None,
+    save_path: str = None,
+    device=torch.device("cpu"),
+    is_half=False,
+):
+    if not save_path:
+        save_path = model_path.rstrip(".pth")
+        save_path += ".half.jit" if is_half else ".jit"
+    if "cuda" in str(device) and ":" not in str(device):
+        device = torch.device("cuda:0")
+    from .get_synthesizer import get_synthesizer
+
+    model, cpt = get_synthesizer(model_path, device)
+    assert isinstance(cpt, dict)
+    model.forward = model.infer
+    inputs = None
+    if mode == "trace":
+        inputs = load_inputs(inputs_path, device, is_half)
+    ckpt = export(model, mode, inputs, device, is_half)
+    cpt.pop("weight")
+    cpt["model"] = ckpt["model"]
+    cpt["device"] = device
+    save(cpt, save_path)
+    return cpt
diff --git a/rvc/lib/jit/get_hubert.py b/rvc/lib/jit/get_hubert.py
index aec7132..a073f74 100644
--- a/rvc/lib/jit/get_hubert.py
+++ b/rvc/lib/jit/get_hubert.py
@@ -1,10 +1,11 @@
 import math
 import random
 from typing import Optional, Tuple
-from fairseq.checkpoint_utils import load_model_ensemble_and_task
+
 import numpy as np
 import torch
 import torch.nn.functional as F
+from fairseq.checkpoint_utils import load_model_ensemble_and_task
 
 # from fairseq.data.data_utils import compute_mask_indices
 from fairseq.utils import index_put