RVC/Retrieval-based-Voice-Conversion-WebUI
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Update real-time gui (#1174)

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* loudness factor control and gpu-accelerated noise reduction

* loudness factor control and gpu-accelerated noise reduction

* loudness factor control and gpu-accelerated noise reduction
This commit is contained in:
yxlllc
2023-09-03 13:57:31 +08:00
committed by GitHub
parent b5050fbf0d
commit 1457169e7a
15 changed files with 434 additions and 63 deletions
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132
gui_v1.py
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@@ -51,7 +51,7 @@ if __name__ == "__main__":
from queue import Empty
import librosa
import noisereduce as nr
from tools.torchgate import TorchGate
import numpy as np
import PySimpleGUI as sg
import sounddevice as sd
@@ -80,15 +80,16 @@ if __name__ == "__main__":
def __init__(self) -> None:
self.pth_path: str = ""
self.index_path: str = ""
self.pitch: int = 12
self.pitch: int = 0
self.samplerate: int = 40000
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.threhold: int = -60
self.crossfade_time: float = 0.04
self.extra_time: float = 2.0
self.I_noise_reduce = False
self.O_noise_reduce = False
self.rms_mix_rate = 0.0
self.index_rate = 0.3
self.n_cpu = min(n_cpu, 6)
self.f0method = "harvest"
@@ -118,14 +119,19 @@ if __name__ == "__main__":
"index_path": " ",
"sg_input_device": input_devices[sd.default.device[0]],
"sg_output_device": output_devices[sd.default.device[1]],
"threhold": "-45",
"threhold": "-60",
"pitch": "0",
"index_rate": "0",
"block_time": "1",
"rms_mix_rate": "0",
"block_time": "0.25",
"crossfade_length": "0.04",
"extra_time": "1",
"extra_time": "2",
"f0method": "rmvpe",
}
data["pm"] = data["f0method"] == "pm"
data["harvest"] = data["f0method"] == "harvest"
data["crepe"] = data["f0method"] == "crepe"
data["rmvpe"] = data["f0method"] == "rmvpe"
return data
def launcher(self):
@@ -198,7 +204,7 @@ if __name__ == "__main__":
key="threhold",
resolution=1,
orientation="h",
default_value=data.get("threhold", ""),
default_value=data.get("threhold", "-60"),
enable_events=True,
),
],
@@ -209,7 +215,7 @@ if __name__ == "__main__":
key="pitch",
resolution=1,
orientation="h",
default_value=data.get("pitch", ""),
default_value=data.get("pitch", "0"),
enable_events=True,
),
],
@@ -220,7 +226,18 @@ if __name__ == "__main__":
key="index_rate",
resolution=0.01,
orientation="h",
default_value=data.get("index_rate", ""),
default_value=data.get("index_rate", "0"),
enable_events=True,
),
],
[
sg.Text(i18n("响度因子")),
sg.Slider(
range=(0.0, 1.0),
key="rms_mix_rate",
resolution=0.01,
orientation="h",
default_value=data.get("rms_mix_rate", "0"),
enable_events=True,
),
],
@@ -267,7 +284,7 @@ if __name__ == "__main__":
key="block_time",
resolution=0.01,
orientation="h",
default_value=data.get("block_time", ""),
default_value=data.get("block_time", "0.25"),
enable_events=True,
),
],
@@ -291,7 +308,7 @@ if __name__ == "__main__":
key="crossfade_length",
resolution=0.01,
orientation="h",
default_value=data.get("crossfade_length", ""),
default_value=data.get("crossfade_length", "0.04"),
enable_events=True,
),
],
@@ -302,7 +319,7 @@ if __name__ == "__main__":
key="extra_time",
resolution=0.01,
orientation="h",
default_value=data.get("extra_time", ""),
default_value=data.get("extra_time", "2.0"),
enable_events=True,
),
],
@@ -369,6 +386,7 @@ if __name__ == "__main__":
"sg_output_device": values["sg_output_device"],
"threhold": values["threhold"],
"pitch": values["pitch"],
"rms_mix_rate": values["rms_mix_rate"],
"index_rate": values["index_rate"],
"block_time": values["block_time"],
"crossfade_length": values["crossfade_length"],
@@ -399,6 +417,8 @@ if __name__ == "__main__":
self.config.index_rate = values["index_rate"]
if hasattr(self, "rvc"):
self.rvc.change_index_rate(values["index_rate"])
elif event == "rms_mix_rate":
self.config.rms_mix_rate = values["rms_mix_rate"]
elif event in ["pm", "harvest", "crepe", "rmvpe"]:
self.config.f0method = event
elif event == "I_noise_reduce":
@@ -433,6 +453,7 @@ if __name__ == "__main__":
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.rms_mix_rate = values["rms_mix_rate"]
self.config.index_rate = values["index_rate"]
self.config.n_cpu = values["n_cpu"]
self.config.f0method = ["pm", "harvest", "crepe", "rmvpe"][
@@ -457,17 +478,14 @@ if __name__ == "__main__":
inp_q,
opt_q,
device,
self.rvc if hasattr(self, "rvc") else None,
self.rvc if hasattr(self, "rvc") else None
)
self.config.samplerate = self.rvc.tgt_sr
self.config.crossfade_time = min(
self.config.crossfade_time, self.config.block_time
)
self.zc = self.rvc.tgt_sr // 100
self.block_frame = (
int(np.round(self.config.block_time * self.config.samplerate / self.zc))
* self.zc
)
