OPENGIT/Retrieval-based-Voice-Conversion-WebUI
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Add directML support to RVC for AMD & Intel GPU supported (#707)

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environment_dml.yaml Normal file
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name: pydml
channels:
- pytorch
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
- defaults
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/fastai/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/
dependencies:
- abseil-cpp=20211102.0=hd77b12b_0
- absl-py=1.3.0=py310haa95532_0
- aiohttp=3.8.3=py310h2bbff1b_0
- aiosignal=1.2.0=pyhd3eb1b0_0
- async-timeout=4.0.2=py310haa95532_0
- attrs=22.1.0=py310haa95532_0
- blas=1.0=mkl
- blinker=1.4=py310haa95532_0
- bottleneck=1.3.5=py310h9128911_0
- brotli=1.0.9=h2bbff1b_7
- brotli-bin=1.0.9=h2bbff1b_7
- brotlipy=0.7.0=py310h2bbff1b_1002
- bzip2=1.0.8=he774522_0
- c-ares=1.19.0=h2bbff1b_0
- ca-certificates=2023.05.30=haa95532_0
- cachetools=4.2.2=pyhd3eb1b0_0
- certifi=2023.5.7=py310haa95532_0
- cffi=1.15.1=py310h2bbff1b_3
- charset-normalizer=2.0.4=pyhd3eb1b0_0
- click=8.0.4=py310haa95532_0
- colorama=0.4.6=py310haa95532_0
- contourpy=1.0.5=py310h59b6b97_0
- cryptography=39.0.1=py310h21b164f_0
- cycler=0.11.0=pyhd3eb1b0_0
- fonttools=4.25.0=pyhd3eb1b0_0
- freetype=2.12.1=ha860e81_0
- frozenlist=1.3.3=py310h2bbff1b_0
- giflib=5.2.1=h8cc25b3_3
- glib=2.69.1=h5dc1a3c_2
- google-auth=2.6.0=pyhd3eb1b0_0
- google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
- grpc-cpp=1.48.2=hf108199_0
- grpcio=1.48.2=py310hf108199_0
- gst-plugins-base=1.18.5=h9e645db_0
- gstreamer=1.18.5=hd78058f_0
- icu=58.2=ha925a31_3
- idna=3.4=py310haa95532_0
- intel-openmp=2023.1.0=h59b6b97_46319
- jpeg=9e=h2bbff1b_1
- kiwisolver=1.4.4=py310hd77b12b_0
- krb5=1.19.4=h5b6d351_0
- lerc=3.0=hd77b12b_0
- libbrotlicommon=1.0.9=h2bbff1b_7
- libbrotlidec=1.0.9=h2bbff1b_7
- libbrotlienc=1.0.9=h2bbff1b_7
- libclang=14.0.6=default_hb5a9fac_1
- libclang13=14.0.6=default_h8e68704_1
- libdeflate=1.17=h2bbff1b_0
- libffi=3.4.4=hd77b12b_0
- libiconv=1.16=h2bbff1b_2
- libogg=1.3.5=h2bbff1b_1
- libpng=1.6.39=h8cc25b3_0
- libprotobuf=3.20.3=h23ce68f_0
- libtiff=4.5.0=h6c2663c_2
- libuv=1.44.2=h2bbff1b_0
- libvorbis=1.3.7=he774522_0
- libwebp=1.2.4=hbc33d0d_1
- libwebp-base=1.2.4=h2bbff1b_1
- libxml2=2.10.3=h0ad7f3c_0
- libxslt=1.1.37=h2bbff1b_0
- lz4-c=1.9.4=h2bbff1b_0
- markdown=3.4.1=py310haa95532_0
- markupsafe=2.1.1=py310h2bbff1b_0
- matplotlib=3.7.1=py310haa95532_1
- matplotlib-base=3.7.1=py310h4ed8f06_1
- mkl=2023.1.0=h8bd8f75_46356
- mkl-service=2.4.0=py310h2bbff1b_1
- mkl_fft=1.3.6=py310h4ed8f06_1
- mkl_random=1.2.2=py310h4ed8f06_1
- multidict=6.0.2=py310h2bbff1b_0
- munkres=1.1.4=py_0
- numexpr=2.8.4=py310h2cd9be0_1
- numpy=1.24.3=py310h055cbcc_1
- numpy-base=1.24.3=py310h65a83cf_1
- oauthlib=3.2.2=py310haa95532_0
- openssl=1.1.1t=h2bbff1b_0
- packaging=23.0=py310haa95532_0
- pandas=1.5.3=py310h4ed8f06_0
- pcre=8.45=hd77b12b_0
- pillow=9.4.0=py310hd77b12b_0
- pip=23.0.1=py310haa95532_0
- ply=3.11=py310haa95532_0
- protobuf=3.20.3=py310hd77b12b_0
- pyasn1=0.4.8=pyhd3eb1b0_0
- pyasn1-modules=0.2.8=py_0
- pycparser=2.21=pyhd3eb1b0_0
- pyjwt=2.4.0=py310haa95532_0
- pyopenssl=23.0.0=py310haa95532_0
- pyparsing=3.0.9=py310haa95532_0
- pyqt=5.15.7=py310hd77b12b_0
- pyqt5-sip=12.11.0=py310hd77b12b_0
- pysocks=1.7.1=py310haa95532_0
- python=3.10.11=h966fe2a_2
- python-dateutil=2.8.2=pyhd3eb1b0_0
- pytorch-mutex=1.0=cpu
- pytz=2022.7=py310haa95532_0
- pyyaml=6.0=py310h2bbff1b_1
- qt-main=5.15.2=he8e5bd7_8
- qt-webengine=5.15.9=hb9a9bb5_5
- qtwebkit=5.212=h2bbfb41_5
