index int64 0 50.7k | project_path stringclasses 279
values | completion_path stringlengths 57 199 | signature_postion stringlengths 3 11 | class_position stringlengths 3 11 | function_name stringlengths 1 62 | class_name stringlengths 0 55 | body_position stringlengths 3 11 | body_position_with_comments stringlengths 3 11 | masked_class stringlengths 14 8.32k | masked_class_with_comment stringlengths 14 8.32k | func_body stringlengths 9 6.72k | func_body_with_comment stringlengths 9 6.72k | len_func_body int64 1 643 | len_input int64 10 2.02k | len_output int64 4 1.9k | total int64 20 2.05k | file_code stringlengths 14 442k | file_code_with_comment stringlengths 14 442k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6,087 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models.py | 1113,1114 | 1101,1131 | forward | MultiPeriodDiscriminatorV2 | 1114,1131 | 1114,1131 | class MultiPeriodDiscriminatorV2(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminatorV2, self).__init__()
# periods = [2, 3, 5, 7, 11, 17]
periods = [2, 3, 5, 7, 11, 17, 23, 37]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
... | class MultiPeriodDiscriminatorV2(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminatorV2, self).__init__()
# periods = [2, 3, 5, 7, 11, 17]
periods = [2, 3, 5, 7, 11, 17, 23, 37]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
... | y_d_rs = [] #
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
# for j in range(len(fmap_r)):
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g... | y_d_rs = [] #
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
# for j in range(len(fmap_r)):
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g... | 42 | 220 | 247 | 467 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,088 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models.py | 178,181 | 148,204 | forward | PosteriorEncoder | 181,191 | 181,191 | class PosteriorEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
):
super(PosteriorEncoder, self).__init__()
self.in_channels = in_channels
... | class PosteriorEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
):
super(PosteriorEncoder, self).__init__()
self.in_channels = in_channels
... | x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
x.dtype
)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(... | x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
x.dtype
)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(... | 42 | 464 | 168 | 632 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,089 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models.py | 117,124 | 82,148 | forward | ResidualCouplingBlock | 124,132 | 124,132 | class ResidualCouplingBlock(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
):
super(ResidualCouplingBlock, self).__init__()
self.channels = channels
... | class ResidualCouplingBlock(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
):
super(ResidualCouplingBlock, self).__init__()
self.channels = channels
... | if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in self.flows[::-1]:
x, _ = flow.forward(x, x_mask, g=g, reverse=reverse)
return x
| if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in self.flows[::-1]:
x, _ = flow.forward(x, x_mask, g=g, reverse=reverse)
return x
| 28 | 530 | 95 | 625 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,090 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models.py | 353,354 | 312,410 | forward | SineGen | 360,410 | 354,410 | class SineGen(torch.nn.Module):
"""Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
samp_rate: sampling rate in Hz
harmonic_num: number of harmonic overtones (default 0)
... | class SineGen(torch.nn.Module):
"""Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
samp_rate: sampling rate in Hz
harmonic_num: number of harmonic overtones (default 0)
... | with torch.no_grad():
f0 = f0[:, None].transpose(1, 2)
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
# fundamental component
f0_buf[:, :, 0] = f0[:, :, 0]
for idx in range(self.harmonic_num):
f0_buf[:, :, id... | """sine_tensor, uv = forward(f0)
input F0: tensor(batchsize=1, length, dim=1)
f0 for unvoiced steps should be 0
output sine_tensor: tensor(batchsize=1, length, dim)
output uv: tensor(batchsize=1, length, 1)
"""
with torch.no_grad():
f0 = f0[:... | 198 | 605 | 835 | 1,440 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,091 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models.py | 452,453 | 410,467 | forward | SourceModuleHnNSF | 453,467 | 453,467 | class SourceModuleHnNSF(torch.nn.Module):
"""SourceModule for hn-nsf
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
add_noise_std=0.003, voiced_threshod=0)
sampling_rate: sampling_rate in Hz
harmonic_num: number of harmonic above F0 (default: 0)
sine_amp: amplitude of sin... | class SourceModuleHnNSF(torch.nn.Module):
"""SourceModule for hn-nsf
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
add_noise_std=0.003, voiced_threshod=0)
sampling_rate: sampling_rate in Hz
harmonic_num: number of harmonic above F0 (default: 0)
sine_amp: amplitude of sin... | # if self.ddtype ==-1:
# self.ddtype = self.l_linear.weight.dtype
sine_wavs, uv, _ = self.l_sin_gen(x, upp)
# print(x.dtype,sine_wavs.dtype,self.l_linear.weight.dtype)
# if self.is_half:
# sine_wavs = sine_wavs.half()
# sine_merge = self.l_tanh(self.l_line... | # if self.ddtype ==-1:
# self.ddtype = self.l_linear.weight.dtype
sine_wavs, uv, _ = self.l_sin_gen(x, upp)
# print(x.dtype,sine_wavs.dtype,self.l_linear.weight.dtype)
# if self.is_half:
# sine_wavs = sine_wavs.half()
# sine_merge = self.l_tanh(self.l_line... | 47 | 553 | 280 | 833 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,094 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models.py | 54,61 | 19,82 | forward | TextEncoder | 61,82 | 61,82 | class TextEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
f0=True,
):
super(TextEncoder, self).__init__()
self.out_chann... | class TextEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
f0=True,
):
super(TextEncoder, self).__init__()
self.out_chann... | if pitch is None:
x = self.emb_phone(phone)
else:
x = self.emb_phone(phone) + self.emb_pitch(pitch)
x = x * math.sqrt(self.hidden_channels) # [b, t, h]
x = self.lrelu(x)
x = torch.transpose(x, 1, -1) # [b, h, t]
x_mask = torch.unsqueeze(commons.s... | if pitch is None:
x = self.emb_phone(phone)
else:
x = self.emb_phone(phone) + self.emb_pitch(pitch)
x = x * math.sqrt(self.hidden_channels) # [b, t, h]
x = self.lrelu(x)
x = torch.transpose(x, 1, -1) # [b, h, t]
x_mask = torch.unsqueeze(commons.s... | 83 | 437 | 308 | 745 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,095 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 857,858 | 800,882 | forward | DiscriminatorP | 858,882 | 858,882 | class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super(DiscriminatorP, self).__init__()
self.period = period
self.use_spectral_norm = use_spectral_norm
norm_f = weight_norm if use_spectral_norm == False else spectral... | class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super(DiscriminatorP, self).__init__()
self.period = period
self.use_spectral_norm = use_spectral_norm
norm_f = weight_norm if use_spectral_norm == False else spectral... | fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
if has_xpu and x.dtype == torch.bfloat16:
x = F.pad(x.to(dtype=torch.float16), (0, n_pad), "reflect").to(
dtyp... | fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
if has_xpu and x.dtype == torch.bfloat16:
x = F.pad(x.to(dtype=torch.float16), (0, n_pad), "reflect").to(
dtyp... | 86 | 530 | 312 | 842 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,096 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 786,787 | 770,800 | forward | DiscriminatorS | 787,800 | 787,800 | class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
... | class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
... | fmap = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
| fmap = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
| 27 | 355 | 121 | 476 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,097 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 249,250 | 201,299 | forward | Generator | 250,270 | 250,270 | class Generator(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=0,
):
super(Generator, se... | class Generator(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=0,
):
super(Generator, se... | x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
... | x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
... | 66 | 921 | 239 | 1,160 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,098 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 528,529 | 454,587 | forward | GeneratorNSF | 529,559 | 529,559 | class GeneratorNSF(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels,
sr,
is_half=False... | class GeneratorNSF(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels,
sr,
is_half=False... | har_source, noi_source, uv = self.m_source(f0, self.upp)
har_source = har_source.transpose(1, 2)
x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
# torch.jit.script() does not support direct indexing of torch modules
# That's why I wrote this
... | har_source, noi_source, uv = self.m_source(f0, self.upp)
har_source = har_source.transpose(1, 2)
x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
# torch.jit.script() does not support direct indexing of torch modules
# That's why I wrote this
... | 135 | 1,223 | 435 | 1,658 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,099 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 722,723 | 710,740 | forward | MultiPeriodDiscriminator | 723,740 | 723,740 | class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminator, self).__init__()
periods = [2, 3, 5, 7, 11, 17]
# periods = [3, 5, 7, 11, 17, 23, 37]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
d... | class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminator, self).__init__()
periods = [2, 3, 5, 7, 11, 17]
# periods = [3, 5, 7, 11, 17, 23, 37]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
d... | y_d_rs = [] #
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
# for j in range(len(fmap_r)):
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g... | y_d_rs = [] #
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
# for j in range(len(fmap_r)):
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g... | 42 | 213 | 247 | 460 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,100 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 752,753 | 740,770 | forward | MultiPeriodDiscriminatorV2 | 753,770 | 753,770 | class MultiPeriodDiscriminatorV2(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminatorV2, self).__init__()
# periods = [2, 3, 5, 7, 11, 17]
periods = [2, 3, 5, 7, 11, 17, 23, 37]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
... | class MultiPeriodDiscriminatorV2(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminatorV2, self).__init__()
# periods = [2, 3, 5, 7, 11, 17]
periods = [2, 3, 5, 7, 11, 17, 23, 37]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
... | y_d_rs = [] #
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
# for j in range(len(fmap_r)):
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g... | y_d_rs = [] #
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
# for j in range(len(fmap_r)):
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g... | 42 | 220 | 247 | 467 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,101 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 175,178 | 145,201 | forward | PosteriorEncoder | 178,188 | 178,188 | class PosteriorEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
):
super(PosteriorEncoder, self).__init__()
self.in_channels = in_channels
... | class PosteriorEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
):
super(PosteriorEncoder, self).__init__()
self.in_channels = in_channels
... | x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
x.dtype
)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(... | x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
x.dtype
)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(... | 42 | 464 | 168 | 632 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,102 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 114,121 | 79,145 | forward | ResidualCouplingBlock | 121,129 | 121,129 | class ResidualCouplingBlock(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
):
super(ResidualCouplingBlock, self).__init__()
self.channels = channels
... | class ResidualCouplingBlock(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
):
super(ResidualCouplingBlock, self).__init__()
self.channels = channels
... | if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x, _ = flow.forward(x, x_mask, g=g, reverse=reverse)
return x
| if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x, _ = flow.forward(x, x_mask, g=g, reverse=reverse)
return x
| 28 | 530 | 94 | 624 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,103 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 340,341 | 299,397 | forward | SineGen | 347,397 | 341,397 | class SineGen(torch.nn.Module):
"""Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
samp_rate: sampling rate in Hz
harmonic_num: number of harmonic overtones (default 0)
... | class SineGen(torch.nn.Module):
"""Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
samp_rate: sampling rate in Hz
harmonic_num: number of harmonic overtones (default 0)
... | with torch.no_grad():
f0 = f0[:, None].transpose(1, 2)
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
# fundamental component
f0_buf[:, :, 0] = f0[:, :, 0]
for idx in range(self.harmonic_num):
f0_buf[:, :, id... | """sine_tensor, uv = forward(f0)
input F0: tensor(batchsize=1, length, dim=1)
f0 for unvoiced steps should be 0
output sine_tensor: tensor(batchsize=1, length, dim)
output uv: tensor(batchsize=1, length, 1)
"""
with torch.no_grad():
f0 = f0[:... | 198 | 605 | 835 | 1,440 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,104 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 439,440 | 397,454 | forward | SourceModuleHnNSF | 440,454 | 440,454 | class SourceModuleHnNSF(torch.nn.Module):
"""SourceModule for hn-nsf
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
add_noise_std=0.003, voiced_threshod=0)
sampling_rate: sampling_rate in Hz
harmonic_num: number of harmonic above F0 (default: 0)
sine_amp: amplitude of sin... | class SourceModuleHnNSF(torch.nn.Module):
"""SourceModule for hn-nsf
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
add_noise_std=0.003, voiced_threshod=0)
sampling_rate: sampling_rate in Hz
harmonic_num: number of harmonic above F0 (default: 0)
sine_amp: amplitude of sin... | # if self.ddtype ==-1:
# self.ddtype = self.l_linear.weight.dtype
sine_wavs, uv, _ = self.l_sin_gen(x, upp)
# print(x.dtype,sine_wavs.dtype,self.l_linear.weight.dtype)
# if self.is_half:
# sine_wavs = sine_wavs.half()
# sine_merge = self.l_tanh(self.l_line... | # if self.ddtype ==-1:
# self.ddtype = self.l_linear.weight.dtype
sine_wavs, uv, _ = self.l_sin_gen(x, upp)
# print(x.dtype,sine_wavs.dtype,self.l_linear.weight.dtype)
# if self.is_half:
# sine_wavs = sine_wavs.half()
# sine_merge = self.l_tanh(self.l_line... | 47 | 553 | 280 | 833 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,105 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 693,694 | 594,710 | forward | SynthesizerTrnMsNSFsidM | 694,710 | 694,710 | class SynthesizerTrnMsNSFsidM(nn.Module):
def __init__(
self,
spec_channels,
segment_size,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
resblock,
resblock_kernel_sizes,
... | class SynthesizerTrnMsNSFsidM(nn.Module):
def __init__(
self,
spec_channels,
segment_size,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
resblock,
resblock_kernel_sizes,
... | if self.speaker_map is not None: # [N, S] * [S, B, 1, H]
g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1]
g = g * self.speaker_map # [N, S, B, 1, H]
g = torch.sum(g, dim=1) # [N, 1, B, 1, H]
g = g.transpose(0, -1).transpose(0, -2).squeeze(0... | if self.speaker_map is not None: # [N, S] * [S, B, 1, H]
g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1]
g = g * self.speaker_map # [N, S, B, 1, H]
g = torch.sum(g, dim=1) # [N, 1, B, 1, H]
g = g.transpose(0, -1).transpose(0, -2).squeeze(0... | 97 | 1,066 | 344 | 1,410 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,106 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/models_onnx.py | 56,63 | 21,79 | forward | TextEncoder | 63,79 | 63,79 | class TextEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
f0=True,
):
super(TextEncoder, self).__init__()
self.out_chann... | class TextEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
f0=True,
):
super(TextEncoder, self).__init__()
self.out_chann... | if pitch is None:
x = self.emb_phone(phone)
else:
x = self.emb_phone(phone) + self.emb_pitch(pitch)
x = x * math.sqrt(self.hidden_channels) # [b, t, h]
x = self.lrelu(x)
x = torch.transpose(x, 1, -1) # [b, h, t]
x_mask = torch.unsqueeze(commons.s... | if pitch is None:
x = self.emb_phone(phone)
else:
x = self.emb_phone(phone) + self.emb_pitch(pitch)
x = x * math.sqrt(self.hidden_channels) # [b, t, h]
x = self.lrelu(x)
x = torch.transpose(x, 1, -1) # [b, h, t]
x_mask = torch.unsqueeze(commons.s... | 62 | 437 | 239 | 676 | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... | import math
import logging
from typing import Optional
logger = logging.getLogger(__name__)
import numpy as np
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_nor... |
6,107 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 579,586 | 552,616 | forward | ConvFlow | 586,616 | 586,616 | class ConvFlow(nn.Module):
def __init__(
self,
in_channels,
filter_channels,
kernel_size,
n_layers,
num_bins=10,
tail_bound=5.0,
):
super(ConvFlow, self).__init__()
self.in_channels = in_channels
self.filter_channels = filter_channe... | class ConvFlow(nn.Module):
def __init__(
self,
in_channels,
filter_channels,
kernel_size,
n_layers,
num_bins=10,
tail_bound=5.0,
):
super(ConvFlow, self).__init__()
self.in_channels = in_channels
self.filter_channels = filter_channe... | x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h = self.pre(x0)
h = self.convs(h, x_mask, g=g)
h = self.proj(h) * x_mask
b, c, t = x0.shape
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
unnormalized_widths = h[..., : self.nu... | x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h = self.pre(x0)
h = self.convs(h, x_mask, g=g)
h = self.proj(h) * x_mask
b, c, t = x0.shape
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
unnormalized_widths = h[..., : self.nu... | 105 | 375 | 429 | 804 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,108 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 77,78 | 35,87 | forward | ConvReluNorm | 78,87 | 78,87 | class ConvReluNorm(nn.Module):
def __init__(
self,
in_channels,
hidden_channels,
out_channels,
kernel_size,
n_layers,
p_dropout,
):
super(ConvReluNorm, self).__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_ch... | class ConvReluNorm(nn.Module):
def __init__(
self,
in_channels,
hidden_channels,
out_channels,
kernel_size,
n_layers,
p_dropout,
):
super(ConvReluNorm, self).__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_ch... | x_org = x
for i in range(self.n_layers):
x = self.conv_layers[i](x * x_mask)
x = self.norm_layers[i](x)
x = self.relu_drop(x)
x = x_org + self.proj(x)
return x * x_mask
| x_org = x
for i in range(self.n_layers):
x = self.conv_layers[i](x * x_mask)
x = self.norm_layers[i](x)
x = self.relu_drop(x)
x = x_org + self.proj(x)
return x * x_mask
| 27 | 478 | 98 | 576 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,109 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 121,122 | 87,136 | forward | DDSConv | 122,136 | 122,136 | class DDSConv(nn.Module):
"""
Dialted and Depth-Separable Convolution
"""
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
super(DDSConv, self).__init__()
self.channels = channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.p_d... | class DDSConv(nn.Module):
"""
Dialted and Depth-Separable Convolution
"""
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
super(DDSConv, self).__init__()
self.channels = channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.p_d... | if g is not None:
x = x + g
for i in range(self.n_layers):
y = self.convs_sep[i](x * x_mask)
y = self.norms_1[i](y)
y = F.gelu(y)
y = self.convs_1x1[i](y)
y = self.norms_2[i](y)
y = F.gelu(y)
y = self.drop(y)... | if g is not None:
x = x + g
for i in range(self.n_layers):
y = self.convs_sep[i](x * x_mask)
y = self.norms_1[i](y)
y = F.gelu(y)
y = self.convs_1x1[i](y)
y = self.norms_2[i](y)
y = F.gelu(y)
y = self.drop(y)... | 46 | 401 | 159 | 560 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,110 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 466,467 | 459,477 | forward | ElementwiseAffine | 467,477 | 467,477 | class ElementwiseAffine(nn.Module):
def __init__(self, channels):
super(ElementwiseAffine, self).__init__()
self.channels = channels
self.m = nn.Parameter(torch.zeros(channels, 1))
self.logs = nn.Parameter(torch.zeros(channels, 1))
def forward(self, x, x_mask, reverse=False, **k... | class ElementwiseAffine(nn.Module):
def __init__(self, channels):
super(ElementwiseAffine, self).__init__()
self.channels = channels
self.m = nn.Parameter(torch.zeros(channels, 1))
self.logs = nn.Parameter(torch.zeros(channels, 1))
def forward(self, x, x_mask, reverse=False, **k... | if not reverse:
y = self.m + torch.exp(self.logs) * x
y = y * x_mask
logdet = torch.sum(self.logs * x_mask, [1, 2])
return y, logdet
else:
