We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Prepare model for inference
**Is your feature request related to a problem? Please describe.**
The first time inference occurs takes the longest, where as the next inferences afterwards are faster. Since normally the first time is in the sounddevice callback, it's likely that audio will not be processed in time and will end up delayed.

**Describe the solution you'd like**
After loading the model, run an initial inference with some dummy data, perhaps torch.zeros of appropriate sizes.

**Additional context**
On my computer with a RTX 3050, the first time inference takes about 3 seconds to complete. Otherwise I get a Realtime coef of ~28


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `src/so_vits_svc_fork/inference_main.py`
Content:
```
1 from __future__ import annotations
2 
3 from logging import getLogger
4 from pathlib import Path
5 from typing import Literal
6 
7 import librosa
8 import numpy as np
9 import soundfile
10 import torch
11 
12 from .inference.infer_tool import RealtimeVC, RealtimeVC2, Svc
13 
14 LOG = getLogger(__name__)
15 
16 
17 def infer(
18     *,
19     # paths
20     input_path: Path | str,
21     output_path: Path | str,
22     model_path: Path | str,
23     config_path: Path | str,
24     # svc config
25     speaker: int | str,
26     cluster_model_path: Path | str | None = None,
27     transpose: int = 0,
28     auto_predict_f0: bool = False,
29     cluster_infer_ratio: float = 0,
30     noise_scale: float = 0.4,
31     f0_method: Literal["crepe", "parselmouth", "dio", "harvest"] = "crepe",
32     # slice config
33     db_thresh: int = -40,
34     pad_seconds: float = 0.5,
35     chunk_seconds: float = 0.5,
36     absolute_thresh: bool = False,
37     device: Literal["cpu", "cuda"] = "cuda" if torch.cuda.is_available() else "cpu",
38 ):
39     model_path = Path(model_path)
40     output_path = Path(output_path)
41     input_path = Path(input_path)
42     config_path = Path(config_path)
43     cluster_model_path = Path(cluster_model_path) if cluster_model_path else None
44     svc_model = Svc(
45         net_g_path=model_path.as_posix(),
46         config_path=config_path.as_posix(),
47         cluster_model_path=cluster_model_path.as_posix()
48         if cluster_model_path
49         else None,
50         device=device,
51     )
52 
53     audio, _ = librosa.load(input_path, sr=svc_model.target_sample)
54     audio = svc_model.infer_silence(
55         audio.astype(np.float32),
56         speaker=speaker,
57         transpose=transpose,
58         auto_predict_f0=auto_predict_f0,
59         cluster_infer_ratio=cluster_infer_ratio,
60         noise_scale=noise_scale,
61         f0_method=f0_method,
62         db_thresh=db_thresh,
63         pad_seconds=pad_seconds,
64         chunk_seconds=chunk_seconds,
65         absolute_thresh=absolute_thresh,
66     )
67 
68     soundfile.write(output_path, audio, svc_model.target_sample)
69 
70 
71 def realtime(
72     *,
73     # paths
74     model_path: Path | str,
75     config_path: Path | str,
76     # svc config
77     speaker: str,
78     cluster_model_path: Path | str | None = None,
79     transpose: int = 0,
80     auto_predict_f0: bool = False,
81     cluster_infer_ratio: float = 0,
82     noise_scale: float = 0.4,
83     f0_method: Literal["crepe", "parselmouth", "dio", "harvest"] = "crepe",
84     # slice config
85     db_thresh: int = -40,
86     pad_seconds: float = 0.5,
87     chunk_seconds: float = 0.5,
88     # realtime config
89     crossfade_seconds: float = 0.05,
90     block_seconds: float = 0.5,
91     version: int = 2,
92     input_device: int | str | None = None,
93     output_device: int | str | None = None,
94     device: Literal["cpu", "cuda"] = "cuda" if torch.cuda.is_available() else "cpu",
95 ):
96     import sounddevice as sd
97 
98     model_path = Path(model_path)
99     config_path = Path(config_path)
100     cluster_model_path = Path(cluster_model_path) if cluster_model_path else None