- re2=2022.04.01=hd77b12b_0
- requests=2.29.0=py310haa95532_0
- requests-oauthlib=1.3.0=py_0
- rsa=4.7.2=pyhd3eb1b0_1
- setuptools=67.8.0=py310haa95532_0
- sip=6.6.2=py310hd77b12b_0
- six=1.16.0=pyhd3eb1b0_1
- sqlite=3.41.2=h2bbff1b_0
- tbb=2021.8.0=h59b6b97_0
- tensorboard=2.10.0=py310haa95532_0
- tensorboard-data-server=0.6.1=py310haa95532_0
- tensorboard-plugin-wit=1.8.1=py310haa95532_0
- tk=8.6.12=h2bbff1b_0
- toml=0.10.2=pyhd3eb1b0_0
- tornado=6.2=py310h2bbff1b_0
- tqdm=4.65.0=py310h9909e9c_0
- typing_extensions=4.5.0=py310haa95532_0
- tzdata=2023c=h04d1e81_0
- urllib3=1.26.16=py310haa95532_0
- vc=14.2=h21ff451_1
- vs2015_runtime=14.27.29016=h5e58377_2
- werkzeug=2.2.3=py310haa95532_0
- wheel=0.38.4=py310haa95532_0
- win_inet_pton=1.1.0=py310haa95532_0
- xz=5.4.2=h8cc25b3_0
- yaml=0.2.5=he774522_0
- yarl=1.8.1=py310h2bbff1b_0
- zlib=1.2.13=h8cc25b3_0
- zstd=1.5.5=hd43e919_0
- pip:
- antlr4-python3-runtime==4.8
- appdirs==1.4.4
- audioread==3.0.0
- bitarray==2.7.4
- cython==0.29.35
- decorator==5.1.1
- fairseq==0.12.2
- faiss-cpu==1.7.4
- filelock==3.12.0
- hydra-core==1.0.7
- jinja2==3.1.2
- joblib==1.2.0
- lazy-loader==0.2
- librosa==0.10.0.post2
- llvmlite==0.40.0
- lxml==4.9.2
- mpmath==1.3.0
- msgpack==1.0.5
- networkx==3.1
- noisereduce==2.0.1
- numba==0.57.0
- omegaconf==2.0.6
- opencv-python==4.7.0.72
- pooch==1.6.0
- portalocker==2.7.0
- pysimplegui==4.60.5
- pywin32==306
- pyworld==0.3.3
- regex==2023.5.5
- sacrebleu==2.3.1
- scikit-learn==1.2.2
- scipy==1.10.1
- sounddevice==0.4.6
- soundfile==0.12.1
- soxr==0.3.5
- sympy==1.12
- tabulate==0.9.0
- threadpoolctl==3.1.0
- torch==2.0.0
- torch-directml==0.2.0.dev230426
- torchaudio==2.0.1
- torchvision==0.15.1
- wget==3.2
prefix: D:\ProgramData\anaconda3_\envs\pydml

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guidml.py Normal file
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"""
0416后的更新：
引入config中half
重建npy而不用填写
v2支持
无f0模型支持
修复
int16：
增加无索引支持
f0算法改harvest(怎么看就只有这个会影响CPU占用)，但是不这么改效果不好
"""
import os, sys, traceback, re
import json
now_dir = os.getcwd()
sys.path.append(now_dir)
from config import Config
Config = Config()
import torch_directml
import PySimpleGUI as sg
import sounddevice as sd
import noisereduce as nr
import numpy as np
from fairseq import checkpoint_utils
import librosa, torch, pyworld, faiss, time, threading
import torch.nn.functional as F
import torchaudio.transforms as tat
import scipy.signal as signal
# import matplotlib.pyplot as plt
from lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from i18n import I18nAuto
i18n = I18nAuto()
device = torch_directml.device(torch_directml.default_device())
current_dir = os.getcwd()
class RVC:
def __init__(
self, key, hubert_path, pth_path, index_path, npy_path, index_rate
) -> None:
"""
初始化
"""
try:
self.f0_up_key = key
self.time_step = 160 / 16000 * 1000
self.f0_min = 50
self.f0_max = 1100
self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
self.sr = 16000
self.window = 160
if index_rate != 0:
self.index = faiss.read_index(index_path)
# self.big_npy = np.load(npy_path)
self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
print("index search enabled")
self.index_rate = index_rate
model_path = hubert_path
print("load model(s) from {}".format(model_path))
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
[model_path],
suffix="",
)
self.model = models[0]
self.model = self.model.to(device)
if Config.is_half:
self.model = self.model.half()
else:
self.model = self.model.float()
self.model.eval()
cpt = torch.load(pth_path, map_location="cpu")
self.tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
