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

* 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
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GPG Key ID: 4AEE18F83AFDEB23
15 changed files with 434 additions and 63 deletions
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13
configs/config.json
View File

@ -4,11 +4,12 @@
"sg_input_device": "VoiceMeeter Output (VB-Audio Vo (MME)",
"sg_output_device": "VoiceMeeter Aux Input (VB-Audio (MME)",
"threhold": -45.0,
"pitch": 0.0,
"index_rate": 1.0,
"block_time": 0.09,
"crossfade_length": 0.15,
"extra_time": 5.0,
"n_cpu": 8.0,
"pitch": 12.0,
"index_rate": 0.0,
"rms_mix_rate": 0.0,
"block_time": 0.25,
"crossfade_length": 0.04,
"extra_time": 2.0,
"n_cpu": 6.0,
"f0method": "rmvpe"
}

132
gui_v1.py
View File

@ -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
)

1
i18n/locale/en_US.json
View File

@ -43,6 +43,7 @@
"后处理重采样至最终采样率，0为不进行重采样": "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling:",
"否": "No",
"响应阈值": "Response threshold",
"响度因子": "loudness factor",
"处理数据": "Process data",
"导出Onnx模型": "Export Onnx Model",
"导出文件格式": "Export file format",

1
i18n/locale/es_ES.json
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@ -43,6 +43,7 @@
"后处理重采样至最终采样率，0为不进行重采样": "Remuestreo posterior al proceso a la tasa de muestreo final, 0 significa no remuestrear",
"否": "No",
"响应阈值": "Umbral de respuesta",
"响度因子": "factor de sonoridad",
"处理数据": "Procesar datos",
"导出Onnx模型": "Exportar modelo Onnx",
"导出文件格式": "Formato de archivo de exportación",

1
i18n/locale/it_IT.json
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@ -43,6 +43,7 @@
"后处理重采样至最终采样率，0为不进行重采样": "Ricampiona l'audio di output in post-elaborazione alla frequenza di campionamento finale. ",
"否": "NO",
"响应阈值": "Soglia di risposta",
"响度因子": "fattore di sonorità",
"处理数据": "Processa dati",
"导出Onnx模型": "Esporta modello Onnx",
"导出文件格式": "Formato file di esportazione",

1
i18n/locale/ja_JP.json
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@ -43,6 +43,7 @@
"后处理重采样至最终采样率，0为不进行重采样": "最終的なサンプリングレートへのポストプロセッシングのリサンプリング リサンプリングしない場合は0",
"否": "いいえ",
"响应阈值": "反応閾値",
"响度因子": "ラウドネス係数",
"处理数据": "データ処理",
"导出Onnx模型": "Onnxに変換",
"导出文件格式": "エクスポート形式",

1
i18n/locale/ru_RU.json
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@ -43,6 +43,7 @@
"后处理重采样至最终采样率，0为不进行重采样": "Изменить частоту дискретизации в выходном файле на финальную. Поставьте 0, чтобы ничего не изменялось:",
"否": "Нет",
"响应阈值": "Порог ответа",
"响度因子": "коэффициент громкости",
"处理数据": "Обработать данные",
"导出Onnx模型": "Экспортировать модель",
"导出文件格式": "Формат выходных файлов",

1
i18n/locale/tr_TR.json
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@ -43,6 +43,7 @@
"后处理重采样至最终采样率，0为不进行重采样": "Son işleme aşamasında çıktı sesini son örnekleme hızına yeniden örnekle. 0 değeri için yeniden örnekleme yapılmaz:",
"否": "Hayır",
"响应阈值": "Tepki eşiği",
"响度因子": "ses yüksekliği faktörü",
"处理数据": "Verileri işle",
"导出Onnx模型": "Onnx Modeli Dışa Aktar",
"导出文件格式": "Dışa aktarma dosya formatı",

1
i18n/locale/zh_CN.json
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@ -43,6 +43,7 @@
"后处理重采样至最终采样率，0为不进行重采样": "后处理重采样至最终采样率，0为不进行重采样",
"否": "否",
"响应阈值": "响应阈值",
"响度因子": "响度因子",
"处理数据": "处理数据",
"导出Onnx模型": "导出Onnx模型",
"导出文件格式": "导出文件格式",

1
i18n/locale/zh_HK.json
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@ -43,6 +43,7 @@
"后处理重采样至最终采样率，0为不进行重采样": "後處理重採樣至最終採樣率，0為不進行重採樣",
"否": "否",
"响应阈值": "響應閾值",
"响度因子": "響度因子",
"处理数据": "處理資料",
"导出Onnx模型": "导出Onnx模型",
"导出文件格式": "導出檔格式",