x = (x - self.m) * torch.exp(-self.logs) * x_mask
return x
| if not reverse:
y = self.m + torch.exp(self.logs) * x
y = y * x_mask
logdet = torch.sum(self.logs * x_mask, [1, 2])
return y, logdet
else:
x = (x - self.m) * torch.exp(-self.logs) * x_mask
return x
| 37 | 124 | 113 | 237 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,111 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 444,451 | 440,459 | forward | Flip | 451,459 | 451,459 | class Flip(nn.Module):
# torch.jit.script() Compiled functions \
# can't take variable number of arguments or \
# use keyword-only arguments with defaults
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = F... | class Flip(nn.Module):
# torch.jit.script() Compiled functions \
# can't take variable number of arguments or \
# use keyword-only arguments with defaults
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = F... | x = torch.flip(x, [1])
if not reverse:
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
else:
return x, torch.zeros([1], device=x.device)
| x = torch.flip(x, [1])
if not reverse:
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
else:
return x, torch.zeros([1], device=x.device)
| 19 | 134 | 94 | 228 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,112 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 29,30 | 20,35 | forward | LayerNorm | 30,35 | 30,35 | class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super(LayerNorm, self).__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
FILL_FUNC_... | class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super(LayerNorm, self).__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
FILL_FUNC_... | x = x.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
| x = x.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
| 14 | 117 | 65 | 182 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,113 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 424,431 | 423,440 | forward | Log | 431,440 | 431,440 | class Log(nn.Module):
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
FILL_FUNC_BODY | class Log(nn.Module):
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
FILL_FUNC_BODY | if not reverse:
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
logdet = torch.sum(-y, [1, 2])
return y, logdet
else:
x = torch.exp(x) * x_mask
return x
| if not reverse:
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
logdet = torch.sum(-y, [1, 2])
return y, logdet
else:
x = torch.exp(x) * x_mask
return x
| 25 | 95 | 94 | 189 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,114 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 328,329 | 252,367 | forward | ResBlock1 | 329,343 | 329,343 | class ResBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,... | class ResBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,... | for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c1(xt)
xt = F.leaky_relu(xt, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
... | for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c1(xt)
xt = F.leaky_relu(xt, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
... | 57 | 808 | 179 | 987 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,115 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 397,398 | 367,423 | forward | ResBlock2 | 398,408 | 398,408 | class ResBlock2(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__()
self.convs = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
... | class ResBlock2(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__()
self.convs = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
... | for c in self.convs:
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
| for c in self.convs:
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
| 38 | 407 | 106 | 513 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,116 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 512,519 | 477,552 | forward | ResidualCouplingLayer | 519,539 | 519,539 | class ResidualCouplingLayer(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=0,
gin_channels=0,
mean_only=False,
):
assert channels % 2 == 0, "channels should be divisible by... | class ResidualCouplingLayer(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=0,
gin_channels=0,
mean_only=False,
):
assert channels % 2 == 0, "channels should be divisible by... | x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h = self.pre(x0) * x_mask
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
l... | x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h = self.pre(x0) * x_mask
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
l... | 84 | 549 | 291 | 840 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,117 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/infer_pack/modules.py | 188,191 | 136,252 | forward | WN | 191,219 | 191,219 | class WN(torch.nn.Module):
def __init__(
self,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
p_dropout=0,
):
super(WN, self).__init__()
assert kernel_size % 2 == 1
self.hidden_channels = hidden_channels
... | class WN(torch.nn.Module):
def __init__(
self,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
p_dropout=0,
):
super(WN, self).__init__()
assert kernel_size % 2 == 1
self.hidden_channels = hidden_channels
... | output = torch.zeros_like(x)
n_channels_tensor = torch.IntTensor([self.hidden_channels])
if g is not None:
g = self.cond_layer(g)
for i, (in_layer, res_skip_layer) in enumerate(
zip(self.in_layers, self.res_skip_layers)
):
x_in = in_layer(x)
... | output = torch.zeros_like(x)
n_channels_tensor = torch.IntTensor([self.hidden_channels])
if g is not None:
g = self.cond_layer(g)
for i, (in_layer, res_skip_layer) in enumerate(
zip(self.in_layers, self.res_skip_layers)
):
x_in = in_layer(x)
... | 101 | 1,029 | 389 | 1,418 | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... | import copy
import math
from typing import Optional, Tuple
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from infer.lib.infer_pack import ... |
6,119 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/jit/get_rmvpe.py | 4,5 | 1,13 | get_rmvpe | 5,13 | 5,13 | import torch
def get_rmvpe(model_path="assets/rmvpe/rmvpe.pt", device=torch.device("cpu")):
FILL_FUNC_BODY | import torch
def get_rmvpe(model_path="assets/rmvpe/rmvpe.pt", device=torch.device("cpu")):
FILL_FUNC_BODY | from infer.lib.rmvpe import E2E
model = E2E(4, 1, (2, 2))
ckpt = torch.load(model_path, map_location=device)
model.load_state_dict(ckpt)
model.eval()
model = model.to(device)
return model
| from infer.lib.rmvpe import E2E
model = E2E(4, 1, (2, 2))
ckpt = torch.load(model_path, map_location=device)
model.load_state_dict(ckpt)
model.eval()
model = model.to(device)
return model
| 21 | 51 | 96 | 147 | import torch
def get_rmvpe(model_path="assets/rmvpe/rmvpe.pt", device=torch.device("cpu")):
FILL_FUNC_BODY | import torch
def get_rmvpe(model_path="assets/rmvpe/rmvpe.pt", device=torch.device("cpu")):
FILL_FUNC_BODY | |
6,121 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/train/losses.py | 15,16 | 1,59 | discriminator_loss | 16,31 | 16,31 | import torch
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
def discriminator_loss(disc_real_outputs, dis... | import torch
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
def discriminator_loss(disc_real_outputs, dis... | loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
dr = dr.float()
dg = dg.float()
r_loss = torch.mean((1 - dr) ** 2)
g_loss = torch.mean(dg**2)
loss += r_loss + g_loss
r_losses.append(r_loss.item())
... | loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
dr = dr.float()
dg = dg.float()
r_loss = torch.mean((1 - dr) ** 2)
g_loss = torch.mean(dg**2)
loss += r_loss + g_loss
r_losses.append(r_loss.item())
... | 42 | 455 | 171 | 626 | import torch
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
def discriminator_loss(disc_real_outputs, dis... | import torch
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
def discriminator_loss(disc_real_outputs, dis... | |
6,122 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/train/mel_processing.py | 92,93 | 1,128 | spec_to_mel_torch | 93,111 | 93,111 | import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
import logging
logger = logging.getLogger(__name__)
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.cl... | import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
import logging
logger = logging.getLogger(__name__)
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.cl... | # MelBasis - Cache if needed
global mel_basis
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(
sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fm... | # MelBasis - Cache if needed
global mel_basis
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(
sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fm... | 59 | 1,134 | 283 | 1,417 | import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
import logging
logger = logging.getLogger(__name__)
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.cl... | import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
import logging
logger = logging.getLogger(__name__)
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.cl... | |
6,124 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/uvr5_pack/lib_v5/model_param_init.py | 36,37 | 1,70 | int_keys | 37,45 | 37,45 | import json
import os
import pathlib
default_param = {}
default_param["bins"] = 768
default_param["unstable_bins"] = 9 # training only
default_param["reduction_bins"] = 762 # training only
default_param["sr"] = 44100
default_param["pre_filter_start"] = 757
default_param["pre_filter_stop"] = 768
default_param["band"]... | import json
import os
import pathlib
default_param = {}
default_param["bins"] = 768
default_param["unstable_bins"] = 9 # training only
default_param["reduction_bins"] = 762 # training only
default_param["sr"] = 44100
default_param["pre_filter_start"] = 757
default_param["pre_filter_stop"] = 768
default_param["band"]... | r = {}
for k, v in d:
if k.isdigit():
k = int(k)
r[k] = v
return r
| r = {}
for k, v in d:
if k.isdigit():
k = int(k)
r[k] = v
return r
| 18 | 599 | 53 | 652 | import json
import os
import pathlib
default_param = {}
default_param["bins"] = 768
default_param["unstable_bins"] = 9 # training only
default_param["reduction_bins"] = 762 # training only
default_param["sr"] = 44100
default_param["pre_filter_start"] = 757
default_param["pre_filter_stop"] = 768
default_param["band"]... | import json
import os
import pathlib
default_param = {}
default_param["bins"] = 768
default_param["unstable_bins"] = 9 # training only
default_param["reduction_bins"] = 762 # training only
default_param["sr"] = 44100
default_param["pre_filter_start"] = 757
default_param["pre_filter_stop"] = 768
default_param["band"]... | |
6,125 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/lib/uvr5_pack/utils.py | 25,26 | 1,122 | inference | 29,102 | 26,102 | import json
import numpy as np
import torch
from tqdm import tqdm
def load_data(file_name: str = "./infer/lib/uvr5_pack/name_params.json") -> dict:
with open(file_name, "r") as f:
data = json.load(f)
return data
def make_padding(width, cropsize, offset):
left = offset
roi_size = cropsize -... | import json
import numpy as np
import torch
from tqdm import tqdm
def load_data(file_name: str = "./infer/lib/uvr5_pack/name_params.json") -> dict:
with open(file_name, "r") as f:
data = json.load(f)
return data
def make_padding(width, cropsize, offset):
left = offset
roi_size = cropsize -... |
def _execute(
X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
):
model.eval()
with torch.no_grad():
preds = []
iterations = [n_window]
total_iterations = sum(iterations)
for i in tqdm(range(n_window)):
... | """
data ： dic configs
"""
def _execute(
X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
):
model.eval()
with torch.no_grad():
preds = []
iterations = [n_window]
total_iterations = sum(iterations)
f... | 213 | 456 | 898 | 1,354 | import json
import numpy as np
import torch
from tqdm import tqdm
def load_data(file_name: str = "./infer/lib/uvr5_pack/name_params.json") -> dict:
with open(file_name, "r") as f:
data = json.load(f)
return data
def make_padding(width, cropsize, offset):
left = offset
roi_size = cropsize -... | import json
import numpy as np
import torch
from tqdm import tqdm
def load_data(file_name: str = "./infer/lib/uvr5_pack/name_params.json") -> dict:
with open(file_name, "r") as f:
data = json.load(f)
return data
def make_padding(width, cropsize, offset):
left = offset
roi_size = cropsize -... | |
6,126 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/modules/gui/utils.py | 5,9 | 1,71 | amp_to_db | 24,28 | 9,28 | 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:
FILL_FUNC_BODY@torch.no_grad()
def temperature_sigmoid(x: torch.Tensor, x0: float, temp_coeff: float) -> torch.Tensor:
"""
Apply a sigmoid function ... | 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:
... | x_db = 20 * torch.log10(x.abs() + eps)
return torch.max(x_db, (x_db.max(-1).values - top_db).unsqueeze(-1))
| """
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]... | 12 | 760 | 64 | 824 | 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:
FILL_FUNC_BODY@torch.no_grad()
def temperature_sigmoid(x: torch.Tensor, x0: float, temp_coeff: float) -> torch.Tensor:
"""
Apply a sigmoid function ... | 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:
... | |
6,128 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/modules/onnx/export.py | 6,7 | 1,53 | export_onnx | 7,53 | 7,53 | import torch
from infer.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
def export_onnx(ModelPath, ExportedPath):
FILL_FUNC_BODY | import torch
from infer.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
def export_onnx(ModelPath, ExportedPath):
FILL_FUNC_BODY | cpt = torch.load(ModelPath, map_location="cpu")
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
test_phone = torch.rand(1, 200, vec_channels) # hidden unit
test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌... | cpt = torch.load(ModelPath, map_location="cpu")
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
test_phone = torch.rand(1, 200, vec_channels) # hidden unit
test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌... | 119 | 62 | 721 | 783 | import torch
from infer.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
def export_onnx(ModelPath, ExportedPath):
FILL_FUNC_BODY | import torch
from infer.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
def export_onnx(ModelPath, ExportedPath):
FILL_FUNC_BODY | |
6,129 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/modules/train/preprocess.py | 29,30 | 1,143 | println | 30,35 | 30,35 | import multiprocessing
import os
import sys
from scipy import signal
now_dir = os.getcwd()
sys.path.append(now_dir)
print(*sys.argv[1:])
inp_root = sys.argv[1]
sr = int(sys.argv[2])
n_p = int(sys.argv[3])
exp_dir = sys.argv[4]
noparallel = sys.argv[5] == "True"
per = float(sys.argv[6])
import os
import traceback
imp... | import multiprocessing
import os
import sys
from scipy import signal
now_dir = os.getcwd()
sys.path.append(now_dir)
print(*sys.argv[1:])
inp_root = sys.argv[1]
sr = int(sys.argv[2])
n_p = int(sys.argv[3])
exp_dir = sys.argv[4]
noparallel = sys.argv[5] == "True"
per = float(sys.argv[6])
import os
import traceback
imp... | print(strr)
f.write("%s\n" % strr)
f.flush()
| print(strr)
f.write("%s\n" % strr)
f.flush()
| 5 | 1,557 | 34 | 1,591 | import multiprocessing
import os
import sys
from scipy import signal
now_dir = os.getcwd()
sys.path.append(now_dir)
print(*sys.argv[1:])
inp_root = sys.argv[1]
sr = int(sys.argv[2])
n_p = int(sys.argv[3])
exp_dir = sys.argv[4]
noparallel = sys.argv[5] == "True"
per = float(sys.argv[6])
import os
import traceback
imp... | import multiprocessing
import os
import sys
from scipy import signal
now_dir = os.getcwd()
sys.path.append(now_dir)
print(*sys.argv[1:])
inp_root = sys.argv[1]
sr = int(sys.argv[2])
n_p = int(sys.argv[3])
exp_dir = sys.argv[4]
noparallel = sys.argv[5] == "True"
per = float(sys.argv[6])
import os
import traceback
imp... | |
6,130 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/modules/train/preprocess.py | 111,112 | 35,134 | pipeline_mp_inp_dir | PreProcess | 112,134 | 112,134 | class PreProcess:
def __init__(self, sr, exp_dir, per=3.7):
self.slicer = Slicer(
sr=sr,
threshold=-42,
min_length=1500,
min_interval=400,
hop_size=15,
max_sil_kept=500,
)
self.sr = sr
self.bh, self.ah = signal.b... | class PreProcess:
def __init__(self, sr, exp_dir, per=3.7):
self.slicer = Slicer(
sr=sr,
threshold=-42,
min_length=1500,
min_interval=400,
hop_size=15,
max_sil_kept=500,
)
self.sr = sr
self.bh, self.ah = signal.b... | try:
infos = [
("%s/%s" % (inp_root, name), idx)
for idx, name in enumerate(sorted(list(os.listdir(inp_root))))
]
if noparallel:
for i in range(n_p):
self.pipeline_mp(infos[i::n_p])
else:
... | try:
infos = [
("%s/%s" % (inp_root, name), idx)
for idx, name in enumerate(sorted(list(os.listdir(inp_root))))
]
if noparallel:
for i in range(n_p):
self.pipeline_mp(infos[i::n_p])
else:
... | 48 | 1,003 | 233 | 1,236 | import multiprocessing
import os
import sys
from scipy import signal
now_dir = os.getcwd()
sys.path.append(now_dir)
print(*sys.argv[1:])
inp_root = sys.argv[1]
sr = int(sys.argv[2])
n_p = int(sys.argv[3])
exp_dir = sys.argv[4]
noparallel = sys.argv[5] == "True"
per = float(sys.argv[6])
import os
import traceback
imp... | import multiprocessing
import os
import sys
from scipy import signal
now_dir = os.getcwd()
sys.path.append(now_dir)
print(*sys.argv[1:])
inp_root = sys.argv[1]
sr = int(sys.argv[2])
n_p = int(sys.argv[3])
exp_dir = sys.argv[4]
noparallel = sys.argv[5] == "True"
per = float(sys.argv[6])
import os
import traceback
imp... |
6,131 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/modules/uvr5/modules.py | 17,18 | 1,109 | uvr | 18,109 | 18,109 | import os
import traceback
import logging
logger = logging.getLogger(__name__)
import ffmpeg
import torch
from configs.config import Config
from infer.modules.uvr5.mdxnet import MDXNetDereverb
from infer.modules.uvr5.vr import AudioPre, AudioPreDeEcho
config = Config()
def uvr(model_name, inp_root, save_root_voca... | import os
import traceback
import logging
logger = logging.getLogger(__name__)
import ffmpeg
import torch
from configs.config import Config
from infer.modules.uvr5.mdxnet import MDXNetDereverb
from infer.modules.uvr5.vr import AudioPre, AudioPreDeEcho
config = Config()
def uvr(model_name, inp_root, save_root_voca... | infos = []
try:
inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
save_root_vocal = (
save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
)
save_root_ins = (
save_root_ins.strip(" ").strip('"').strip("\n").stri... | infos = []
try:
inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
save_root_vocal = (
save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
)
save_root_ins = (
save_root_ins.strip(" ").strip('"').strip("\n").stri... | 229 | 133 | 1,160 | 1,293 | import os
import traceback
import logging
logger = logging.getLogger(__name__)
import ffmpeg
import torch
from configs.config import Config
from infer.modules.uvr5.mdxnet import MDXNetDereverb
from infer.modules.uvr5.vr import AudioPre, AudioPreDeEcho
config = Config()
def uvr(model_name, inp_root, save_root_voca... | import os
import traceback
import logging
logger = logging.getLogger(__name__)
import ffmpeg
import torch
from configs.config import Config
from infer.modules.uvr5.mdxnet import MDXNetDereverb
from infer.modules.uvr5.vr import AudioPre, AudioPreDeEcho
config = Config()
def uvr(model_name, inp_root, save_root_voca... | |
6,132 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/infer/modules/vc/utils.py | 22,23 | 1,34 | load_hubert | 23,34 | 23,34 | import os
from fairseq import checkpoint_utils
def get_index_path_from_model(sid):
return next(
(
f
for f in [
os.path.join(root, name)
for root, _, files in os.walk(os.getenv("index_root"), topdown=False)
for name in files
... | import os
from fairseq import checkpoint_utils
def get_index_path_from_model(sid):
return next(
(
f
for f in [
os.path.join(root, name)
for root, _, files in os.walk(os.getenv("index_root"), topdown=False)
for name in files
... | models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
["assets/hubert/hubert_base.pt"],
suffix="",
)
hubert_model = models[0]
hubert_model = hubert_model.to(config.device)
if config.is_half:
hubert_model = hubert_model.half()
else:
hubert_model = hubert_mo... | models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
["assets/hubert/hubert_base.pt"],
suffix="",
)
hubert_model = models[0]
hubert_model = hubert_model.to(config.device)
if config.is_half:
hubert_model = hubert_model.half()
else:
hubert_model = hubert_mo... | 25 | 158 | 141 | 299 | import os
from fairseq import checkpoint_utils
def get_index_path_from_model(sid):
return next(
(
f
for f in [
os.path.join(root, name)
for root, _, files in os.walk(os.getenv("index_root"), topdown=False)
for name in files
... | import os
from fairseq import checkpoint_utils
def get_index_path_from_model(sid):
return next(
(
f
for f in [
os.path.join(root, name)
for root, _, files in os.walk(os.getenv("index_root"), topdown=False)
for name in files
... | |
6,134 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/tools/cmd/calc_rvc_model_similarity.py | 56,57 | 1,97 | main | 57,93 | 57,93 | # This code references https://huggingface.co/JosephusCheung/ASimilarityCalculatior/blob/main/qwerty.py
# Fill in the path of the model to be queried and the root directory of the reference models, and this script will return the similarity between the model to be queried and all reference models.
