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Fix bugs in `install.sh`, reduce log noise, and improve error reporting (#2464)

Browse Source 
* Update Install.sh

* Format Code

* Delete dev null

* Update README, Support Dark Mode in CSS/JS
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38 changed files with 982 additions and 770 deletions
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3
GPT_SoVITS/AR/models/t2s_lightning_module.py
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@ -28,7 +28,8 @@ class Text2SemanticLightningModule(LightningModule):
self.load_state_dict(
torch.load(
pretrained_s1,
map_location="cpu", weights_only=False,
map_location="cpu",
weights_only=False,
)["weight"],
)
)

74
GPT_SoVITS/TTS_infer_pack/TTS.py
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@ -32,19 +32,21 @@ from transformers import AutoModelForMaskedLM, AutoTokenizer
from tools.audio_sr import AP_BWE
from tools.i18n.i18n import I18nAuto, scan_language_list
from tools.my_utils import load_audio
from TTS_infer_pack.text_segmentation_method import splits
from TTS_infer_pack.TextPreprocessor import TextPreprocessor
from sv import SV
resample_transform_dict={}
def resample(audio_tensor, sr0,sr1,device):
resample_transform_dict = {}
def resample(audio_tensor, sr0, sr1, device):
global resample_transform_dict
key="%s-%s-%s"%(sr0,sr1,str(device))
key = "%s-%s-%s" % (sr0, sr1, str(device))
if key not in resample_transform_dict:
resample_transform_dict[key] = torchaudio.transforms.Resample(
sr0, sr1
).to(device)
resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
return resample_transform_dict[key](audio_tensor)
language = os.environ.get("language", "Auto")
language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
i18n = I18nAuto(language=language)
@ -111,6 +113,7 @@ def speed_change(input_audio: np.ndarray, speed: float, sr: int):
return processed_audio
class DictToAttrRecursive(dict):
def __init__(self, input_dict):
super().__init__(input_dict)
@ -479,7 +482,7 @@ class TTS:
def init_vits_weights(self, weights_path: str):
self.configs.vits_weights_path = weights_path
version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(weights_path)
if "Pro"in model_version:
if "Pro" in model_version:
self.init_sv_model()
path_sovits = self.configs.default_configs[model_version]["vits_weights_path"]
@ -498,9 +501,9 @@ class TTS:
else:
hps["model"]["version"] = "v2"
version = hps["model"]["version"]
v3v4set={"v3", "v4"}
v3v4set = {"v3", "v4"}
if model_version not in v3v4set:
if "Pro"not in model_version:
if "Pro" not in model_version:
model_version = version
else:
hps["model"]["version"] = model_version
@ -542,7 +545,7 @@ class TTS:
if "pretrained" not in weights_path and hasattr(vits_model, "enc_q"):
del vits_model.enc_q
self.is_v2pro=model_version in {"v2Pro","v2ProPlus"}
self.is_v2pro = model_version in {"v2Pro", "v2ProPlus"}
if if_lora_v3 == False:
print(
@ -632,7 +635,9 @@ class TTS:
)
self.vocoder.remove_weight_norm()
state_dict_g = torch.load(
"%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu", weights_only=False
"%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,),
map_location="cpu",
weights_only=False,
)
print("loading vocoder", self.vocoder.load_state_dict(state_dict_g))
@ -752,11 +757,13 @@ class TTS:
if raw_sr != self.configs.sampling_rate:
audio = raw_audio.to(self.configs.device)
if (audio.shape[0] == 2): audio = audio.mean(0).unsqueeze(0)
if audio.shape[0] == 2:
audio = audio.mean(0).unsqueeze(0)
audio = resample(audio, raw_sr, self.configs.sampling_rate, self.configs.device)
else:
audio = raw_audio.to(self.configs.device)
if (audio.shape[0] == 2): audio = audio.mean(0).unsqueeze(0)
if audio.shape[0] == 2:
audio = audio.mean(0).unsqueeze(0)
maxx = audio.abs().max()
if maxx > 1:
@ -775,8 +782,9 @@ class TTS:
audio = resample(audio, self.configs.sampling_rate, 16000, self.configs.device)
if self.configs.is_half:
audio = audio.half()
else:audio=None
return spec,audio
else:
audio = None
return spec, audio
def _set_prompt_semantic(self, ref_wav_path: str):
zero_wav = np.zeros(
@ -1073,7 +1081,10 @@ class TTS:
###### setting reference audio and prompt text preprocessing ########
t0 = time.perf_counter()
if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"] or (self.is_v2pro and self.prompt_cache["refer_spec"][0][1] is None)):
if (ref_audio_path is not None) and (
ref_audio_path != self.prompt_cache["ref_audio_path"]
or (self.is_v2pro and self.prompt_cache["refer_spec"][0][1] is None)
):
if not os.path.exists(ref_audio_path):
raise ValueError(f"{ref_audio_path} not exists")
self.set_ref_audio(ref_audio_path)
@ -1212,9 +1223,10 @@ class TTS:
t_34 += t4 - t3
refer_audio_spec = []
if self.is_v2pro:sv_emb=[]
for spec,audio_tensor in self.prompt_cache["refer_spec"]:
spec=spec.to(dtype=self.precision, device=self.configs.device)
if self.is_v2pro:
sv_emb = []
for spec, audio_tensor in self.prompt_cache["refer_spec"]:
spec = spec.to(dtype=self.precision, device=self.configs.device)
refer_audio_spec.append(spec)
if self.is_v2pro:
sv_emb.append(self.sv_model.compute_embedding3(audio_tensor))
@ -1249,10 +1261,14 @@ class TTS:
torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
)
_batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
if self.is_v2pro!=True:
_batch_audio_fragment = self.vits_model.decode(all_pred_semantic, _batch_phones, refer_audio_spec, speed=speed_factor).detach()[0, 0, :]
if self.is_v2pro != True:
_batch_audio_fragment = self.vits_model.decode(
all_pred_semantic, _batch_phones, refer_audio_spec, speed=speed_factor
).detach()[0, 0, :]
else:
_batch_audio_fragment = self.vits_model.decode(all_pred_semantic, _batch_phones, refer_audio_spec, speed=speed_factor,sv_emb=sv_emb).detach()[0, 0, :]
_batch_audio_fragment = self.vits_model.decode(
all_pred_semantic, _batch_phones, refer_audio_spec, speed=speed_factor, sv_emb=sv_emb
).detach()[0, 0, :]
audio_frag_end_idx.insert(0, 0)
batch_audio_fragment = [
_batch_audio_fragment[audio_frag_end_idx[i - 1] : audio_frag_end_idx[i]]
@ -1266,9 +1282,13 @@ class TTS:
pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)
) # .unsqueeze(0)#mq要多unsqueeze一次
if self.is_v2pro != True:
audio_fragment = self.vits_model.decode(_pred_semantic, phones, refer_audio_spec, speed=speed_factor).detach()[0, 0, :]
audio_fragment = self.vits_model.decode(
_pred_semantic, phones, refer_audio_spec, speed=speed_factor
).detach()[0, 0, :]
else:
audio_fragment = self.vits_model.decode(_pred_semantic, phones, refer_audio_spec, speed=speed_factor,sv_emb=sv_emb).detach()[0, 0, :]
audio_fragment = self.vits_model.decode(
_pred_semantic, phones, refer_audio_spec, speed=speed_factor, sv_emb=sv_emb
).detach()[0, 0, :]
batch_audio_fragment.append(audio_fragment) ###试试重建不带上prompt部分
else:
if parallel_infer:
@ -1410,7 +1430,7 @@ class TTS:
raw_entry = self.prompt_cache["refer_spec"][0]
if isinstance(raw_entry, tuple):
raw_entry = raw_entry[0]
refer_audio_spec = raw_entry.to(dtype=self.precision,device=self.configs.device)
refer_audio_spec = raw_entry.to(dtype=self.precision, device=self.configs.device)
fea_ref, ge = self.vits_model.decode_encp(prompt_semantic_tokens, prompt_phones, refer_audio_spec)
ref_audio: torch.Tensor = self.prompt_cache["raw_audio"]
@ -1480,7 +1500,7 @@ class TTS:
raw_entry = self.prompt_cache["refer_spec"][0]
if isinstance(raw_entry, tuple):
raw_entry = raw_entry[0]
refer_audio_spec = raw_entry.to(dtype=self.precision,device=self.configs.device)
refer_audio_spec = raw_entry.to(dtype=self.precision, device=self.configs.device)
fea_ref, ge = self.vits_model.decode_encp(prompt_semantic_tokens, prompt_phones, refer_audio_spec)
ref_audio: torch.Tensor = self.prompt_cache["raw_audio"]

4
GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py
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@ -160,7 +160,9 @@ class TextPreprocessor:
else:
for tmp in LangSegmenter.getTexts(text):
if langlist:
if (tmp["lang"] == "en" and langlist[-1] == "en") or (tmp["lang"] != "en" and langlist[-1] != "en"):
if (tmp["lang"] == "en" and langlist[-1] == "en") or (
tmp["lang"] != "en" and langlist[-1] != "en"
):
textlist[-1] += tmp["text"]
continue
if tmp["lang"] == "en":

162
GPT_SoVITS/eres2net/ERes2Net.py
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@ -1,13 +1,12 @@
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker.
ERes2Net incorporates both local and global feature fusion techniques to improve the performance.
The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal.
The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
"""
Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker.
ERes2Net incorporates both local and global feature fusion techniques to improve the performance.
The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal.
The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
"""
import torch
import math
@ -16,15 +15,14 @@ import torch.nn.functional as F
import pooling_layers as pooling_layers
from fusion import AFF
class ReLU(nn.Hardtanh):
class ReLU(nn.Hardtanh):
def __init__(self, inplace=False):
super(ReLU, self).__init__(0, 20, inplace)
def __repr__(self):
inplace_str = 'inplace' if self.inplace else ''
return self.__class__.__name__ + ' (' \
+ inplace_str + ')'
inplace_str = "inplace" if self.inplace else ""
return self.__class__.__name__ + " (" + inplace_str + ")"
class BasicBlockERes2Net(nn.Module):
@ -32,28 +30,28 @@ class BasicBlockERes2Net(nn.Module):
def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
super(BasicBlockERes2Net, self).__init__()
width = int(math.floor(planes*(baseWidth/64.0)))
self.conv1 = nn.Conv2d(in_planes, width*scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width*scale)
width = int(math.floor(planes * (baseWidth / 64.0)))
self.conv1 = nn.Conv2d(in_planes, width * scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width * scale)
self.nums = scale
convs=[]
bns=[]
convs = []
bns = []
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.relu = ReLU(inplace=True)
self.conv3 = nn.Conv2d(width*scale, planes*self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*self.expansion)
self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1,
stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes))
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes),
)
self.stride = stride
self.width = width
self.scale = scale
@ -64,18 +62,18 @@ class BasicBlockERes2Net(nn.Module):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out,self.width,1)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i==0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
out = sp
else:
out = torch.cat((out,sp),1)
if i == 0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i == 0:
out = sp
else:
out = torch.cat((out, sp), 1)
out = self.conv3(out)
out = self.bn3(out)
@ -86,22 +84,23 @@ class BasicBlockERes2Net(nn.Module):
return out
class BasicBlockERes2Net_diff_AFF(nn.Module):
expansion = 2
def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
super(BasicBlockERes2Net_diff_AFF, self).__init__()
width = int(math.floor(planes*(baseWidth/64.0)))
self.conv1 = nn.Conv2d(in_planes, width*scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width*scale)
width = int(math.floor(planes * (baseWidth / 64.0)))
self.conv1 = nn.Conv2d(in_planes, width * scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width * scale)
self.nums = scale
convs=[]
fuse_models=[]
bns=[]
convs = []
fuse_models = []
bns = []
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
for j in range(self.nums - 1):
fuse_models.append(AFF(channels=width))
@ -109,15 +108,15 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
self.bns = nn.ModuleList(bns)
self.fuse_models = nn.ModuleList(fuse_models)
self.relu = ReLU(inplace=True)
self.conv3 = nn.Conv2d(width*scale, planes*self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*self.expansion)
self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1,
stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes))
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes),
)
self.stride = stride
self.width = width
self.scale = scale
@ -128,19 +127,19 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out,self.width,1)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i==0:
if i == 0:
sp = spx[i]
else:
sp = self.fuse_models[i-1](sp, spx[i])
sp = self.fuse_models[i - 1](sp, spx[i])
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
if i == 0:
out = sp
else:
out = torch.cat((out,sp),1)
out = torch.cat((out, sp), 1)
out = self.conv3(out)
out = self.bn3(out)
@ -151,16 +150,19 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
return out
class ERes2Net(nn.Module):
def __init__(self,
block=BasicBlockERes2Net,
block_fuse=BasicBlockERes2Net_diff_AFF,
num_blocks=[3, 4, 6, 3],
m_channels=32,
feat_dim=80,
embedding_size=192,
pooling_func='TSTP',
two_emb_layer=False):
def __init__(
self,
block=BasicBlockERes2Net,
block_fuse=BasicBlockERes2Net_diff_AFF,
num_blocks=[3, 4, 6, 3],
m_channels=32,
feat_dim=80,
embedding_size=192,
pooling_func="TSTP",
two_emb_layer=False,
):
super(ERes2Net, self).__init__()
self.in_planes = m_channels
self.feat_dim = feat_dim
@ -176,20 +178,24 @@ class ERes2Net(nn.Module):
self.layer4 = self._make_layer(block_fuse, m_channels * 8, num_blocks[3], stride=2)
# Downsampling module for each layer
self.layer1_downsample = nn.Conv2d(m_channels * 2, m_channels * 4, kernel_size=3, stride=2, padding=1, bias=False)
self.layer2_downsample = nn.Conv2d(m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2, bias=False)
self.layer3_downsample = nn.Conv2d(m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2, bias=False)
self.layer1_downsample = nn.Conv2d(
m_channels * 2, m_channels * 4, kernel_size=3, stride=2, padding=1, bias=False
)
self.layer2_downsample = nn.Conv2d(
m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2, bias=False
)
self.layer3_downsample = nn.Conv2d(
m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2, bias=False
)
# Bottom-up fusion module
self.fuse_mode12 = AFF(channels=m_channels * 4)
self.fuse_mode123 = AFF(channels=m_channels * 8)
self.fuse_mode1234 = AFF(channels=m_channels * 16)
self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
self.pool = getattr(pooling_layers, pooling_func)(
in_dim=self.stats_dim * block.expansion)
self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats,
embedding_size)
self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2
self.pool = getattr(pooling_layers, pooling_func)(in_dim=self.stats_dim * block.expansion)
self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats, embedding_size)
if self.two_emb_layer:
self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
self.seg_2 = nn.Linear(embedding_size, embedding_size)
@ -212,7 +218,7 @@ class ERes2Net(nn.Module):
out1 = self.layer1(out)
out2 = self.layer2(out1)
out1_downsample = self.layer1_downsample(out1)
fuse_out12 = self.fuse_mode12(out2, out1_downsample)
fuse_out12 = self.fuse_mode12(out2, out1_downsample)
out3 = self.layer3(out2)
fuse_out12_downsample = self.layer2_downsample(fuse_out12)
fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
@ -243,18 +249,16 @@ class ERes2Net(nn.Module):
fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
out4 = self.layer4(out3)
fuse_out123_downsample = self.layer3_downsample(fuse_out123)
fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample).flatten(start_dim=1,end_dim=2).mean(-1)
fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample).flatten(start_dim=1, end_dim=2).mean(-1)
return fuse_out1234
if __name__ == '__main__':
if __name__ == "__main__":
x = torch.zeros(10, 300, 80)
model = ERes2Net(feat_dim=80, embedding_size=192, pooling_func='TSTP')
model = ERes2Net(feat_dim=80, embedding_size=192, pooling_func="TSTP")
model.eval()
out = model(x)
print(out.shape) # torch.Size([10, 192])
print(out.shape) # torch.Size([10, 192])
num_params = sum(param.numel() for param in model.parameters())
print("{} M".format(num_params / 1e6)) # 6.61M
print("{} M".format(num_params / 1e6)) # 6.61M