self.if_f0 = cpt.get("f0", 1)
self.version = cpt.get("version", "v1")
if self.version == "v1":
if self.if_f0 == 1:
self.net_g = SynthesizerTrnMs256NSFsid(
*cpt["config"], is_half=Config.is_half
)
else:
self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif self.version == "v2":
if self.if_f0 == 1:
self.net_g = SynthesizerTrnMs768NSFsid(
*cpt["config"], is_half=Config.is_half
)
else:
self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del self.net_g.enc_q
print(self.net_g.load_state_dict(cpt["weight"], strict=False))
self.net_g.eval().to(device)
if Config.is_half:
self.net_g = self.net_g.half()
else:
self.net_g = self.net_g.float()
except:
print(traceback.format_exc())
def get_f0(self, x, f0_up_key, inp_f0=None):
x_pad = 1
f0_min = 50
f0_max = 1100
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
f0, t = pyworld.harvest(
x.astype(np.double),
fs=self.sr,
f0_ceil=f0_max,
f0_floor=f0_min,
frame_period=10,
)
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
f0 = signal.medfilt(f0, 3)
f0 *= pow(2, f0_up_key / 12)
# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
tf0 = self.sr // self.window # 每秒f0点数
if inp_f0 is not None:
delta_t = np.round(
(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
).astype("int16")
replace_f0 = np.interp(
list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
)
shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
f0bak = f0.copy()
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
f0_mel_max - f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
f0_coarse = np.rint(f0_mel).astype(np.int)
return f0_coarse, f0bak # 1-0
def infer(self, feats: torch.Tensor) -> np.ndarray:
"""
推理函数
"""
audio = feats.clone().cpu().numpy()
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
if Config.is_half:
feats = feats.half()
else:
feats = feats.float()
inputs = {
"source": feats.to(device),
"padding_mask": padding_mask.to(device),
"output_layer": 9 if self.version == "v1" else 12,
}
torch.cuda.synchronize()
with torch.no_grad():
logits = self.model.extract_features(**inputs)
feats = (
self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
)
####索引优化
try:
if (
hasattr(self, "index")
and hasattr(self, "big_npy")
and self.index_rate != 0
):
npy = feats[0].cpu().numpy().astype("float32")
score, ix = self.index.search(npy, k=8)
weight = np.square(1 / score)
weight /= weight.sum(axis=1, keepdims=True)
npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
if Config.is_half:
npy = npy.astype("float16")
feats = (
torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
+ (1 - self.index_rate) * feats
)
else:
print("index search FAIL or disabled")
except:
traceback.print_exc()
print("index search FAIL")
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
torch.cuda.synchronize()
print(feats.shape)
if self.if_f0 == 1:
pitch, pitchf = self.get_f0(audio, self.f0_up_key)
p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存
else:
pitch, pitchf = None, None
p_len = min(feats.shape[1], 13000) # 太大了爆显存
torch.cuda.synchronize()
# print(feats.shape,pitch.shape)
feats = feats[:, :p_len, :]
if self.if_f0 == 1:
pitch = pitch[:p_len]
pitchf = pitchf[:p_len]
pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
p_len = torch.LongTensor([p_len]).to(device)
ii = 0 # sid
sid = torch.LongTensor([ii]).to(device)
with torch.no_grad():
if self.if_f0 == 1:
infered_audio = (
self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
.data.cpu()
.float()
)
else:
infered_audio = (
self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
)
torch.cuda.synchronize()
return infered_audio
class GUIConfig:
def __init__(self) -> None:
self.hubert_path: str = ""