1
i18n/locale/zh_SG.json
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@ -43,6 +43,7 @@
"后处理重采样至最终采样率，0为不进行重采样": "後處理重採樣至最終採樣率，0為不進行重採樣",
"否": "否",
"响应阈值": "響應閾值",
"响度因子": "響度因子",
"处理数据": "處理資料",
"导出Onnx模型": "导出Onnx模型",
"导出文件格式": "導出檔格式",

1
i18n/locale/zh_TW.json
View File

@ -43,6 +43,7 @@
"后处理重采样至最终采样率，0为不进行重采样": "後處理重採樣至最終採樣率，0為不進行重採樣",
"否": "否",
"响应阈值": "響應閾值",
"响度因子": "響度因子",
"处理数据": "處理資料",
"导出Onnx模型": "导出Onnx模型",
"导出文件格式": "導出檔格式",

12
tools/torchgate/__init__.py Normal file
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@ -0,0 +1,12 @@
"""
TorchGating is a PyTorch-based implementation of Spectral Gating
================================================
Author: Asaf Zorea
Contents
--------
torchgate imports all the functions from PyTorch, and in addition provides:
TorchGating --- A PyTorch module that applies a spectral gate to an input signal
"""
from .torchgate import TorchGate

264
tools/torchgate/torchgate.py Normal file
View File

@ -0,0 +1,264 @@
import torch
from torch.nn.functional import conv1d, conv2d
from typing import Union, Optional
from .utils import linspace, temperature_sigmoid, amp_to_db
class TorchGate(torch.nn.Module):
"""
A PyTorch module that applies a spectral gate to an input signal.
Arguments:
sr {int} -- Sample rate of the input signal.
nonstationary {bool} -- Whether to use non-stationary or stationary masking (default: {False}).
n_std_thresh_stationary {float} -- Number of standard deviations above mean to threshold noise for
stationary masking (default: {1.5}).
n_thresh_nonstationary {float} -- Number of multiplies above smoothed magnitude spectrogram. for
non-stationary masking (default: {1.3}).
temp_coeff_nonstationary {float} -- Temperature coefficient for non-stationary masking (default: {0.1}).
n_movemean_nonstationary {int} -- Number of samples for moving average smoothing in non-stationary masking
(default: {20}).
prop_decrease {float} -- Proportion to decrease signal by where the mask is zero (default: {1.0}).
n_fft {int} -- Size of FFT for STFT (default: {1024}).
win_length {[int]} -- Window length for STFT. If None, defaults to `n_fft` (default: {None}).
hop_length {[int]} -- Hop length for STFT. If None, defaults to `win_length` // 4 (default: {None}).
freq_mask_smooth_hz {float} -- Frequency smoothing width for mask (in Hz). If None, no smoothing is applied
(default: {500}).
time_mask_smooth_ms {float} -- Time smoothing width for mask (in ms). If None, no smoothing is applied
(default: {50}).
"""
@torch.no_grad()
def __init__(
self,
sr: int,
nonstationary: bool = False,
n_std_thresh_stationary: float = 1.5,
n_thresh_nonstationary: float = 1.3,
temp_coeff_nonstationary: float = 0.1,
n_movemean_nonstationary: int = 20,
prop_decrease: float = 1.0,
n_fft: int = 1024,
win_length: bool = None,
hop_length: int = None,
freq_mask_smooth_hz: float = 500,
time_mask_smooth_ms: float = 50,
):
super().__init__()
# General Params
self.sr = sr
self.nonstationary = nonstationary
assert 0.0 <= prop_decrease <= 1.0
self.prop_decrease = prop_decrease
# STFT Params
self.n_fft = n_fft
self.win_length = self.n_fft if win_length is None else win_length
self.hop_length = self.win_length // 4 if hop_length is None else hop_length
# Stationary Params
self.n_std_thresh_stationary = n_std_thresh_stationary
# Non-Stationary Params
self.temp_coeff_nonstationary = temp_coeff_nonstationary
self.n_movemean_nonstationary = n_movemean_nonstationary
self.n_thresh_nonstationary = n_thresh_nonstationary
# Smooth Mask Params
self.freq_mask_smooth_hz = freq_mask_smooth_hz
self.time_mask_smooth_ms = time_mask_smooth_ms
self.register_buffer("smoothing_filter", self._generate_mask_smoothing_filter())
@torch.no_grad()
def _generate_mask_smoothing_filter(self) -> Union[torch.Tensor, None]:
"""
A PyTorch module that applies a spectral gate to an input signal using the STFT.
Returns:
smoothing_filter (torch.Tensor): a 2D tensor representing the smoothing filter,
with shape (n_grad_freq, n_grad_time), where n_grad_freq is the number of frequency
bins to smooth and n_grad_time is the number of time frames to smooth.
If both self.freq_mask_smooth_hz and self.time_mask_smooth_ms are None, returns None.
"""
if self.freq_mask_smooth_hz is None and self.time_mask_smooth_ms is None:
return None
n_grad_freq = (
1
if self.freq_mask_smooth_hz is None
else int(self.freq_mask_smooth_hz / (self.sr / (self.n_fft / 2)))
)
if n_grad_freq < 1:
raise ValueError(
f"freq_mask_smooth_hz needs to be at least {int((self.sr / (self._n_fft / 2)))} Hz"
)
n_grad_time = (
1
if self.time_mask_smooth_ms is None