101     svc_model = Svc(
102         net_g_path=model_path.as_posix(),
103         config_path=config_path.as_posix(),
104         cluster_model_path=cluster_model_path.as_posix()
105         if cluster_model_path
106         else None,
107         device=device,
108     )
109     if version == 1:
110         model = RealtimeVC(
111             svc_model=svc_model,
112             crossfade_len=int(crossfade_seconds * svc_model.target_sample),
113         )
114     else:
115         model = RealtimeVC2(
116             svc_model=svc_model,
117         )
118 
119     # LOG all device info
120     devices = sd.query_devices()
121     LOG.info(f"Device: {devices}")
122     if isinstance(input_device, str):
123         input_device_candidates = [
124             i for i, d in enumerate(devices) if d["name"] == input_device
125         ]
126         if len(input_device_candidates) == 0:
127             LOG.warning(f"Input device {input_device} not found, using default")
128             input_device = None
129         else:
130             input_device = input_device_candidates[0]
131     if isinstance(output_device, str):
132         output_device_candidates = [
133             i for i, d in enumerate(devices) if d["name"] == output_device
134         ]
135         if len(output_device_candidates) == 0:
136             LOG.warning(f"Output device {output_device} not found, using default")
137             output_device = None
138         else:
139             output_device = output_device_candidates[0]
140     if input_device is None or input_device >= len(devices):
141         input_device = sd.default.device[0]
142     if output_device is None or output_device >= len(devices):
143         output_device = sd.default.device[1]
144     LOG.info(
145         f"Input Device: {devices[input_device]['name']}, Output Device: {devices[output_device]['name']}"
146     )
147 
148     def callback(
149         indata: np.ndarray,
150         outdata: np.ndarray,
151         frames: int,
152         time: int,
153         status: sd.CallbackFlags,
154     ) -> None:
155         LOG.debug(
156             f"Frames: {frames}, Status: {status}, Shape: {indata.shape}, Time: {time}"
157         )
158 
159         kwargs = dict(
160             input_audio=indata.mean(axis=1).astype(np.float32),
161             # svc config
162             speaker=speaker,
163             transpose=transpose,
164             auto_predict_f0=auto_predict_f0,
165             cluster_infer_ratio=cluster_infer_ratio,
166             noise_scale=noise_scale,
167             f0_method=f0_method,
168             # slice config
169             db_thresh=db_thresh,
170             # pad_seconds=pad_seconds,
171             chunk_seconds=chunk_seconds,
172         )
173         if version == 1:
174             kwargs["pad_seconds"] = pad_seconds
175         outdata[:] = model.process(
176             **kwargs,
177         ).reshape(-1, 1)
178 
179     with sd.Stream(
180         device=(input_device, output_device),
181         channels=1,
182         callback=callback,
183         samplerate=svc_model.target_sample,
184         blocksize=int(block_seconds * svc_model.target_sample),
185     ):
186         while True:
187             sd.sleep(1)
188
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/src/so_vits_svc_fork/inference_main.py b/src/so_vits_svc_fork/inference_main.py
--- a/src/so_vits_svc_fork/inference_main.py
+++ b/src/so_vits_svc_fork/inference_main.py
@@ -145,6 +145,21 @@
         f"Input Device: {devices[input_device]['name']}, Output Device: {devices[output_device]['name']}"
     )
 
+    # the model realtime coef is somewhat significantly low only in the first inference
+    # there could be no better way to warm up the model than to do a dummy inference
+    # (there are not differences in the behavior of the model between the first and the later inferences)
+    # so we do a dummy inference to warm up the model (1 second of audio)
+    LOG.info("Warming up the model...")