self.pth_path: str = ""
self.index_path: str = ""
self.npy_path: str = ""
self.pitch: int = 12
self.samplerate: int = 44100
self.block_time: float = 1.0 # s
self.buffer_num: int = 1
self.threhold: int = -30
self.crossfade_time: float = 0.08
self.extra_time: float = 0.04
self.I_noise_reduce = False
self.O_noise_reduce = False
self.index_rate = 0.3
class GUI:
def __init__(self) -> None:
self.config = GUIConfig()
self.flag_vc = False
self.launcher()
def load(self):
(
input_devices,
output_devices,
input_devices_indices,
output_devices_indices,
) = self.get_devices()
try:
with open("values1.json", "r") as j:
data = json.load(j)
except:
with open("values1.json", "w") as j:
data = {
"pth_path": "",
"index_path": "",
"sg_input_device": input_devices[
input_devices_indices.index(sd.default.device[0])
],
"sg_output_device": output_devices[
output_devices_indices.index(sd.default.device[1])
],
"threhold": "-45",
"pitch": "0",
"index_rate": "0",
"block_time": "1",
"crossfade_length": "0.04",
"extra_time": "1",
}
return data
def launcher(self):
data = self.load()
sg.theme("LightBlue3")
input_devices, output_devices, _, _ = self.get_devices()
layout = [
[
sg.Frame(
title=i18n("加载模型"),
layout=[
[
sg.Input(
default_text="hubert_base.pt",
key="hubert_path",
disabled=True,
),
sg.FileBrowse(
i18n("Hubert模型"),
initial_folder=os.path.join(os.getcwd()),
file_types=(("pt files", "*.pt"),),
),
],
[
sg.Input(
default_text=data.get("pth_path", ""),
key="pth_path",
),
sg.FileBrowse(
i18n("选择.pth文件"),
initial_folder=os.path.join(os.getcwd(), "weights"),
file_types=(("weight files", "*.pth"),),
),
],
[
sg.Input(
default_text=data.get("index_path", ""),
key="index_path",
),
sg.FileBrowse(
i18n("选择.index文件"),
initial_folder=os.path.join(os.getcwd(), "logs"),
file_types=(("index files", "*.index"),),
),
],
[
sg.Input(
default_text="你不需要填写这个You don't need write this.",
key="npy_path",
disabled=True,
),
sg.FileBrowse(
i18n("选择.npy文件"),
initial_folder=os.path.join(os.getcwd(), "logs"),
file_types=(("feature files", "*.npy"),),
),
],
],
)
],
[
sg.Frame(
layout=[
[
sg.Text(i18n("输入设备")),
sg.Combo(
input_devices,
key="sg_input_device",
default_value=data.get("sg_input_device", ""),
),
],
[
sg.Text(i18n("输出设备")),
sg.Combo(
output_devices,
key="sg_output_device",
default_value=data.get("sg_output_device", ""),
),
],
],
title=i18n("音频设备(请使用同种类驱动)"),
)
],
[
sg.Frame(
layout=[
[
sg.Text(i18n("响应阈值")),
sg.Slider(
range=(-60, 0),
key="threhold",
resolution=1,
orientation="h",
default_value=data.get("threhold", ""),
),
],
[
sg.Text(i18n("音调设置")),
sg.Slider(
range=(-24, 24),
key="pitch",
resolution=1,
orientation="h",
default_value=data.get("pitch", ""),
),
],
[
sg.Text(i18n("Index Rate")),
sg.Slider(
range=(0.0, 1.0),
key="index_rate",
resolution=0.01,
orientation="h",
default_value=data.get("index_rate", ""),
),
],
],
title=i18n("常规设置"),
),
sg.Frame(
layout=[
[
sg.Text(i18n("采样长度")),
sg.Slider(
range=(0.1, 3.0),
key="block_time",
resolution=0.1,
orientation="h",
default_value=data.get("block_time", ""),
),
],
[
sg.Text(i18n("淡入淡出长度")),
sg.Slider(
range=(0.01, 0.15),
key="crossfade_length",
resolution=0.01,
orientation="h",
default_value=data.get("crossfade_length", ""),
),
],
[
sg.Text(i18n("额外推理时长")),
sg.Slider(
range=(0.05, 3.00),
key="extra_time",
resolution=0.01,
orientation="h",
default_value=data.get("extra_time", ""),
),
],
[
sg.Checkbox(i18n("输入降噪"), key="I_noise_reduce"),
sg.Checkbox(i18n("输出降噪"), key="O_noise_reduce"),
],
],
title=i18n("性能设置"),
),
],
[
sg.Button(i18n("开始音频转换"), key="start_vc"),
sg.Button(i18n("停止音频转换"), key="stop_vc"),
sg.Text(i18n("推理时间(ms):")),
sg.Text("0", key="infer_time"),
],
]