self.block_frame = int(np.round(self.config.block_time * self.config.samplerate / self.zc)) * self.zc
self.block_frame_16k = 160 * self.block_frame // self.zc
self.crossfade_frame = int(
self.config.crossfade_time * self.config.samplerate
@@ -489,9 +507,7 @@ if __name__ == "__main__":
),
dtype="float32",
)
self.input_wav_res: torch.Tensor = torch.zeros(
160 * len(self.input_wav) // self.zc
)
self.input_wav_res: torch.Tensor= torch.zeros(160 * len(self.input_wav) // self.zc, device=device,dtype=torch.float32)
self.output_wav_cache: torch.Tensor = torch.zeros(
int(
np.ceil(
@@ -540,6 +556,8 @@ if __name__ == "__main__":
self.resampler = tat.Resample(
orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
).to(device)
self.input_tg = TorchGate(sr=16000, nonstationary=True, n_fft=640).to(device)
self.output_tg = TorchGate(sr=self.config.samplerate, nonstationary=True, n_fft=4*self.zc).to(device)
thread_vc = threading.Thread(target=self.soundinput)
thread_vc.start()
@@ -568,9 +586,6 @@ if __name__ == "__main__":
"""
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(
@@ -584,18 +599,13 @@ if __name__ == "__main__":
if db_threhold[i]:
indata[i * hop_length : (i + 1) * hop_length] = 0
self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :]
self.input_wav[-self.block_frame :] = indata
self.input_wav[-self.block_frame: ] = indata
# infer
inp = torch.from_numpy(
self.input_wav[-self.block_frame - 2 * self.zc :]
).to(device)
self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[
self.block_frame_16k :
].clone()
self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(inp)[
160:
]
inp = torch.from_numpy(self.input_wav[-self.block_frame-2*self.zc :]).to(device)
self.input_wav_res[ : -self.block_frame_16k] = self.input_wav_res[self.block_frame_16k :].clone()
self.input_wav_res[-self.block_frame_16k-160 :] = self.resampler(inp)[160 :]
if self.config.I_noise_reduce:
self.input_wav_res[-self.block_frame_16k-320 :] = self.input_tg(self.input_wav_res[None, -self.block_frame_16k-800 :])[0, 480 : ]
rate = (
self.crossfade_frame + self.sola_search_frame + self.block_frame
) / (
@@ -605,11 +615,11 @@ if __name__ == "__main__":
+ self.block_frame
)
f0_extractor_frame = self.block_frame_16k + 800
if self.config.f0method == "rmvpe":
if self.config.f0method == 'rmvpe':
f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1)
res2 = self.rvc.infer(
self.input_wav_res,
self.input_wav_res[-f0_extractor_frame:].cpu().numpy(),
self.input_wav_res[-f0_extractor_frame :].cpu().numpy(),
self.block_frame_16k,
rate,
self.pitch,
@@ -620,6 +630,27 @@ if __name__ == "__main__":
infer_wav = self.output_wav_cache[
-self.crossfade_frame - self.sola_search_frame - self.block_frame :
]
if self.config.O_noise_reduce:
infer_wav = self.output_tg(infer_wav.unsqueeze(0)).squeeze(0)
if self.config.rms_mix_rate < 1:
rms1 = librosa.feature.rms(
y=self.input_wav[-self.crossfade_frame - self.sola_search_frame - self.block_frame :],
frame_length=frame_length,
hop_length=hop_length
)
rms1 = torch.from_numpy(rms1).to(device)
rms1 = F.interpolate(
rms1.unsqueeze(0), size=infer_wav.shape[0], mode="linear"
).squeeze()
rms2 = librosa.feature.rms(
y=infer_wav[:].cpu().numpy(), frame_length=frame_length, hop_length=hop_length
)
rms2 = torch.from_numpy(rms2).to(device)
rms2 = F.interpolate(
rms2.unsqueeze(0), size=infer_wav.shape[0], mode="linear"
).squeeze()
rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-3)
infer_wav *= torch.pow(rms1 / rms2, torch.tensor(1 - self.config.rms_mix_rate))
# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
cor_nom = F.conv1d(
infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
@@ -659,25 +690,10 @@ if __name__ == "__main__":
self.sola_buffer[:] = (
infer_wav[-self.crossfade_frame :] * self.fade_out_window
)
if self.config.O_noise_reduce:
if sys.platform == "darwin":
noise_reduced_signal = nr.reduce_noise(
y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
)
outdata[:] = noise_reduced_signal[:, np.newaxis]
else:
outdata[:] = np.tile(
nr.reduce_noise(
y=self.output_wav[:].cpu().numpy(),
sr=self.config.samplerate,
),
(2, 1),
).T
if sys.platform == "darwin":
outdata[:] = self.output_wav[:].cpu().numpy()[:, np.newaxis]
else:
if sys.platform == "darwin":
outdata[:] = self.output_wav[:].cpu().numpy()[:, np.newaxis]
else:
outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
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))
logger.info("Infer time: %.2f", total_time)
@@ -733,7 +749,9 @@ if __name__ == "__main__":
sd.default.device[1] = output_device_indices[
output_devices.index(output_device)
]
logger.info("Input device: %s:%d", str(sd.default.device[0]), input_device)
logger.info(
"Input device: %s:%d", str(sd.default.device[0]), input_device
)
logger.info(
"Output device: %s:%d", str(sd.default.device[1]), output_device
)