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chore: code cleanup by ruff fix
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.ruff.toml

+3
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@@ -1 +1,4 @@
1+
select = ["E", "F", "I"]
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3+
# Never enforce `E501` (line length violations).
4+
ignore = ["E501"]

cluster/__init__.py

+1
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,7 @@
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import torch
22
from sklearn.cluster import KMeans
33

4+
45
def get_cluster_model(ckpt_path):
56
checkpoint = torch.load(ckpt_path)
67
kmeans_dict = {}

cluster/kmeans.py

+6-2
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,11 @@
1-
import torch,pynvml
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from torch.nn.functional import normalize
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from time import time
2+
43
import numpy as np
4+
import pynvml
5+
import torch
6+
from torch.nn.functional import normalize
7+
8+
59
# device=torch.device("cuda:0")
610
def _kpp(data: torch.Tensor, k: int, sample_size: int = -1):
711
""" Picks k points in the data based on the kmeans++ method.

cluster/train_cluster.py

+8-8
Original file line numberDiff line numberDiff line change
@@ -1,17 +1,17 @@
1-
import time
2-
import tqdm
1+
import argparse
2+
import logging
33
import os
4+
import time
45
from pathlib import Path
5-
import logging
6-
import argparse
7-
from kmeans import KMeansGPU
8-
import torch
6+
97
import numpy as np
10-
from sklearn.cluster import KMeans,MiniBatchKMeans
8+
import torch
9+
import tqdm
10+
from kmeans import KMeansGPU
11+
from sklearn.cluster import KMeans, MiniBatchKMeans
1112

1213
logging.basicConfig(level=logging.INFO)
1314
logger = logging.getLogger(__name__)
14-
import torch
1515

1616
def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False,use_gpu=False):#gpu_minibatch真拉,虽然库支持但是也不考虑
1717
logger.info(f"Loading features from {in_dir}")

data_utils.py

+3-2
Original file line numberDiff line numberDiff line change
@@ -1,12 +1,13 @@
11
import os
22
import random
3+
34
import numpy as np
45
import torch
56
import torch.utils.data
67

78
import utils
8-
from modules.mel_processing import spectrogram_torch, spectrogram_torch
9-
from utils import load_wav_to_torch, load_filepaths_and_text
9+
from modules.mel_processing import spectrogram_torch
10+
from utils import load_filepaths_and_text, load_wav_to_torch
1011

1112
# import h5py
1213

diffusion/data_loaders.py

+6-4
Original file line numberDiff line numberDiff line change
@@ -1,12 +1,14 @@
11
import os
22
import random
3-
import numpy as np
3+
44
import librosa
5+
import numpy as np
56
import torch
6-
import random
7-
from utils import repeat_expand_2d
8-
from tqdm import tqdm
97
from torch.utils.data import Dataset
8+
from tqdm import tqdm
9+
10+
from utils import repeat_expand_2d
11+
1012

1113
def traverse_dir(
1214
root_dir,

diffusion/diffusion.py

+8-3
Original file line numberDiff line numberDiff line change
@@ -1,9 +1,10 @@
11
from collections import deque
22
from functools import partial
33
from inspect import isfunction
4-
import torch.nn.functional as F
4+
55
import numpy as np
66
import torch
7+
import torch.nn.functional as F
78
from torch import nn
89
from tqdm import tqdm
910

@@ -254,7 +255,11 @@ def forward(self,
254255

255256
if method is not None and infer_speedup > 1:
256257
if method == 'dpm-solver' or method == 'dpm-solver++':
257-
from .dpm_solver_pytorch import NoiseScheduleVP, model_wrapper, DPM_Solver
258+
from .dpm_solver_pytorch import (
259+
DPM_Solver,
260+
NoiseScheduleVP,
261+
model_wrapper,
262+
)
258263
# 1. Define the noise schedule.
259264
noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t])
260265