self.window = sg.Window("RVC - GUI", layout=layout)
self.event_handler()
def event_handler(self):
while True:
event, values = self.window.read()
if event == sg.WINDOW_CLOSED:
self.flag_vc = False
exit()
if event == "start_vc" and self.flag_vc == False:
if self.set_values(values) == True:
print("using_cuda:" + str(torch.cuda.is_available()))
self.start_vc()
settings = {
"pth_path": values["pth_path"],
"index_path": values["index_path"],
"sg_input_device": values["sg_input_device"],
"sg_output_device": values["sg_output_device"],
"threhold": values["threhold"],
"pitch": values["pitch"],
"index_rate": values["index_rate"],
"block_time": values["block_time"],
"crossfade_length": values["crossfade_length"],
"extra_time": values["extra_time"],
}
with open("values1.json", "w") as j:
json.dump(settings, j)
if event == "stop_vc" and self.flag_vc == True:
self.flag_vc = False
def set_values(self, values):
if len(values["pth_path"].strip()) == 0:
sg.popup(i18n("请选择pth文件"))
return False
if len(values["index_path"].strip()) == 0:
sg.popup(i18n("请选择index文件"))
return False
pattern = re.compile("[^\x00-\x7F]+")
if pattern.findall(values["hubert_path"]):
sg.popup(i18n("hubert模型路径不可包含中文"))
return False
if pattern.findall(values["pth_path"]):
sg.popup(i18n("pth文件路径不可包含中文"))
return False
if pattern.findall(values["index_path"]):
sg.popup(i18n("index文件路径不可包含中文"))
return False
self.set_devices(values["sg_input_device"], values["sg_output_device"])
self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
self.config.pth_path = values["pth_path"]
self.config.index_path = values["index_path"]
self.config.npy_path = values["npy_path"]
self.config.threhold = values["threhold"]
self.config.pitch = values["pitch"]
self.config.block_time = values["block_time"]
self.config.crossfade_time = values["crossfade_length"]
self.config.extra_time = values["extra_time"]
self.config.I_noise_reduce = values["I_noise_reduce"]
self.config.O_noise_reduce = values["O_noise_reduce"]
self.config.index_rate = values["index_rate"]
return True
def start_vc(self):
torch.cuda.empty_cache()
self.flag_vc = True
self.block_frame = int(self.config.block_time * self.config.samplerate)
self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
self.sola_search_frame = int(0.012 * self.config.samplerate)
self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s
self.extra_frame = int(self.config.extra_time * self.config.samplerate)
self.rvc = None
self.rvc = RVC(
self.config.pitch,
self.config.hubert_path,
self.config.pth_path,
self.config.index_path,
self.config.npy_path,
self.config.index_rate,
)
self.input_wav: np.ndarray = np.zeros(
self.extra_frame
+ self.crossfade_frame
+ self.sola_search_frame
+ self.block_frame,
dtype="float32",
)
self.output_wav: torch.Tensor = torch.zeros(
self.block_frame, device=device, dtype=torch.float32
)
self.sola_buffer: torch.Tensor = torch.zeros(
self.crossfade_frame, device=device, dtype=torch.float32
)
self.fade_in_window: torch.Tensor = torch.linspace(
0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
)
self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
self.resampler1 = tat.Resample(
orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
)
self.resampler2 = tat.Resample(
orig_freq=self.rvc.tgt_sr,
new_freq=self.config.samplerate,
dtype=torch.float32,
)
thread_vc = threading.Thread(target=self.soundinput)
thread_vc.start()
def soundinput(self):
"""
接受音频输入
"""
with sd.Stream(
channels=2,
callback=self.audio_callback,
blocksize=self.block_frame,
samplerate=self.config.samplerate,
dtype="float32",
):
while self.flag_vc:
time.sleep(self.config.block_time)
print("Audio block passed.")