208
GPT_SoVITS/eres2net/ERes2NetV2.py
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@ -1,14 +1,12 @@
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
To further improve the short-duration feature extraction capability of ERes2Net, we expand the channel dimension
within each stage. However, this modification also increases the number of model parameters and computational complexity.
To alleviate this problem, we propose an improved ERes2NetV2 by pruning redundant structures, ultimately reducing
both the model parameters and its computational cost.
"""
To further improve the short-duration feature extraction capability of ERes2Net, we expand the channel dimension
within each stage. However, this modification also increases the number of model parameters and computational complexity.
To alleviate this problem, we propose an improved ERes2NetV2 by pruning redundant structures, ultimately reducing
both the model parameters and its computational cost.
"""
import torch
import math
@ -17,47 +15,42 @@ import torch.nn.functional as F
import pooling_layers as pooling_layers
from fusion import AFF
class ReLU(nn.Hardtanh):
class ReLU(nn.Hardtanh):
def __init__(self, inplace=False):
super(ReLU, self).__init__(0, 20, inplace)
def __repr__(self):
inplace_str = 'inplace' if self.inplace else ''
return self.__class__.__name__ + ' (' \
+ inplace_str + ')'
inplace_str = "inplace" if self.inplace else ""
return self.__class__.__name__ + " (" + inplace_str + ")"
class BasicBlockERes2NetV2(nn.Module):
def __init__(self, in_planes, planes, stride=1, baseWidth=26, scale=2, expansion=2):
super(BasicBlockERes2NetV2, self).__init__()
width = int(math.floor(planes*(baseWidth/64.0)))
self.conv1 = nn.Conv2d(in_planes, width*scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width*scale)
width = int(math.floor(planes * (baseWidth / 64.0)))
self.conv1 = nn.Conv2d(in_planes, width * scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width * scale)
self.nums = scale
self.expansion = expansion
convs=[]
bns=[]
convs = []
bns = []
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.relu = ReLU(inplace=True)
self.conv3 = nn.Conv2d(width*scale, planes*self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*self.expansion)
self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False),
nn.BatchNorm2d(self.expansion * planes))
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes),
)
self.stride = stride
self.width = width
self.scale = scale
@ -68,18 +61,18 @@ class BasicBlockERes2NetV2(nn.Module):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out,self.width,1)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i==0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
out = sp
else:
out = torch.cat((out,sp),1)
if i == 0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i == 0:
out = sp
else:
out = torch.cat((out, sp), 1)
out = self.conv3(out)
out = self.bn3(out)
@ -90,22 +83,22 @@ class BasicBlockERes2NetV2(nn.Module):
return out
class BasicBlockERes2NetV2AFF(nn.Module):
class BasicBlockERes2NetV2AFF(nn.Module):
def __init__(self, in_planes, planes, stride=1, baseWidth=26, scale=2, expansion=2):
super(BasicBlockERes2NetV2AFF, self).__init__()
width = int(math.floor(planes*(baseWidth/64.0)))
self.conv1 = nn.Conv2d(in_planes, width*scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width*scale)
width = int(math.floor(planes * (baseWidth / 64.0)))
self.conv1 = nn.Conv2d(in_planes, width * scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width * scale)
self.nums = scale
self.expansion = expansion
convs=[]
fuse_models=[]
bns=[]
convs = []
fuse_models = []
bns = []
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
for j in range(self.nums - 1):
fuse_models.append(AFF(channels=width, r=4))
@ -113,18 +106,15 @@ class BasicBlockERes2NetV2AFF(nn.Module):
self.bns = nn.ModuleList(bns)
self.fuse_models = nn.ModuleList(fuse_models)
self.relu = ReLU(inplace=True)
self.conv3 = nn.Conv2d(width*scale, planes*self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*self.expansion)
self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False),
nn.BatchNorm2d(self.expansion * planes))
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes),
)
self.stride = stride
self.width = width
self.scale = scale
@ -135,19 +125,19 @@ class BasicBlockERes2NetV2AFF(nn.Module):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out,self.width,1)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i==0:
if i == 0:
sp = spx[i]
else:
sp = self.fuse_models[i-1](sp, spx[i])
sp = self.fuse_models[i - 1](sp, spx[i])
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
if i == 0:
out = sp
else:
out = torch.cat((out,sp),1)
out = torch.cat((out, sp), 1)
out = self.conv3(out)
out = self.bn3(out)
@ -158,19 +148,22 @@ class BasicBlockERes2NetV2AFF(nn.Module):
return out
class ERes2NetV2(nn.Module):
def __init__(self,
block=BasicBlockERes2NetV2,
block_fuse=BasicBlockERes2NetV2AFF,
num_blocks=[3, 4, 6, 3],
m_channels=64,
feat_dim=80,
embedding_size=192,
baseWidth=26,
scale=2,
expansion=2,
pooling_func='TSTP',
two_emb_layer=False):
def __init__(
self,
block=BasicBlockERes2NetV2,
block_fuse=BasicBlockERes2NetV2AFF,
num_blocks=[3, 4, 6, 3],
m_channels=64,
feat_dim=80,
embedding_size=192,
baseWidth=26,
scale=2,
expansion=2,
pooling_func="TSTP",
two_emb_layer=False,
):
super(ERes2NetV2, self).__init__()
self.in_planes = m_channels
self.feat_dim = feat_dim
@ -181,42 +174,29 @@ class ERes2NetV2(nn.Module):
self.scale = scale
self.expansion = expansion
self.conv1 = nn.Conv2d(1,
m_channels,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(m_channels)
self.layer1 = self._make_layer(block,
m_channels,
num_blocks[0],
stride=1)
self.layer2 = self._make_layer(block,
m_channels * 2,
num_blocks[1],
stride=2)
self.layer3 = self._make_layer(block_fuse,
m_channels * 4,
num_blocks[2],
stride=2)
self.layer4 = self._make_layer(block_fuse,
m_channels * 8,
num_blocks[3],
stride=2)
self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, m_channels * 2, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block_fuse, m_channels * 4, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block_fuse, m_channels * 8, num_blocks[3], stride=2)
# Downsampling module
self.layer3_ds = nn.Conv2d(m_channels * 4 * self.expansion, m_channels * 8 * self.expansion, kernel_size=3, \
padding=1, stride=2, bias=False)
self.layer3_ds = nn.Conv2d(
m_channels * 4 * self.expansion,
m_channels * 8 * self.expansion,
kernel_size=3,
padding=1,
stride=2,
bias=False,
)
# Bottom-up fusion module
self.fuse34 = AFF(channels=m_channels * 8 * self.expansion, r=4)
self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
self.pool = getattr(pooling_layers, pooling_func)(
in_dim=self.stats_dim * self.expansion)
self.seg_1 = nn.Linear(self.stats_dim * self.expansion * self.n_stats,
embedding_size)
self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2
self.pool = getattr(pooling_layers, pooling_func)(in_dim=self.stats_dim * self.expansion)
self.seg_1 = nn.Linear(self.stats_dim * self.expansion * self.n_stats, embedding_size)
if self.two_emb_layer:
self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
self.seg_2 = nn.Linear(embedding_size, embedding_size)
@ -228,7 +208,11 @@ class ERes2NetV2(nn.Module):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride, baseWidth=self.baseWidth, scale=self.scale, expansion=self.expansion))
layers.append(
block(
self.in_planes, planes, stride, baseWidth=self.baseWidth, scale=self.scale, expansion=self.expansion
)
)
self.in_planes = planes * self.expansion
return nn.Sequential(*layers)
@ -264,7 +248,7 @@ class ERes2NetV2(nn.Module):
out3_ds = self.layer3_ds(out3)
fuse_out34 = self.fuse34(out4, out3_ds)
# print(111111111,fuse_out34.shape)#111111111 torch.Size([16, 2048, 10, 72])
return fuse_out34.flatten(start_dim=1,end_dim=2).mean(-1)
return fuse_out34.flatten(start_dim=1, end_dim=2).mean(-1)
# stats = self.pool(fuse_out34)
#
# embed_a = self.seg_1(stats)
@ -276,17 +260,13 @@ class ERes2NetV2(nn.Module):
# else:
# return embed_a
if __name__ == '__main__':
if __name__ == "__main__":
x = torch.randn(1, 300, 80)
model = ERes2NetV2(feat_dim=80, embedding_size=192, m_channels=64, baseWidth=26, scale=2, expansion=2)
model.eval()
y = model(x)
print(y.size())
macs, num_params = profile(model, inputs=(x, ))
print("Params: {} M".format(num_params / 1e6)) # 17.86 M
print("MACs: {} G".format(macs / 1e9)) # 12.69 G
macs, num_params = profile(model, inputs=(x,))
print("Params: {} M".format(num_params / 1e6)) # 17.86 M
print("MACs: {} G".format(macs / 1e9)) # 12.69 G

163
GPT_SoVITS/eres2net/ERes2Net_huge.py
Unescape Escape View File

@ -1,14 +1,13 @@
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
""" Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker.
ERes2Net incorporates both local and global feature fusion techniques to improve the performance.
The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal.
The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
ERes2Net-huge is an upgraded version of ERes2Net that uses a larger number of parameters to achieve better
recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance.
"""Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker.
ERes2Net incorporates both local and global feature fusion techniques to improve the performance.
The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal.
The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
ERes2Net-huge is an upgraded version of ERes2Net that uses a larger number of parameters to achieve better
recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance.
"""
import pdb
import torch
import math
@ -17,15 +16,14 @@ import torch.nn.functional as F
import pooling_layers as pooling_layers
from fusion import AFF
class ReLU(nn.Hardtanh):
class ReLU(nn.Hardtanh):
def __init__(self, inplace=False):
super(ReLU, self).__init__(0, 20, inplace)
def __repr__(self):
inplace_str = 'inplace' if self.inplace else ''
return self.__class__.__name__ + ' (' \
+ inplace_str + ')'
inplace_str = "inplace" if self.inplace else ""
return self.__class__.__name__ + " (" + inplace_str + ")"
class BasicBlockERes2Net(nn.Module):
@ -33,27 +31,28 @@ class BasicBlockERes2Net(nn.Module):
def __init__(self, in_planes, planes, stride=1, baseWidth=24, scale=3):
super(BasicBlockERes2Net, self).__init__()
width = int(math.floor(planes*(baseWidth/64.0)))
self.conv1 = nn.Conv2d(in_planes, width*scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width*scale)
width = int(math.floor(planes * (baseWidth / 64.0)))
self.conv1 = nn.Conv2d(in_planes, width * scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width * scale)
self.nums = scale
convs=[]
bns=[]
convs = []
bns = []
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.relu = ReLU(inplace=True)
self.conv3 = nn.Conv2d(width*scale, planes*self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*self.expansion)
self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes))
nn.BatchNorm2d(self.expansion * planes),
)
self.stride = stride
self.width = width
self.scale = scale
@ -64,18 +63,18 @@ class BasicBlockERes2Net(nn.Module):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out,self.width,1)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i==0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
out = sp
else:
out = torch.cat((out,sp),1)
if i == 0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i == 0:
out = sp
else:
out = torch.cat((out, sp), 1)
out = self.conv3(out)
out = self.bn3(out)
@ -86,22 +85,23 @@ class BasicBlockERes2Net(nn.Module):
return out
class BasicBlockERes2Net_diff_AFF(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1, baseWidth=24, scale=3):
super(BasicBlockERes2Net_diff_AFF, self).__init__()
width = int(math.floor(planes*(baseWidth/64.0)))
self.conv1 = nn.Conv2d(in_planes, width*scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width*scale)
width = int(math.floor(planes * (baseWidth / 64.0)))
self.conv1 = nn.Conv2d(in_planes, width * scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width * scale)
self.nums = scale
convs=[]
fuse_models=[]
bns=[]
convs = []
fuse_models = []
bns = []
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
for j in range(self.nums - 1):
fuse_models.append(AFF(channels=width))
@ -109,14 +109,15 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
self.bns = nn.ModuleList(bns)
self.fuse_models = nn.ModuleList(fuse_models)
self.relu = ReLU(inplace=True)
self.conv3 = nn.Conv2d(width*scale, planes*self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*self.expansion)
self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes))
nn.BatchNorm2d(self.expansion * planes),
)
self.stride = stride
self.width = width
self.scale = scale
@ -127,20 +128,19 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out,self.width,1)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i==0:
if i == 0:
sp = spx[i]
else:
sp = self.fuse_models[i-1](sp, spx[i])
sp = self.fuse_models[i - 1](sp, spx[i])
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
if i == 0:
out = sp
else:
out = torch.cat((out,sp),1)
out = torch.cat((out, sp), 1)
out = self.conv3(out)
out = self.bn3(out)
@ -151,16 +151,19 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
return out
class ERes2Net(nn.Module):
def __init__(self,
block=BasicBlockERes2Net,
block_fuse=BasicBlockERes2Net_diff_AFF,
num_blocks=[3, 4, 6, 3],
m_channels=64,
feat_dim=80,
embedding_size=192,
pooling_func='TSTP',
two_emb_layer=False):
def __init__(
self,
block=BasicBlockERes2Net,
block_fuse=BasicBlockERes2Net_diff_AFF,
num_blocks=[3, 4, 6, 3],
m_channels=64,
feat_dim=80,
embedding_size=192,
pooling_func="TSTP",
two_emb_layer=False,
):
super(ERes2Net, self).__init__()
self.in_planes = m_channels
self.feat_dim = feat_dim
@ -176,17 +179,22 @@ class ERes2Net(nn.Module):
self.layer3 = self._make_layer(block_fuse, m_channels * 4, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block_fuse, m_channels * 8, num_blocks[3], stride=2)
self.layer1_downsample = nn.Conv2d(m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2, bias=False)
self.layer2_downsample = nn.Conv2d(m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2, bias=False)
self.layer3_downsample = nn.Conv2d(m_channels * 16, m_channels * 32, kernel_size=3, padding=1, stride=2, bias=False)
self.layer1_downsample = nn.Conv2d(
m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2, bias=False
)
self.layer2_downsample = nn.Conv2d(
m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2, bias=False
)
self.layer3_downsample = nn.Conv2d(
m_channels * 16, m_channels * 32, kernel_size=3, padding=1, stride=2, bias=False
)
self.fuse_mode12 = AFF(channels=m_channels * 8)
self.fuse_mode123 = AFF(channels=m_channels * 16)
self.fuse_mode1234 = AFF(channels=m_channels * 32)
self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
self.pool = getattr(pooling_layers, pooling_func)(
in_dim=self.stats_dim * block.expansion)
self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2
self.pool = getattr(pooling_layers, pooling_func)(in_dim=self.stats_dim * block.expansion)
self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats, embedding_size)
if self.two_emb_layer:
self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
@ -229,7 +237,7 @@ class ERes2Net(nn.Module):
else:
return embed_a
def forward2(self, x,if_mean):
def forward2(self, x, if_mean):
x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
x = x.unsqueeze_(1)
@ -243,14 +251,13 @@ class ERes2Net(nn.Module):
fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
out4 = self.layer4(out3)
fuse_out123_downsample = self.layer3_downsample(fuse_out123)
fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample).flatten(start_dim=1,end_dim=2)#bs,20480,T
if(if_mean==False):
mean=fuse_out1234[0].transpose(1,0)#(T,20480),bs=T
fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample).flatten(start_dim=1, end_dim=2) # bs,20480,T
if if_mean == False:
mean = fuse_out1234[0].transpose(1, 0) # (T,20480),bs=T
else:
mean = fuse_out1234.mean(2)#bs,20480
mean_std=torch.cat([mean,torch.zeros_like(mean)],1)
return self.seg_1(mean_std)#(T,192)
mean = fuse_out1234.mean(2) # bs,20480
mean_std = torch.cat([mean, torch.zeros_like(mean)], 1)
return self.seg_1(mean_std) # (T,192)
# stats = self.pool(fuse_out1234)
# if self.two_emb_layer:
@ -275,12 +282,8 @@ class ERes2Net(nn.Module):
fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
out4 = self.layer4(out3)
fuse_out123_downsample = self.layer3_downsample(fuse_out123)
fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample).flatten(start_dim=1,end_dim=2).mean(-1)
fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample).flatten(start_dim=1, end_dim=2).mean(-1)
return fuse_out1234
# print(fuse_out1234.shape)
# print(fuse_out1234.flatten(start_dim=1,end_dim=2).shape)
# pdb.set_trace()

