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chore: make Ruff happy

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magic-akari 2023-07-05 14:52:16 +08:00 committed by YuriHead
parent fab65746d6
commit 4ae070bb77
5 changed files with 20 additions and 12 deletions
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2
.ruff.toml
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@ -1,4 +1,4 @@
select = ["E", "F", "I"] select = ["E", "F", "I"]
# Never enforce `E501` (line length violations). # Never enforce `E501` (line length violations).
ignore = ["E501"] ignore = ["E501", "E741"]

4
modules/DSConv.py
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@ -1,6 +1,6 @@
import torch
import torch.nn as nn import torch.nn as nn
from torch.nn.utils import weight_norm, remove_weight_norm from torch.nn.utils import remove_weight_norm, weight_norm
class Depthwise_Separable_Conv1D(nn.Module): class Depthwise_Separable_Conv1D(nn.Module):
def __init__( def __init__(

4
modules/mel_processing.py
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@ -53,7 +53,9 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
y = y.squeeze(1) y = y.squeeze(1)
y_dtype = y.dtype y_dtype = y.dtype
if y.dtype == torch.bfloat16: y = y.to(torch.float32) if y.dtype == torch.bfloat16:
y = y.to(torch.float32)
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True) center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
spec = torch.view_as_real(spec).to(y_dtype) spec = torch.view_as_real(spec).to(y_dtype)

10
modules/modules.py
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@ -1,12 +1,14 @@
import torch import torch
from torch import nn from torch import nn
from torch.nn import Conv1d
from torch.nn import functional as F from torch.nn import functional as F
from modules.DSConv import weight_norm_modules, remove_weight_norm_modules, Depthwise_Separable_Conv1D
import modules.commons as commons import modules.commons as commons
from modules.commons import init_weights, get_padding from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
LRELU_SLOPE = 0.1 LRELU_SLOPE = 0.1

12
webUI.py
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@ -6,6 +6,7 @@ import subprocess
import time import time
import traceback import traceback
from itertools import chain from itertools import chain
from pathlib import Path
# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt") # os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
import gradio as gr import gradio as gr
@ -15,7 +16,6 @@ import numpy as np
import soundfile import soundfile
import torch import torch
from scipy.io import wavfile from scipy.io import wavfile
from pathlib import Path
from compress_model import removeOptimizer from compress_model import removeOptimizer
from inference.infer_tool import Svc from inference.infer_tool import Svc
@ -172,14 +172,18 @@ def vc_fn(sid, input_audio, output_format, vc_transform, auto_f0,cluster_ratio,
model.clear_empty() model.clear_empty()
#os.remove(temp_path) #os.remove(temp_path)
#构建保存文件的路径，并保存到results文件夹内 #构建保存文件的路径，并保存到results文件夹内
timestamp = str(int(time.time())) str(int(time.time()))
if not os.path.exists("results"): if not os.path.exists("results"):
os.makedirs("results") os.makedirs("results")
key = "auto" if auto_f0 else f"{int(vc_transform)}key" key = "auto" if auto_f0 else f"{int(vc_transform)}key"
cluster = "_" if cluster_ratio == 0 else f"_{cluster_ratio}_" cluster = "_" if cluster_ratio == 0 else f"_{cluster_ratio}_"
isdiffusion = "sovits" isdiffusion = "sovits"
if model.shallow_diffusion : isdiffusion = "sovdiff" if model.shallow_diffusion:
if model.only_diffusion : isdiffusion = "diff" isdiffusion = "sovdiff"
if model.only_diffusion:
isdiffusion = "diff"
output_file_name = 'result_'+truncated_basename+f'_{sid}_{key}{cluster}{isdiffusion}.{output_format}' output_file_name = 'result_'+truncated_basename+f'_{sid}_{key}{cluster}{isdiffusion}.{output_format}'
output_file = os.path.join("results", output_file_name) output_file = os.path.join("results", output_file_name)
soundfile.write(output_file, _audio, model.target_sample, format=output_format) soundfile.write(output_file, _audio, model.target_sample, format=output_format)