+    svc_model.infer(
+        speaker=speaker,
+        transpose=transpose,
+        auto_predict_f0=auto_predict_f0,
+        cluster_infer_ratio=cluster_infer_ratio,
+        noise_scale=noise_scale,
+        f0_method=f0_method,
+        audio=np.zeros(svc_model.target_sample, dtype=np.float32),
+    )
+
     def callback(
         indata: np.ndarray,
         outdata: np.ndarray,

{"golden_diff": "diff --git a/src/so_vits_svc_fork/inference_main.py b/src/so_vits_svc_fork/inference_main.py\n--- a/src/so_vits_svc_fork/inference_main.py\n+++ b/src/so_vits_svc_fork/inference_main.py\n@@ -145,6 +145,21 @@\n         f\"Input Device: {devices[input_device]['name']}, Output Device: {devices[output_device]['name']}\"\n     )\n \n+    # the model realtime coef is somewhat significantly low only in the first inference\n+    # there could be no better way to warm up the model than to do a dummy inference\n+    # (there are not differences in the behavior of the model between the first and the later inferences)\n+    # so we do a dummy inference to warm up the model (1 second of audio)\n+    LOG.info(\"Warming up the model...\")\n+    svc_model.infer(\n+        speaker=speaker,\n+        transpose=transpose,\n+        auto_predict_f0=auto_predict_f0,\n+        cluster_infer_ratio=cluster_infer_ratio,\n+        noise_scale=noise_scale,\n+        f0_method=f0_method,\n+        audio=np.zeros(svc_model.target_sample, dtype=np.float32),\n+    )\n+\n     def callback(\n         indata: np.ndarray,\n         outdata: np.ndarray,\n", "issue": "Prepare model for inference\n**Is your feature request related to a problem? Please describe.**\r\nThe first time inference occurs takes the longest, where as the next inferences afterwards are faster. Since normally the first time is in the sounddevice callback, it's likely that audio will not be processed in time and will end up delayed.\r\n\r\n**Describe the solution you'd like**\r\nAfter loading the model, run an initial inference with some dummy data, perhaps torch.zeros of appropriate sizes.\r\n\r\n**Additional context**\r\nOn my computer with a RTX 3050, the first time inference takes about 3 seconds to complete. Otherwise I get a Realtime coef of ~28\r\n\n", "before_files": [{"content": "from __future__ import annotations\n\nfrom logging import getLogger\nfrom pathlib import Path\nfrom typing import Literal\n\nimport librosa\nimport numpy as np\nimport soundfile\nimport torch\n\nfrom .inference.infer_tool import RealtimeVC, RealtimeVC2, Svc\n\nLOG = getLogger(__name__)\n\n\ndef infer(\n    *,\n    # paths\n    input_path: Path | str,\n    output_path: Path | str,\n    model_path: Path | str,\n    config_path: Path | str,\n    # svc config\n    speaker: int | str,\n    cluster_model_path: Path | str | None = None,\n    transpose: int = 0,\n    auto_predict_f0: bool = False,\n    cluster_infer_ratio: float = 0,\n    noise_scale: float = 0.4,\n    f0_method: Literal[\"crepe\", \"parselmouth\", \"dio\", \"harvest\"] = \"crepe\",\n    # slice config\n    db_thresh: int = -40,\n    pad_seconds: float = 0.5,\n    chunk_seconds: float = 0.5,\n    absolute_thresh: bool = False,\n    device: Literal[\"cpu\", \"cuda\"] = \"cuda\" if torch.cuda.is_available() else \"cpu\",\n):\n    model_path = Path(model_path)\n    output_path = Path(output_path)\n    input_path = Path(input_path)\n    config_path = Path(config_path)\n    cluster_model_path = Path(cluster_model_path) if cluster_model_path else None\n    svc_model = Svc(\n        net_g_path=model_path.as_posix(),\n        config_path=config_path.as_posix(),\n        cluster_model_path=cluster_model_path.as_posix()\n        if cluster_model_path\n        else None,\n        device=device,\n    )\n\n    audio, _ = librosa.load(input_path, sr=svc_model.target_sample)\n    