4
GPT_SoVITS/eres2net/fusion.py
Unescape Escape View File

@ -6,7 +6,6 @@ import torch.nn as nn
class AFF(nn.Module):
def __init__(self, channels=64, r=4):
super(AFF, self).__init__()
inter_channels = int(channels // r)
@ -23,7 +22,6 @@ class AFF(nn.Module):
xa = torch.cat((x, ds_y), dim=1)
x_att = self.local_att(xa)
x_att = 1.0 + torch.tanh(x_att)
xo = torch.mul(x, x_att) + torch.mul(ds_y, 2.0-x_att)
xo = torch.mul(x, x_att) + torch.mul(ds_y, 2.0 - x_att)
return xo

49
GPT_SoVITS/eres2net/kaldi.py
Unescape Escape View File

@ -144,7 +144,7 @@ def _get_waveform_and_window_properties(
)
assert 0 < window_shift, "`window_shift` must be greater than 0"
assert padded_window_size % 2 == 0, (
"the padded `window_size` must be divisible by two." " use `round_to_power_of_two` or change `frame_length`"
"the padded `window_size` must be divisible by two. use `round_to_power_of_two` or change `frame_length`"
)
assert 0.0 <= preemphasis_coefficient <= 1.0, "`preemphasis_coefficient` must be between [0,1]"
assert sample_frequency > 0, "`sample_frequency` must be greater than zero"
@ -441,7 +441,9 @@ def get_mel_banks(
high_freq: float,
vtln_low: float,
vtln_high: float,
vtln_warp_factor: float,device=None,dtype=None
vtln_warp_factor: float,
device=None,
dtype=None,
) -> Tuple[Tensor, Tensor]:
"""
Returns:
@ -457,9 +459,9 @@ def get_mel_banks(
if high_freq <= 0.0:
high_freq += nyquist
assert (
(0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq)
), "Bad values in options: low-freq {} and high-freq {} vs. nyquist {}".format(low_freq, high_freq, nyquist)
assert (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq), (
"Bad values in options: low-freq {} and high-freq {} vs. nyquist {}".format(low_freq, high_freq, nyquist)
)
# fft-bin width [think of it as Nyquist-freq / half-window-length]
fft_bin_width = sample_freq / window_length_padded
@ -475,7 +477,7 @@ def get_mel_banks(
assert vtln_warp_factor == 1.0 or (
(low_freq < vtln_low < high_freq) and (0.0 < vtln_high < high_freq) and (vtln_low < vtln_high)
), "Bad values in options: vtln-low {} and vtln-high {}, versus " "low-freq {} and high-freq {}".format(
), "Bad values in options: vtln-low {} and vtln-high {}, versus low-freq {} and high-freq {}".format(
vtln_low, vtln_high, low_freq, high_freq
)
@ -508,9 +510,12 @@ def get_mel_banks(
bins[up_idx] = up_slope[up_idx]
bins[down_idx] = down_slope[down_idx]
return bins.to(device=device,dtype=dtype)#, center_freqs
return bins.to(device=device, dtype=dtype) # , center_freqs
cache = {}
cache={}
def fbank(
waveform: Tensor,
blackman_coeff: float = 0.42,
@ -620,14 +625,34 @@ def fbank(
# size (num_mel_bins, padded_window_size // 2)
# print(num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp)
cache_key="%s-%s-%s-%s-%s-%s-%s-%s-%s-%s"%(num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp,device,dtype)
cache_key = "%s-%s-%s-%s-%s-%s-%s-%s-%s-%s" % (
num_mel_bins,
padded_window_size,
sample_frequency,
low_freq,
high_freq,
vtln_low,
vtln_high,
vtln_warp,
device,
dtype,
)
if cache_key not in cache:
mel_energies = get_mel_banks(
num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp,device,dtype
num_mel_bins,
padded_window_size,
sample_frequency,
low_freq,
high_freq,
vtln_low,
vtln_high,
vtln_warp,
device,
dtype,
)
cache[cache_key]=mel_energies
cache[cache_key] = mel_energies
else:
mel_energies=cache[cache_key]
mel_energies = cache[cache_key]
# pad right column with zeros and add dimension, size (num_mel_bins, padded_window_size // 2 + 1)
mel_energies = torch.nn.functional.pad(mel_energies, (0, 1), mode="constant", value=0)

27
GPT_SoVITS/eres2net/pooling_layers.py
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@ -1,7 +1,7 @@
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
""" This implementation is adapted from https://github.com/wenet-e2e/wespeaker."""
"""This implementation is adapted from https://github.com/wenet-e2e/wespeaker."""
import torch
import torch.nn as nn
@ -11,6 +11,7 @@ class TAP(nn.Module):
"""
Temporal average pooling, only first-order mean is considered
"""
def __init__(self, **kwargs):
super(TAP, self).__init__()
@ -25,6 +26,7 @@ class TSDP(nn.Module):
"""
Temporal standard deviation pooling, only second-order std is considered
"""
def __init__(self, **kwargs):
super(TSDP, self).__init__()
@ -41,6 +43,7 @@ class TSTP(nn.Module):
x-vector
Comment: simple concatenation can not make full use of both statistics
"""
def __init__(self, **kwargs):
super(TSTP, self).__init__()
@ -56,9 +59,10 @@ class TSTP(nn.Module):
class ASTP(nn.Module):
""" Attentive statistics pooling: Channel- and context-dependent
statistics pooling, first used in ECAPA_TDNN.
"""Attentive statistics pooling: Channel- and context-dependent
statistics pooling, first used in ECAPA_TDNN.
"""
def __init__(self, in_dim, bottleneck_dim=128, global_context_att=False):
super(ASTP, self).__init__()
self.global_context_att = global_context_att
@ -66,15 +70,10 @@ class ASTP(nn.Module):
# Use Conv1d with stride == 1 rather than Linear, then we don't
# need to transpose inputs.
if global_context_att:
self.linear1 = nn.Conv1d(
in_dim * 3, bottleneck_dim,
kernel_size=1) # equals W and b in the paper
self.linear1 = nn.Conv1d(in_dim * 3, bottleneck_dim, kernel_size=1) # equals W and b in the paper
else:
self.linear1 = nn.Conv1d(
in_dim, bottleneck_dim,
kernel_size=1) # equals W and b in the paper
self.linear2 = nn.Conv1d(bottleneck_dim, in_dim,
kernel_size=1) # equals V and k in the paper
self.linear1 = nn.Conv1d(in_dim, bottleneck_dim, kernel_size=1) # equals W and b in the paper
self.linear2 = nn.Conv1d(bottleneck_dim, in_dim, kernel_size=1) # equals V and k in the paper
def forward(self, x):
"""
@ -88,15 +87,13 @@ class ASTP(nn.Module):
if self.global_context_att:
context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
context_std = torch.sqrt(
torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
context_std = torch.sqrt(torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
x_in = torch.cat((x, context_mean, context_std), dim=1)
else:
x_in = x
# DON'T use ReLU here! ReLU may be hard to converge.
alpha = torch.tanh(
self.linear1(x_in)) # alpha = F.relu(self.linear1(x_in))
alpha = torch.tanh(self.linear1(x_in)) # alpha = F.relu(self.linear1(x_in))
alpha = torch.softmax(self.linear2(alpha), dim=2)
mean = torch.sum(alpha * x, dim=2)
var = torch.sum(alpha * (x**2), dim=2) - mean**2

59
GPT_SoVITS/export_torch_script.py
Unescape Escape View File

@ -37,10 +37,13 @@ default_config = {
}
sv_cn_model = None
def init_sv_cn(device, is_half):
global sv_cn_model
sv_cn_model = SV(device, is_half)
def load_sovits_new(sovits_path):
f = open(sovits_path, "rb")
meta = f.read(2)
@ -129,7 +132,9 @@ def sample(
@torch.jit.script
def spectrogram_torch(hann_window:Tensor, y: Tensor, n_fft: int, sampling_rate: int, hop_size: int, win_size: int, center: bool = False):
def spectrogram_torch(
hann_window: Tensor, y: Tensor, n_fft: int, sampling_rate: int, hop_size: int, win_size: int, center: bool = False
):
# hann_window = torch.hann_window(win_size, device=y.device, dtype=y.dtype)
y = torch.nn.functional.pad(
y.unsqueeze(1),
@ -380,8 +385,9 @@ class VitsModel(nn.Module):
self.vq_model = self.vq_model.half()
self.vq_model = self.vq_model.to(device)
self.vq_model.eval()
self.hann_window = torch.hann_window(self.hps.data.win_length, device=device, dtype= torch.float16 if is_half else torch.float32)
self.hann_window = torch.hann_window(
self.hps.data.win_length, device=device, dtype=torch.float16 if is_half else torch.float32
)
def forward(self, text_seq, pred_semantic, ref_audio, speed=1.0, sv_emb=None):
refer = spectrogram_torch(
@ -667,7 +673,9 @@ def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path, export_be
ref_seq = torch.LongTensor([ref_seq_id]).to(device)
ref_bert = ref_bert_T.T.to(ref_seq.device)
text_seq_id, text_bert_T, norm_text = get_phones_and_bert(
"这是一个简单的示例，真没想到这么简单就完成了。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.", "auto", "v2"
"这是一个简单的示例，真没想到这么简单就完成了。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.",
"auto",
"v2",
)
text_seq = torch.LongTensor([text_seq_id]).to(device)
text_bert = text_bert_T.T.to(text_seq.device)
@ -675,7 +683,7 @@ def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path, export_be
ssl_content = ssl(ref_audio).to(device)
# vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
vits = VitsModel(vits_path,device=device,is_half=False)
vits = VitsModel(vits_path, device=device, is_half=False)
vits.eval()
# gpt_path = "GPT_weights_v2/xw-e15.ckpt"
@ -726,7 +734,7 @@ def export_prov2(
is_half=True,
):
if sv_cn_model == None:
init_sv_cn(device,is_half)
init_sv_cn(device, is_half)
if not os.path.exists(output_path):
os.makedirs(output_path)
@ -747,9 +755,7 @@ def export_prov2(
print(f"device: {device}")
ref_seq_id, ref_bert_T, ref_norm_text = get_phones_and_bert(
ref_text, "all_zh", "v2"
)
ref_seq_id, ref_bert_T, ref_norm_text = get_phones_and_bert(ref_text, "all_zh", "v2")
ref_seq = torch.LongTensor([ref_seq_id]).to(device)
ref_bert = ref_bert_T.T
if is_half:
@ -757,7 +763,9 @@ def export_prov2(
ref_bert = ref_bert.to(ref_seq.device)
text_seq_id, text_bert_T, norm_text = get_phones_and_bert(
"这是一个简单的示例，真没想到这么简单就完成了。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.", "auto", "v2"
"这是一个简单的示例，真没想到这么简单就完成了。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.",
"auto",
"v2",
)
text_seq = torch.LongTensor([text_seq_id]).to(device)
text_bert = text_bert_T.T
@ -773,7 +781,7 @@ def export_prov2(
sv_model = ExportERes2NetV2(sv_cn_model)
# vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
vits = VitsModel(vits_path, version,is_half=is_half,device=device)
vits = VitsModel(vits_path, version, is_half=is_half, device=device)
vits.eval()
# gpt_path = "GPT_weights_v2/xw-e15.ckpt"
@ -871,7 +879,7 @@ class GPT_SoVITS(nn.Module):
class ExportERes2NetV2(nn.Module):
def __init__(self, sv_cn_model:SV):
def __init__(self, sv_cn_model: SV):
super(ExportERes2NetV2, self).__init__()
self.bn1 = sv_cn_model.embedding_model.bn1
self.conv1 = sv_cn_model.embedding_model.conv1
@ -898,11 +906,11 @@ class ExportERes2NetV2(nn.Module):
out4 = self.layer4(out3)
out3_ds = self.layer3_ds(out3)
fuse_out34 = self.fuse34(out4, out3_ds)
return fuse_out34.flatten(start_dim=1,end_dim=2).mean(-1)
return fuse_out34.flatten(start_dim=1, end_dim=2).mean(-1)
class GPT_SoVITS_V2Pro(nn.Module):
def __init__(self, t2s: T2SModel, vits: VitsModel,sv_model:ExportERes2NetV2):
def __init__(self, t2s: T2SModel, vits: VitsModel, sv_model: ExportERes2NetV2):
super().__init__()
self.t2s = t2s
self.vits = vits
@ -930,6 +938,7 @@ class GPT_SoVITS_V2Pro(nn.Module):
audio = self.vits(text_seq, pred_semantic, ref_audio_sr, speed, sv_emb)
return audio
def test():
parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
parser.add_argument("--gpt_model", required=True, help="Path to the GPT model file")
@ -1046,24 +1055,14 @@ def export_symbel(version="v2"):
def main():
parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
parser.add_argument("--gpt_model", required=True, help="Path to the GPT model file")
parser.add_argument(
"--sovits_model", required=True, help="Path to the SoVITS model file"
)
parser.add_argument(
"--ref_audio", required=True, help="Path to the reference audio file"
)
parser.add_argument(
"--ref_text", required=True, help="Path to the reference text file"
)
parser.add_argument(
"--output_path", required=True, help="Path to the output directory"
)
parser.add_argument(
"--export_common_model", action="store_true", help="Export Bert and SSL model"
)
parser.add_argument("--sovits_model", required=True, help="Path to the SoVITS model file")
parser.add_argument("--ref_audio", required=True, help="Path to the reference audio file")
parser.add_argument("--ref_text", required=True, help="Path to the reference text file")
parser.add_argument("--output_path", required=True, help="Path to the output directory")
parser.add_argument("--export_common_model", action="store_true", help="Export Bert and SSL model")
parser.add_argument("--device", help="Device to use")
parser.add_argument("--version", help="version of the model", default="v2")
parser.add_argument("--no-half", action="store_true", help = "Do not use half precision for model weights")
parser.add_argument("--no-half", action="store_true", help="Do not use half precision for model weights")
args = parser.parse_args()
if args.version in ["v2Pro", "v2ProPlus"]:

22
GPT_SoVITS/export_torch_script_v3v4.py
Unescape Escape View File

@ -406,7 +406,7 @@ class GPTSoVITSV3(torch.nn.Module):
chunk_len = 934 - fea_ref.shape[2]
wav_gen_list = []
idx = 0
fea_todo = fea_todo[:,:,:-5]
fea_todo = fea_todo[:, :, :-5]
wav_gen_length = fea_todo.shape[2] * 256
while 1:
# current_time = datetime.now()
@ -438,7 +438,8 @@ class GPTSoVITSV3(torch.nn.Module):
wav_gen = torch.cat(wav_gen_list, 2)
return wav_gen[0][0][:wav_gen_length]
class GPTSoVITSV4(torch.nn.Module):
def __init__(self, gpt_sovits_half, cfm, hifigan):
super().__init__()
@ -465,7 +466,7 @@ class GPTSoVITSV4(torch.nn.Module):
chunk_len = 1000 - fea_ref.shape[2]
wav_gen_list = []
idx = 0
fea_todo = fea_todo[:,:,:-10]
fea_todo = fea_todo[:, :, :-10]
wav_gen_length = fea_todo.shape[2] * 480
while 1:
# current_time = datetime.now()
@ -581,6 +582,7 @@ from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
v3v4set = {"v3", "v4"}
def get_sovits_weights(sovits_path):
path_sovits_v3 = "GPT_SoVITS/pretrained_models/s2Gv3.pth"
is_exist_s2gv3 = os.path.exists(path_sovits_v3)
@ -703,14 +705,13 @@ def export_cfm(
return export_cfm
def export_1(ref_wav_path,ref_wav_text,version="v3"):
def export_1(ref_wav_path, ref_wav_text, version="v3"):
if version == "v3":
sovits = get_sovits_weights("GPT_SoVITS/pretrained_models/s2Gv3.pth")
init_bigvgan()
else:
sovits = get_sovits_weights("GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth")
init_hifigan()
dict_s1 = torch.load("GPT_SoVITS/pretrained_models/s1v3.ckpt")
raw_t2s = get_raw_t2s_model(dict_s1).to(device)
@ -755,9 +756,7 @@ def export_1(ref_wav_path,ref_wav_text,version="v3"):
# phones1, bert1, norm_text1 = get_phones_and_bert(
# "你这老坏蛋，我找了你这么久，真没想到在这里找到你。他说。", "all_zh", "v3"
# )
phones1, bert1, norm_text1 = get_phones_and_bert(
ref_wav_text, "auto", "v3"
)
phones1, bert1, norm_text1 = get_phones_and_bert(ref_wav_text, "auto", "v3")
phones2, bert2, norm_text2 = get_phones_and_bert(
"这是一个简单的示例，真没想到这么简单就完成了。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.",
"auto",
@ -918,7 +917,7 @@ def export_1(ref_wav_path,ref_wav_text,version="v3"):
hifigan_model_ = torch.jit.trace(hifigan_model, optimize=True, example_inputs=(cmf_res_rand,))
hifigan_model_.save("onnx/ad/hifigan_model.pt")
wav_gen = hifigan_model(cmf_res)
print("wav_gen:", wav_gen.shape, wav_gen.dtype)
audio = wav_gen[0][0].cpu().detach().numpy()
@ -1205,7 +1204,6 @@ def export_2(version="v3"):
gpt_sovits_v3v4 = gpt_sovits_v3 if version == "v3" else gpt_sovits_v4
sr = 24000 if version == "v3" else 48000
time.sleep(5)
# print("thread:", torch.get_num_threads())
# print("thread:", torch.get_num_interop_threads())
@ -1216,14 +1214,14 @@ def export_2(version="v3"):
"汗流浃背了呀!老弟~ My uncle has two dogs. One is big and the other is small. He likes them very much. He often plays with them. He takes them for a walk every day. He says they are his good friends. He is very happy with them. 最后还是我得了 MVP....",
gpt_sovits_v3v4,
"out.wav",
sr
sr,
)
test_export(
"你小子是什么来路.汗流浃背了呀!老弟~ My uncle has two dogs. He is very happy with them. 最后还是我得了 MVP!",
gpt_sovits_v3v4,
"out2.wav",
sr
sr,
)
# test_export(

8
GPT_SoVITS/f5_tts/model/backbones/dit.py
Unescape Escape View File

@ -143,9 +143,9 @@ class DiT(nn.Module):
drop_audio_cond=False, # cfg for cond audio
drop_text=False, # cfg for text
# mask: bool["b n"] | None = None, # noqa: F722
infer=False, # bool
text_cache=None, # torch tensor as text_embed
dt_cache=None, # torch tensor as dt
infer=False, # bool
text_cache=None, # torch tensor as text_embed
dt_cache=None, # torch tensor as dt
):
x = x0.transpose(2, 1)
cond = cond0.transpose(2, 1)
@ -191,4 +191,4 @@ class DiT(nn.Module):
if infer:
return output, text_embed, dt
else:
return output
return output

41
GPT_SoVITS/module/data_utils.py
Unescape Escape View File

@ -21,7 +21,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
3) computes spectrograms from audio files.
"""
def __init__(self, hparams, version=None,val=False):
def __init__(self, hparams, version=None, val=False):
exp_dir = hparams.exp_dir
self.path2 = "%s/2-name2text.txt" % exp_dir
self.path4 = "%s/4-cnhubert" % exp_dir
@ -29,7 +29,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
assert os.path.exists(self.path2)
assert os.path.exists(self.path4)
assert os.path.exists(self.path5)
self.is_v2Pro=version in {"v2Pro","v2ProPlus"}
self.is_v2Pro = version in {"v2Pro", "v2ProPlus"}
if self.is_v2Pro:
self.path7 = "%s/7-sv_cn" % exp_dir
assert os.path.exists(self.path7)
@ -118,7 +118,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
ssl = F.pad(ssl.float(), (0, 1), mode="replicate").to(typee)
ssl.requires_grad = False
if self.is_v2Pro:
sv_emb=torch.load("%s/%s.pt" % (self.path7, audiopath), map_location="cpu")
sv_emb = torch.load("%s/%s.pt" % (self.path7, audiopath), map_location="cpu")
except:
traceback.print_exc()
spec = torch.zeros(1025, 100)
@ -126,10 +126,10 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
ssl = torch.zeros(1, 768, 100)
text = text[-1:]
if self.is_v2Pro:
sv_emb=torch.zeros(1,20480)
sv_emb = torch.zeros(1, 20480)
print("load audio or ssl error!!!!!!", audiopath)
if self.is_v2Pro:
return (ssl, spec, wav, text,sv_emb)
return (ssl, spec, wav, text, sv_emb)
else:
return (ssl, spec, wav, text)
@ -192,9 +192,9 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
class TextAudioSpeakerCollate:
"""Zero-pads model inputs and targets"""
def __init__(self, return_ids=False,version=None):
def __init__(self, return_ids=False, version=None):
self.return_ids = return_ids
self.is_v2Pro=version in {"v2Pro","v2ProPlus"}
self.is_v2Pro = version in {"v2Pro", "v2ProPlus"}
def __call__(self, batch):
"""Collate's training batch from normalized text, audio and speaker identities
@ -228,7 +228,7 @@ class TextAudioSpeakerCollate:
text_padded.zero_()
if self.is_v2Pro:
sv_embs=torch.FloatTensor(len(batch),20480)
sv_embs = torch.FloatTensor(len(batch), 20480)
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
@ -250,11 +250,30 @@ class TextAudioSpeakerCollate:
text_lengths[i] = text.size(0)
if self.is_v2Pro:
sv_embs[i]=row[4]
sv_embs[i] = row[4]
if self.is_v2Pro:
return ssl_padded, ssl_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, text_padded, text_lengths,sv_embs
return (
ssl_padded,
ssl_lengths,
spec_padded,
spec_lengths,
wav_padded,
wav_lengths,
text_padded,
text_lengths,
sv_embs,
)
else:
return ssl_padded, ssl_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, text_padded, text_lengths
return (
ssl_padded,
ssl_lengths,
spec_padded,
spec_lengths,
wav_padded,
wav_lengths,
text_padded,
text_lengths,
)
class TextAudioSpeakerLoaderV3(torch.utils.data.Dataset):

71
GPT_SoVITS/module/models.py
Unescape Escape View File

@ -586,12 +586,17 @@ class DiscriminatorS(torch.nn.Module):
return x, fmap
v2pro_set={"v2Pro","v2ProPlus"}
v2pro_set = {"v2Pro", "v2ProPlus"}
class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, use_spectral_norm=False,version=None):
def __init__(self, use_spectral_norm=False, version=None):
super(MultiPeriodDiscriminator, self).__init__()
if version in v2pro_set:periods = [2, 3, 5, 7, 11,17,23]
else:periods = [2, 3, 5, 7, 11]
if version in v2pro_set:
periods = [2, 3, 5, 7, 11, 17, 23]
else:
periods = [2, 3, 5, 7, 11]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
@ -787,6 +792,7 @@ class CodePredictor(nn.Module):
return pred_codes.transpose(0, 1)
class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
@ -886,13 +892,13 @@ class SynthesizerTrn(nn.Module):
self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024)
self.freeze_quantizer = freeze_quantizer
self.is_v2pro=self.version in v2pro_set
self.is_v2pro = self.version in v2pro_set
if self.is_v2pro:
self.sv_emb = nn.Linear(20480, gin_channels)
self.ge_to512 = nn.Linear(gin_channels, 512)
self.prelu = nn.PReLU(num_parameters=gin_channels)
def forward(self, ssl, y, y_lengths, text, text_lengths,sv_emb=None):
def forward(self, ssl, y, y_lengths, text, text_lengths, sv_emb=None):
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
if self.version == "v1":
ge = self.ref_enc(y * y_mask, y_mask)
@ -952,7 +958,7 @@ class SynthesizerTrn(nn.Module):
return o, y_mask, (z, z_p, m_p, logs_p)
@torch.no_grad()
def decode(self, codes, text, refer,noise_scale=0.5, speed=1, sv_emb=None):
def decode(self, codes, text, refer, noise_scale=0.5, speed=1, sv_emb=None):
def get_ge(refer, sv_emb):
ge = None
if refer is not None:
@ -970,8 +976,8 @@ class SynthesizerTrn(nn.Module):
if type(refer) == list:
ges = []
for idx,_refer in enumerate(refer):
ge = get_ge(_refer, sv_emb[idx]if self.is_v2pro else None)
for idx, _refer in enumerate(refer):
ge = get_ge(_refer, sv_emb[idx] if self.is_v2pro else None)
ges.append(ge)
ge = torch.stack(ges, 0).mean(0)
else:
@ -983,7 +989,14 @@ class SynthesizerTrn(nn.Module):
quantized = self.quantizer.decode(codes)
if self.semantic_frame_rate == "25hz":
quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, self.ge_to512(ge.transpose(2,1)).transpose(2,1)if self.is_v2pro else ge, speed)
x, m_p, logs_p, y_mask = self.enc_p(
quantized,
y_lengths,
text,
text_lengths,
self.ge_to512(ge.transpose(2, 1)).transpose(2, 1) if self.is_v2pro else ge,
speed,
)
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
z = self.flow(z_p, y_mask, g=ge, reverse=True)
@ -996,6 +1009,7 @@ class SynthesizerTrn(nn.Module):
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
return codes.transpose(0, 1)
class CFM(torch.nn.Module):
def __init__(self, in_channels, dit):
super().__init__()
@ -1029,7 +1043,18 @@ class CFM(torch.nn.Module):
t_tensor = torch.ones(x.shape[0], device=x.device, dtype=mu.dtype) * t
# v_pred = model(x, t_tensor, d_tensor, **extra_args)
v_pred, text_emb, dt = self.estimator(
x, prompt_x, x_lens, t_tensor, d_tensor, mu, use_grad_ckpt=False, drop_audio_cond=False, drop_text=False, infer=True, text_cache=text_cache, dt_cache=dt_cache
x,
prompt_x,
x_lens,
t_tensor,
d_tensor,
mu,
use_grad_ckpt=False,
drop_audio_cond=False,
drop_text=False,
infer=True,
text_cache=text_cache,
dt_cache=dt_cache,
)
v_pred = v_pred.transpose(2, 1)
if self.use_conditioner_cache:
@ -1037,18 +1062,18 @@ class CFM(torch.nn.Module):
dt_cache = dt
if inference_cfg_rate > 1e-5:
neg, text_cfg_emb, _ = self.estimator(
x,
prompt_x,
x_lens,
t_tensor,
d_tensor,
mu,
use_grad_ckpt=False,
drop_audio_cond=True,
drop_text=True,
infer=True,
text_cache=text_cfg_cache,
dt_cache=dt_cache
x,
prompt_x,
x_lens,
t_tensor,
d_tensor,
mu,
use_grad_ckpt=False,
drop_audio_cond=True,
drop_text=True,
infer=True,
text_cache=text_cfg_cache,
dt_cache=dt_cache,
)
neg = neg.transpose(2, 1)
if self.use_conditioner_cache:

8
GPT_SoVITS/module/models_onnx.py
Unescape Escape View File

@ -762,7 +762,9 @@ class CodePredictor(nn.Module):
return pred_codes.transpose(0, 1)
v2pro_set={"v2Pro","v2ProPlus"}
v2pro_set = {"v2Pro", "v2ProPlus"}
class SynthesizerTrn(nn.Module):
"""
@ -868,7 +870,7 @@ class SynthesizerTrn(nn.Module):
# self.enc_p.text_embedding.requires_grad_(False)
# self.enc_p.encoder_text.requires_grad_(False)
# self.enc_p.mrte.requires_grad_(False)
self.is_v2pro=self.version in v2pro_set
self.is_v2pro = self.version in v2pro_set
if self.is_v2pro:
self.sv_emb = nn.Linear(20480, gin_channels)
self.ge_to512 = nn.Linear(gin_channels, 512)
@ -891,7 +893,7 @@ class SynthesizerTrn(nn.Module):
quantized = dquantized.contiguous().view(1, self.ssl_dim, -1)
if self.is_v2pro:
ge_ = self.ge_to512(ge.transpose(2,1)).transpose(2,1)
ge_ = self.ge_to512(ge.transpose(2, 1)).transpose(2, 1)
x, m_p, logs_p, y_mask = self.enc_p(quantized, text, ge_, speed)
else:
x, m_p, logs_p, y_mask = self.enc_p(quantized, text, ge, speed)

7
GPT_SoVITS/module/modules.py
Unescape Escape View File

@ -1,5 +1,4 @@
import math
import pdb
import numpy as np
import torch
@ -720,10 +719,10 @@ class MelStyleEncoder(nn.Module):
else:
len_ = (~mask).sum(dim=1).unsqueeze(1)
x = x.masked_fill(mask.unsqueeze(-1), 0)
dtype=x.dtype
dtype = x.dtype
x = x.float()
x=torch.div(x,len_.unsqueeze(1))
out=x.sum(dim=1).to(dtype)
x = torch.div(x, len_.unsqueeze(1))
out = x.sum(dim=1).to(dtype)
return out
def forward(self, x, mask=None):

50
GPT_SoVITS/prepare_datasets/2-get-sv.py
Unescape Escape View File

@ -10,7 +10,6 @@ i_part = os.environ.get("i_part")
all_parts = os.environ.get("all_parts")
if "_CUDA_VISIBLE_DEVICES" in os.environ:
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
from feature_extractor import cnhubert
opt_dir = os.environ.get("opt_dir")
sv_path = os.environ.get("sv_path")
@ -19,19 +18,18 @@ import torch
is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
import traceback
import numpy as np
from scipy.io import wavfile
import torchaudio
now_dir = os.getcwd()
sys.path.append(now_dir)
sys.path.append(f"{now_dir}/GPT_SoVITS/eres2net")
from tools.my_utils import load_audio, clean_path
from tools.my_utils import clean_path
from time import time as ttime
import shutil
from ERes2NetV2 import ERes2NetV2
import kaldi as Kaldi
def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path
dir = os.path.dirname(path)
name = os.path.basename(path)
@ -56,37 +54,45 @@ if torch.cuda.is_available():
else:
device = "cpu"
class SV:
def __init__(self,device,is_half):
pretrained_state = torch.load(sv_path, map_location='cpu')
embedding_model = ERes2NetV2(baseWidth=24,scale=4,expansion=4)
def __init__(self, device, is_half):
pretrained_state = torch.load(sv_path, map_location="cpu")
embedding_model = ERes2NetV2(baseWidth=24, scale=4, expansion=4)
embedding_model.load_state_dict(pretrained_state)
embedding_model.eval()
self.embedding_model=embedding_model
self.res=torchaudio.transforms.Resample(32000, 16000).to(device)
self.embedding_model = embedding_model
self.res = torchaudio.transforms.Resample(32000, 16000).to(device)
if is_half == False:
self.embedding_model=self.embedding_model.to(device)
self.embedding_model = self.embedding_model.to(device)
else:
self.embedding_model=self.embedding_model.half().to(device)
self.is_half=is_half
self.embedding_model = self.embedding_model.half().to(device)
self.is_half = is_half
def compute_embedding3(self,wav):#(1,x)#-1~1
def compute_embedding3(self, wav): # (1,x)#-1~1
with torch.no_grad():
wav=self.res(wav)
if self.is_half==True:wav=wav.half()
feat = torch.stack([Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav])
wav = self.res(wav)
if self.is_half == True:
wav = wav.half()
feat = torch.stack(
[Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav]
)
sv_emb = self.embedding_model.forward3(feat)
return sv_emb
sv=SV(device,is_half)
sv = SV(device, is_half)
def name2go(wav_name, wav_path):
sv_cn_path = "%s/%s.pt" % (sv_cn_dir, wav_name)
if os.path.exists(sv_cn_path):return
wav_path="%s/%s" % (wav32dir, wav_name)
wav32k,sr0 = torchaudio.load(wav_path)
assert sr0==32000
if os.path.exists(sv_cn_path):
return
wav_path = "%s/%s" % (wav32dir, wav_name)
wav32k, sr0 = torchaudio.load(wav_path)
assert sr0 == 32000
wav32k = wav32k.to(device)
emb=sv.compute_embedding3(wav32k).cpu() # torch.Size([1, 20480])
emb = sv.compute_embedding3(wav32k).cpu() # torch.Size([1, 20480])
my_save(emb, sv_cn_path)