import os
import logg... | # This code references https://huggingface.co/JosephusCheung/ASimilarityCalculatior/blob/main/qwerty.py
# Fill in the path of the model to be queried and the root directory of the reference models, and this script will return the similarity between the model to be queried and all reference models.
import os
import logg... | torch.manual_seed(114514)
model_a = torch.load(path, map_location="cpu")["weight"]
logger.info("Query:\t\t%s\t%s" % (path, model_hash(path)))
map_attn_a = {}
map_rand_input = {}
for n in range(6):
hidden_dim, embed_dim, _ = model_a[
f"enc_p.encoder.attn_layers.{n}.conv_v.we... | torch.manual_seed(114514)
model_a = torch.load(path, map_location="cpu")["weight"]
logger.info("Query:\t\t%s\t%s" % (path, model_hash(path)))
map_attn_a = {}
map_rand_input = {}
for n in range(6):
hidden_dim, embed_dim, _ = model_a[
f"enc_p.encoder.attn_layers.{n}.conv_v.we... | 83 | 761 | 468 | 1,229 | # This code references https://huggingface.co/JosephusCheung/ASimilarityCalculatior/blob/main/qwerty.py
# Fill in the path of the model to be queried and the root directory of the reference models, and this script will return the similarity between the model to be queried and all reference models.
import os
import logg... | # This code references https://huggingface.co/JosephusCheung/ASimilarityCalculatior/blob/main/qwerty.py
# Fill in the path of the model to be queried and the root directory of the reference models, and this script will return the similarity between the model to be queried and all reference models.
import os
import logg... | |
6,135 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/tools/cmd/infer_batch_rvc.py | 41,42 | 1,73 | main | 42,71 | 42,71 | import argparse
import os
import sys
print("Command-line arguments:", sys.argv)
now_dir = os.getcwd()
sys.path.append(now_dir)
import sys
import tqdm as tq
from dotenv import load_dotenv
from scipy.io import wavfile
from configs.config import Config
from infer.modules.vc.modules import VC
def arg_parse() -> tuple... | import argparse
import os
import sys
print("Command-line arguments:", sys.argv)
now_dir = os.getcwd()
sys.path.append(now_dir)
import sys
import tqdm as tq
from dotenv import load_dotenv
from scipy.io import wavfile
from configs.config import Config
from infer.modules.vc.modules import VC
def arg_parse() -> tuple... | load_dotenv()
args = arg_parse()
config = Config()
config.device = args.device if args.device else config.device
config.is_half = args.is_half if args.is_half else config.is_half
vc = VC(config)
vc.get_vc(args.model_name)
audios = os.listdir(args.input_path)
for file in tq.tqdm(audio... | load_dotenv()
args = arg_parse()
config = Config()
config.device = args.device if args.device else config.device
config.is_half = args.is_half if args.is_half else config.is_half
vc = VC(config)
vc.get_vc(args.model_name)
audios = os.listdir(args.input_path)
for file in tq.tqdm(audio... | 62 | 527 | 333 | 860 | import argparse
import os
import sys
print("Command-line arguments:", sys.argv)
now_dir = os.getcwd()
sys.path.append(now_dir)
import sys
import tqdm as tq
from dotenv import load_dotenv
from scipy.io import wavfile
from configs.config import Config
from infer.modules.vc.modules import VC
def arg_parse() -> tuple... | import argparse
import os
import sys
print("Command-line arguments:", sys.argv)
now_dir = os.getcwd()
sys.path.append(now_dir)
import sys
import tqdm as tq
from dotenv import load_dotenv
from scipy.io import wavfile
from configs.config import Config
from infer.modules.vc.modules import VC
def arg_parse() -> tuple... | |
6,136 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/tools/cmd/infer_cli.py | 19,20 | 1,68 | arg_parse | 20,41 | 20,41 | import argparse
import os
import sys
now_dir = os.getcwd()
sys.path.append(now_dir)
from dotenv import load_dotenv
from scipy.io import wavfile
from configs.config import Config
from infer.modules.vc.modules import VC
####
# USAGE
#
# In your Terminal or CMD or whatever
def arg_parse() -> tuple:
FILL_FUNC_BODYdef ... | import argparse
import os
import sys
now_dir = os.getcwd()
sys.path.append(now_dir)
from dotenv import load_dotenv
from scipy.io import wavfile
from configs.config import Config
from infer.modules.vc.modules import VC
####
# USAGE
#
# In your Terminal or CMD or whatever
def arg_parse() -> tuple:
FILL_FUNC_BODYdef ... | parser = argparse.ArgumentParser()
parser.add_argument("--f0up_key", type=int, default=0)
parser.add_argument("--input_path", type=str, help="input path")
parser.add_argument("--index_path", type=str, help="index path")
parser.add_argument("--f0method", type=str, default="harvest", help="harvest or ... | parser = argparse.ArgumentParser()
parser.add_argument("--f0up_key", type=int, default=0)
parser.add_argument("--input_path", type=str, help="input path")
parser.add_argument("--index_path", type=str, help="index path")
parser.add_argument("--f0method", type=str, default="harvest", help="harvest or ... | 71 | 358 | 406 | 764 | import argparse
import os
import sys
now_dir = os.getcwd()
sys.path.append(now_dir)
from dotenv import load_dotenv
from scipy.io import wavfile
from configs.config import Config
from infer.modules.vc.modules import VC
####
# USAGE
#
# In your Terminal or CMD or whatever
def arg_parse() -> tuple:
FILL_FUNC_BODYdef ... | import argparse
import os
import sys
now_dir = os.getcwd()
sys.path.append(now_dir)
from dotenv import load_dotenv
from scipy.io import wavfile
from configs.config import Config
from infer.modules.vc.modules import VC
####
# USAGE
#
# In your Terminal or CMD or whatever
def arg_parse() -> tuple:
FILL_FUNC_BODYdef ... | |
6,137 | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI | /home/hieuvd/dung_repo/raw/stars_01/RVC-Project_Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/tools/download_models.py | 10,11 | 1,80 | dl_model | 11,19 | 11,19 | import os
from pathlib import Path
import requests
RVC_DOWNLOAD_LINK = "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/"
BASE_DIR = Path(__file__).resolve().parent.parent
def dl_model(link, model_name, dir_name):
FILL_FUNC_BODYif __name__ == "__main__":
print("Downloading hubert_base.pt...")
... | import os
from pathlib import Path
import requests
RVC_DOWNLOAD_LINK = "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/"
BASE_DIR = Path(__file__).resolve().parent.parent
def dl_model(link, model_name, dir_name):
FILL_FUNC_BODYif __name__ == "__main__":
print("Downloading hubert_base.pt...")
... | with requests.get(f"{link}{model_name}") as r:
r.raise_for_status()
os.makedirs(os.path.dirname(dir_name / model_name), exist_ok=True)
with open(dir_name / model_name, "wb") as f:
for chunk in r.iter_content(chunk_size=8192):
f.write(chunk)
| with requests.get(f"{link}{model_name}") as r:
r.raise_for_status()
os.makedirs(os.path.dirname(dir_name / model_name), exist_ok=True)
with open(dir_name / model_name, "wb") as f:
for chunk in r.iter_content(chunk_size=8192):
f.write(chunk)
| 21 | 994 | 119 | 1,113 | import os
from pathlib import Path
import requests
RVC_DOWNLOAD_LINK = "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/"
BASE_DIR = Path(__file__).resolve().parent.parent
def dl_model(link, model_name, dir_name):
FILL_FUNC_BODYif __name__ == "__main__":
print("Downloading hubert_base.pt...")
... | import os
from pathlib import Path
import requests
RVC_DOWNLOAD_LINK = "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/"
BASE_DIR = Path(__file__).resolve().parent.parent
def dl_model(link, model_name, dir_name):
FILL_FUNC_BODYif __name__ == "__main__":
print("Downloading hubert_base.pt...")
... | |
6,303 | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper/benchmark/speed_benchmark.py | 18,19 | 1,32 | measure_speed | 19,30 | 19,30 | import argparse
import timeit
from typing import Callable
from utils import inference
parser = argparse.ArgumentParser(description="Speed benchmark")
parser.add_argument(
"--repeat",
type=int,
default=3,
help="Times an experiment will be run.",
)
args = parser.parse_args()
def measure_speed(func: C... | import argparse
import timeit
from typing import Callable
from utils import inference
parser = argparse.ArgumentParser(description="Speed benchmark")
parser.add_argument(
"--repeat",
type=int,
default=3,
help="Times an experiment will be run.",
)
args = parser.parse_args()
def measure_speed(func: C... | # as written in https://docs.python.org/3/library/timeit.html#timeit.Timer.repeat,
# min should be taken rather than the average
runtimes = timeit.repeat(
func,
repeat=args.repeat,
number=10,
)
print(runtimes)
print("Min execution time: %.3fs" % (min(runtimes) / 10.0))
| # as written in https://docs.python.org/3/library/timeit.html#timeit.Timer.repeat,
# min should be taken rather than the average
runtimes = timeit.repeat(
func,
repeat=args.repeat,
number=10,
)
print(runtimes)
print("Min execution time: %.3fs" % (min(runtimes) / 10.0))
| 30 | 133 | 123 | 256 | import argparse
import timeit
from typing import Callable
from utils import inference
parser = argparse.ArgumentParser(description="Speed benchmark")
parser.add_argument(
"--repeat",
type=int,
default=3,
help="Times an experiment will be run.",
)
args = parser.parse_args()
def measure_speed(func: C... | import argparse
import timeit
from typing import Callable
from utils import inference
parser = argparse.ArgumentParser(description="Speed benchmark")
parser.add_argument(
"--repeat",
type=int,
default=3,
help="Times an experiment will be run.",
)
args = parser.parse_args()
def measure_speed(func: C... | |
6,304 | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper/benchmark/utils.py | 12,13 | 1,40 | inference | 13,18 | 13,18 | import logging
from threading import Thread
from typing import Optional
from faster_whisper import WhisperModel
model_path = "large-v3"
model = WhisperModel(model_path, device="cuda")
def inference():
FILL_FUNC_BODYdef get_logger(name: Optional[str] = None) -> logging.Logger:
formatter = logging.Formatter("%(l... | import logging
from threading import Thread
from typing import Optional
from faster_whisper import WhisperModel
model_path = "large-v3"
model = WhisperModel(model_path, device="cuda")
def inference():
FILL_FUNC_BODYdef get_logger(name: Optional[str] = None) -> logging.Logger:
formatter = logging.Formatter("%(l... | segments, info = model.transcribe("benchmark.m4a", language="fr")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
| segments, info = model.transcribe("benchmark.m4a", language="fr")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
| 17 | 265 | 71 | 336 | import logging
from threading import Thread
from typing import Optional
from faster_whisper import WhisperModel
model_path = "large-v3"
model = WhisperModel(model_path, device="cuda")
def inference():
FILL_FUNC_BODYdef get_logger(name: Optional[str] = None) -> logging.Logger:
formatter = logging.Formatter("%(l... | import logging
from threading import Thread
from typing import Optional
from faster_whisper import WhisperModel
model_path = "large-v3"
model = WhisperModel(model_path, device="cuda")
def inference():
FILL_FUNC_BODYdef get_logger(name: Optional[str] = None) -> logging.Logger:
formatter = logging.Formatter("%(l... | |
6,305 | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper/benchmark/utils.py | 35,36 | 28,40 | run | MyThread | 36,38 | 36,38 | class MyThread(Thread):
def __init__(self, func, params):
super(MyThread, self).__init__()
self.func = func
self.params = params
self.result = None
def run(self):
FILL_FUNC_BODY def get_result(self):
return self.result
| class MyThread(Thread):
def __init__(self, func, params):
super(MyThread, self).__init__()
self.func = func
self.params = params
self.result = None
def run(self):
FILL_FUNC_BODY def get_result(self):
return self.result
| self.result = self.func(*self.params)
| self.result = self.func(*self.params)
| 3 | 86 | 17 | 103 | import logging
from threading import Thread
from typing import Optional
from faster_whisper import WhisperModel
model_path = "large-v3"
model = WhisperModel(model_path, device="cuda")
def inference():
segments, info = model.transcribe("benchmark.m4a", language="fr")
for segment in segments:
print("... | import logging
from threading import Thread
from typing import Optional
from faster_whisper import WhisperModel
model_path = "large-v3"
model = WhisperModel(model_path, device="cuda")
def inference():
segments, info = model.transcribe("benchmark.m4a", language="fr")
for segment in segments:
print("... |
6,306 | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper/faster_whisper/audio.py | 19,24 | 1,120 | decode_audio | 37,75 | 24,75 | """We use the PyAV library to decode the audio: https://github.com/PyAV-Org/PyAV
The advantage of PyAV is that it bundles the FFmpeg libraries so there is no additional
system dependencies. FFmpeg does not need to be installed on the system.