audio = svc_model.infer_silence(\n        audio.astype(np.float32),\n        speaker=speaker,\n        transpose=transpose,\n        auto_predict_f0=auto_predict_f0,\n        cluster_infer_ratio=cluster_infer_ratio,\n        noise_scale=noise_scale,\n        f0_method=f0_method,\n        db_thresh=db_thresh,\n        pad_seconds=pad_seconds,\n        chunk_seconds=chunk_seconds,\n        absolute_thresh=absolute_thresh,\n    )\n\n    soundfile.write(output_path, audio, svc_model.target_sample)\n\n\ndef realtime(\n    *,\n    # paths\n    model_path: Path | str,\n    config_path: Path | str,\n    # svc config\n    speaker: str,\n    cluster_model_path: Path | str | None = None,\n    transpose: int = 0,\n    auto_predict_f0: bool = False,\n    cluster_infer_ratio: float = 0,\n    noise_scale: float = 0.4,\n    f0_method: Literal[\"crepe\", \"parselmouth\", \"dio\", \"harvest\"] = \"crepe\",\n    # slice config\n    db_thresh: int = -40,\n    pad_seconds: float = 0.5,\n    chunk_seconds: float = 0.5,\n    # realtime config\n    crossfade_seconds: float = 0.05,\n    block_seconds: float = 0.5,\n    version: int = 2,\n    input_device: int | str | None = None,\n    output_device: int | str | None = None,\n    device: Literal[\"cpu\", \"cuda\"] = \"cuda\" if torch.cuda.is_available() else \"cpu\",\n):\n    import sounddevice as sd\n\n    model_path = Path(model_path)\n    config_path = Path(config_path)\n    cluster_model_path = Path(cluster_model_path) if cluster_model_path else None\n    svc_model = Svc(\n        net_g_path=model_path.as_posix(),\n        config_path=config_path.as_posix(),\n        cluster_model_path=cluster_model_path.as_posix()\n        if cluster_model_path\n        else None,\n        device=device,\n    )\n    if version == 1:\n        model = RealtimeVC(\n            svc_model=svc_model,\n            crossfade_len=int(crossfade_seconds * svc_model.target_sample),\n        )\n    else:\n        model = RealtimeVC2(\n            svc_model=svc_model,\n        )\n\n    # LOG all device info\n    devices = sd.query_devices()\n    LOG.info(f\"Device: {devices}\")\n    if isinstance(input_device, str):\n        input_device_candidates = [\n            i for i, d in enumerate(devices) if d[\"name\"] == input_device\n        ]\n        if len(input_device_candidates) == 0:\n            LOG.warning(f\"Input device {input_device} not found, using default\")\n            input_device = None\n        else:\n            input_device = input_device_candidates[0]\n    if isinstance(output_device, str):\n        output_device_candidates = [\n            i for i, d in enumerate(devices) if d[\"name\"] == output_device\n        ]\n        if len(output_device_candidates) == 0:\n            LOG.warning(f\"Output device {output_device} not found, using default\")\n            output_device = None\n        else:\n            output_device = output_device_candidates[0]\n    if input_device is None or input_device >= len(devices):\n        input_device = sd.default.device[0]\n    if output_device is None or output_device >= len(devices):\n        output_device = sd.default.device[1]\n    LOG.info(\n        f\"Input Device: {devices[input_device]['name']}, Output Device: {devices[output_device]['name']}\"\n    )\n\n    def callback(\n        indata: np.ndarray,\n        outdata: np.ndarray,\n        frames: int,\n        time: int,\n        status: sd.CallbackFlags,\n    ) -> None:\n        LOG.debug(\n            f\"Frames: {frames}, Status: {status}, Shape: {indata.shape}, Time: {time}\"\n        )\n\n        kwargs = dict(\n            input_audio=indata.mean(axis=1).astype(np.float32),\n            # svc config\n            speaker=speaker,\n            transpose=transpose,\n            auto_predict_f0=auto_predict_f0,\n            