16
GPT_SoVITS/process_ckpt.py
Unescape Escape View File

@ -17,15 +17,16 @@ def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path
shutil.move(tmp_path, "%s/%s" % (dir, name))
from io import BytesIO
model_version2byte={
"v3":b"03",
"v4":b"04",
"v2Pro":b"05",
"v2ProPlus":b"06",
model_version2byte = {
"v3": b"03",
"v4": b"04",
"v2Pro": b"05",
"v2ProPlus": b"06",
}
def my_save2(fea, path, model_version):
bio = BytesIO()
torch.save(fea, bio)
@ -50,7 +51,7 @@ def savee(ckpt, name, epoch, steps, hps, model_version=None, lora_rank=None):
if lora_rank:
opt["lora_rank"] = lora_rank
my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), model_version)
elif (model_version!=None and "Pro"in model_version):
elif model_version != None and "Pro" in model_version:
my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), model_version)
else:
my_save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
@ -58,6 +59,7 @@ def savee(ckpt, name, epoch, steps, hps, model_version=None, lora_rank=None):
except:
return traceback.format_exc()
"""
00:v1
01:v2

93
GPT_SoVITS/s2_train.py
Unescape Escape View File

@ -36,7 +36,7 @@ from module.models import (
MultiPeriodDiscriminator,
SynthesizerTrn,
)
from process_ckpt import savee,my_save2
from process_ckpt import savee
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = False
@ -87,11 +87,30 @@ def run(rank, n_gpus, hps):
if torch.cuda.is_available():
torch.cuda.set_device(rank)
train_dataset = TextAudioSpeakerLoader(hps.data,version=hps.model.version)
train_dataset = TextAudioSpeakerLoader(hps.data, version=hps.model.version)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size,
[32,300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,],
[
32,
300,
400,
500,
600,
700,
800,
900,
1000,
1100,
1200,
1300,
1400,
1500,
1600,
1700,
1800,
1900,
],
num_replicas=n_gpus,
rank=rank,
shuffle=True,
@ -130,9 +149,9 @@ def run(rank, n_gpus, hps):
)
net_d = (
MultiPeriodDiscriminator(hps.model.use_spectral_norm,version=hps.model.version).cuda(rank)
MultiPeriodDiscriminator(hps.model.use_spectral_norm, version=hps.model.version).cuda(rank)
if torch.cuda.is_available()
else MultiPeriodDiscriminator(hps.model.use_spectral_norm,version=hps.model.version).to(device)
else MultiPeriodDiscriminator(hps.model.use_spectral_norm, version=hps.model.version).to(device)
)
for name, param in net_g.named_parameters():
if not param.requires_grad:
@ -235,7 +254,7 @@ def run(rank, n_gpus, hps):
print(
"loaded pretrained %s" % hps.train.pretrained_s2D,
net_d.module.load_state_dict(
torch.load(hps.train.pretrained_s2D, map_location="cpu", weights_only=False)["weight"],strict=False
torch.load(hps.train.pretrained_s2D, map_location="cpu", weights_only=False)["weight"], strict=False
)
if torch.cuda.is_available()
else net_d.load_state_dict(
@ -310,17 +329,44 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
net_g.train()
net_d.train()
for batch_idx, data in enumerate(tqdm(train_loader)):
if hps.model.version in {"v2Pro","v2ProPlus"}:
ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths,sv_emb=data
if hps.model.version in {"v2Pro", "v2ProPlus"}:
ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths, sv_emb = data
else:
ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths=data
ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths = data
if torch.cuda.is_available():
spec, spec_lengths = (spec.cuda(rank,non_blocking=True,),spec_lengths.cuda(rank,non_blocking=True,),)
y, y_lengths = (y.cuda(rank,non_blocking=True,),y_lengths.cuda(rank,non_blocking=True,),)
spec, spec_lengths = (
spec.cuda(
rank,
non_blocking=True,
),
spec_lengths.cuda(
rank,
non_blocking=True,
),
)
y, y_lengths = (
y.cuda(
rank,
non_blocking=True,
),
y_lengths.cuda(
rank,
non_blocking=True,
),
)
ssl = ssl.cuda(rank, non_blocking=True)
ssl.requires_grad = False
# ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
text, text_lengths = (text.cuda(rank,non_blocking=True,),text_lengths.cuda(rank,non_blocking=True,),)
text, text_lengths = (
text.cuda(
rank,
non_blocking=True,
),
text_lengths.cuda(
rank,
non_blocking=True,
),
)
if hps.model.version in {"v2Pro", "v2ProPlus"}:
sv_emb = sv_emb.cuda(rank, non_blocking=True)
else:
@ -334,9 +380,19 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
sv_emb = sv_emb.to(device)
with autocast(enabled=hps.train.fp16_run):
if hps.model.version in {"v2Pro", "v2ProPlus"}:
(y_hat,kl_ssl,ids_slice,x_mask,z_mask,(z, z_p, m_p, logs_p, m_q, logs_q),stats_ssl) = net_g(ssl, spec, spec_lengths, text, text_lengths,sv_emb)
(y_hat, kl_ssl, ids_slice, x_mask, z_mask, (z, z_p, m_p, logs_p, m_q, logs_q), stats_ssl) = net_g(
ssl, spec, spec_lengths, text, text_lengths, sv_emb
)
else:
(y_hat,kl_ssl,ids_slice,x_mask,z_mask,(z, z_p, m_p, logs_p, m_q, logs_q),stats_ssl,) = net_g(ssl, spec, spec_lengths, text, text_lengths)
(
y_hat,
kl_ssl,
ids_slice,
x_mask,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
stats_ssl,
) = net_g(ssl, spec, spec_lengths, text, text_lengths)
mel = spec_to_mel_torch(
spec,
@ -508,7 +564,14 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
% (
hps.name,
epoch,
savee(ckpt,hps.name + "_e%s_s%s" % (epoch, global_step),epoch,global_step,hps,model_version=None if hps.model.version not in {"v2Pro","v2ProPlus"}else hps.model.version),
savee(
ckpt,
hps.name + "_e%s_s%s" % (epoch, global_step),
epoch,
global_step,
hps,
model_version=None if hps.model.version not in {"v2Pro", "v2ProPlus"} else hps.model.version,
),
)
)

30
GPT_SoVITS/sv.py
Unescape Escape View File

@ -1,24 +1,32 @@
import sys,os,torch
import sys
import os
import torch
sys.path.append(f"{os.getcwd()}/GPT_SoVITS/eres2net")
sv_path = "GPT_SoVITS/pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt"
from ERes2NetV2 import ERes2NetV2
import kaldi as Kaldi
class SV:
def __init__(self,device,is_half):
pretrained_state = torch.load(sv_path, map_location='cpu', weights_only=False)
embedding_model = ERes2NetV2(baseWidth=24,scale=4,expansion=4)
def __init__(self, device, is_half):
pretrained_state = torch.load(sv_path, map_location="cpu", weights_only=False)
embedding_model = ERes2NetV2(baseWidth=24, scale=4, expansion=4)
embedding_model.load_state_dict(pretrained_state)
embedding_model.eval()
self.embedding_model=embedding_model
self.embedding_model = embedding_model
if is_half == False:
self.embedding_model=self.embedding_model.to(device)
self.embedding_model = self.embedding_model.to(device)
else:
self.embedding_model=self.embedding_model.half().to(device)
self.is_half=is_half
self.embedding_model = self.embedding_model.half().to(device)
self.is_half = is_half
def compute_embedding3(self,wav):
def compute_embedding3(self, wav):
with torch.no_grad():
if self.is_half==True:wav=wav.half()
feat = torch.stack([Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav])
if self.is_half == True:
wav = wav.half()
feat = torch.stack(
[Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav]
)
sv_emb = self.embedding_model.forward3(feat)
return sv_emb

107
GPT_SoVITS/text/LangSegmenter/langsegmenter.py
Unescape Escape View File

@ -3,38 +3,44 @@ import re
# jieba静音
import jieba
jieba.setLogLevel(logging.CRITICAL)
# 更改fast_langdetect大模型位置
from pathlib import Path
import fast_langdetect
fast_langdetect.infer._default_detector = fast_langdetect.infer.LangDetector(fast_langdetect.infer.LangDetectConfig(cache_dir=Path(__file__).parent.parent.parent / "pretrained_models" / "fast_langdetect"))
fast_langdetect.infer._default_detector = fast_langdetect.infer.LangDetector(
fast_langdetect.infer.LangDetectConfig(
cache_dir=Path(__file__).parent.parent.parent / "pretrained_models" / "fast_langdetect"
)
)
from split_lang import LangSplitter
def full_en(text):
pattern = r'^(?=.*[A-Za-z])[A-Za-z0-9\s\u0020-\u007E\u2000-\u206F\u3000-\u303F\uFF00-\uFFEF]+$'
pattern = r"^(?=.*[A-Za-z])[A-Za-z0-9\s\u0020-\u007E\u2000-\u206F\u3000-\u303F\uFF00-\uFFEF]+$"
return bool(re.match(pattern, text))
def full_cjk(text):
# 来自wiki
cjk_ranges = [
(0x4E00, 0x9FFF), # CJK Unified Ideographs
(0x3400, 0x4DB5), # CJK Extension A
(0x20000, 0x2A6DD), # CJK Extension B
(0x2A700, 0x2B73F), # CJK Extension C
(0x2B740, 0x2B81F), # CJK Extension D
(0x2B820, 0x2CEAF), # CJK Extension E
(0x2CEB0, 0x2EBEF), # CJK Extension F
(0x30000, 0x3134A), # CJK Extension G
(0x31350, 0x323AF), # CJK Extension H
(0x2EBF0, 0x2EE5D), # CJK Extension H
(0x4E00, 0x9FFF), # CJK Unified Ideographs
(0x3400, 0x4DB5), # CJK Extension A
(0x20000, 0x2A6DD), # CJK Extension B
(0x2A700, 0x2B73F), # CJK Extension C
(0x2B740, 0x2B81F), # CJK Extension D
(0x2B820, 0x2CEAF), # CJK Extension E
(0x2CEB0, 0x2EBEF), # CJK Extension F
(0x30000, 0x3134A), # CJK Extension G
(0x31350, 0x323AF), # CJK Extension H
(0x2EBF0, 0x2EE5D), # CJK Extension H
]
pattern = r'[0-9、-〜。！？.!?… /]+$'
pattern = r"[0-9、-〜。！？.!?… /]+$"
cjk_text = ""
for char in text:
@ -45,7 +51,7 @@ def full_cjk(text):
return cjk_text
def split_jako(tag_lang,item):
def split_jako(tag_lang, item):
if tag_lang == "ja":
pattern = r"([\u3041-\u3096\u3099\u309A\u30A1-\u30FA\u30FC]+(?:[0-9、-〜。！？.!?… ]+[\u3041-\u3096\u3099\u309A\u30A1-\u30FA\u30FC]*)*)"
else:
@ -53,41 +59,40 @@ def split_jako(tag_lang,item):
lang_list: list[dict] = []
tag = 0
for match in re.finditer(pattern, item['text']):
for match in re.finditer(pattern, item["text"]):
if match.start() > tag:
lang_list.append({'lang':item['lang'],'text':item['text'][tag:match.start()]})
lang_list.append({"lang": item["lang"], "text": item["text"][tag : match.start()]})
tag = match.end()
lang_list.append({'lang':tag_lang,'text':item['text'][match.start():match.end()]})
lang_list.append({"lang": tag_lang, "text": item["text"][match.start() : match.end()]})
if tag < len(item['text']):
lang_list.append({'lang':item['lang'],'text':item['text'][tag:len(item['text'])]})
if tag < len(item["text"]):
lang_list.append({"lang": item["lang"], "text": item["text"][tag : len(item["text"])]})
return lang_list
def merge_lang(lang_list, item):
if lang_list and item['lang'] == lang_list[-1]['lang']:
lang_list[-1]['text'] += item['text']
if lang_list and item["lang"] == lang_list[-1]["lang"]:
lang_list[-1]["text"] += item["text"]
else:
lang_list.append(item)
return lang_list
class LangSegmenter():
class LangSegmenter:
# 默认过滤器, 基于gsv目前四种语言
DEFAULT_LANG_MAP = {
"zh": "zh",
"yue": "zh", # 粤语
"wuu": "zh", # 吴语
"zh-cn": "zh",
"zh-tw": "x", # 繁体设置为x
"zh-tw": "x", # 繁体设置为x
"ko": "ko",
"ja": "ja",
"en": "en",
}
def getTexts(text):
lang_splitter = LangSplitter(lang_map=LangSegmenter.DEFAULT_LANG_MAP)
substr = lang_splitter.split_by_lang(text=text)
@ -95,18 +100,18 @@ class LangSegmenter():
lang_list: list[dict] = []
for _, item in enumerate(substr):
dict_item = {'lang':item.lang,'text':item.text}
dict_item = {"lang": item.lang, "text": item.text}
# 处理短英文被识别为其他语言的问题
if full_en(dict_item['text']):
dict_item['lang'] = 'en'
lang_list = merge_lang(lang_list,dict_item)
if full_en(dict_item["text"]):
dict_item["lang"] = "en"
lang_list = merge_lang(lang_list, dict_item)
continue
# 处理非日语夹日文的问题(不包含CJK)
ja_list: list[dict] = []
if dict_item['lang'] != 'ja':
ja_list = split_jako('ja',dict_item)
if dict_item["lang"] != "ja":
ja_list = split_jako("ja", dict_item)
if not ja_list:
ja_list.append(dict_item)
@ -115,8 +120,8 @@ class LangSegmenter():
ko_list: list[dict] = []
temp_list: list[dict] = []
for _, ko_item in enumerate(ja_list):
if ko_item["lang"] != 'ko':
ko_list = split_jako('ko',ko_item)
if ko_item["lang"] != "ko":
ko_list = split_jako("ko", ko_item)
if ko_list:
temp_list.extend(ko_list)
@ -126,50 +131,50 @@ class LangSegmenter():
# 未存在非日韩文夹日韩文
if len(temp_list) == 1:
# 未知语言检查是否为CJK
if dict_item['lang'] == 'x':
cjk_text = full_cjk(dict_item['text'])
if dict_item["lang"] == "x":
cjk_text = full_cjk(dict_item["text"])
if cjk_text:
dict_item = {'lang':'zh','text':cjk_text}
lang_list = merge_lang(lang_list,dict_item)
dict_item = {"lang": "zh", "text": cjk_text}
lang_list = merge_lang(lang_list, dict_item)
else:
lang_list = merge_lang(lang_list,dict_item)
lang_list = merge_lang(lang_list, dict_item)
continue
else:
lang_list = merge_lang(lang_list,dict_item)
lang_list = merge_lang(lang_list, dict_item)
continue
# 存在非日韩文夹日韩文
for _, temp_item in enumerate(temp_list):
# 未知语言检查是否为CJK
if temp_item['lang'] == 'x':
cjk_text = full_cjk(dict_item['text'])
if temp_item["lang"] == "x":
cjk_text = full_cjk(dict_item["text"])
if cjk_text:
dict_item = {'lang':'zh','text':cjk_text}
lang_list = merge_lang(lang_list,dict_item)
dict_item = {"lang": "zh", "text": cjk_text}
lang_list = merge_lang(lang_list, dict_item)
else:
lang_list = merge_lang(lang_list,dict_item)
lang_list = merge_lang(lang_list, dict_item)
else:
lang_list = merge_lang(lang_list,temp_item)
lang_list = merge_lang(lang_list, temp_item)
temp_list = lang_list
lang_list = []
for _, temp_item in enumerate(temp_list):
if temp_item['lang'] == 'x':
if temp_item["lang"] == "x":
if lang_list:
temp_item['lang'] = lang_list[-1]['lang']
temp_item["lang"] = lang_list[-1]["lang"]
elif len(temp_list) > 1:
temp_item['lang'] = temp_list[1]['lang']
temp_item["lang"] = temp_list[1]["lang"]
else:
temp_item['lang'] = 'zh'
temp_item["lang"] = "zh"
lang_list = merge_lang(lang_list,temp_item)
lang_list = merge_lang(lang_list, temp_item)
return lang_list
if __name__ == "__main__":
text = "MyGO?,你也喜欢まいご吗？"
print(LangSegmenter.getTexts(text))
text = "ねえ、知ってる？最近、僕は天文学を勉強してるんだ。君の瞳が星空みたいにキラキラしてるからさ。"
print(LangSegmenter.getTexts(text))
print(LangSegmenter.getTexts(text))