else int(self.time_mask_smooth_ms / ((self.hop_length / self.sr) * 1000))
)
if n_grad_time < 1:
raise ValueError(
f"time_mask_smooth_ms needs to be at least {int((self.hop_length / self.sr) * 1000)} ms"
)
if n_grad_time == 1 and n_grad_freq == 1:
return None
v_f = torch.cat(
[
linspace(0, 1, n_grad_freq + 1, endpoint=False),
linspace(1, 0, n_grad_freq + 2),
]
)[1:-1]
v_t = torch.cat(
[
linspace(0, 1, n_grad_time + 1, endpoint=False),
linspace(1, 0, n_grad_time + 2),
]
)[1:-1]
smoothing_filter = torch.outer(v_f, v_t).unsqueeze(0).unsqueeze(0)
return smoothing_filter / smoothing_filter.sum()
@torch.no_grad()
def _stationary_mask(
self, X_db: torch.Tensor, xn: Optional[torch.Tensor] = None
) -> torch.Tensor:
"""
Computes a stationary binary mask to filter out noise in a log-magnitude spectrogram.
Arguments:
X_db (torch.Tensor): 2D tensor of shape (frames, freq_bins) containing the log-magnitude spectrogram.
xn (torch.Tensor): 1D tensor containing the audio signal corresponding to X_db.
Returns:
sig_mask (torch.Tensor): Binary mask of the same shape as X_db, where values greater than the threshold
are set to 1, and the rest are set to 0.
"""
if xn is not None:
XN = torch.stft(
xn,
n_fft=self.n_fft,
hop_length=self.hop_length,
win_length=self.win_length,
return_complex=True,
pad_mode="constant",
center=True,
window=torch.hann_window(self.win_length).to(xn.device),
)
XN_db = amp_to_db(XN).to(dtype=X_db.dtype)
else:
XN_db = X_db
# calculate mean and standard deviation along the frequency axis
std_freq_noise, mean_freq_noise = torch.std_mean(XN_db, dim=-1)
# compute noise threshold
noise_thresh = mean_freq_noise + std_freq_noise * self.n_std_thresh_stationary
# create binary mask by thresholding the spectrogram
sig_mask = X_db > noise_thresh.unsqueeze(2)
return sig_mask
@torch.no_grad()
def _nonstationary_mask(self, X_abs: torch.Tensor) -> torch.Tensor:
"""
Computes a non-stationary binary mask to filter out noise in a log-magnitude spectrogram.
Arguments:
X_abs (torch.Tensor): 2D tensor of shape (frames, freq_bins) containing the magnitude spectrogram.
Returns:
sig_mask (torch.Tensor): Binary mask of the same shape as X_abs, where values greater than the threshold
are set to 1, and the rest are set to 0.
"""
X_smoothed = (
conv1d(
X_abs.reshape(-1, 1, X_abs.shape[-1]),
torch.ones(
self.n_movemean_nonstationary,
dtype=X_abs.dtype,
device=X_abs.device,
).view(1, 1, -1),
padding="same",
).view(X_abs.shape)
/ self.n_movemean_nonstationary
)
# Compute slowness ratio and apply temperature sigmoid
slowness_ratio = (X_abs - X_smoothed) / (X_smoothed + 1e-6)
sig_mask = temperature_sigmoid(
slowness_ratio, self.n_thresh_nonstationary, self.temp_coeff_nonstationary
)
return sig_mask
def forward(
self, x: torch.Tensor, xn: Optional[torch.Tensor] = None
) -> torch.Tensor:
"""
Apply the proposed algorithm to the input signal.
Arguments:
x (torch.Tensor): The input audio signal, with shape (batch_size, signal_length).
xn (Optional[torch.Tensor]): The noise signal used for stationary noise reduction. If `None`, the input
signal is used as the noise signal. Default: `None`.
Returns:
torch.Tensor: The denoised audio signal, with the same shape as the input signal.
"""
assert x.ndim == 2
if x.shape[-1] < self.win_length * 2:
raise Exception(f"x must be bigger than {self.win_length * 2}")
assert xn is None or xn.ndim == 1 or xn.ndim == 2
if xn is not None and xn.shape[-1] < self.win_length * 2:
raise Exception(f"xn must be bigger than {self.win_length * 2}")
# Compute short-time Fourier transform (STFT)
X = torch.stft(
x,
n_fft=self.n_fft,
hop_length=self.hop_length,
win_length=self.win_length,
return_complex=True,
pad_mode="constant",
center=True,
window=torch.hann_window(self.win_length).to(x.device),
)
# Compute signal mask based on stationary or nonstationary assumptions
if self.nonstationary:
sig_mask = self._nonstationary_mask(X.abs())
else:
sig_mask = self._stationary_mask(amp_to_db(X), xn)
# Propagate decrease in signal power
sig_mask = self.prop_decrease * (sig_mask * 1.0 - 1.0) + 1.0
# Smooth signal mask with 2D convolution
if self.smoothing_filter is not None:
sig_mask = conv2d(
sig_mask.unsqueeze(1),
self.smoothing_filter.to(sig_mask.dtype),
padding="same",
)
# Apply signal mask to STFT magnitude and phase components
Y = X * sig_mask.squeeze(1)
# Inverse STFT to obtain time-domain signal
y = torch.istft(
Y,
n_fft=self.n_fft,
hop_length=self.hop_length,
win_length=self.win_length,
center=True,
window=torch.hann_window(self.win_length).to(Y.device),
)
return y.to(dtype=x.dtype)