However, the API is quite low-level so we need to manipulate audio frames di... | """We use the PyAV library to decode the audio: https://github.com/PyAV-Org/PyAV
The advantage of PyAV is that it bundles the FFmpeg libraries so there is no additional
system dependencies. FFmpeg does not need to be installed on the system.
However, the API is quite low-level so we need to manipulate audio frames di... | resampler = av.audio.resampler.AudioResampler(
format="s16",
layout="mono" if not split_stereo else "stereo",
rate=sampling_rate,
)
raw_buffer = io.BytesIO()
dtype = None
with av.open(input_file, mode="r", metadata_errors="ignore") as container:
frames = container.d... | """Decodes the audio.
Args:
input_file: Path to the input file or a file-like object.
sampling_rate: Resample the audio to this sample rate.
split_stereo: Return separate left and right channels.
Returns:
A float32 Numpy array.
If `split_stereo` is enabled, the function retu... | 101 | 692 | 396 | 1,088 | """We use the PyAV library to decode the audio: https://github.com/PyAV-Org/PyAV
The advantage of PyAV is that it bundles the FFmpeg libraries so there is no additional
system dependencies. FFmpeg does not need to be installed on the system.
However, the API is quite low-level so we need to manipulate audio frames di... | """We use the PyAV library to decode the audio: https://github.com/PyAV-Org/PyAV
The advantage of PyAV is that it bundles the FFmpeg libraries so there is no additional
system dependencies. FFmpeg does not need to be installed on the system.
However, the API is quite low-level so we need to manipulate audio frames di... | |
6,307 | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper/faster_whisper/feature_extractor.py | 74,75 | 5,171 | fram_wave | FeatureExtractor | 83,118 | 75,118 | class FeatureExtractor:
def __init__(
self,
feature_size=80,
sampling_rate=16000,
hop_length=160,
chunk_length=30,
n_fft=400,
):
self.n_fft = n_fft
self.hop_length = hop_length
self.chunk_length = chunk_length
self.n_samples = chunk... | class FeatureExtractor:
def __init__(
self,
feature_size=80,
sampling_rate=16000,
hop_length=160,
chunk_length=30,
n_fft=400,
):
self.n_fft = n_fft
self.hop_length = hop_length
self.chunk_length = chunk_length
self.n_samples = chunk... | frames = []
for i in range(0, waveform.shape[0] + 1, self.hop_length):
half_window = (self.n_fft - 1) // 2 + 1
if center:
start = i - half_window if i > half_window else 0
end = (
i + half_window
if i < wavef... | """
Transform a raw waveform into a list of smaller waveforms.
The window length defines how much of the signal is
contain in each frame (smalle waveform), while the hope length defines the step
between the beginning of each new frame.
Centering is done by reflecting the ... | 113 | 1,616 | 397 | 2,013 | import numpy as np
# Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/feature_extraction_whisper.py # noqa: E501
class FeatureExtractor:
def __init__(
self,
feature_size=80,
sampling_rate=16000,
hop_length=160,
chunk_length... | import numpy as np
# Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/feature_extraction_whisper.py # noqa: E501
class FeatureExtractor:
def __init__(
self,
feature_size=80,
sampling_rate=16000,
hop_length=160,
chunk_length... |
6,308 | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper/faster_whisper/tokenizer.py | 108,111 | 9,172 | split_to_word_tokens | Tokenizer | 111,119 | 111,119 | class Tokenizer:
"""Simple wrapper around a tokenizers.Tokenizer."""
def __init__(
self,
tokenizer: tokenizers.Tokenizer,
multilingual: bool,
task: Optional[str] = None,
language: Optional[str] = None,
):
self.tokenizer = tokenizer
if multilingual:
... | class Tokenizer:
"""Simple wrapper around a tokenizers.Tokenizer."""
def __init__(
self,
tokenizer: tokenizers.Tokenizer,
multilingual: bool,
task: Optional[str] = None,
language: Optional[str] = None,
):
self.tokenizer = tokenizer
if multilingual:
... | if self.language_code in {"zh", "ja", "th", "lo", "my", "yue"}:
# These languages don't typically use spaces, so it is difficult to split words
# without morpheme analysis. Here, we instead split words at any
# position where the tokens are decoded as valid unicode points
... | if self.language_code in {"zh", "ja", "th", "lo", "my", "yue"}:
# These languages don't typically use spaces, so it is difficult to split words
# without morpheme analysis. Here, we instead split words at any
# position where the tokens are decoded as valid unicode points
... | 49 | 1,544 | 127 | 1,671 | import string
from functools import cached_property
from typing import List, Optional, Tuple
import tokenizers
class Tokenizer:
"""Simple wrapper around a tokenizers.Tokenizer."""
def __init__(
self,
tokenizer: tokenizers.Tokenizer,
multilingual: bool,
task: Optional[str] = ... | import string
from functools import cached_property
from typing import List, Optional, Tuple
import tokenizers
class Tokenizer:
"""Simple wrapper around a tokenizers.Tokenizer."""
def __init__(
self,
tokenizer: tokenizers.Tokenizer,
multilingual: bool,
task: Optional[str] = ... |
6,312 | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper/faster_whisper/vad.py | 264,265 | 244,292 | get_initial_state | SileroVADModel | 265,269 | 265,269 | class SileroVADModel:
def __init__(self, path):
try:
import onnxruntime
except ImportError as e:
raise RuntimeError(
"Applying the VAD filter requires the onnxruntime package"
) from e
opts = onnxruntime.SessionOptions()
opts.inter... | class SileroVADModel:
def __init__(self, path):
try:
import onnxruntime
except ImportError as e:
raise RuntimeError(
"Applying the VAD filter requires the onnxruntime package"
) from e
opts = onnxruntime.SessionOptions()
opts.inter... | h = np.zeros((2, batch_size, 64), dtype=np.float32)
c = np.zeros((2, batch_size, 64), dtype=np.float32)
return h, c
| h = np.zeros((2, batch_size, 64), dtype=np.float32)
c = np.zeros((2, batch_size, 64), dtype=np.float32)
return h, c
| 15 | 406 | 67 | 473 | import bisect
import functools
import os
import warnings
from typing import List, NamedTuple, Optional
import numpy as np
from faster_whisper.utils import get_assets_path
# The code below is adapted from https://github.com/snakers4/silero-vad.
class VadOptions(NamedTuple):
"""VAD options.
Attributes:
... | import bisect
import functools
import os
import warnings
from typing import List, NamedTuple, Optional
import numpy as np
from faster_whisper.utils import get_assets_path
# The code below is adapted from https://github.com/snakers4/silero-vad.
class VadOptions(NamedTuple):
"""VAD options.
Attributes:
... |
6,313 | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper/faster_whisper/vad.py | 229,230 | 199,237 | get_chunk_index | SpeechTimestampsMap | 230,237 | 230,237 | class SpeechTimestampsMap:
"""Helper class to restore original speech timestamps."""
def __init__(self, chunks: List[dict], sampling_rate: int, time_precision: int = 2):
self.sampling_rate = sampling_rate
self.time_precision = time_precision
self.chunk_end_sample = []
self.total... | class SpeechTimestampsMap:
"""Helper class to restore original speech timestamps."""
def __init__(self, chunks: List[dict], sampling_rate: int, time_precision: int = 2):
self.sampling_rate = sampling_rate
self.time_precision = time_precision
self.chunk_end_sample = []
self.total... | sample = int(time * self.sampling_rate)
return min(
bisect.bisect(self.chunk_end_sample, sample),
len(self.chunk_end_sample) - 1,
)
| sample = int(time * self.sampling_rate)
return min(
bisect.bisect(self.chunk_end_sample, sample),
len(self.chunk_end_sample) - 1,
)
| 13 | 342 | 68 | 410 | import bisect
import functools
import os
import warnings
from typing import List, NamedTuple, Optional
import numpy as np
from faster_whisper.utils import get_assets_path
# The code below is adapted from https://github.com/snakers4/silero-vad.
class VadOptions(NamedTuple):
"""VAD options.
Attributes:
... | import bisect
import functools
import os
import warnings
from typing import List, NamedTuple, Optional
import numpy as np
from faster_whisper.utils import get_assets_path
# The code below is adapted from https://github.com/snakers4/silero-vad.
class VadOptions(NamedTuple):
"""VAD options.
Attributes:
... |
6,314 | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper | /home/hieuvd/dung_repo/raw/stars_01/SYSTRAN_faster-whisper/faster-whisper/setup.py | 22,23 | 1,69 | get_requirements | 23,27 | 23,27 | import os
from setuptools import find_packages, setup
base_dir = os.path.dirname(os.path.abspath(__file__))
def get_long_description():
readme_path = os.path.join(base_dir, "README.md")
with open(readme_path, encoding="utf-8") as readme_file:
return readme_file.read()
def get_project_version():
... | import os
from setuptools import find_packages, setup
base_dir = os.path.dirname(os.path.abspath(__file__))
def get_long_description():
readme_path = os.path.join(base_dir, "README.md")
with open(readme_path, encoding="utf-8") as readme_file:
return readme_file.read()
def get_project_version():
... | with open(path, encoding="utf-8") as requirements:
return [requirement.strip() for requirement in requirements]
| with open(path, encoding="utf-8") as requirements:
return [requirement.strip() for requirement in requirements]
| 11 | 673 | 37 | 710 | import os
from setuptools import find_packages, setup
base_dir = os.path.dirname(os.path.abspath(__file__))
def get_long_description():
readme_path = os.path.join(base_dir, "README.md")
with open(readme_path, encoding="utf-8") as readme_file:
return readme_file.read()
def get_project_version():
... | import os
from setuptools import find_packages, setup
base_dir = os.path.dirname(os.path.abspath(__file__))
def get_long_description():
readme_path = os.path.join(base_dir, "README.md")
with open(readme_path, encoding="utf-8") as readme_file:
return readme_file.read()
def get_project_version():
... | |
6,634 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM-6B/ChatGLM-6B | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM-6B/ChatGLM-6B/cli_demo.py | 29,30 | 1,59 | main | 30,57 | 30,57 | import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()
os_name = pla... | import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()
os_name = pla... | history = []
global stop_stream
print("欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
while True:
query = input("\n用户：")
if query.strip() == "stop":
break
if query.strip() == "clear":
history = []
os.system(clear_command)
pr... | history = []
global stop_stream
print("欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
while True:
query = input("\n用户：")
if query.strip() == "stop":
break
if query.strip() == "clear":
history = []
os.system(clear_command)
pr... | 68 | 290 | 302 | 592 | import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()
os_name = pla... | import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()
os_name = pla... | |
6,635 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM-6B/ChatGLM-6B | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM-6B/ChatGLM-6B/cli_demo_vision.py | 31,32 | 1,65 | main | 32,63 | 32,63 | import os
import platform
import signal
import sys
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
model = model.eval(... | import os
import platform
import signal
import sys
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
model = model.eval(... | global stop_stream
while True:
history = []
prefix = "欢迎使用 VisualGLM-6B 模型，输入图片路径和内容即可进行对话，clear 清空对话历史，stop 终止程序"
print(prefix)
image_path = input("\n请输入图片路径：")
if image_path == "stop":
break
prefix = prefix + "\n" + image_path
query = "描述这张图片... | global stop_stream
while True:
history = []
prefix = "欢迎使用 VisualGLM-6B 模型，输入图片路径和内容即可进行对话，clear 清空对话历史，stop 终止程序"
print(prefix)
image_path = input("\n请输入图片路径：")
if image_path == "stop":
break
prefix = prefix + "\n" + image_path
query = "描述这张图片... | 85 | 265 | 338 | 603 | import os
import platform
import signal
import sys
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
model = model.eval(... | import os
import platform
import signal
import sys
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
model = model.eval(... | |
6,637 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM-6B/ChatGLM-6B | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM-6B/ChatGLM-6B/utils.py | 8,9 | 1,55 | auto_configure_device_map | 9,38 | 9,38 | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | # transformer.word_embeddings 占用1层
# transformer.final_layernorm 和 lm_head 占用1层
# transformer.layers 占用 28 层
# 总共30层分配到num_gpus张卡上
num_trans_layers = 28
per_gpu_layers = 30 / num_gpus
# bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
# windows下 model.device 会被设... | # transformer.word_embeddings 占用1层
# transformer.final_layernorm 和 lm_head 占用1层
# transformer.layers 占用 28 层
# 总共30层分配到num_gpus张卡上
num_trans_layers = 28
per_gpu_layers = 30 / num_gpus
# bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
# windows下 model.device 会被设... | 82 | 277 | 418 | 695 | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | |
6,638 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM2-6B/ChatGLM2-6B | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM2-6B/ChatGLM2-6B/cli_demo.py | 19,20 | 1,61 | build_prompt | 20,27 | 20,27 | import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
# 多显卡支持，使用下面两行代替上面一行，将num_gpus改为你实际的显卡数量... | import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
# 多显卡支持，使用下面两行代替上面一行，将num_gpus改为你实际的显卡数量... | prompt = "欢迎使用 ChatGLM2-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序"
for query, response in history:
prompt += f"\n\n用户：{query}"
prompt += f"\n\nChatGLM2-6B：{response}"
return prompt
| prompt = "欢迎使用 ChatGLM2-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序"
for query, response in history:
prompt += f"\n\n用户：{query}"
prompt += f"\n\nChatGLM2-6B：{response}"
return prompt
| 20 | 574 | 99 | 673 | import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
# 多显卡支持，使用下面两行代替上面一行，将num_gpus改为你实际的显卡数量... | import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
# 多显卡支持，使用下面两行代替上面一行，将num_gpus改为你实际的显卡数量... | |
6,639 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM2-6B/ChatGLM2-6B | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM2-6B/ChatGLM2-6B/ptuning/trainer.py | 37,38 | 32,71 | _save | PrefixTrainer | 38,71 | 38,71 | class PrefixTrainer(Trainer):
def __init__(self, *args, save_changed=False, **kwargs):
self.save_changed = save_changed
super().__init__(*args, **kwargs)
def _save(self, output_dir: Optional[str] = None, state_dict=None):
FILL_FUNC_BODY | class PrefixTrainer(Trainer):
def __init__(self, *args, save_changed=False, **kwargs):
self.save_changed = save_changed
super().__init__(*args, **kwargs)
def _save(self, output_dir: Optional[str] = None, state_dict=None):
FILL_FUNC_BODY | # If we are executing this function, we are the process zero, so we don't check for that.
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving model checkpoint to {output_dir}")
# Save a trained mo... | # If we are executing this function, we are the process zero, so we don't check for that.
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving model checkpoint to {output_dir}")
# Save a trained mo... | 140 | 92 | 549 | 641 | # coding=utf-8
# Copyright 2020-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | # coding=utf-8
# Copyright 2020-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... |
6,640 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM2-6B/ChatGLM2-6B | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM2-6B/ChatGLM2-6B/utils.py | 8,9 | 1,60 | auto_configure_device_map | 9,45 | 9,45 | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | # transformer.word_embeddings 占用1层
# transformer.final_layernorm 和 lm_head 占用1层
# transformer.layers 占用 28 层
# 总共30层分配到num_gpus张卡上
num_trans_layers = 28
per_gpu_layers = 30 / num_gpus
# bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
# windows下 model.device 会被设... | # transformer.word_embeddings 占用1层
# transformer.final_layernorm 和 lm_head 占用1层
# transformer.layers 占用 28 层
# 总共30层分配到num_gpus张卡上
num_trans_layers = 28
per_gpu_layers = 30 / num_gpus
# bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
# windows下 model.device 会被设... | 92 | 275 | 520 | 795 | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | |
6,641 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM2-6B/ChatGLM2-6B | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM2-6B/ChatGLM2-6B/web_demo2.py | 12,14 | 1,71 | get_model | 14,23 | 14,23 | from transformers import AutoModel, AutoTokenizer
import streamlit as st
st.set_page_config(
page_title="ChatGLM2-6b 演示",
page_icon=":robot:",
layout='wide'
)
@st.cache_resource
def get_model():
FILL_FUNC_BODYtokenizer, model = get_model()
st.title("ChatGLM2-6B")
max_length = st.sidebar.slider(
'm... | from transformers import AutoModel, AutoTokenizer
import streamlit as st
st.set_page_config(
page_title="ChatGLM2-6b 演示",
page_icon=":robot:",
layout='wide'
)
@st.cache_resource
def get_model():
FILL_FUNC_BODYtokenizer, model = get_model()
st.title("ChatGLM2-6B")
max_length = st.sidebar.slider(
'm... | tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
# 多显卡支持，使用下面两行代替上面一行，将num_gpus改为你实际的显卡数量
# from utils import load_model_on_gpus
# model = load_model_on_gpus("THUDM/chatglm2-6b... | tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
# 多显卡支持，使用下面两行代替上面一行，将num_gpus改为你实际的显卡数量
# from utils import load_model_on_gpus
# model = load_model_on_gpus("THUDM/chatglm2-6b... | 26 | 674 | 175 | 849 | from transformers import AutoModel, AutoTokenizer
import streamlit as st
st.set_page_config(
page_title="ChatGLM2-6b 演示",
page_icon=":robot:",
layout='wide'
)
@st.cache_resource
def get_model():
FILL_FUNC_BODYtokenizer, model = get_model()
st.title("ChatGLM2-6B")
max_length = st.sidebar.slider(
'm... | from transformers import AutoModel, AutoTokenizer
import streamlit as st
st.set_page_config(
page_title="ChatGLM2-6b 演示",
page_icon=":robot:",
layout='wide'
)
@st.cache_resource
def get_model():
FILL_FUNC_BODYtokenizer, model = get_model()
st.title("ChatGLM2-6B")
max_length = st.sidebar.slider(
'm... | |
6,642 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/Intel_device_demo/ipex_llm_cpu_demo/openai_api_request.py | 20,21 | 1,90 | function_chat | 21,56 | 21,56 | """
This script is an example of using the OpenAI API to create various interactions with a ChatGLM3 model.
