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guidml.py
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guidml.py
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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()