@@ -332,7 +337,7 @@ def wrapped(x, t, **kwargs):
332337
infer_speedup, cond=cond
333338
)
334339
elif method == 'unipc':
335-
from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC
340+
from .uni_pc import NoiseScheduleVP, UniPC, model_wrapper
336341
# 1. Define the noise schedule.
337342
noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t])
338343

diffusion/diffusion_onnx.py

+9-5
Original file line numberDiff line numberDiff line change
@@ -1,14 +1,14 @@
1+
import math
12
from collections import deque
23
from functools import partial
34
from inspect import isfunction
4-
import torch.nn.functional as F
5+
56
import numpy as np
6-
from torch.nn import Conv1d
7-
from torch.nn import Mish
87
import torch
8+
import torch.nn.functional as F
99
from torch import nn
10+
from torch.nn import Conv1d, Mish
1011
from tqdm import tqdm
11-
import math
1212

1313

1414
def exists(x):
@@ -390,7 +390,11 @@ def org_forward(self,
390390

391391
if method is not None and infer_speedup > 1:
392392
if method == 'dpm-solver':
393-
from .dpm_solver_pytorch import NoiseScheduleVP, model_wrapper, DPM_Solver
393+
from .dpm_solver_pytorch import (
394+
DPM_Solver,
395+
NoiseScheduleVP,
396+
model_wrapper,
397+
)
394398
# 1. Define the noise schedule.
395399
noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t])
396400

diffusion/infer_gt_mel.py

+1
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@@ -1,5 +1,6 @@
11
import torch
22
import torch.nn.functional as F
3+
34
from diffusion.unit2mel import load_model_vocoder
45

56

diffusion/logger/saver.py

+5-3
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@@ -2,14 +2,16 @@
22
author: wayn391@mastertones
33
'''
44

5+
import datetime
56
import os
67
import time
7-
import yaml
8-
import datetime
9-
import torch
8+
109
import matplotlib.pyplot as plt
10+
import torch
11+
import yaml
1112
from torch.utils.tensorboard import SummaryWriter
1213

14+
1315
class Saver(object):
1416
def __init__(
1517
self,

diffusion/logger/utils.py

+4-2
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@@ -1,7 +1,9 @@
1-
import os
2-
import yaml
31
import json
2+
import os
3+
44
import torch
5+
import yaml
6+
57

68
def traverse_dir(
79
root_dir,

diffusion/onnx_export.py

+5-3
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@@ -1,10 +1,12 @@
1-
from diffusion_onnx import GaussianDiffusion
21
import os
3-
import yaml
2+
3+
import numpy as np
44
import torch
55
import torch.nn as nn
6-
import numpy as np
76
import torch.nn.functional as F
7+
import yaml
8+
from diffusion_onnx import GaussianDiffusion
9+
810

911
class DotDict(dict):
1012
def __getattr__(*args):

diffusion/solver.py

+6-3
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@@ -1,12 +1,15 @@
11
import time
2+
3+
import librosa
24
import numpy as np
35
import torch
4-
import librosa
5-
from diffusion.logger.saver import Saver
6-
from diffusion.logger import utils
76
from torch import autocast
87
from torch.cuda.amp import GradScaler
98

9+
from diffusion.logger import utils
10+
from diffusion.logger.saver import Saver
11+
12+
1013
def test(args, model, vocoder, loader_test, saver):
1114
print(' [*] testing...')
1215
model.eval()

diffusion/uni_pc.py

+2-1
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@@ -1,6 +1,7 @@
1-
import torch
21
import math
32

3+
import torch
4+
45

56
class NoiseScheduleVP:
67
def __init__(

diffusion/unit2mel.py

+6-3
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@@ -1,11 +1,14 @@
11
import os
2-
import yaml
2+
3+
import numpy as np
34
import torch
45
import torch.nn as nn
5-
import numpy as np
6+
import yaml
7+
68
from .diffusion import GaussianDiffusion
7-
from .wavenet import WaveNet
89
from .vocoder import Vocoder
10+
from .wavenet import WaveNet
11+
912

1013
class DotDict(dict):
1114
def __getattr__(*args):

diffusion/vocoder.py

+4-3
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@@ -1,9 +1,10 @@
11
import torch
2-
from vdecoder.nsf_hifigan.nvSTFT import STFT
3-
from vdecoder.nsf_hifigan.models import load_model,load_config
42
from torchaudio.transforms import Resample
53