It includes functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print ... | """
This script is an example of using the OpenAI API to create various interactions with a ChatGLM3 model.
It includes functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print ... | messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
... | messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
... | 84 | 504 | 319 | 823 | """
This script is an example of using the OpenAI API to create various interactions with a ChatGLM3 model.
It includes functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print ... | """
This script is an example of using the OpenAI API to create various interactions with a ChatGLM3 model.
It includes functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print ... | |
6,643 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/Intel_device_demo/ipex_llm_cpu_demo/utils.py | 46,48 | 1,187 | generate_stream_chatglm3 | 48,122 | 48,122 | import gc
import json
import torch
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.generation.logits_process import LogitsProcessor
from typing import Union, Tuple
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(
self, input_ids: torch.LongTensor, score... | import gc
import json
import torch
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.generation.logits_process import LogitsProcessor
from typing import Union, Tuple
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(
self, input_ids: torch.LongTensor, score... | messages = params["messages"]
tools = params["tools"]
temperature = float(params.get("temperature", 1.0))
repetition_penalty = float(params.get("repetition_penalty", 1.0))
top_p = float(params.get("top_p", 1.0))
max_new_tokens = int(params.get("max_tokens", 256))
echo = params.get("echo", Tr... | messages = params["messages"]
tools = params["tools"]
temperature = float(params.get("temperature", 1.0))
repetition_penalty = float(params.get("repetition_penalty", 1.0))
top_p = float(params.get("top_p", 1.0))
max_new_tokens = int(params.get("max_tokens", 256))
echo = params.get("echo", Tr... | 186 | 958 | 875 | 1,833 | import gc
import json
import torch
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.generation.logits_process import LogitsProcessor
from typing import Union, Tuple
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(
self, input_ids: torch.LongTensor, score... | import gc
import json
import torch
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.generation.logits_process import LogitsProcessor
from typing import Union, Tuple
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(
self, input_ids: torch.LongTensor, score... | |
6,644 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/basic_demo/cli_demo.py | 28,29 | 1,58 | main | 29,56 | 29,56 | import os
import platform
from transformers import AutoTokenizer, AutoModel
MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
model = AutoModel.from_pretrained(M... | import os
import platform
from transformers import AutoTokenizer, AutoModel
MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
model = AutoModel.from_pretrained(M... | past_key_values, history = None, []
global stop_stream
print(welcome_prompt)
while True:
query = input("\n用户：")
if query.strip() == "stop":
break
if query.strip() == "clear":
past_key_values, history = None, []
os.system(clear_command)
... | past_key_values, history = None, []
global stop_stream
print(welcome_prompt)
while True:
query = input("\n用户：")
if query.strip() == "stop":
break
if query.strip() == "clear":
past_key_values, history = None, []
os.system(clear_command)
... | 61 | 362 | 284 | 646 | import os
import platform
from transformers import AutoTokenizer, AutoModel
MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
model = AutoModel.from_pretrained(M... | import os
import platform
from transformers import AutoTokenizer, AutoModel
MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
model = AutoModel.from_pretrained(M... | |
6,645 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/basic_demo/cli_demo_bad_word_ids.py | 44,45 | 1,88 | main | 45,86 | 45,86 | """
This script demonstrates how to use the `bad_words_ids` argument in the context of a conversational AI model to filter out unwanted words or phrases from the model's responses. It's designed to showcase a fundamental method of content moderation within AI-generated text, particularly useful in scenarios where maint... | """
This script demonstrates how to use the `bad_words_ids` argument in the context of a conversational AI model to filter out unwanted words or phrases from the model's responses. It's designed to showcase a fundamental method of content moderation within AI-generated text, particularly useful in scenarios where maint... | past_key_values, history = None, []
global stop_stream
print(welcome_prompt)
while True:
query = input("\n用户：")
if query.strip().lower() == "stop":
break
if query.strip().lower() == "clear":
past_key_values, history = None, []
os.system(clear_c... | past_key_values, history = None, []
global stop_stream
print(welcome_prompt)
while True:
query = input("\n用户：")
if query.strip().lower() == "stop":
break
if query.strip().lower() == "clear":
past_key_values, history = None, []
os.system(clear_c... | 122 | 577 | 455 | 1,032 | """
This script demonstrates how to use the `bad_words_ids` argument in the context of a conversational AI model to filter out unwanted words or phrases from the model's responses. It's designed to showcase a fundamental method of content moderation within AI-generated text, particularly useful in scenarios where maint... | """
This script demonstrates how to use the `bad_words_ids` argument in the context of a conversational AI model to filter out unwanted words or phrases from the model's responses. It's designed to showcase a fundamental method of content moderation within AI-generated text, particularly useful in scenarios where maint... | |
6,646 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/basic_demo/web_demo_streamlit.py | 28,30 | 1,87 | get_model | 30,37 | 30,37 | """
This script is a simple web demo based on Streamlit, showcasing the use of the ChatGLM3-6B model. For a more comprehensive web demo,
it is recommended to use 'composite_demo'.
Usage:
- Run the script using Streamlit: `streamlit run web_demo_streamlit.py`
- Adjust the model parameters from the sidebar.
- Enter ques... | """
This script is a simple web demo based on Streamlit, showcasing the use of the ChatGLM3-6B model. For a more comprehensive web demo,
it is recommended to use 'composite_demo'.
Usage:
- Run the script using Streamlit: `streamlit run web_demo_streamlit.py`
- Adjust the model parameters from the sidebar.
- Enter ques... |
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True, device_map="auto").eval()
return tokenizer, model
# 加载Chatglm3的model和tokenizer
|
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True, device_map="auto").eval()
return tokenizer, model
# 加载Chatglm3的model和tokenizer
| 14 | 930 | 95 | 1,025 | """
This script is a simple web demo based on Streamlit, showcasing the use of the ChatGLM3-6B model. For a more comprehensive web demo,
it is recommended to use 'composite_demo'.
Usage:
- Run the script using Streamlit: `streamlit run web_demo_streamlit.py`
- Adjust the model parameters from the sidebar.
- Enter ques... | """
This script is a simple web demo based on Streamlit, showcasing the use of the ChatGLM3-6B model. For a more comprehensive web demo,
it is recommended to use 'composite_demo'.
Usage:
- Run the script using Streamlit: `streamlit run web_demo_streamlit.py`
- Adjust the model parameters from the sidebar.
- Enter ques... | |
6,647 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/composite_demo/tool_registry.py | 119,123 | 1,139 | get_shell | 126,136 | 123,136 | """
This code is the tool registration part. By registering the tool, the model can call the tool.
This code provides extended functionality to the model, enabling it to call and interact with a variety of utilities
through defined interfaces.
"""
import copy
import inspect
from pprint import pformat
import traceback
... | """
This code is the tool registration part. By registering the tool, the model can call the tool.
This code provides extended functionality to the model, enabling it to call and interact with a variety of utilities
through defined interfaces.
"""
import copy
import inspect
from pprint import pformat
import traceback
... | if not isinstance(query, str):
raise TypeError("Command must be a string")
try:
result = subprocess.run(query, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
text=True)
return result.stdout
except subprocess.CalledProcessError ... | """
Use shell to run command
"""
if not isinstance(query, str):
raise TypeError("Command must be a string")
try:
result = subprocess.run(query, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
text=True)
return result.... | 27 | 1,221 | 109 | 1,330 | """
This code is the tool registration part. By registering the tool, the model can call the tool.
This code provides extended functionality to the model, enabling it to call and interact with a variety of utilities
through defined interfaces.
"""
import copy
import inspect
from pprint import pformat
import traceback
... | """
This code is the tool registration part. By registering the tool, the model can call the tool.
This code provides extended functionality to the model, enabling it to call and interact with a variety of utilities
through defined interfaces.
"""
import copy
import inspect
from pprint import pformat
import traceback
... | |
6,648 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/langchain_demo/ChatGLM3.py | 37,38 | 8,144 | _tool_history | ChatGLM3 | 38,81 | 38,81 | class ChatGLM3(LLM):
max_token: int = 8192
do_sample: bool = True
temperature: float = 0.8
top_p = 0.8
tokenizer: object = None
model: object = None
history: List = []
has_search: bool = False
def __init__(self):
super().__init__()
@property
def _llm_type(self) -> s... | class ChatGLM3(LLM):
max_token: int = 8192
do_sample: bool = True
temperature: float = 0.8
top_p = 0.8
tokenizer: object = None
model: object = None
history: List = []
has_search: bool = False
def __init__(self):
super().__init__()
@property
def _llm_type(self) -> s... | ans = []
tool_prompts = prompt.split(
"You have access to the following tools:\n\n")[1].split("\n\nUse a json blob")[0].split("\n")
tools_json = []
for tool_desc in tool_prompts:
name = tool_desc.split(":")[0]
description = tool_desc.split(", args:")... | ans = []
tool_prompts = prompt.split(
"You have access to the following tools:\n\n")[1].split("\n\nUse a json blob")[0].split("\n")
tools_json = []
for tool_desc in tool_prompts:
name = tool_desc.split(":")[0]
description = tool_desc.split(", args:")... | 125 | 927 | 509 | 1,436 | import ast
import json
from langchain.llms.base import LLM
from transformers import AutoTokenizer, AutoModel, AutoConfig
from typing import List, Optional
class ChatGLM3(LLM):
max_token: int = 8192
do_sample: bool = True
temperature: float = 0.8
top_p = 0.8
tokenizer: object = None
model: obje... | import ast
import json
from langchain.llms.base import LLM
from transformers import AutoTokenizer, AutoModel, AutoConfig
from typing import List, Optional
class ChatGLM3(LLM):
max_token: int = 8192
do_sample: bool = True
temperature: float = 0.8
top_p = 0.8
tokenizer: object = None
model: obje... |
6,649 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/langchain_demo/tools/Calculator.py | 20,21 | 12,55 | parameter_validation | Calculator | 27,34 | 21,34 | class Calculator(BaseTool, abc.ABC):
name = "Calculator"
description = "Useful for when you need to calculate math problems"
args_schema: Type[BaseModel] = CalculatorInput
def __init__(self):
super().__init__()
def parameter_validation(self, para: str):
FILL_FUNC_BODY def _run(self, cal... | class Calculator(BaseTool, abc.ABC):
name = "Calculator"
description = "Useful for when you need to calculate math problems"
args_schema: Type[BaseModel] = CalculatorInput
def __init__(self):
super().__init__()
def parameter_validation(self, para: str):
"""
You can write yo... | symbols = ["math", "sqrt", "log", "sin", "cos", "tan", "pi"]
for sym in symbols:
para = para.replace(sym, "")
patten = re.compile("[+*/\-%\d()=\s.]{3,}")
if re.findall(patten, para):
return True
| """
You can write your own parameter validation rules here,
you can refer to the code given here.
:param para:
:return:
"""
symbols = ["math", "sqrt", "log", "sin", "cos", "tan", "pi"]
for sym in symbols:
para = para.replace(sym, "")
pa... | 25 | 453 | 96 | 549 | import abc
import re
from typing import Type
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
class CalculatorInput(BaseModel):
calculation: str = Field(description="calculation to perform")
class Calculator(BaseTool, abc.ABC):
name = "Calculator"
description = "Useful for when... | import abc
import re
from typing import Type
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
class CalculatorInput(BaseModel):
calculation: str = Field(description="calculation to perform")
class Calculator(BaseTool, abc.ABC):
name = "Calculator"
description = "Useful for when... |
6,650 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/langchain_demo/tools/DistanceConversion.py | 21,22 | 13,47 | _run | DistanceConverter | 22,47 | 22,47 | class DistanceConverter(BaseTool, abc.ABC):
name = "DistanceConverter"
description = "Converts distance between meters, kilometers, and feet"
args_schema: Type[BaseModel] = DistanceConversionInput
def __init__(self):
super().__init__()
def _run(self, distance: float, unit: str, to_unit: st... | class DistanceConverter(BaseTool, abc.ABC):
name = "DistanceConverter"
description = "Converts distance between meters, kilometers, and feet"
args_schema: Type[BaseModel] = DistanceConversionInput
def __init__(self):
super().__init__()
def _run(self, distance: float, unit: str, to_unit: st... | unit_conversions = {
"m_to_km": 0.001,
"km_to_m": 1000,
"feet_to_m": 0.3048,
"m_to_feet": 3.28084,
"km_to_feet": 3280.84,
"feet_to_km": 0.0003048
}
if unit == to_unit:
return f"{distance} {unit} is equal to {dis... | unit_conversions = {
"m_to_km": 0.001,
"km_to_m": 1000,
"feet_to_m": 0.3048,
"m_to_feet": 3.28084,
"km_to_feet": 3280.84,
"feet_to_km": 0.0003048
}
if unit == to_unit:
return f"{distance} {unit} is equal to {dis... | 81 | 113 | 334 | 447 | import abc
from typing import Type
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
class DistanceConversionInput(BaseModel):
distance: float = Field(description="The numerical value of the distance to convert")
unit: str = Field(description="The current unit of the distance (m, km, ... | import abc
from typing import Type
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
class DistanceConversionInput(BaseModel):
distance: float = Field(description="The numerical value of the distance to convert")
unit: str = Field(description="The current unit of the distance (m, km, ... |
6,651 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/langchain_demo/tools/Weather.py | 20,21 | 12,34 | _run | Weather | 21,34 | 21,34 | class Weather(BaseTool):
name = "weather"
description = "Use for searching weather at a specific location"
args_schema: Type[BaseModel] = WeatherInput
def __init__(self):
super().__init__()
def _run(self, location: str) -> dict[str, Any]:
FILL_FUNC_BODY | class Weather(BaseTool):
name = "weather"
description = "Use for searching weather at a specific location"
args_schema: Type[BaseModel] = WeatherInput
def __init__(self):
super().__init__()
def _run(self, location: str) -> dict[str, Any]:
FILL_FUNC_BODY | api_key = os.environ["SENIVERSE_KEY"]
url = f"https://api.seniverse.com/v3/weather/now.json?key={api_key}&location={location}&language=zh-Hans&unit=c"
response = requests.get(url)
if response.status_code == 200:
data = response.json()
weather = {
"... | api_key = os.environ["SENIVERSE_KEY"]
url = f"https://api.seniverse.com/v3/weather/now.json?key={api_key}&location={location}&language=zh-Hans&unit=c"
response = requests.get(url)
if response.status_code == 200:
data = response.json()
weather = {
"... | 34 | 92 | 185 | 277 | import os
import requests
from typing import Type, Any
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
class WeatherInput(BaseModel):
location: str = Field(description="the location need to check the weather")
class Weather(BaseTool):
name = "weather"
description = "Use for se... | import os
import requests
from typing import Type, Any
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
class WeatherInput(BaseModel):
location: str = Field(description="the location need to check the weather")
class Weather(BaseTool):
name = "weather"
description = "Use for se... |
6,652 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/openai_api_demo/openai_api_request.py | 86,87 | 1,100 | embedding | 87,95 | 87,95 | """
This script is an example of using the OpenAI API to create various interactions with a ChatGLM3 model.
It includes functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print ... | """
This script is an example of using the OpenAI API to create various interactions with a ChatGLM3 model.