6
GPT_SoVITS/text/g2pw/onnx_api.py
Unescape Escape View File

@ -3,7 +3,6 @@
import json
import os
import traceback
import warnings
import zipfile
from typing import Any, Dict, List, Tuple
@ -23,8 +22,9 @@ from .utils import load_config
onnxruntime.set_default_logger_severity(3)
try:
onnxruntime.preload_dlls()
except:pass
#traceback.print_exc()
except:
pass
# traceback.print_exc()
warnings.filterwarnings("ignore")
model_version = "1.1"

6
GPT_SoVITS/text/tone_sandhi.py
Unescape Escape View File

@ -655,11 +655,7 @@ class ToneSandhi:
while i < len(seg):
word, pos = seg[i]
merged = False
if (
i - 1 >= 0
and word == "一"
and i + 1 < len(seg)
):
if i - 1 >= 0 and word == "一" and i + 1 < len(seg):
last = new_seg[-1] if new_seg else seg[i - 1]
if last[0] == seg[i + 1][0] and last[1] == "v" and seg[i + 1][1] == "v":
combined = last[0] + "一" + seg[i + 1][0]

17
README.md
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@ -9,10 +9,14 @@ A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.<br><br>
<!-- img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br> -->
[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/🤗%20-online%20demo-yellow.svg?style=for-the-badge)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb)
[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/HuggingFace-demo-blue.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits)
[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e)
[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/)
[![Change Log](https://img.shields.io/badge/Change%20Log-View%20Updates-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/en/Changelog_EN.md)
**English** | [**中文简体**](./docs/cn/README.md) | [**日本語**](./docs/ja/README.md) | [**한국어**](./docs/ko/README.md) | [**Türkçe**](./docs/tr/README.md)
@ -128,8 +132,9 @@ Due to rapid development in the codebase and a slower Docker image release cycle
- Check [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) for the latest available image tags
- Choose an appropriate image tag for your environment
- `Lite` means the Docker image does not include ASR models and UVR5 models. You can manually download the UVR5 models, while the program will automatically download the ASR models as needed
- `Lite` means the Docker image **does not include** ASR models and UVR5 models. You can manually download the UVR5 models, while the program will automatically download the ASR models as needed
- The appropriate architecture image (amd64/arm64) will be automatically pulled during Docker Compose
- Docker Compose will mount **all files** in the current directory. Please switch to the project root directory and **pull the latest code** before using the Docker image
- Optionally, build the image locally using the provided Dockerfile for the most up-to-date changes
#### Environment Variables
@ -333,7 +338,7 @@ Use v4 from v1/v2/v3 environment:
New Features:
1. Slightly higher VRAM usage than v2, surpassing v4's performance, with v2's hardware cost and speed.
[more details](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7))
[more details](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7)>)
2.v1/v2 and the v2Pro series share the same characteristics, while v3/v4 have similar features. For training sets with average audio quality, v1/v2/v2Pro can deliver decent results, but v3/v4 cannot. Additionally, the synthesized tone and timebre of v3/v4 lean more toward the reference audio rather than the overall training set.

84
api.py
Unescape Escape View File

@ -199,6 +199,8 @@ def is_full(*items): # 任意一项为空返回False
bigvgan_model = hifigan_model = sv_cn_model = None
def clean_hifigan_model():
global hifigan_model
if hifigan_model:
@ -208,6 +210,8 @@ def clean_hifigan_model():
torch.cuda.empty_cache()
except:
pass
def clean_bigvgan_model():
global bigvgan_model
if bigvgan_model:
@ -217,6 +221,8 @@ def clean_bigvgan_model():
torch.cuda.empty_cache()
except:
pass
def clean_sv_cn_model():
global sv_cn_model
if sv_cn_model:
@ -229,7 +235,7 @@ def clean_sv_cn_model():
def init_bigvgan():
global bigvgan_model, hifigan_model,sv_cn_model
global bigvgan_model, hifigan_model, sv_cn_model
from BigVGAN import bigvgan
bigvgan_model = bigvgan.BigVGAN.from_pretrained(
@ -247,7 +253,7 @@ def init_bigvgan():
def init_hifigan():
global hifigan_model, bigvgan_model,sv_cn_model
global hifigan_model, bigvgan_model, sv_cn_model
hifigan_model = Generator(
initial_channel=100,
resblock="1",
@ -262,7 +268,9 @@ def init_hifigan():
hifigan_model.eval()
hifigan_model.remove_weight_norm()
state_dict_g = torch.load(
"%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu", weights_only=False
"%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,),
map_location="cpu",
weights_only=False,
)
print("loading vocoder", hifigan_model.load_state_dict(state_dict_g))
if is_half == True:
@ -272,19 +280,21 @@ def init_hifigan():
from sv import SV
def init_sv_cn():
global hifigan_model, bigvgan_model, sv_cn_model
sv_cn_model = SV(device, is_half)
resample_transform_dict={}
def resample(audio_tensor, sr0,sr1,device):
resample_transform_dict = {}
def resample(audio_tensor, sr0, sr1, device):
global resample_transform_dict
key="%s-%s-%s"%(sr0,sr1,str(device))
key = "%s-%s-%s" % (sr0, sr1, str(device))
if key not in resample_transform_dict:
resample_transform_dict[key] = torchaudio.transforms.Resample(
sr0, sr1
).to(device)
resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
return resample_transform_dict[key](audio_tensor)
@ -370,6 +380,7 @@ from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
def get_sovits_weights(sovits_path):
from config import pretrained_sovits_name
path_sovits_v3 = pretrained_sovits_name["v3"]
path_sovits_v4 = pretrained_sovits_name["v4"]
is_exist_s2gv3 = os.path.exists(path_sovits_v3)
@ -628,15 +639,17 @@ class DictToAttrRecursive(dict):
def get_spepc(hps, filename, dtype, device, is_v2pro=False):
sr1=int(hps.data.sampling_rate)
audio, sr0=torchaudio.load(filename)
if sr0!=sr1:
audio=audio.to(device)
if(audio.shape[0]==2):audio=audio.mean(0).unsqueeze(0)
audio=resample(audio,sr0,sr1,device)
sr1 = int(hps.data.sampling_rate)
audio, sr0 = torchaudio.load(filename)
if sr0 != sr1:
audio = audio.to(device)
if audio.shape[0] == 2:
audio = audio.mean(0).unsqueeze(0)
audio = resample(audio, sr0, sr1, device)
else:
audio=audio.to(device)
if(audio.shape[0]==2):audio=audio.mean(0).unsqueeze(0)
audio = audio.to(device)
if audio.shape[0] == 2:
audio = audio.mean(0).unsqueeze(0)
maxx = audio.abs().max()
if maxx > 1:
@ -649,10 +662,10 @@ def get_spepc(hps, filename, dtype, device, is_v2pro=False):
hps.data.win_length,
center=False,
)
spec=spec.to(dtype)
if is_v2pro==True:
audio=resample(audio,sr1,16000,device).to(dtype)
return spec,audio
spec = spec.to(dtype)
if is_v2pro == True:
audio = resample(audio, sr1, 16000, device).to(dtype)
return spec, audio
def pack_audio(audio_bytes, data, rate):
@ -872,29 +885,29 @@ def get_tts_wav(
prompt_semantic = codes[0, 0]
prompt = prompt_semantic.unsqueeze(0).to(device)
is_v2pro = version in {"v2Pro","v2ProPlus"}
is_v2pro = version in {"v2Pro", "v2ProPlus"}
if version not in {"v3", "v4"}:
refers = []
if is_v2pro:
sv_emb= []
sv_emb = []
if sv_cn_model == None:
init_sv_cn()
if inp_refs:
for path in inp_refs:
try:#####这里加上提取sv的逻辑，要么一堆sv一堆refer，要么单个sv单个refer
refer,audio_tensor = get_spepc(hps, path.name, dtype, device, is_v2pro)
try: #####这里加上提取sv的逻辑，要么一堆sv一堆refer，要么单个sv单个refer
refer, audio_tensor = get_spepc(hps, path.name, dtype, device, is_v2pro)
refers.append(refer)
if is_v2pro:
sv_emb.append(sv_cn_model.compute_embedding3(audio_tensor))
except Exception as e:
logger.error(e)
if len(refers) == 0:
refers,audio_tensor = get_spepc(hps, ref_wav_path, dtype, device, is_v2pro)
refers=[refers]
refers, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device, is_v2pro)
refers = [refers]
if is_v2pro:
sv_emb=[sv_cn_model.compute_embedding3(audio_tensor)]
sv_emb = [sv_cn_model.compute_embedding3(audio_tensor)]
else:
refer,audio_tensor = get_spepc(hps, ref_wav_path, dtype, device)
refer, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device)
t1 = ttime()
# os.environ['version'] = version
@ -937,14 +950,22 @@ def get_tts_wav(
if version not in {"v3", "v4"}:
if is_v2pro:
audio = (
vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed,sv_emb=sv_emb)
vq_model.decode(
pred_semantic,
torch.LongTensor(phones2).to(device).unsqueeze(0),
refers,
speed=speed,
sv_emb=sv_emb,
)
.detach()
.cpu()
.numpy()[0, 0]
)
else:
audio = (
vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed)
vq_model.decode(
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed
)
.detach()
.cpu()
.numpy()[0, 0]
@ -1108,7 +1129,6 @@ def handle(
if not default_refer.is_ready():
return JSONResponse({"code": 400, "message": "未指定参考音频且接口无预设"}, status_code=400)
if cut_punc == None:
text = cut_text(text, default_cut_punc)
else:

14
config.py
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@ -144,7 +144,8 @@ webui_port_subfix = 9871
api_port = 9880
#Thanks to the contribution of @Karasukaigan and @XXXXRT666
# Thanks to the contribution of @Karasukaigan and @XXXXRT666
def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, float]:
cpu = torch.device("cpu")
cuda = torch.device(f"cuda:{idx}")
@ -157,10 +158,13 @@ def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, flo
mem_gb = mem_bytes / (1024**3) + 0.4
major, minor = capability
sm_version = major + minor / 10.0
is_16_series = bool(re.search(r"16\d{2}", name))and sm_version == 7.5
if mem_gb < 4 or sm_version < 5.3:return cpu, torch.float32, 0.0, 0.0
if sm_version == 6.1 or is_16_series==True:return cuda, torch.float32, sm_version, mem_gb
if sm_version > 6.1:return cuda, torch.float16, sm_version, mem_gb
is_16_series = bool(re.search(r"16\d{2}", name)) and sm_version == 7.5
if mem_gb < 4 or sm_version < 5.3:
return cpu, torch.float32, 0.0, 0.0
if sm_version == 6.1 or is_16_series == True:
return cuda, torch.float32, sm_version, mem_gb
if sm_version > 6.1:
return cuda, torch.float16, sm_version, mem_gb
return cpu, torch.float32, 0.0, 0.0

16
docker-compose.yaml
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@ -12,10 +12,6 @@ services:
- "9880:9880"
volumes:
- .:/workspace/GPT-SoVITS
- /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models
- /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel
- /dev/null:/workspace/GPT-SoVITS/tools/asr/models
- /dev/null:/workspace/GPT-SoVITS/tools/uvr5/uvr5_weights
environment:
- is_half=true
tty: true
@ -34,10 +30,6 @@ services:
- "9880:9880"
volumes:
- .:/workspace/GPT-SoVITS
- /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models
- /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel
- /dev/null:/workspace/GPT-SoVITS/tools/asr/models
- /dev/null:/workspace/GPT-SoVITS/tools/uvr5/uvr5_weights
- tools/asr/models:/workspace/models/asr_models
- tools/uvr5/uvr5_weights:/workspace/models/uvr5_weights
environment:
@ -58,10 +50,6 @@ services:
- "9880:9880"
volumes:
- .:/workspace/GPT-SoVITS
- /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models
- /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel
- /dev/null:/workspace/GPT-SoVITS/tools/asr/models
- /dev/null:/workspace/GPT-SoVITS/tools/uvr5/uvr5_weights
environment:
- is_half=true
tty: true
@ -80,10 +68,6 @@ services:
- "9880:9880"
volumes:
- .:/workspace/GPT-SoVITS
- /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models
- /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel
- /dev/null:/workspace/GPT-SoVITS/tools/asr/models
- /dev/null:/workspace/GPT-SoVITS/tools/uvr5/uvr5_weights
- tools/asr/models:/workspace/models/asr_models
- tools/uvr5/uvr5_weights:/workspace/models/uvr5_weights
environment:

17
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@ -7,12 +7,14 @@
<a href="https://trendshift.io/repositories/7033" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7033" alt="RVC-Boss%2FGPT-SoVITS | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
<!-- img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br> -->
[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb)
[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/HuggingFace-demo-blue.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits)
[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/🤗%20-online%20demo-yellow.svg?style=for-the-badge)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e)
[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/)
[![Change Log](https://img.shields.io/badge/更新日志-查看更新-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/cn/Changelog_CN.md)
[**English**](../../README.md) | **中文简体** | [**日本語**](../ja/README.md) | [**한국어**](../ko/README.md) | [**Türkçe**](../tr/README.md)
@ -128,8 +130,9 @@ brew install ffmpeg
- 前往 [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) 查看最新可用的镜像标签(tags)
- 根据你的运行环境选择合适的镜像标签
- `Lite` Docker 镜像不包含 ASR 模型和 UVR5 模型. 你可以自行下载 UVR5 模型, ASR 模型则会在需要时由程序自动下载
- `Lite` Docker 镜像**不包含** ASR 模型和 UVR5 模型. 你可以自行下载 UVR5 模型, ASR 模型则会在需要时由程序自动下载
- 在使用 Docker Compose 时, 会自动拉取适配的架构镜像 (amd64 或 arm64)
- Docker Compose 将会挂载当前目录的**所有文件**, 请在使用 Docker 镜像前先切换到项目根目录并**拉取代码更新**
- 可选：为了获得最新的更改, 你可以使用提供的 Dockerfile 在本地构建镜像
#### 环境变量
@ -329,7 +332,7 @@ python webui.py
新特性：
1. **相比 V2 占用稍高显存, 性能超过 V4, 在保留 V2 硬件成本和推理速度优势的同时实现更高音质.**
[更多详情](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7))
[更多详情](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7)>)
2. V1/V2 与 V2Pro 系列具有相同特性, V3/V4 则具备相近功能. 对于平均音频质量较低的训练集, V1/V2/V2Pro 可以取得较好的效果, 但 V3/V4 无法做到. 此外, V3/V4 合成的声音更偏向参考音频, 而不是整体训练集的风格.