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import torch
from torch.types import Number
@torch.no_grad()
def amp_to_db(x: torch.Tensor, eps=torch.finfo(torch.float64).eps, top_db=40) -> torch.Tensor:
"""
Convert the input tensor from amplitude to decibel scale.
Arguments:
x {[torch.Tensor]} -- [Input tensor.]
Keyword Arguments:
eps {[float]} -- [Small value to avoid numerical instability.]
(default: {torch.finfo(torch.float64).eps})
top_db {[float]} -- [threshold the output at ``top_db`` below the peak]
` (default: {40})
Returns:
[torch.Tensor] -- [Output tensor in decibel scale.]
"""
x_db = 20 * torch.log10(x.abs() + eps)
return torch.max(x_db, (x_db.max(-1).values - top_db).unsqueeze(-1))
@torch.no_grad()
def temperature_sigmoid(x: torch.Tensor, x0: float, temp_coeff: float) -> torch.Tensor:
"""
Apply a sigmoid function with temperature scaling.
Arguments:
x {[torch.Tensor]} -- [Input tensor.]
x0 {[float]} -- [Parameter that controls the threshold of the sigmoid.]
temp_coeff {[float]} -- [Parameter that controls the slope of the sigmoid.]
Returns:
[torch.Tensor] -- [Output tensor after applying the sigmoid with temperature scaling.]
"""
return torch.sigmoid((x - x0) / temp_coeff)
@torch.no_grad()
def linspace(start: Number, stop: Number, num: int = 50, endpoint: bool = True, **kwargs) -> torch.Tensor:
"""
Generate a linearly spaced 1-D tensor.
Arguments:
start {[Number]} -- [The starting value of the sequence.]
stop {[Number]} -- [The end value of the sequence, unless `endpoint` is set to False.
In that case, the sequence consists of all but the last of ``num + 1``
evenly spaced samples, so that `stop` is excluded. Note that the step
size changes when `endpoint` is False.]
Keyword Arguments:
num {[int]} -- [Number of samples to generate. Default is 50. Must be non-negative.]
endpoint {[bool]} -- [If True, `stop` is the last sample. Otherwise, it is not included.
Default is True.]
**kwargs -- [Additional arguments to be passed to the underlying PyTorch `linspace` function.]
Returns:
[torch.Tensor] -- [1-D tensor of `num` equally spaced samples from `start` to `stop`.]
"""
if endpoint:
return torch.linspace(start, stop, num, **kwargs)
else:
return torch.linspace(start, stop, num + 1, **kwargs)[:-1]