It includes functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print ... | response = client.embeddings.create(
model="bge-large-zh-1.5",
input=["你好，给我讲一个故事，大概100字"],
)
embeddings = response.data[0].embedding
print("嵌入完成，维度：", len(embeddings))
| response = client.embeddings.create(
model="bge-large-zh-1.5",
input=["你好，给我讲一个故事，大概100字"],
)
embeddings = response.data[0].embedding
print("嵌入完成，维度：", len(embeddings))
| 11 | 799 | 93 | 892 | """
This script is an example of using the OpenAI API to create various interactions with a ChatGLM3 model.
It includes functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print ... | """
This script is an example of using the OpenAI API to create various interactions with a ChatGLM3 model.
It includes functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print ... | |
6,653 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/openai_api_demo/utils.py | 19,20 | 1,193 | process_response | 20,46 | 20,46 | import gc
import json
import torch
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.generation.logits_process import LogitsProcessor
from typing import Union, Tuple
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(
self, input_ids: torch.LongTensor, score... | import gc
import json
import torch
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.generation.logits_process import LogitsProcessor
from typing import Union, Tuple
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(
self, input_ids: torch.LongTensor, score... | content = ""
for response in output.split("<|assistant|>"):
metadata, content = response.split("\n", maxsplit=1)
if not metadata.strip():
content = content.strip()
content = content.replace("[[训练时间]]", "2023年")
else:
if use_tool:
conten... | content = ""
for response in output.split("<|assistant|>"):
metadata, content = response.split("\n", maxsplit=1)
if not metadata.strip():
content = content.strip()
content = content.replace("[[训练时间]]", "2023年")
else:
if use_tool:
conten... | 55 | 1,617 | 229 | 1,846 | import gc
import json
import torch
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.generation.logits_process import LogitsProcessor
from typing import Union, Tuple
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(
self, input_ids: torch.LongTensor, score... | import gc
import json
import torch
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.generation.logits_process import LogitsProcessor
from typing import Union, Tuple
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(
self, input_ids: torch.LongTensor, score... | |
6,654 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/openai_api_demo/zhipu_api_request.py | 57,58 | 1,101 | simple_chat | 58,87 | 58,87 | """
This script is an example of using the Zhipu API to create various interactions with a ChatGLM3 model. It includes
functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print e... | """
This script is an example of using the Zhipu API to create various interactions with a ChatGLM3 model. It includes
functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print e... | messages = [
{
"role": "system",
"content": "You are ChatGLM3, a large language model trained by Zhipu.AI. Follow "
"the user's instructions carefully. Respond using markdown.",
},
{
"role": "user",
"content": "你好，请你介绍一下c... | messages = [
{
"role": "system",
"content": "You are ChatGLM3, a large language model trained by Zhipu.AI. Follow "
"the user's instructions carefully. Respond using markdown.",
},
{
"role": "user",
"content": "你好，请你介绍一下c... | 60 | 670 | 278 | 948 | """
This script is an example of using the Zhipu API to create various interactions with a ChatGLM3 model. It includes
functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print e... | """
This script is an example of using the Zhipu API to create various interactions with a ChatGLM3 model. It includes
functions to:
1. Conduct a basic chat session, asking about weather conditions in multiple cities.
2. Initiate a simple chat in Chinese, asking the model to tell a short story.
3. Retrieve and print e... | |
6,655 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/tools_using_demo/cli_demo_tool.py | 103,104 | 1,130 | main | 104,128 | 104,128 | """
This demo script is designed for interacting with the ChatGLM3-6B in Function, to show Function Call capabilities.
"""
import os
import platform
import torch
from transformers import AutoTokenizer, AutoModel
MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_... | """
This demo script is designed for interacting with the ChatGLM3-6B in Function, to show Function Call capabilities.
"""
import os
import platform
import torch
from transformers import AutoTokenizer, AutoModel
MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_... | past_key_values, history = None, [system_item]
role = "user"
global stop_stream
print("欢迎使用 ChatGLM3-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
while True:
query = input("\n用户：") if role == "user" else input("\n结果：")
if query.strip() == "stop":
break
if query.strip... | past_key_values, history = None, [system_item]
role = "user"
global stop_stream
print("欢迎使用 ChatGLM3-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
while True:
query = input("\n用户：") if role == "user" else input("\n结果：")
if query.strip() == "stop":
break
if query.strip... | 73 | 860 | 310 | 1,170 | """
This demo script is designed for interacting with the ChatGLM3-6B in Function, to show Function Call capabilities.
"""
import os
import platform
import torch
from transformers import AutoTokenizer, AutoModel
MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_... | """
This demo script is designed for interacting with the ChatGLM3-6B in Function, to show Function Call capabilities.
"""
import os
import platform
import torch
from transformers import AutoTokenizer, AutoModel
MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_... | |
6,656 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_ChatGLM3/ChatGLM3/tools_using_demo/tool_register.py | 89,93 | 1,119 | get_weather | 96,116 | 93,116 | """
这段代码是工具注册的部分，通过注册工具，让模型实现工具调用
"""
import inspect
import traceback
from copy import deepcopy
from pprint import pformat
from types import GenericAlias
from typing import get_origin, Annotated
_TOOL_HOOKS = {}
_TOOL_DESCRIPTIONS = {}
def register_tool(func: callable):
tool_name = func.__name__
tool_descrip... | """
这段代码是工具注册的部分，通过注册工具，让模型实现工具调用
"""
import inspect
import traceback
from copy import deepcopy
from pprint import pformat
from types import GenericAlias
from typing import get_origin, Annotated
_TOOL_HOOKS = {}
_TOOL_DESCRIPTIONS = {}
def register_tool(func: callable):
tool_name = func.__name__
tool_descrip... |
if not isinstance(city_name, str):
raise TypeError("City name must be a string")
key_selection = {
"current_condition": ["temp_C", "FeelsLikeC", "humidity", "weatherDesc", "observation_time"],
}
import requests
try:
resp = requests.get(f"https://wttr.in/{city_name}?format=j... | """
Get the current weather for `city_name`
"""
if not isinstance(city_name, str):
raise TypeError("City name must be a string")
key_selection = {
"current_condition": ["temp_C", "FeelsLikeC", "humidity", "weatherDesc", "observation_time"],
}
import requests
try:
... | 60 | 940 | 223 | 1,163 | """
这段代码是工具注册的部分，通过注册工具，让模型实现工具调用
"""
import inspect
import traceback
from copy import deepcopy
from pprint import pformat
from types import GenericAlias
from typing import get_origin, Annotated
_TOOL_HOOKS = {}
_TOOL_DESCRIPTIONS = {}
def register_tool(func: callable):
tool_name = func.__name__
tool_descrip... | """
这段代码是工具注册的部分，通过注册工具，让模型实现工具调用
"""
import inspect
import traceback
from copy import deepcopy
from pprint import pformat
from types import GenericAlias
from typing import get_origin, Annotated
_TOOL_HOOKS = {}
_TOOL_DESCRIPTIONS = {}
def register_tool(func: callable):
tool_name = func.__name__
tool_descrip... | |
6,734 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_CodeGeeX2/CodeGeeX2 | /home/hieuvd/dung_repo/raw/stars_01/THUDM_CodeGeeX2/CodeGeeX2/demo/gpus.py | 8,9 | 1,60 | auto_configure_device_map | 9,45 | 9,45 | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | # transformer.word_embeddings 占用1层
# transformer.final_layernorm 和 lm_head 占用1层
# transformer.layers 占用 28 层
# 总共30层分配到num_gpus张卡上
num_trans_layers = 28
per_gpu_layers = 30 / num_gpus
# bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
# windows下 model.device 会被设... | # transformer.word_embeddings 占用1层
# transformer.final_layernorm 和 lm_head 占用1层
# transformer.layers 占用 28 层
# 总共30层分配到num_gpus张卡上
num_trans_layers = 28
per_gpu_layers = 30 / num_gpus
# bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
# windows下 model.device 会被设... | 92 | 275 | 526 | 801 | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | import os
from typing import Dict, Tuple, Union, Optional
from torch.nn import Module
from transformers import AutoModel
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
FILL_FUNC_BODYdef load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
device_map:... | |
8,044 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/cli2.py | 27,28 | 1,75 | run_server | 28,39 | 28,39 | import os
import sys
import subprocess
from time import sleep
import shutil
from sys import platform
from multiprocessing import Process
from superagi.lib.logger import logger
def check_command(command, message):
if not shutil.which(command):
logger.info(message)
sys.exit(1)
def run_npm_commands... | import os
import sys
import subprocess
from time import sleep
import shutil
from sys import platform
from multiprocessing import Process
from superagi.lib.logger import logger
def check_command(command, message):
if not shutil.which(command):
logger.info(message)
sys.exit(1)
def run_npm_commands... | tgwui_process = Process(target=subprocess.run, args=(["python", "test.py","--name",a_name,"--description",a_description,"--goals"]+goals,), kwargs={"shell": shell})
api_process = Process(target=subprocess.run, args=(["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"],), kwargs={"shell": shell})
c... | tgwui_process = Process(target=subprocess.run, args=(["python", "test.py","--name",a_name,"--description",a_description,"--goals"]+goals,), kwargs={"shell": shell})
api_process = Process(target=subprocess.run, args=(["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"],), kwargs={"shell": shell})
c... | 42 | 615 | 286 | 901 | import os
import sys
import subprocess
from time import sleep
import shutil
from sys import platform
from multiprocessing import Process
from superagi.lib.logger import logger
def check_command(command, message):
if not shutil.which(command):
logger.info(message)
sys.exit(1)
def run_npm_commands... | import os
import sys
import subprocess
from time import sleep
import shutil
from sys import platform
from multiprocessing import Process
from superagi.lib.logger import logger
def check_command(command, message):
if not shutil.which(command):
logger.info(message)
sys.exit(1)
def run_npm_commands... | |
8,045 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/run_gui.py | 13,14 | 1,52 | run_npm_commands | 14,22 | 14,22 | import os
import sys
import subprocess
from time import sleep
import shutil
from superagi.lib.logger import logger
def check_command(command, message):
if not shutil.which(command):
logger.info(message)
sys.exit(1)
def run_npm_commands():
FILL_FUNC_BODYdef run_server():
api_process = subproces... | import os
import sys
import subprocess
from time import sleep
import shutil
from superagi.lib.logger import logger
def check_command(command, message):
if not shutil.which(command):
logger.info(message)
sys.exit(1)
def run_npm_commands():
FILL_FUNC_BODYdef run_server():
api_process = subproces... | os.chdir("gui")
try:
subprocess.run(["npm", "install"], check=True)
except subprocess.CalledProcessError:
logger.error(f"Error during '{' '.join(sys.exc_info()[1].cmd)}'. Exiting.")
sys.exit(1)
os.chdir("..")
| os.chdir("gui")
try:
subprocess.run(["npm", "install"], check=True)
except subprocess.CalledProcessError:
logger.error(f"Error during '{' '.join(sys.exc_info()[1].cmd)}'. Exiting.")
sys.exit(1)
os.chdir("..")
| 14 | 423 | 97 | 520 | import os
import sys
import subprocess
from time import sleep
import shutil
from superagi.lib.logger import logger
def check_command(command, message):
if not shutil.which(command):
logger.info(message)
sys.exit(1)
def run_npm_commands():
FILL_FUNC_BODYdef run_server():
api_process = subproces... | import os
import sys
import subprocess
from time import sleep
import shutil
from superagi.lib.logger import logger
def check_command(command, message):
if not shutil.which(command):
logger.info(message)
sys.exit(1)
def run_npm_commands():
FILL_FUNC_BODYdef run_server():
api_process = subproces... | |
8,046 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/agent/agent_workflow_step_wait_handler.py | 32,33 | 10,46 | handle_next_step | AgentWaitStepHandler | 34,46 | 33,46 | class AgentWaitStepHandler:
"""Handle Agent Wait Step in the agent workflow."""
def __init__(self, session, agent_id, agent_execution_id):
self.session = session
self.agent_id = agent_id
self.agent_execution_id = agent_execution_id
def execute_step(self):
"""Execute the age... | class AgentWaitStepHandler:
"""Handle Agent Wait Step in the agent workflow."""
def __init__(self, session, agent_id, agent_execution_id):
self.session = session
self.agent_id = agent_id
self.agent_execution_id = agent_execution_id
def execute_step(self):
"""Execute the age... |
execution = AgentExecution.get_agent_execution_from_id(self.session, self.agent_execution_id)
workflow_step = AgentWorkflowStep.find_by_id(self.session, execution.current_agent_step_id)
step_response = "default"
next_step = AgentWorkflowStep.fetch_next_step(self.session, workflow_step.i... | """Handle next step of agent workflow in case of wait step."""
execution = AgentExecution.get_agent_execution_from_id(self.session, self.agent_execution_id)
workflow_step = AgentWorkflowStep.find_by_id(self.session, execution.current_agent_step_id)
step_response = "default"
next... | 35 | 305 | 230 | 535 | from datetime import datetime
from superagi.agent.types.agent_execution_status import AgentExecutionStatus
from superagi.lib.logger import logger
from superagi.models.agent_execution import AgentExecution
from superagi.models.workflows.agent_workflow_step import AgentWorkflowStep
from superagi.models.workflows.agent_w... | from datetime import datetime
from superagi.agent.types.agent_execution_status import AgentExecutionStatus
from superagi.lib.logger import logger
from superagi.models.agent_execution import AgentExecution
from superagi.models.workflows.agent_workflow_step import AgentWorkflowStep
from superagi.models.workflows.agent_w... |
8,047 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/agent/output_parser.py | 29,30 | 27,50 | parse | AgentSchemaOutputParser | 30,50 | 30,50 | class AgentSchemaOutputParser(BaseOutputParser):
"""Parses the output from the agent schema"""
def parse(self, response: str) -> AgentGPTAction:
FILL_FUNC_BODY | class AgentSchemaOutputParser(BaseOutputParser):
"""Parses the output from the agent schema"""
def parse(self, response: str) -> AgentGPTAction:
FILL_FUNC_BODY | if response.startswith("```") and response.endswith("```"):
response = "```".join(response.split("```")[1:-1])
response = JsonCleaner.extract_json_section(response)
# ast throws error if true/false params passed in json
response = JsonCleaner.clean_boolean(response)
... | if response.startswith("```") and response.endswith("```"):
response = "```".join(response.split("```")[1:-1])
response = JsonCleaner.extract_json_section(response)
# ast throws error if true/false params passed in json
response = JsonCleaner.clean_boolean(response)
... | 63 | 52 | 251 | 303 | import json
from abc import ABC, abstractmethod
from typing import Dict, NamedTuple, List
import re
import ast
import json
from superagi.helper.json_cleaner import JsonCleaner
from superagi.lib.logger import logger
class AgentGPTAction(NamedTuple):
name: str
args: Dict
class AgentTasks(NamedTuple):
task... | import json
from abc import ABC, abstractmethod
from typing import Dict, NamedTuple, List
import re
import ast
import json
from superagi.helper.json_cleaner import JsonCleaner
from superagi.lib.logger import logger
class AgentGPTAction(NamedTuple):
name: str
args: Dict
class AgentTasks(NamedTuple):
task... |
8,048 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/agent/output_parser.py | 52,53 | 50,71 | parse | AgentSchemaToolOutputParser | 53,71 | 53,71 | class AgentSchemaToolOutputParser(BaseOutputParser):
"""Parses the output from the agent schema for the tool"""
def parse(self, response: str) -> AgentGPTAction:
FILL_FUNC_BODY | class AgentSchemaToolOutputParser(BaseOutputParser):
"""Parses the output from the agent schema for the tool"""
def parse(self, response: str) -> AgentGPTAction:
FILL_FUNC_BODY | if response.startswith("```") and response.endswith("```"):
response = "```".join(response.split("```")[1:-1])
response = JsonCleaner.extract_json_section(response)
# ast throws error if true/false params passed in json
response = JsonCleaner.clean_boolean(response)
... | if response.startswith("```") and response.endswith("```"):
response = "```".join(response.split("```")[1:-1])
response = JsonCleaner.extract_json_section(response)
# ast throws error if true/false params passed in json
response = JsonCleaner.clean_boolean(response)
... | 63 | 56 | 241 | 297 | import json
from abc import ABC, abstractmethod
from typing import Dict, NamedTuple, List
import re
import ast
import json
from superagi.helper.json_cleaner import JsonCleaner
from superagi.lib.logger import logger
class AgentGPTAction(NamedTuple):
name: str
args: Dict
class AgentTasks(NamedTuple):
task... | import json
from abc import ABC, abstractmethod
from typing import Dict, NamedTuple, List
import re
import ast
import json
from superagi.helper.json_cleaner import JsonCleaner
from superagi.lib.logger import logger
class AgentGPTAction(NamedTuple):
name: str
args: Dict
class AgentTasks(NamedTuple):
task... |
8,049 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/agent/tool_builder.py | 21,22 | 13,30 | get_tool_config | DBToolkitConfiguration | 22,30 | 22,30 | class DBToolkitConfiguration(BaseToolkitConfiguration):
session = None
toolkit_id: int
def __init__(self, session=None, toolkit_id=None):
self.session = session
self.toolkit_id = toolkit_id
def get_tool_config(self, key: str):
FILL_FUNC_BODY | class DBToolkitConfiguration(BaseToolkitConfiguration):
session = None
toolkit_id: int
def __init__(self, session=None, toolkit_id=None):
self.session = session
self.toolkit_id = toolkit_id
def get_tool_config(self, key: str):
FILL_FUNC_BODY | tool_config = self.session.query(ToolConfig).filter_by(key=key, toolkit_id=self.toolkit_id).first()
if tool_config and tool_config.value:
if is_encrypted(tool_config.value):
return decrypt_data(tool_config.value)
else:
return tool_config.value
... | tool_config = self.session.query(ToolConfig).filter_by(key=key, toolkit_id=self.toolkit_id).first()
if tool_config and tool_config.value:
if is_encrypted(tool_config.value):
return decrypt_data(tool_config.value)
else:
return tool_config.value
... | 17 | 91 | 114 | 205 | import importlib
import os
from superagi.config.config import get_config
from superagi.llms.llm_model_factory import get_model
from superagi.models.tool import Tool
from superagi.models.tool_config import ToolConfig
from superagi.models.agent import Agent
from superagi.resource_manager.file_manager import FileManager
f... | import importlib
import os
from superagi.config.config import get_config
from superagi.llms.llm_model_factory import get_model
from superagi.models.tool import Tool
from superagi.models.tool_config import ToolConfig
from superagi.models.agent import Agent
from superagi.resource_manager.file_manager import FileManager
f... |
8,050 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/agent/tool_builder.py | 50,51 | 30,124 | build_tool | ToolBuilder | 60,84 | 51,84 | class ToolBuilder:
def __init__(self, session, agent_id: int, agent_execution_id: int = None):
self.session = session
self.agent_id = agent_id
self.agent_execution_id = agent_execution_id
def __validate_filename(self, filename):
"""
Validate the filename by removing the ... | class ToolBuilder:
def __init__(self, session, agent_id: int, agent_execution_id: int = None):
self.session = session
self.agent_id = agent_id
self.agent_execution_id = agent_execution_id
def __validate_filename(self, filename):
"""
Validate the filename by removing the ... | file_name = self.__validate_filename(filename=tool.file_name)
tools_dir=""
tool_paths = ["superagi/tools", "superagi/tools/external_tools", "superagi/tools/marketplace_tools"]
for tool_path in tool_paths:
if os.path.exists(os.path.join(os.getcwd(), tool_path) + '/' + tool.fo... | """
Create an object of a agent usable tool dynamically.