print("ENDing VC")
def audio_callback(
self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
):
"""
音频处理
"""
start_time = time.perf_counter()
indata = librosa.to_mono(indata.T)
if self.config.I_noise_reduce:
indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
"""noise gate"""
frame_length = 2048
hop_length = 1024
rms = librosa.feature.rms(
y=indata, frame_length=frame_length, hop_length=hop_length
)
db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
# print(rms.shape,db.shape,db)
for i in range(db_threhold.shape[0]):
if db_threhold[i]:
indata[i * hop_length : (i + 1) * hop_length] = 0
self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
# infer
print("input_wav:" + str(self.input_wav.shape))
# print('infered_wav:'+str(infer_wav.shape))
infer_wav: torch.Tensor = self.resampler2(
self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
)[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
device
)
print("infer_wav:" + str(infer_wav.shape))
# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
cor_nom = F.conv1d(
infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
self.sola_buffer[None, None, :],
)
cor_den = torch.sqrt(
F.conv1d(
infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
** 2,
torch.ones(1, 1, self.crossfade_frame, device=device),
)
+ 1e-8
)
sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
print("sola offset: " + str(int(sola_offset)))
# crossfade
self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
self.output_wav[: self.crossfade_frame] *= self.fade_in_window
self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
if sola_offset < self.sola_search_frame:
self.sola_buffer[:] = (
infer_wav[
-self.sola_search_frame
- self.crossfade_frame
+ sola_offset : -self.sola_search_frame
+ sola_offset
]
* self.fade_out_window
)
else:
self.sola_buffer[:] = (
infer_wav[-self.crossfade_frame :] * self.fade_out_window
)
if self.config.O_noise_reduce:
outdata[:] = np.tile(
nr.reduce_noise(
y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
),
(2, 1),
).T
else:
outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
total_time = time.perf_counter() - start_time
self.window["infer_time"].update(int(total_time * 1000))
print("infer time:" + str(total_time))
def get_devices(self, update: bool = True):
"""获取设备列表"""
if update:
sd._terminate()
sd._initialize()
devices = sd.query_devices()
hostapis = sd.query_hostapis()
for hostapi in hostapis:
for device_idx in hostapi["devices"]:
devices[device_idx]["hostapi_name"] = hostapi["name"]
input_devices = [
f"{d['name']} ({d['hostapi_name']})"
for d in devices
if d["max_input_channels"] > 0
]
output_devices = [
f"{d['name']} ({d['hostapi_name']})"
for d in devices
if d["max_output_channels"] > 0
]
input_devices_indices = [
d["index"] if "index" in d else d["name"]
for d in devices
if d["max_input_channels"] > 0
]
output_devices_indices = [
d["index"] if "index" in d else d["name"]
for d in devices
if d["max_output_channels"] > 0
]
return (
input_devices,
output_devices,
input_devices_indices,
output_devices_indices,
)
def set_devices(self, input_device, output_device):
"""设置输出设备"""
(
input_devices,
output_devices,
input_device_indices,
output_device_indices,
) = self.get_devices()
sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
sd.default.device[1] = output_device_indices[
output_devices.index(output_device)
]
print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
gui = GUI()

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