21
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@ -5,12 +5,16 @@
[![madewithlove](https://img.shields.io/badge/made_with-%E2%9D%A4-red?style=for-the-badge&labelColor=orange)](https://github.com/RVC-Boss/GPT-SoVITS)
<img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br>
<a href="https://trendshift.io/repositories/7033" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7033" alt="RVC-Boss%2FGPT-SoVITS | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/🤗%20-online%20demo-yellow.svg?style=for-the-badge)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb)
[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/HuggingFace-demo-blue.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits)
[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e)
[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/)
[![Change Log](https://img.shields.io/badge/Change%20Log-View%20Updates-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/ja/Changelog_JA.md)
[**English**](../../README.md) | [**中文简体**](../cn/README.md) | **日本語** | [**한국어**](../ko/README.md) | [**Türkçe**](../tr/README.md)
@ -122,8 +126,9 @@ brew install ffmpeg
- [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) で最新のイメージタグを確認してください
- 環境に合った適切なイメージタグを選択してください
- `Lite` とは、Docker イメージに ASR モデルおよび UVR5 モデルが含まれていないことを意味します. UVR5 モデルは手動でダウンロードし、ASR モデルは必要に応じてプログラムが自動的にダウンロードします
- `Lite` とは、Docker イメージに ASR モデルおよび UVR5 モデルが**含まれていない**ことを意味します. UVR5 モデルは手動でダウンロードし、ASR モデルは必要に応じてプログラムが自動的にダウンロードします
- Docker Compose 実行時に、対応するアーキテクチャ (amd64 または arm64) のイメージが自動的に取得されます
- Docker Compose は現在のディレクトリ内の**すべてのファイル**をマウントします. Docker イメージを使用する前に、プロジェクトのルートディレクトリに移動し、**コードを最新の状態に更新**してください
- オプション：最新の変更を反映させるため、提供されている Dockerfile を使ってローカルでイメージをビルドすることも可能です
#### 環境変数
@ -304,7 +309,7 @@ v2 環境から v3 を使用する方法:
新機能:
1. **V4 は、V3 で発生していた非整数倍アップサンプリングによる金属音の問題を修正し、音声がこもる問題を防ぐためにネイティブに 48kHz 音声を出力します（V3 はネイティブに 24kHz 音声のみ出力）**. 作者は V4 を V3 の直接的な置き換えとして推奨していますが、さらなるテストが必要です.
[詳細はこちら](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3v4%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
[詳細はこちら](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3v4%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
V1/V2/V3 環境から V4 への移行方法:
@ -319,7 +324,7 @@ V1/V2/V3 環境から V4 への移行方法:
新機能:
1. **V2 と比較してやや高いメモリ使用量ですが、ハードウェアコストと推論速度は維持しつつ、V4 よりも高い性能と音質を実現します. **
[詳細はこちら](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7))
[詳細はこちら](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7)>)
2. V1/V2 と V2Pro シリーズは類似した特徴を持ち、V3/V4 も同様の機能を持っています. 平均音質が低いトレーニングセットの場合、V1/V2/V2Pro は良好な結果を出すことができますが、V3/V4 では対応できません. また、V3/V4 の合成音声はトレーニング全体ではなく、より参考音声に寄った音質になります.

19
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@ -5,12 +5,16 @@
[![madewithlove](https://img.shields.io/badge/made_with-%E2%9D%A4-red?style=for-the-badge&labelColor=orange)](https://github.com/RVC-Boss/GPT-SoVITS)
<img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br>
<a href="https://trendshift.io/repositories/7033" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7033" alt="RVC-Boss%2FGPT-SoVITS | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/🤗%20-online%20demo-yellow.svg?style=for-the-badge)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb)
[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/HuggingFace-demo-blue.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits)
[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e)
[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/)
[![Change Log](https://img.shields.io/badge/Change%20Log-View%20Updates-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/ko/Changelog_KO.md)
[**English**](../../README.md) | [**中文简体**](../cn/README.md) | [**日本語**](../ja/README.md) | **한국어** | [**Türkçe**](../tr/README.md)
@ -122,8 +126,9 @@ brew install ffmpeg
- [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits)에서 최신 이미지 태그를 확인하세요
- 환경에 맞는 적절한 이미지 태그를 선택하세요
- `Lite` 는 Docker 이미지에 ASR 모델과 UVR5 모델이 포함되어 있지 않음을 의미합니다. UVR5 모델은 사용자가 직접 다운로드해야 하며, ASR 모델은 필요 시 프로그램이 자동으로 다운로드합니다
- `Lite` 는 Docker 이미지에 ASR 모델과 UVR5 모델이 **포함되어 있지 않음**을 의미합니다. UVR5 모델은 사용자가 직접 다운로드해야 하며, ASR 모델은 필요 시 프로그램이 자동으로 다운로드합니다
- Docker Compose 실행 시, 해당 아키텍처에 맞는 이미지(amd64 또는 arm64)가 자동으로 다운로드됩니다
- Docker Compose는 현재 디렉터리의 **모든 파일**을 마운트합니다. Docker 이미지를 사용하기 전에 프로젝트 루트 디렉터리로 이동하여 코드를 **최신 상태로 업데이트**하세요
- 선택 사항: 최신 변경사항을 반영하려면 제공된 Dockerfile을 사용하여 로컬에서 직접 이미지를 빌드할 수 있습니다
#### 환경 변수
@ -319,7 +324,7 @@ V1/V2/V3 환경에서 V4로 전환 방법:
신규 기능:
1. **V2보다 약간 높은 VRAM 사용량이지만 성능은 V4보다 우수하며, V2 수준의 하드웨어 비용과 속도를 유지합니다**.
[자세히 보기](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7))
[자세히 보기](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7)>)
2. V1/V2와 V2Pro 시리즈는 유사한 특징을 가지며, V3/V4도 비슷한 기능을 가지고 있습니다. 평균 음질이 낮은 학습 데이터셋에서는 V1/V2/V2Pro가 좋은 결과를 내지만 V3/V4는 그렇지 못합니다. 또한 V3/V4의 합성 음색은 전체 학습 데이터셋보다는 참고 음성에 더 가깝습니다.

17
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@ -7,12 +7,14 @@ Güçlü Birkaç Örnekli Ses Dönüştürme ve Metinden Konuşmaya Web Arayüz
<a href="https://trendshift.io/repositories/7033" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7033" alt="RVC-Boss%2FGPT-SoVITS | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
<!-- img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br> -->
[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb)
[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/HuggingFace-demo-blue.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits)
[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[![Huggingface](https://img.shields.io/badge/🤗%20-online%20demo-yellow.svg?style=for-the-badge)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e)
[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/)
[![Change Log](https://img.shields.io/badge/Change%20Log-View%20Updates-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/tr/Changelog_TR.md)
[**English**](../../README.md) | [**中文简体**](../cn/README.md) | [**日本語**](../ja/README.md) | [**한국어**](../ko/README.md) | **Türkçe**
@ -124,8 +126,9 @@ Kod tabanı hızla geliştiği halde Docker imajları daha yavaş yayınlandığ
- En güncel kullanılabilir imaj etiketlerini görmek için [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) adresini kontrol edin
- Ortamınıza uygun bir imaj etiketi seçin
- `Lite`, Docker imajında ASR modelleri ve UVR5 modellerinin bulunmadığı anlamına gelir. UVR5 modellerini manuel olarak indirebilirsiniz; ASR modelleri ise gerektiğinde program tarafından otomatik olarak indirilir
- `Lite`, Docker imajında ASR modelleri ve UVR5 modellerinin **bulunmadığı** anlamına gelir. UVR5 modellerini manuel olarak indirebilirsiniz; ASR modelleri ise gerektiğinde program tarafından otomatik olarak indirilir
- Docker Compose sırasında, uygun mimariye (amd64 veya arm64) ait imaj otomatik olarak indirilir
- Docker Compose, mevcut dizindeki **tüm dosyaları** bağlayacaktır. Docker imajını kullanmadan önce lütfen proje kök dizinine geçin ve **en son kodu çekin**
- Opsiyonel: En güncel değişiklikleri almak için, sağlanan Dockerfile ile yerel olarak imajı kendiniz oluşturabilirsiniz
#### Ortam Değişkenleri
@ -323,7 +326,7 @@ V1/V2/V3 ortamından V4'e geçiş:
Yeni Özellikler:
1. **V2 ile karşılaştırıldığında biraz daha yüksek VRAM kullanımı sağlar ancak V4'ten daha iyi performans gösterir; aynı donanım maliyeti ve hız avantajını korur**.
[Daha fazla bilgi](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7))
[Daha fazla bilgi](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7)>)
2. V1/V2 ve V2Pro serisi benzer özelliklere sahipken, V3/V4 de yakın işlevleri paylaşır. Ortalama kalite düşük olan eğitim setleriyle V1/V2/V2Pro iyi sonuçlar verebilir ama V3/V4 veremez. Ayrıca, V3/V4’ün ürettiği ses tonu genel eğitim setine değil, referans ses örneğine daha çok benzemektedir.