Args:
tool (Tool) : Tool object from which agent tool would be made.
Returns:
object: The object of the agent usable tool.
"""
file_name = self.__validate_filename(filename=tool.file_name)
... | 73 | 875 | 330 | 1,205 | import importlib
import os
from superagi.config.config import get_config
from superagi.llms.llm_model_factory import get_model
from superagi.models.tool import Tool
from superagi.models.tool_config import ToolConfig
from superagi.models.agent import Agent
from superagi.resource_manager.file_manager import FileManager
f... | import importlib
import os
from superagi.config.config import get_config
from superagi.llms.llm_model_factory import get_model
from superagi.models.tool import Tool
from superagi.models.tool_config import ToolConfig
from superagi.models.agent import Agent
from superagi.resource_manager.file_manager import FileManager
f... |
8,051 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/agent/types/agent_execution_status.py | 11,13 | 4,19 | get_agent_execution_status | AgentExecutionStatus | 13,19 | 13,19 | class AgentExecutionStatus(Enum):
RUNNING = 'RUNNING'
WAITING_FOR_PERMISSION = 'WAITING_FOR_PERMISSION'
ITERATION_LIMIT_EXCEEDED = 'ITERATION_LIMIT_EXCEEDED'
WAIT_STEP = 'WAIT_STEP'
COMPLETED = 'COMPLETED'
@classmethod
def get_agent_execution_status(cls, store):
FILL_FUNC_BODY | class AgentExecutionStatus(Enum):
RUNNING = 'RUNNING'
WAITING_FOR_PERMISSION = 'WAITING_FOR_PERMISSION'
ITERATION_LIMIT_EXCEEDED = 'ITERATION_LIMIT_EXCEEDED'
WAIT_STEP = 'WAIT_STEP'
COMPLETED = 'COMPLETED'
@classmethod
def get_agent_execution_status(cls, store):
FILL_FUNC_BODY | if store is None:
raise ValueError("Storage type cannot be None.")
store = store.upper()
if store in cls.__members__:
return cls[store]
raise ValueError(f"{store} is not a valid storage name.")
| if store is None:
raise ValueError("Storage type cannot be None.")
store = store.upper()
if store in cls.__members__:
return cls[store]
raise ValueError(f"{store} is not a valid storage name.")
| 27 | 128 | 70 | 198 | from enum import Enum
class AgentExecutionStatus(Enum):
RUNNING = 'RUNNING'
WAITING_FOR_PERMISSION = 'WAITING_FOR_PERMISSION'
ITERATION_LIMIT_EXCEEDED = 'ITERATION_LIMIT_EXCEEDED'
WAIT_STEP = 'WAIT_STEP'
COMPLETED = 'COMPLETED'
@classmethod
def get_agent_execution_status(cls, store):
FILL... | from enum import Enum
class AgentExecutionStatus(Enum):
RUNNING = 'RUNNING'
WAITING_FOR_PERMISSION = 'WAITING_FOR_PERMISSION'
ITERATION_LIMIT_EXCEEDED = 'ITERATION_LIMIT_EXCEEDED'
WAIT_STEP = 'WAIT_STEP'
COMPLETED = 'COMPLETED'
@classmethod
def get_agent_execution_status(cls, store):
FILL... |
8,052 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/agent/types/agent_workflow_step_action_types.py | 10,12 | 4,18 | get_agent_workflow_action_type | AgentWorkflowStepAction | 12,18 | 12,18 | class AgentWorkflowStepAction(Enum):
ITERATION_WORKFLOW = 'ITERATION_WORKFLOW'
TOOL = 'TOOL'
WAIT_STEP = 'WAIT_STEP'
@classmethod
def get_agent_workflow_action_type(cls, store):
FILL_FUNC_BODY | class AgentWorkflowStepAction(Enum):
ITERATION_WORKFLOW = 'ITERATION_WORKFLOW'
TOOL = 'TOOL'
WAIT_STEP = 'WAIT_STEP'
@classmethod
def get_agent_workflow_action_type(cls, store):
FILL_FUNC_BODY | if store is None:
raise ValueError("Storage type cannot be None.")
store = store.upper()
if store in cls.__members__:
return cls[store]
raise ValueError(f"{store} is not a valid storage name.")
| if store is None:
raise ValueError("Storage type cannot be None.")
store = store.upper()
if store in cls.__members__:
return cls[store]
raise ValueError(f"{store} is not a valid storage name.")
| 27 | 90 | 70 | 160 | from enum import Enum
class AgentWorkflowStepAction(Enum):
ITERATION_WORKFLOW = 'ITERATION_WORKFLOW'
TOOL = 'TOOL'
WAIT_STEP = 'WAIT_STEP'
@classmethod
def get_agent_workflow_action_type(cls, store):
FILL_FUNC_BODY | from enum import Enum
class AgentWorkflowStepAction(Enum):
ITERATION_WORKFLOW = 'ITERATION_WORKFLOW'
TOOL = 'TOOL'
WAIT_STEP = 'WAIT_STEP'
@classmethod
def get_agent_workflow_action_type(cls, store):
FILL_FUNC_BODY |
8,053 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/agent/types/wait_step_status.py | 9,11 | 4,17 | get_agent_workflow_step_wait_status | AgentWorkflowStepWaitStatus | 11,17 | 11,17 | class AgentWorkflowStepWaitStatus(Enum):
PENDING = 'PENDING'
WAITING = 'WAITING'
COMPLETED = 'COMPLETED'
@classmethod
def get_agent_workflow_step_wait_status(cls, store):
FILL_FUNC_BODY | class AgentWorkflowStepWaitStatus(Enum):
PENDING = 'PENDING'
WAITING = 'WAITING'
COMPLETED = 'COMPLETED'
@classmethod
def get_agent_workflow_step_wait_status(cls, store):
FILL_FUNC_BODY | if store is None:
raise ValueError("Storage type cannot be None.")
store = store.upper()
if store in cls.__members__:
return cls[store]
raise ValueError(f"{store} is not a valid storage name.")
| if store is None:
raise ValueError("Storage type cannot be None.")
store = store.upper()
if store in cls.__members__:
return cls[store]
raise ValueError(f"{store} is not a valid storage name.")
| 27 | 81 | 70 | 151 | from enum import Enum
class AgentWorkflowStepWaitStatus(Enum):
PENDING = 'PENDING'
WAITING = 'WAITING'
COMPLETED = 'COMPLETED'
@classmethod
def get_agent_workflow_step_wait_status(cls, store):
FILL_FUNC_BODY | from enum import Enum
class AgentWorkflowStepWaitStatus(Enum):
PENDING = 'PENDING'
WAITING = 'WAITING'
COMPLETED = 'COMPLETED'
@classmethod
def get_agent_workflow_step_wait_status(cls, store):
FILL_FUNC_BODY |
8,055 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/agent/workflow_seed.py | 229,231 | 218,270 | build_task_based_agents | IterationWorkflowSeed | 231,254 | 231,254 | class IterationWorkflowSeed:
@classmethod
def build_single_step_agent(cls, session):
iteration_workflow = IterationWorkflow.find_or_create_by_name(session, "Goal Based Agent-I", "Goal Based Agent")
output = AgentPromptTemplate.get_super_agi_single_prompt()
IterationWorkflowStep.find_or_c... | class IterationWorkflowSeed:
@classmethod
def build_single_step_agent(cls, session):
iteration_workflow = IterationWorkflow.find_or_create_by_name(session, "Goal Based Agent-I", "Goal Based Agent")
output = AgentPromptTemplate.get_super_agi_single_prompt()
IterationWorkflowStep.find_or_c... | iteration_workflow = IterationWorkflow.find_or_create_by_name(session, "Dynamic Task Queue-I",
"Dynamic Task Queue", has_task_queue=True)
output = AgentPromptTemplate.analyse_task()
workflow_step1 = IterationWorkflowStep.find... | iteration_workflow = IterationWorkflow.find_or_create_by_name(session, "Dynamic Task Queue-I",
"Dynamic Task Queue", has_task_queue=True)
output = AgentPromptTemplate.analyse_task()
workflow_step1 = IterationWorkflowStep.find... | 53 | 492 | 353 | 845 | from superagi.agent.agent_prompt_builder import AgentPromptBuilder
from superagi.agent.agent_prompt_template import AgentPromptTemplate
from superagi.models.workflows.agent_workflow import AgentWorkflow
from superagi.models.workflows.agent_workflow_step import AgentWorkflowStep
from superagi.models.workflows.iteration_... | from superagi.agent.agent_prompt_builder import AgentPromptBuilder
from superagi.agent.agent_prompt_template import AgentPromptTemplate
from superagi.models.workflows.agent_workflow import AgentWorkflow
from superagi.models.workflows.agent_workflow_step import AgentWorkflowStep
from superagi.models.workflows.iteration_... |
8,056 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/controllers/types/models_types.py | 7,9 | 3,15 | get_models_types | ModelsTypes | 9,15 | 9,15 | class ModelsTypes(Enum):
MARKETPLACE = "Marketplace"
CUSTOM = "Custom"
@classmethod
def get_models_types(cls, model_type):
FILL_FUNC_BODY | class ModelsTypes(Enum):
MARKETPLACE = "Marketplace"
CUSTOM = "Custom"
@classmethod
def get_models_types(cls, model_type):
FILL_FUNC_BODY | if model_type is None:
raise ValueError("Queue status type cannot be None.")
model_type = model_type.upper()
if model_type in cls.__members__:
return cls[model_type]
raise ValueError(f"{model_type} is not a valid storage name.") | if model_type is None:
raise ValueError("Queue status type cannot be None.")
model_type = model_type.upper()
if model_type in cls.__members__:
return cls[model_type]
raise ValueError(f"{model_type} is not a valid storage name.") | 28 | 61 | 82 | 143 | from enum import Enum
class ModelsTypes(Enum):
MARKETPLACE = "Marketplace"
CUSTOM = "Custom"
@classmethod
def get_models_types(cls, model_type):
FILL_FUNC_BODY | from enum import Enum
class ModelsTypes(Enum):
MARKETPLACE = "Marketplace"
CUSTOM = "Custom"
@classmethod
def get_models_types(cls, model_type):
FILL_FUNC_BODY |
8,057 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/helper/feed_parser.py | 8,9 | 1,59 | parse_feed | 19,59 | 9,59 | import json
from datetime import datetime
from superagi.helper.time_helper import get_time_difference
from superagi.lib.logger import logger
def parse_feed(feed):
FILL_FUNC_BODY | import json
from datetime import datetime
from superagi.helper.time_helper import get_time_difference
from superagi.lib.logger import logger
def parse_feed(feed):
"""
Helper function to parse the feed.
Args:
feed (AgentExecutionFeed): The feed to be parsed.
Returns:
dict: Parsed fee... |
# Get the current time
feed.time_difference = get_time_difference(feed.updated_at, str(datetime.now()))
# Check if the feed belongs to an assistant role
if feed.role == "assistant":
try:
# Parse the feed as JSON
parsed = json.loads(feed.feed, strict=False)
... | """
Helper function to parse the feed.
Args:
feed (AgentExecutionFeed): The feed to be parsed.
Returns:
dict: Parsed feed information with role, feed content, and updated timestamp.
If parsing fails, the original feed is returned.
"""
# Get the current time
f... | 155 | 129 | 571 | 700 | import json
from datetime import datetime
from superagi.helper.time_helper import get_time_difference
from superagi.lib.logger import logger
def parse_feed(feed):
FILL_FUNC_BODY | import json
from datetime import datetime
from superagi.helper.time_helper import get_time_difference
from superagi.lib.logger import logger
def parse_feed(feed):
"""
Helper function to parse the feed.
Args:
feed (AgentExecutionFeed): The feed to be parsed.
Returns:
dict: Parsed fee... | |
8,058 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/helper/imap_email.py | 23,24 | 4,40 | adjust_imap_folder | ImapEmail | 34,40 | 24,40 | class ImapEmail:
def imap_open(self, imap_folder, email_sender, email_password, imap_server) -> imaplib.IMAP4_SSL:
"""
Function to open an IMAP connection to the email server.
Args:
imap_folder (str): The folder to open.
email_sender (str): The email address of the ... | class ImapEmail:
def imap_open(self, imap_folder, email_sender, email_password, imap_server) -> imaplib.IMAP4_SSL:
"""
Function to open an IMAP connection to the email server.
Args:
imap_folder (str): The folder to open.
email_sender (str): The email address of the ... | if "@gmail" in email_sender.lower():
if "sent" in imap_folder.lower():
return '"[Gmail]/Sent Mail"'
if "draft" in imap_folder.lower():
return '"[Gmail]/Drafts"'
return imap_folder
| """
Function to adjust the IMAP folder based on the email address of the sender.
Args:
imap_folder (str): The folder to open.
email_sender (str): The email address of the sender.
Returns:
str: The adjusted IMAP folder.
"""
if "@gmail"... | 19 | 301 | 82 | 383 | import imaplib
class ImapEmail:
def imap_open(self, imap_folder, email_sender, email_password, imap_server) -> imaplib.IMAP4_SSL:
"""
Function to open an IMAP connection to the email server.
Args:
imap_folder (str): The folder to open.
email_sender (str): The emai... | import imaplib
class ImapEmail:
def imap_open(self, imap_folder, email_sender, email_password, imap_server) -> imaplib.IMAP4_SSL:
"""
Function to open an IMAP connection to the email server.