6-
4+
from vdecoder.nsf_hifigan.models import load_config, load_model
5+
from vdecoder.nsf_hifigan.nvSTFT import STFT
6+
7+
78
class Vocoder:
89
def __init__(self, vocoder_type, vocoder_ckpt, device = None):
910
if device is None:

flask_api.py

+1-1
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@@ -7,7 +7,7 @@
77
from flask import Flask, request, send_file
88
from flask_cors import CORS
99

10-
from inference.infer_tool import Svc, RealTimeVC
10+
from inference.infer_tool import RealTimeVC, Svc
1111

1212
app = Flask(__name__)
1313

flask_api_full_song.py

+2-2
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@@ -1,10 +1,10 @@
11
import io
2+
23
import numpy as np
34
import soundfile
45
from flask import Flask, request, send_file
56

6-
from inference import infer_tool
7-
from inference import slicer
7+
from inference import infer_tool, slicer
88

99
app = Flask(__name__)
1010

inference/infer_tool.py

+5-5
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@@ -1,26 +1,26 @@
1+
import gc
12
import hashlib
23
import io
34
import json
45
import logging
56
import os
7+
import pickle
68
import time
79
from pathlib import Path
8-
from inference import slicer
9-
import gc
1010

1111
import librosa
1212
import numpy as np
13+
1314
# import onnxruntime
1415
import soundfile
1516
import torch
1617
import torchaudio
1718

1819
import cluster
1920
import utils
20-
from models import SynthesizerTrn
21-
import pickle
22-
2321
from diffusion.unit2mel import load_model_vocoder
22+
from inference import slicer
23+
from models import SynthesizerTrn
2424

2525
logging.getLogger('matplotlib').setLevel(logging.WARNING)
2626

inference/infer_tool_grad.py

+4-2
Original file line numberDiff line numberDiff line change
@@ -1,16 +1,18 @@
1+
import io
12
import logging
23
import os
3-
import io
4+
45
import librosa
56
import numpy as np
6-
from inference import slicer
77
import parselmouth
88
import soundfile
99
import torch
1010
import torchaudio
1111

1212
import utils
13+
from inference import slicer
1314
from models import SynthesizerTrn
15+
1416
logging.getLogger('numba').setLevel(logging.WARNING)
1517
logging.getLogger('matplotlib').setLevel(logging.WARNING)
1618

inference_main.py

+3-1
Original file line numberDiff line numberDiff line change
@@ -1,8 +1,10 @@
11
import logging
2-
from spkmix import spk_mix_map
2+
33
import soundfile
4+
45
from inference import infer_tool
56
from inference.infer_tool import Svc
7+
from spkmix import spk_mix_map
68

79
logging.getLogger('numba').setLevel(logging.WARNING)
810
chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")

models.py

+3-4
Original file line numberDiff line numberDiff line change
@@ -1,18 +1,17 @@
11
import torch
22
from torch import nn
3+
from torch.nn import Conv1d, Conv2d
34
from torch.nn import functional as F
5+
from torch.nn.utils import spectral_norm, weight_norm
46

57
import modules.attentions as attentions
68
import modules.commons as commons
79
import modules.modules as modules
8-
9-
from torch.nn import Conv1d, Conv2d
10-
from torch.nn.utils import weight_norm, spectral_norm
11-
1210
import utils
1311
from modules.commons import get_padding
1412
from utils import f0_to_coarse
1513

14+
1615
class ResidualCouplingBlock(nn.Module):
1716
def __init__(self,
1817
channels,

modules/F0Predictor/CrepeF0Predictor.py

+4-2
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@@ -1,7 +1,9 @@
1-
from modules.F0Predictor.F0Predictor import F0Predictor
2-
from modules.F0Predictor.crepe import CrepePitchExtractor
31
import torch
42

3+
from modules.F0Predictor.crepe import CrepePitchExtractor
4+
from modules.F0Predictor.F0Predictor import F0Predictor
5+
6+
57
class CrepeF0Predictor(F0Predictor):
68
def __init__(self,hop_length=512,f0_min=50,f0_max=1100,device=None,sampling_rate=44100,threshold=0.05,model="full"):
79
self.F0Creper = CrepePitchExtractor(hop_length=hop_length,f0_min=f0_min,f0_max=f0_max,device=device,threshold=threshold,model=model)

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