224
install.sh
Unescape Escape View File

@ -5,14 +5,62 @@ SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" &>/dev/null && pwd)"
cd "$SCRIPT_DIR" || exit 1
set -e
RESET="\033[0m"
BOLD="\033[1m"
ERROR="\033[1;31m[ERROR]: $RESET"
WARNING="\033[1;33m[WARNING]: $RESET"
INFO="\033[1;32m[INFO]: $RESET"
SUCCESS="\033[1;34m[SUCCESS]: $RESET"
set -eE
set -o errtrace
trap 'on_error $LINENO "$BASH_COMMAND" $?' ERR
# shellcheck disable=SC2317
on_error() {
local lineno="$1"
local cmd="$2"
local code="$3"
echo -e "${ERROR}${BOLD}Command \"${cmd}\" Failed${RESET} at ${BOLD}Line ${lineno}${RESET} with Exit Code ${BOLD}${code}${RESET}"
echo -e "${ERROR}${BOLD}Call Stack:${RESET}"
for ((i = ${#FUNCNAME[@]} - 1; i >= 1; i--)); do
echo -e " in ${BOLD}${FUNCNAME[i]}()${RESET} at ${BASH_SOURCE[i]}:${BOLD}${BASH_LINENO[i - 1]}${RESET}"
done
exit "$code"
}
run_conda_quiet() {
local output
output=$(conda install --yes --quiet "$@" 2>&1) || {
echo -e "${ERROR} Conda install failed:\n$output"
exit 1
}
}
run_pip_quiet() {
local output
output=$(pip install "$@" 2>&1) || {
echo -e "${ERROR} Pip install failed:\n$output"
exit 1
}
}
run_wget_quiet() {
local output
output=$(wget --tries=25 --wait=5 --read-timeout=40 --retry-on-http-error=404 "$@" 2>&1) || {
echo -e "${ERROR} Wget failed:\n$output"
exit 1
}
}
if ! command -v conda &>/dev/null; then
echo "Conda Not Found"
echo -e "${ERROR}Conda Not Found"
exit 1
fi
trap 'echo "Error Occured at \"$BASH_COMMAND\" with exit code $?"; exit 1' ERR
run_conda_quiet gcc
USE_CUDA=false
USE_ROCM=false
@ -34,8 +82,8 @@ print_help() {
echo " -h, --help Show this help message and exit"
echo ""
echo "Examples:"
echo " bash install.sh --source HF --download-uvr5"
echo " bash install.sh --source ModelScope"
echo " bash install.sh --device CU128 --source HF --download-uvr5"
echo " bash install.sh --device MPS --source ModelScope"
}
# Show help if no arguments provided
@ -59,8 +107,8 @@ while [[ $# -gt 0 ]]; do
USE_MODELSCOPE=true
;;
*)
echo "Error: Invalid Download Source: $2"
echo "Choose From: [HF, HF-Mirror, ModelScope]"
echo -e "${ERROR}Error: Invalid Download Source: $2"
echo -e "${ERROR}Choose From: [HF, HF-Mirror, ModelScope]"
exit 1
;;
esac
@ -86,8 +134,8 @@ while [[ $# -gt 0 ]]; do
USE_CPU=true
;;
*)
echo "Error: Invalid Device: $2"
echo "Choose From: [CU126, CU128, ROCM, MPS, CPU]"
echo -e "${ERROR}Error: Invalid Device: $2"
echo -e "${ERROR}Choose From: [CU126, CU128, ROCM, MPS, CPU]"
exit 1
;;
esac
@ -102,22 +150,23 @@ while [[ $# -gt 0 ]]; do
exit 0
;;
*)
echo "Unknown Argument: $1"
echo "Use -h or --help to see available options."
echo -e "${ERROR}Unknown Argument: $1"
echo ""
print_help
exit 1
;;
esac
done
if ! $USE_CUDA && ! $USE_ROCM && ! $USE_CPU; then
echo "Error: Device is REQUIRED"
echo -e "${ERROR}Error: Device is REQUIRED"
echo ""
print_help
exit 1
fi
if ! $USE_HF && ! $USE_HF_MIRROR && ! $USE_MODELSCOPE; then
echo "Error: Download Source is REQUIRED"
echo -e "${ERROR}Error: Download Source is REQUIRED"
echo ""
print_help
exit 1
@ -125,55 +174,65 @@ fi
# 安装构建工具
# Install build tools
echo -e "${INFO}Detected system: $(uname -s) $(uname -r) $(uname -m)"
if [ "$(uname)" != "Darwin" ]; then
gcc_major_version=$(command -v gcc >/dev/null 2>&1 && gcc -dumpversion | cut -d. -f1 || echo 0)
if [ "$gcc_major_version" -lt 11 ]; then
echo "Installing GCC & G++..."
conda install -c conda-forge gcc=11 gxx=11 -q -y
echo -e "${INFO}Installing GCC & G++..."
run_conda_quiet gcc=11 gxx=11
echo -e "${SUCCESS}GCC & G++ Installed..."
else
echo "GCC >=11"
echo -e "${INFO}Detected GCC Version: $gcc_major_version"
echo -e "${INFO}Skip Installing GCC & G++ From Conda-Forge"
fi
else
if ! xcode-select -p &>/dev/null; then
echo "Installing Xcode Command Line Tools..."
echo -e "${INFO}Installing Xcode Command Line Tools..."
xcode-select --install
fi
echo "Waiting For Xcode Command Line Tools Installation Complete..."
while true; do
sleep 20
if xcode-select -p &>/dev/null; then
echo "Xcode Command Line Tools Installed"
break
else
echo "Installing，Please Wait..."
echo -e "${INFO}Waiting For Xcode Command Line Tools Installation Complete..."
while true; do
sleep 20
if xcode-select -p &>/dev/null; then
echo -e "${SUCCESS}Xcode Command Line Tools Installed"
break
else
echo -e "${INFO}Installing，Please Wait..."
fi
done
else
XCODE_PATH=$(xcode-select -p)
if [[ "$XCODE_PATH" == *"Xcode.app"* ]]; then
echo -e "${WARNING} Detected Xcode path: $XCODE_PATH"
echo -e "${WARNING} If your Xcode version does not match your macOS version, it may cause unexpected issues during compilation or package builds."
fi
done
conda install -c conda-forge -q -y
fi
fi
echo "Installing ffmpeg and cmake..."
conda install ffmpeg cmake make -q -y
echo -e "${INFO}Installing FFmpeg & CMake..."
run_conda_quiet ffmpeg cmake make
echo -e "${SUCCESS}FFmpeg & CMake Installed"
echo "Installing unzip..."
conda install unzip -y --quiet
echo -e "${INFO}Installing unzip..."
run_conda_quiet unzip
echo -e "${SUCCESS}unzip Installed"
if [ "$USE_HF" = "true" ]; then
echo "Download Model From HuggingFace"
echo -e "${INFO}Download Model From HuggingFace"
PRETRINED_URL="https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/pretrained_models.zip"
G2PW_URL="https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip"
UVR5_URL="https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/uvr5_weights.zip"
NLTK_URL="https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/nltk_data.zip"
PYOPENJTALK_URL="https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/open_jtalk_dic_utf_8-1.11.tar.gz"
elif [ "$USE_HF_MIRROR" = "true" ]; then
echo "Download Model From HuggingFace-Mirror"
echo -e "${INFO}Download Model From HuggingFace-Mirror"
PRETRINED_URL="https://hf-mirror.com/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/pretrained_models.zip"
G2PW_URL="https://hf-mirror.com/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip"
UVR5_URL="https://hf-mirror.com/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/uvr5_weights.zip"
NLTK_URL="https://hf-mirror.com/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/nltk_data.zip"
PYOPENJTALK_URL="https://hf-mirror.com/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/open_jtalk_dic_utf_8-1.11.tar.gz"
elif [ "$USE_MODELSCOPE" = "true" ]; then
echo "Download Model From ModelScope"
echo -e "${INFO}Download Model From ModelScope"
PRETRINED_URL="https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/pretrained_models.zip"
G2PW_URL="https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/G2PWModel.zip"
UVR5_URL="https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/uvr5_weights.zip"
@ -181,118 +240,129 @@ elif [ "$USE_MODELSCOPE" = "true" ]; then
PYOPENJTALK_URL="https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/open_jtalk_dic_utf_8-1.11.tar.gz"
fi
if [ "$WORKFLOW" = "true" ]; then
WGET_CMD=(wget -nv --tries=25 --wait=5 --read-timeout=40 --retry-on-http-error=404)
else
WGET_CMD=(wget --tries=25 --wait=5 --read-timeout=40 --retry-on-http-error=404)
fi
if find -L "GPT_SoVITS/pretrained_models" -mindepth 1 ! -name '.gitignore' | grep -q .; then
echo "Pretrained Model Exists"
echo -e "${INFO}Pretrained Model Exists"
echo -e "${INFO}Skip Downloading Pretrained Models"
else
echo "Download Pretrained Models"
"${WGET_CMD[@]}" "$PRETRINED_URL"
echo -e "${INFO}Downloading Pretrained Models..."
rm -rf pretrained_models.zip
run_wget_quiet "$PRETRINED_URL"
unzip -q -o pretrained_models.zip -d GPT_SoVITS
rm -rf pretrained_models.zip
echo -e "${SUCCESS}Pretrained Models Downloaded"
fi
if [ ! -d "GPT_SoVITS/text/G2PWModel" ]; then
echo "Download G2PWModel"
"${WGET_CMD[@]}" "$G2PW_URL"
echo -e "${INFO}Downloading G2PWModel.."
rm -rf G2PWModel.zip
run_wget_quiet "$G2PW_URL"
unzip -q -o G2PWModel.zip -d GPT_SoVITS/text
rm -rf G2PWModel.zip
echo -e "${SUCCESS}G2PWModel Downloaded"
else
echo "G2PWModel Exists"
echo -e "${INFO}G2PWModel Exists"
echo -e "${INFO}Skip Downloading G2PWModel"
fi
if [ "$DOWNLOAD_UVR5" = "true" ]; then
if find -L "tools/uvr5/uvr5_weights" -mindepth 1 ! -name '.gitignore' | grep -q .; then
echo "UVR5 Model Exists"
echo -e"${INFO}UVR5 Models Exists"
echo -e "${INFO}Skip Downloading UVR5 Models"
else
echo "Download UVR5 Model"
"${WGET_CMD[@]}" "$UVR5_URL"
echo -e "${INFO}Downloading UVR5 Models..."
rm -rf uvr5_weights.zip
run_wget_quiet "$UVR5_URL"
unzip -q -o uvr5_weights.zip -d tools/uvr5
rm -rf uvr5_weights.zip
echo -e "${SUCCESS}UVR5 Models Downloaded"
fi
fi
if [ "$USE_CUDA" = true ] && [ "$WORKFLOW" = false ]; then
echo "Checking for CUDA installation..."
echo -e "${INFO}Checking For Nvidia Driver Installation..."
if command -v nvidia-smi &>/dev/null; then
echo "CUDA found."
echo "${INFO}Nvidia Driver Founded"
else
echo -e "${WARNING}Nvidia Driver Not Found, Fallback to CPU"
USE_CUDA=false
USE_CPU=true
echo "CUDA not found."
fi
fi
if [ "$USE_ROCM" = true ] && [ "$WORKFLOW" = false ]; then
echo "Checking for ROCm installation..."
echo -e "${INFO}Checking For ROCm Installation..."
if [ -d "/opt/rocm" ]; then
echo "ROCm found."
echo -e "${INFO}ROCm Founded"
if grep -qi "microsoft" /proc/version; then
echo "You are running WSL."
echo -e "${INFO}WSL2 Founded"
IS_WSL=true
else
echo "You are NOT running WSL."
IS_WSL=false
fi
else
echo -e "${WARNING}ROCm Not Found, Fallback to CPU"
USE_ROCM=false
USE_CPU=true
echo "ROCm not found."
fi
fi
if [ "$USE_CUDA" = true ] && [ "$WORKFLOW" = false ]; then
echo "Installing PyTorch with CUDA support..."
if [ "$CUDA" = 128 ]; then
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu128
echo -e "${INFO}Installing PyTorch For CUDA 12.8..."
run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/cu128"
elif [ "$CUDA" = 126 ]; then
pip install torch==2.6 torchaudio --index-url https://download.pytorch.org/whl/cu126
echo -e "${INFO}Installing PyTorch For CUDA 12.6..."
run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/cu126"
fi
elif [ "$USE_ROCM" = true ] && [ "$WORKFLOW" = false ]; then
echo "Installing PyTorch with ROCm support..."
pip install torch==2.6 torchaudio --index-url https://download.pytorch.org/whl/rocm6.2
echo -e "${INFO}Installing PyTorch For ROCm 6.2..."
run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/rocm6.2"
elif [ "$USE_CPU" = true ] && [ "$WORKFLOW" = false ]; then
echo "Installing PyTorch for CPU..."
pip install torch==2.6 torchaudio --index-url https://download.pytorch.org/whl/cpu
echo -e "${INFO}Installing PyTorch For CPU..."
run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/cpu"
elif [ "$WORKFLOW" = false ]; then
echo "Unknown Err"
echo -e "${ERROR}Unknown Err"
exit 1
fi
echo -e "${SUCCESS}PyTorch Installed"
echo "Installing Python dependencies from requirements.txt..."
echo -e "${INFO}Installing Python Dependencies From requirements.txt..."
# 刷新环境
# Refresh environment
hash -r
pip install -r extra-req.txt --no-deps --quiet
run_pip_quiet -r extra-req.txt --no-deps
pip install -r requirements.txt --quiet
run_pip_quiet -r requirements.txt
echo -e "${SUCCESS}Python Dependencies Installed"
PY_PREFIX=$(python -c "import sys; print(sys.prefix)")
PYOPENJTALK_PREFIX=$(python -c "import os, pyopenjtalk; print(os.path.dirname(pyopenjtalk.__file__))")
"${WGET_CMD[@]}" "$NLTK_URL" -O nltk_data.zip
echo -e "${INFO}Downloading NLTK Data..."
rm -rf nltk_data.zip
run_wget_quiet "$NLTK_URL" -O nltk_data.zip
unzip -q -o nltk_data -d "$PY_PREFIX"
rm -rf nltk_data.zip
echo -e "${SUCCESS}NLTK Data Downloaded"
"${WGET_CMD[@]}" "$PYOPENJTALK_URL" -O open_jtalk_dic_utf_8-1.11.tar.gz
tar -xvzf open_jtalk_dic_utf_8-1.11.tar.gz -C "$PYOPENJTALK_PREFIX"
echo -e "${INFO}Downloading Open JTalk Dict..."
rm -rf open_jtalk_dic_utf_8-1.11.tar.gz
run_wget_quiet "$PYOPENJTALK_URL" -O open_jtalk_dic_utf_8-1.11.tar.gz
tar -xzf open_jtalk_dic_utf_8-1.11.tar.gz -C "$PYOPENJTALK_PREFIX"
rm -rf open_jtalk_dic_utf_8-1.11.tar.gz
echo -e "${SUCCESS}Open JTalk Dic Downloaded"
if [ "$USE_ROCM" = true ] && [ "$IS_WSL" = true ]; then
echo "Update to WSL compatible runtime lib..."
echo -e "${INFO}Updating WSL Compatible Runtime Lib For ROCm..."
location=$(pip show torch | grep Location | awk -F ": " '{print $2}')
cd "${location}"/torch/lib/ || exit
rm libhsa-runtime64.so*
cp /opt/rocm/lib/libhsa-runtime64.so.1.2 libhsa-runtime64.so
echo -e "${SUCCESS}ROCm Runtime Lib Updated..."
fi
echo "Installation completed successfully!"
echo -e "${SUCCESS}Installation Completed"

82
tools/assets.py
Unescape Escape View File

@ -1,81 +1,38 @@
js = """
function createGradioAnimation() {
function deleteTheme() {
const params = new URLSearchParams(window.location.search);
if (params.get('__theme') !== 'light') {
params.set('__theme', 'light'); // 仅当 __theme 不是 'light' 时设置为 'light'
window.location.search = params.toString(); // 更新 URL，触发页面刷新
}
var container = document.createElement('div');
container.id = 'gradio-animation';
container.style.fontSize = '2em';
container.style.fontWeight = '500';
container.style.textAlign = 'center';
container.style.marginBottom = '20px';
container.style.fontFamily = '-apple-system, sans-serif, Arial, Calibri';
var text = 'Welcome to GPT-SoVITS !';
for (var i = 0; i < text.length; i++) {
(function(i){
setTimeout(function(){
var letter = document.createElement('span');
letter.style.opacity = '0';
letter.style.transition = 'opacity 0.5s';
letter.innerText = text[i];
container.appendChild(letter);
const params = new URLSearchParams(window.location.search);
if (params.has('__theme')) {
params.delete('__theme');
const newUrl = `${window.location.pathname}?${params.toString()}`;
window.location.replace(newUrl);
}
setTimeout(function() {
letter.style.opacity = '1';
}, 50);
}, i * 250);
})(i);
}
return 'Animation created';
}
"""
css = """
/* CSSStyleRule */
.markdown {
background-color: lightblue;
padding: 6px 10px;
}
.checkbox_info {
color: var(--block-title-text-color) !important;
font-size: var(--block-title-text-size) !important;
font-weight: var(--block-title-text-weight) !important;
height: 22px;
margin-bottom: 8px !important;
}
::selection {
background: #ffc078; !important;
}
#checkbox_train_dpo input[type="checkbox"]{
margin-top: 6px;
}
#checkbox_train_dpo span {
margin-top: 6px;
}
#checkbox_align_train {
padding-top: 18px;
padding-bottom: 18px;
@media (prefers-color-scheme: light) {
.markdown {
background-color: lightblue;
color: #000;
}
}
#checkbox_align_infer input[type="checkbox"] {
margin-top: 10px;
@media (prefers-color-scheme: dark) {
.markdown {
background-color: #4b4b4b;
color: rgb(244, 244, 245);
}
}
#checkbox_align_infer span {
margin-top: 10px;
::selection {
background: #ffc078 !important;
}
footer {
@ -91,6 +48,7 @@ footer * {
}
"""
top_html = """
<div align="center">
<div style="margin-bottom: 5px; font-size: 15px;">{}</div>

2
tools/my_utils.py
Unescape Escape View File

@ -109,7 +109,7 @@ def check_details(path_list=None, is_train=False, is_dataset_processing=False):
if os.path.exists(wav_path):
...
else:
gr.Warning(wav_path+i18n("路径错误"))
gr.Warning(wav_path + i18n("路径错误"))
return
if is_train:
path_list.append(os.path.join(path_list[0], "2-name2text.txt"))

5
tools/subfix_webui.py
Unescape Escape View File

@ -1,5 +1,6 @@
import sys
from tools.i18n.i18n import I18nAuto, scan_language_list
language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else "Auto"
i18n = I18nAuto(language=language)
import argparse
@ -309,7 +310,9 @@ if __name__ == "__main__":
with gr.Blocks(analytics_enabled=False) as demo:
gr.Markdown(
value=i18n("Submit Text: 将当前页所有文本框内容手工保存到内存和文件(翻页前后或者退出标注页面前如果没点这个按钮，你再翻回来就回滚了，白忙活。)")
value=i18n(
"Submit Text: 将当前页所有文本框内容手工保存到内存和文件(翻页前后或者退出标注页面前如果没点这个按钮，你再翻回来就回滚了，白忙活。)"
)
)
with gr.Row():
btn_change_index = gr.Button("Change Index")

4
tools/uvr5/mdxnet.py
Unescape Escape View File

@ -190,14 +190,14 @@ class Predictor:
opt_path_vocal = path_vocal[:-4] + ".%s" % format
opt_path_other = path_other[:-4] + ".%s" % format
if os.path.exists(path_vocal):
os.system("ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path_vocal, opt_path_vocal))
os.system('ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path_vocal, opt_path_vocal))
if os.path.exists(opt_path_vocal):
try:
os.remove(path_vocal)
except:
pass
if os.path.exists(path_other):
os.system("ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path_other, opt_path_other))
os.system('ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path_other, opt_path_other))
if os.path.exists(opt_path_other):
try:
os.remove(path_other)

8
tools/uvr5/vr.py
Unescape Escape View File

@ -140,7 +140,7 @@ class AudioPre:
)
if os.path.exists(path):
opt_format_path = path[:-4] + ".%s" % format
cmd="ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path, opt_format_path)
cmd = 'ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path, opt_format_path)
print(cmd)
os.system(cmd)
if os.path.exists(opt_format_path):
@ -177,7 +177,7 @@ class AudioPre:
)
if os.path.exists(path):
opt_format_path = path[:-4] + ".%s" % format
cmd="ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path, opt_format_path)
cmd = 'ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path, opt_format_path)
print(cmd)
os.system(cmd)
if os.path.exists(opt_format_path):
@ -307,7 +307,7 @@ class AudioPreDeEcho:
)
if os.path.exists(path):
opt_format_path = path[:-4] + ".%s" % format
cmd="ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path, opt_format_path)
cmd = 'ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path, opt_format_path)
print(cmd)
os.system(cmd)
if os.path.exists(opt_format_path):
@ -340,7 +340,7 @@ class AudioPreDeEcho:
)
if os.path.exists(path):
opt_format_path = path[:-4] + ".%s" % format
cmd="ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path, opt_format_path)
cmd = 'ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path, opt_format_path)
print(cmd)
os.system(cmd)
if os.path.exists(opt_format_path):

4
webui.py
Unescape Escape View File

@ -507,7 +507,7 @@ def open1Ba(
):
global p_train_SoVITS
if p_train_SoVITS == None:
exp_name=exp_name.rstrip(" ")
exp_name = exp_name.rstrip(" ")
config_file = (
"GPT_SoVITS/configs/s2.json"
if version not in {"v2Pro", "v2ProPlus"}
@ -604,7 +604,7 @@ def open1Bb(
):
global p_train_GPT
if p_train_GPT == None:
exp_name=exp_name.rstrip(" ")
exp_name = exp_name.rstrip(" ")
with open(
"GPT_SoVITS/configs/s1longer.yaml" if version == "v1" else "GPT_SoVITS/configs/s1longer-v2.yaml"
) as f:

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