cluster_infer_ratio=cluster_infer_ratio,\n            noise_scale=noise_scale,\n            f0_method=f0_method,\n            # slice config\n            db_thresh=db_thresh,\n            # pad_seconds=pad_seconds,\n            chunk_seconds=chunk_seconds,\n        )\n        if version == 1:\n            kwargs[\"pad_seconds\"] = pad_seconds\n        outdata[:] = model.process(\n            **kwargs,\n        ).reshape(-1, 1)\n\n    with sd.Stream(\n        device=(input_device, output_device),\n        channels=1,\n        callback=callback,\n        samplerate=svc_model.target_sample,\n        blocksize=int(block_seconds * svc_model.target_sample),\n    ):\n        while True:\n            sd.sleep(1)\n", "path": "src/so_vits_svc_fork/inference_main.py"}], "after_files": [{"content": "from __future__ import annotations\n\nfrom logging import getLogger\nfrom pathlib import Path\nfrom typing import Literal\n\nimport librosa\nimport numpy as np\nimport soundfile\nimport torch\n\nfrom .inference.infer_tool import RealtimeVC, RealtimeVC2, Svc\n\nLOG = getLogger(__name__)\n\n\ndef infer(\n    *,\n    # paths\n    input_path: Path | str,\n    output_path: Path | str,\n    model_path: Path | str,\n    config_path: Path | str,\n    # svc config\n    speaker: int | str,\n    cluster_model_path: Path | str | None = None,\n    transpose: int = 0,\n    auto_predict_f0: bool = False,\n    cluster_infer_ratio: float = 0,\n    noise_scale: float = 0.4,\n    f0_method: Literal[\"crepe\", \"parselmouth\", \"dio\", \"harvest\"] = \"crepe\",\n    # slice config\n    db_thresh: int = -40,\n    pad_seconds: float = 0.5,\n    chunk_seconds: float = 0.5,\n    absolute_thresh: bool = False,\n    device: Literal[\"cpu\", \"cuda\"] = \"cuda\" if torch.cuda.is_available() else \"cpu\",\n):\n    model_path = Path(model_path)\n    output_path = Path(output_path)\n    input_path = Path(input_path)\n    config_path = Path(config_path)\n    cluster_model_path = Path(cluster_model_path) if cluster_model_path else None\n    svc_model = Svc(\n        net_g_path=model_path.as_posix(),\n        config_path=config_path.as_posix(),\n        cluster_model_path=cluster_model_path.as_posix()\n        if cluster_model_path\n        else None,\n        device=device,\n    )\n\n    audio, _ = librosa.load(input_path, sr=svc_model.target_sample)\n    audio = svc_model.infer_silence(\n        audio.astype(np.float32),\n        speaker=speaker,\n        transpose=transpose,\n        auto_predict_f0=auto_predict_f0,\n        cluster_infer_ratio=cluster_infer_ratio,\n        noise_scale=noise_scale,\n        f0_method=f0_method,\n        db_thresh=db_thresh,\n        pad_seconds=pad_seconds,\n        chunk_seconds=chunk_seconds,\n        absolute_thresh=absolute_thresh,\n    )\n\n    soundfile.write(output_path, audio, svc_model.target_sample)\n\n\ndef realtime(\n    *,\n    # paths\n    model_path: Path | str,\n    config_path: Path | str,\n    # svc config\n    speaker: str,\n    cluster_model_path: Path | str | None = None,\n    transpose: int = 0,\n    auto_predict_f0: bool = False,\n    cluster_infer_ratio: float = 0,\n    noise_scale: float = 0.4,\n    f0_method: Literal[\"crepe\", \"parselmouth\", \"dio\", \"harvest\"] = \"crepe\",\n    # slice config\n    db_thresh: int = -40,\n    pad_seconds: float = 0.5,\n    chunk_seconds: float = 0.5,\n    # realtime config\n    crossfade_seconds: float = 0.05,\n    block_seconds: float = 0.5,\n    version: int = 2,\n    input_device: int | str | None = None,\n    output_device: int | str | None = None,\n    device: Literal[\"cpu\", \"cuda\"] = \"cuda\" if torch.cuda.is_available() else \"cpu\",\n):\n    import sounddevice as sd\n\n    model_path = Path(model_path)\n    config_path = Path(config_path)\n    cluster_model_path = Path(cluster_model_path) if