Args:
imap_folder (str): The folder to open.
email_sender (str): The emai... |
8,059 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/helper/models_helper.py | 4,6 | 3,20 | validate_end_point | ModelsHelper | 6,20 | 6,20 | class ModelsHelper:
@staticmethod
def validate_end_point(model_api_key, end_point, model_provider):
FILL_FUNC_BODY | class ModelsHelper:
@staticmethod
def validate_end_point(model_api_key, end_point, model_provider):
FILL_FUNC_BODY | response = {"success": True}
if (model_provider == 'Hugging Face'):
try:
result = HuggingFace(api_key=model_api_key, end_point=end_point).verify_end_point()
except Exception as e:
response['success'] = False
response['error'] = str... | response = {"success": True}
if (model_provider == 'Hugging Face'):
try:
result = HuggingFace(api_key=model_api_key, end_point=end_point).verify_end_point()
except Exception as e:
response['success'] = False
response['error'] = str... | 30 | 44 | 121 | 165 | from superagi.llms.hugging_face import HuggingFace
class ModelsHelper:
@staticmethod
def validate_end_point(model_api_key, end_point, model_provider):
FILL_FUNC_BODY | from superagi.llms.hugging_face import HuggingFace
class ModelsHelper:
@staticmethod
def validate_end_point(model_api_key, end_point, model_provider):
FILL_FUNC_BODY |
8,060 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/helper/prompt_reader.py | 5,7 | 4,28 | read_tools_prompt | PromptReader | 7,17 | 7,17 | class PromptReader:
@staticmethod
def read_tools_prompt(current_file: str, prompt_file: str) -> str:
FILL_FUNC_BODY @staticmethod
def read_agent_prompt(current_file: str, prompt_file: str) -> str:
file_path = str(Path(current_file).resolve().parent) + "/prompts/" + prompt_file
try:
... | class PromptReader:
@staticmethod
def read_tools_prompt(current_file: str, prompt_file: str) -> str:
FILL_FUNC_BODY @staticmethod
def read_agent_prompt(current_file: str, prompt_file: str) -> str:
file_path = str(Path(current_file).resolve().parent) + "/prompts/" + prompt_file
try:
... | file_path = str(Path(current_file).resolve().parent) + "/prompts/" + prompt_file
try:
f = open(file_path, "r")
file_content = f.read()
f.close()
except FileNotFoundError as e:
print(e.__str__())
raise e
return file_content
| file_path = str(Path(current_file).resolve().parent) + "/prompts/" + prompt_file
try:
f = open(file_path, "r")
file_content = f.read()
f.close()
except FileNotFoundError as e:
print(e.__str__())
raise e
return file_content
| 25 | 167 | 100 | 267 | from pathlib import Path
class PromptReader:
@staticmethod
def read_tools_prompt(current_file: str, prompt_file: str) -> str:
FILL_FUNC_BODY @staticmethod
def read_agent_prompt(current_file: str, prompt_file: str) -> str:
file_path = str(Path(current_file).resolve().parent) + "/prompts/" + prom... | from pathlib import Path
class PromptReader:
@staticmethod
def read_tools_prompt(current_file: str, prompt_file: str) -> str:
FILL_FUNC_BODY @staticmethod
def read_agent_prompt(current_file: str, prompt_file: str) -> str:
file_path = str(Path(current_file).resolve().parent) + "/prompts/" + prom... |
8,061 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/helper/resource_helper.py | 101,103 | 12,183 | get_root_output_dir | ResourceHelper | 105,114 | 103,114 | class ResourceHelper:
@classmethod
def make_written_file_resource(cls, file_name: str, agent: Agent, agent_execution: AgentExecution, session):
"""
Function to create a Resource object for a written file.
Args:
file_name (str): The name of the file.
agent (Agent)... | class ResourceHelper:
@classmethod
def make_written_file_resource(cls, file_name: str, agent: Agent, agent_execution: AgentExecution, session):
"""
Function to create a Resource object for a written file.
Args:
file_name (str): The name of the file.
agent (Agent)... | root_dir = get_config('RESOURCES_OUTPUT_ROOT_DIR')
if root_dir is not None:
root_dir = root_dir if root_dir.startswith("/") else os.getcwd() + "/" + root_dir
root_dir = root_dir if root_dir.endswith("/") else root_dir + "/"
else:
root_dir = os.getcwd() + "/"
... | """Get root dir of the resource.
"""
root_dir = get_config('RESOURCES_OUTPUT_ROOT_DIR')
if root_dir is not None:
root_dir = root_dir if root_dir.startswith("/") else os.getcwd() + "/" + root_dir
root_dir = root_dir if root_dir.endswith("/") else root_dir + "/"
... | 36 | 1,907 | 128 | 2,035 | import os
from superagi.config.config import get_config
from superagi.helper.s3_helper import S3Helper
from superagi.lib.logger import logger
from superagi.models.agent import Agent
from superagi.models.agent_execution import AgentExecution
from superagi.models.resource import Resource
from superagi.types.storage_type... | import os
from superagi.config.config import get_config
from superagi.helper.s3_helper import S3Helper
from superagi.lib.logger import logger
from superagi.models.agent import Agent
from superagi.models.agent_execution import AgentExecution
from superagi.models.resource import Resource
from superagi.types.storage_type... |
8,062 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/helper/time_helper.py | 4,5 | 1,44 | get_time_difference | 5,36 | 5,36 | from datetime import datetime
def get_time_difference(timestamp1, timestamp2):
FILL_FUNC_BODYdef parse_interval_to_seconds(interval: str) -> int:
units = {"Minutes": 60, "Hours": 3600, "Days": 86400, "Weeks": 604800, "Months": 2592000}
interval = ' '.join(interval.split())
value, unit = interval.split(" "... | from datetime import datetime
def get_time_difference(timestamp1, timestamp2):
FILL_FUNC_BODYdef parse_interval_to_seconds(interval: str) -> int:
units = {"Minutes": 60, "Hours": 3600, "Days": 86400, "Weeks": 604800, "Months": 2592000}
interval = ' '.join(interval.split())
value, unit = interval.split(" "... | time_format = "%Y-%m-%d %H:%M:%S.%f"
# Parse the given timestamp
parsed_timestamp1 = datetime.strptime(str(timestamp1), time_format)
parsed_timestamp2 = datetime.strptime(timestamp2, time_format)
# Calculate the time difference
time_difference = parsed_timestamp2 - parsed_timestamp1
# Con... | time_format = "%Y-%m-%d %H:%M:%S.%f"
# Parse the given timestamp
parsed_timestamp1 = datetime.strptime(str(timestamp1), time_format)
parsed_timestamp2 = datetime.strptime(timestamp2, time_format)
# Calculate the time difference
time_difference = parsed_timestamp2 - parsed_timestamp1
# Con... | 170 | 146 | 487 | 633 | from datetime import datetime
def get_time_difference(timestamp1, timestamp2):
FILL_FUNC_BODYdef parse_interval_to_seconds(interval: str) -> int:
units = {"Minutes": 60, "Hours": 3600, "Days": 86400, "Weeks": 604800, "Months": 2592000}
interval = ' '.join(interval.split())
value, unit = interval.split(" "... | from datetime import datetime
def get_time_difference(timestamp1, timestamp2):
FILL_FUNC_BODYdef parse_interval_to_seconds(interval: str) -> int:
units = {"Minutes": 60, "Hours": 3600, "Days": 86400, "Weeks": 604800, "Months": 2592000}
interval = ' '.join(interval.split())
value, unit = interval.split(" "... | |
8,063 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/lib/logger.py | 50,51 | 33,79 | _make_custom_log_record | Logger | 51,53 | 51,53 | class Logger(metaclass=SingletonMeta):
def __init__(self, logger_name='Super AGI', log_level=logging.DEBUG):
if not hasattr(self, 'logger'):
self.logger = logging.getLogger(logger_name)
self.logger.setLevel(log_level)
self.logger.makeRecord = self._make_custom_log_record
... | class Logger(metaclass=SingletonMeta):
def __init__(self, logger_name='Super AGI', log_level=logging.DEBUG):
if not hasattr(self, 'logger'):
self.logger = logging.getLogger(logger_name)
self.logger.setLevel(log_level)
self.logger.makeRecord = self._make_custom_log_record
... | return CustomLogRecord(name, level, fn, lno, msg, args, exc_info, func=func, extra=extra, sinfo=sinfo)
| return CustomLogRecord(name, level, fn, lno, msg, args, exc_info, func=func, extra=extra, sinfo=sinfo)
| 11 | 478 | 41 | 519 | import logging
import inspect
class CustomLogRecord(logging.LogRecord):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
frame = inspect.currentframe().f_back
while frame:
if frame.f_globals['__name__'] != __name__ and frame.f_globals['__name__'] != 'logg... | import logging
import inspect
class CustomLogRecord(logging.LogRecord):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
frame = inspect.currentframe().f_back
while frame:
if frame.f_globals['__name__'] != __name__ and frame.f_globals['__name__'] != 'logg... |
8,064 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/llms/local_llm.py | 35,36 | 7,93 | chat_completion | LocalLLM | 46,61 | 36,61 | class LocalLLM(BaseLlm):
def __init__(self, temperature=0.6, max_tokens=get_config("MAX_MODEL_TOKEN_LIMIT"), top_p=1,
frequency_penalty=0,
presence_penalty=0, number_of_results=1, model=None, api_key='EMPTY', context_length=4096):
"""
Args:
model (str): ... | class LocalLLM(BaseLlm):
def __init__(self, temperature=0.6, max_tokens=get_config("MAX_MODEL_TOKEN_LIMIT"), top_p=1,
frequency_penalty=0,
presence_penalty=0, number_of_results=1, model=None, api_key='EMPTY', context_length=4096):
"""
Args:
model (str): ... | try:
if self.llm_model is None or self.llm_grammar is None:
logger.error("Model not found.")
return {"error": "Model loading error", "message": "Model not found. Please check your model path and try again."}
else:
response = self.llm_model.... | """
Call the chat completion.
Args:
messages (list): The messages.
max_tokens (int): The maximum number of tokens.
Returns:
dict: The response.
"""
try:
if self.llm_model is None or self.llm_grammar is None:
... | 62 | 621 | 265 | 886 | from superagi.config.config import get_config
from superagi.lib.logger import logger
from superagi.llms.base_llm import BaseLlm
from superagi.helper.llm_loader import LLMLoader
class LocalLLM(BaseLlm):
def __init__(self, temperature=0.6, max_tokens=get_config("MAX_MODEL_TOKEN_LIMIT"), top_p=1,
fr... | from superagi.config.config import get_config
from superagi.lib.logger import logger
from superagi.llms.base_llm import BaseLlm
from superagi.helper.llm_loader import LLMLoader
class LocalLLM(BaseLlm):
def __init__(self, temperature=0.6, max_tokens=get_config("MAX_MODEL_TOKEN_LIMIT"), top_p=1,
fr... |
8,065 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/llms/utils/huggingface_utils/tasks.py | 31,32 | 11,43 | _validate_params | TaskParameters | 32,43 | 32,43 | class TaskParameters:
def __init__(self) -> None:
self.params = self._generate_params()
self._validate_params()
def get_params(self, task, **kwargs) -> Dict[str, Union[int, float, bool, str]]:
# Return the task parameters and override with any kwargs
# This allows us to override... | class TaskParameters:
def __init__(self) -> None:
self.params = self._generate_params()
self._validate_params()
def get_params(self, task, **kwargs) -> Dict[str, Union[int, float, bool, str]]:
# Return the task parameters and override with any kwargs
# This allows us to override... | assert len(self.params) == len(Tasks), "Not all tasks have parameters defined"
for task in Tasks:
assert task in self.params, f"Task {task} does not have parameters defined"
# params = self.params[task]
# assert isinstance(params, dict), f"Task {task} parameters are ... | assert len(self.params) == len(Tasks), "Not all tasks have parameters defined"
for task in Tasks:
assert task in self.params, f"Task {task} does not have parameters defined"
# params = self.params[task]
# assert isinstance(params, dict), f"Task {task} parameters are ... | 73 | 211 | 171 | 382 | from enum import Enum
from dataclasses import dataclass
from typing import List, Dict, Union, Optional
# Define an Enum for the different tasks
class Tasks(Enum):
TEXT_GENERATION = "text-generation"
class TaskParameters:
def __init__(self) -> None:
self.params = self._generate_params()
self.... | from enum import Enum
from dataclasses import dataclass
from typing import List, Dict, Union, Optional
# Define an Enum for the different tasks
class Tasks(Enum):
TEXT_GENERATION = "text-generation"
class TaskParameters:
def __init__(self) -> None:
self.params = self._generate_params()
self.... |
8,066 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/resource_manager/resource_summary.py | 33,34 | 13,91 | add_to_vector_store_and_create_summary | ResourceSummarizer | 43,50 | 34,50 | class ResourceSummarizer:
"""Class to summarize a resource."""
def __init__(self, session, agent_id: int, model: str):
self.session = session
self.agent_id = agent_id
self.organisation_id = self.__get_organisation_id()
self.model = model
def __get_organisation_id(self):
... | class ResourceSummarizer:
"""Class to summarize a resource."""
def __init__(self, session, agent_id: int, model: str):
self.session = session
self.agent_id = agent_id
self.organisation_id = self.__get_organisation_id()
self.model = model
def __get_organisation_id(self):
... | model_api_key = self.__get_model_api_key()
try:
ResourceManager(str(self.agent_id)).save_document_to_vector_store(documents, str(resource_id), model_api_key,
self.__get_model_source())
except Exception as e:
... | """
Add a file to the vector store and generate a summary for it.
Args:
agent_id (str): ID of the agent.
resource_id (int): ID of the resource.
openai_api_key (str): OpenAI API key.
documents (list): List of documents.
"""
model_ap... | 21 | 919 | 116 | 1,035 | from datetime import datetime
import logging
from superagi.lib.logger import logger
from superagi.models.agent import Agent
from superagi.models.agent_config import AgentConfiguration
from superagi.models.configuration import Configuration
from superagi.models.resource import Resource
from superagi.resource_manager.lla... | from datetime import datetime
import logging
from superagi.lib.logger import logger
from superagi.models.agent import Agent
from superagi.models.agent_config import AgentConfiguration
from superagi.models.configuration import Configuration
from superagi.models.resource import Resource
from superagi.resource_manager.lla... |
8,067 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/tools/apollo/apollo_toolkit.py | 16,17 | 9,18 | get_env_keys | ApolloToolkit | 17,18 | 17,18 | class ApolloToolkit(BaseToolkit, ABC):
name: str = "ApolloToolkit"
description: str = "Apollo Tool kit contains all tools related to apollo.io tasks"
def get_tools(self) -> List[BaseTool]:
return [ApolloSearchTool()]
def get_env_keys(self) -> List[str]:
FILL_FUNC_BODY | class ApolloToolkit(BaseToolkit, ABC):
name: str = "ApolloToolkit"
description: str = "Apollo Tool kit contains all tools related to apollo.io tasks"
def get_tools(self) -> List[BaseTool]:
return [ApolloSearchTool()]
def get_env_keys(self) -> List[str]:
FILL_FUNC_BODY | return [ToolConfiguration(key="APOLLO_SEARCH_KEY", key_type=ToolConfigKeyType.STRING, is_required=True)]
| return [ToolConfiguration(key="APOLLO_SEARCH_KEY", key_type=ToolConfigKeyType.STRING, is_required=True)]
| 4 | 102 | 38 | 140 | from abc import ABC
from typing import List
from superagi.tools.apollo.apollo_search import ApolloSearchTool
from superagi.tools.base_tool import BaseToolkit, BaseTool, ToolConfiguration
from superagi.types.key_type import ToolConfigKeyType
class ApolloToolkit(BaseToolkit, ABC):
name: str = "ApolloToolkit"
d... | from abc import ABC
from typing import List
from superagi.tools.apollo.apollo_search import ApolloSearchTool
from superagi.tools.base_tool import BaseToolkit, BaseTool, ToolConfiguration
from superagi.types.key_type import ToolConfigKeyType
class ApolloToolkit(BaseToolkit, ABC):
name: str = "ApolloToolkit"
d... |
8,068 | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI | /home/hieuvd/dung_repo/raw/stars_01/TransformerOptimus_SuperAGI/SuperAGI/superagi/tools/code/coding_toolkit.py | 15,16 | 11,20 | get_tools | CodingToolkit | 16,18 | 16,18 | class CodingToolkit(BaseToolkit, ABC):
name: str = "CodingToolkit"
description: str = "Coding Tool kit contains all tools related to coding tasks"
def get_tools(self) -> List[BaseTool]:
FILL_FUNC_BODY def get_env_keys(self) -> List[ToolConfiguration]:
return []
| class CodingToolkit(BaseToolkit, ABC):
name: str = "CodingToolkit"
description: str = "Coding Tool kit contains all tools related to coding tasks"
def get_tools(self) -> List[BaseTool]:
FILL_FUNC_BODY def get_env_keys(self) -> List[ToolConfiguration]:
return []
| return [CodingTool(), WriteSpecTool(), WriteTestTool(), ImproveCodeTool()]
| return [CodingTool(), WriteSpecTool(), WriteTestTool(), ImproveCodeTool()]
| 5 | 93 | 25 | 118 | from abc import ABC
from typing import List
from superagi.tools.base_tool import BaseToolkit, BaseTool, ToolConfiguration
from superagi.tools.code.improve_code import ImproveCodeTool
from superagi.tools.code.write_code import CodingTool
from superagi.tools.code.write_spec import WriteSpecTool
from superagi.tools.code.... | from abc import ABC
from typing import List
from superagi.tools.base_tool import BaseToolkit, BaseTool, ToolConfiguration
from superagi.tools.code.improve_code import ImproveCodeTool
from superagi.tools.code.write_code import CodingTool
from superagi.tools.code.write_spec import WriteSpecTool
from superagi.tools.code.... |
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