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from typing import Any
from multiprocessing import cpu_count
from pathlib import Path
from scipy.io import wavfile
import torch
from fairseq import checkpoint_utils
from ultimate_rvc.typing_extra import F0Method
from ultimate_rvc.vc.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from ultimate_rvc.vc.my_utils import load_audio
from ultimate_rvc.vc.vc_infer_pipeline import VC
SRC_DIR = Path(__file__).resolve().parent.parent
class Config:
def __init__(self, device, is_half):
self.device = device
self.is_half = is_half
self.n_cpu = 0
self.gpu_name = None
self.gpu_mem = None
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
def device_config(self) -> tuple:
if torch.cuda.is_available():
i_device = int(self.device.split(":")[-1])
self.gpu_name = torch.cuda.get_device_name(i_device)
if (
("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
or "P40" in self.gpu_name.upper()
or "1060" in self.gpu_name
or "1070" in self.gpu_name
or "1080" in self.gpu_name
):
print("16 series/10 series P40 forced single precision")
self.is_half = False
for config_file in ["32k.json", "40k.json", "48k.json"]:
with open(SRC_DIR / "vc" / "configs" / config_file) as f:
strr = f.read().replace("true", "false")
with open(SRC_DIR / "vc" / "configs" / config_file, "w") as f:
f.write(strr)
with open(
SRC_DIR / "vc" / "trainset_preprocess_pipeline_print.py",
) as f:
strr = f.read().replace("3.7", "3.0")
with open(
SRC_DIR / "vc" / "trainset_preprocess_pipeline_print.py",
"w",
) as f:
f.write(strr)
else:
self.gpu_name = None
self.gpu_mem = int(
torch.cuda.get_device_properties(i_device).total_memory
/ 1024
/ 1024
/ 1024
+ 0.4,
)
if self.gpu_mem <= 4:
with open(
SRC_DIR / "vc" / "trainset_preprocess_pipeline_print.py",
) as f:
strr = f.read().replace("3.7", "3.0")
with open(
SRC_DIR / "vc" / "trainset_preprocess_pipeline_print.py",
"w",
) as f:
f.write(strr)
elif torch.backends.mps.is_available():
print("No supported N-card found, use MPS for inference")
self.device = "mps"
else:
print("No supported N-card found, use CPU for inference")
self.device = "cpu"
self.is_half = True
if self.n_cpu == 0:
self.n_cpu = cpu_count()
if self.is_half:
# 6G memory config
x_pad = 3
x_query = 10
x_center = 60
x_max = 65
else:
# 5G memory config
x_pad = 1
x_query = 6
x_center = 38
x_max = 41
if self.gpu_mem != None and self.gpu_mem <= 4:
x_pad = 1
x_query = 5
x_center = 30
x_max = 32
return x_pad, x_query, x_center, x_max
def load_hubert(device: str, model_path: str, *, is_half: bool) -> torch.nn.Module:
models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
[model_path],
suffix="",
)
hubert = models[0]
hubert.to(device)
hubert.half() if is_half else hubert.float()
hubert.eval()
return hubert
def get_vc(
device: str,
config: Config,
model_path: str,
*,
is_half: bool,
) -> tuple[dict[str, Any], str, torch.nn.Module, int, VC]:
cpt = torch.load(model_path, map_location="cpu")
if "config" not in cpt or "weight" not in cpt:
err_msg = (
f"Incorrect format for {model_path}. Use a voice model trained using RVC v2"
)
raise ValueError(err_msg)
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=is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
else:
err_msg = f"Unsupported model version: {version}"
raise ValueError(err_msg)
del net_g.enc_q
net_g.load_state_dict(cpt["weight"], strict=False)
net_g.eval().to(device)
net_g.half() if is_half else net_g.float()
vc = VC(tgt_sr, config)
return cpt, version, net_g, tgt_sr, vc
def rvc_infer(
index_path: str,
index_rate: float,
input_path: str,
output_path: str,
pitch_change: int,
f0_method: F0Method,
cpt: dict[str, Any],
version: str,
net_g: torch.nn.Module,
filter_radius: int,
tgt_sr: int,
rms_mix_rate: float,
protect: float,
crepe_hop_length: int,
vc: VC,
hubert_model: torch.nn.Module,
resample_sr: int,
) -> None:
audio = load_audio(input_path, 16000)
times = [0, 0, 0]
if_f0 = cpt.get("f0", 1)
audio_opt, output_sr = vc.pipeline(
hubert_model,
net_g,
0,
audio,
input_path,
times,
pitch_change,
f0_method,
index_path,
index_rate,
if_f0,
filter_radius,
tgt_sr,
resample_sr,
rms_mix_rate,
version,
protect,
crepe_hop_length,
)
wavfile.write(output_path, output_sr, audio_opt)
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