cluster_model_path else None\n    svc_model = Svc(\n        net_g_path=model_path.as_posix(),\n        config_path=config_path.as_posix(),\n        cluster_model_path=cluster_model_path.as_posix()\n        if cluster_model_path\n        else None,\n        device=device,\n    )\n    if version == 1:\n        model = RealtimeVC(\n            svc_model=svc_model,\n            crossfade_len=int(crossfade_seconds * svc_model.target_sample),\n        )\n    else:\n        model = RealtimeVC2(\n            svc_model=svc_model,\n        )\n\n    # LOG all device info\n    devices = sd.query_devices()\n    LOG.info(f\"Device: {devices}\")\n    if isinstance(input_device, str):\n        input_device_candidates = [\n            i for i, d in enumerate(devices) if d[\"name\"] == input_device\n        ]\n        if len(input_device_candidates) == 0:\n            LOG.warning(f\"Input device {input_device} not found, using default\")\n            input_device = None\n        else:\n            input_device = input_device_candidates[0]\n    if isinstance(output_device, str):\n        output_device_candidates = [\n            i for i, d in enumerate(devices) if d[\"name\"] == output_device\n        ]\n        if len(output_device_candidates) == 0:\n            LOG.warning(f\"Output device {output_device} not found, using default\")\n            output_device = None\n        else:\n            output_device = output_device_candidates[0]\n    if input_device is None or input_device >= len(devices):\n        input_device = sd.default.device[0]\n    if output_device is None or output_device >= len(devices):\n        output_device = sd.default.device[1]\n    LOG.info(\n        f\"Input Device: {devices[input_device]['name']}, Output Device: {devices[output_device]['name']}\"\n    )\n\n    # the model realtime coef is somewhat significantly low only in the first inference\n    # there could be no better way to warm up the model than to do a dummy inference\n    # (there are not differences in the behavior of the model between the first and the later inferences)\n    # so we do a dummy inference to warm up the model (1 second of audio)\n    LOG.info(\"Warming up the model...\")\n    svc_model.infer(\n        speaker=speaker,\n        transpose=transpose,\n        auto_predict_f0=auto_predict_f0,\n        cluster_infer_ratio=cluster_infer_ratio,\n        noise_scale=noise_scale,\n        f0_method=f0_method,\n        audio=np.zeros(svc_model.target_sample, dtype=np.float32),\n    )\n\n    def callback(\n        indata: np.ndarray,\n        outdata: np.ndarray,\n        frames: int,\n        time: int,\n        status: sd.CallbackFlags,\n    ) -> None:\n        LOG.debug(\n            f\"Frames: {frames}, Status: {status}, Shape: {indata.shape}, Time: {time}\"\n        )\n\n        kwargs = dict(\n            input_audio=indata.mean(axis=1).astype(np.float32),\n            # svc config\n            speaker=speaker,\n            transpose=transpose,\n            auto_predict_f0=auto_predict_f0,\n            cluster_infer_ratio=cluster_infer_ratio,\n            noise_scale=noise_scale,\n            f0_method=f0_method,\n            # slice config\n            db_thresh=db_thresh,\n            # pad_seconds=pad_seconds,\n            chunk_seconds=chunk_seconds,\n        )\n        if version == 1:\n            kwargs[\"pad_seconds\"] = pad_seconds\n        outdata[:] = model.process(\n            **kwargs,\n        ).reshape(-1, 1)\n\n    with sd.Stream(\n        device=(input_device, output_device),\n        channels=1,\n        callback=callback,\n        samplerate=svc_model.target_sample,\n        blocksize=int(block_seconds * svc_model.target_sample),\n    ):\n        while True:\n            sd.sleep(1)\n", "path": "src/so_vits_svc_fork/inference_main.py"}]}