OPENGIT/Retrieval-based-Voice-Conversion-WebUI
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Remove part of the non-inference-related modules

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VSlobolinskyi 2025-03-18 10:10:54 +02:00
parent df1a890ca1
commit d6de82afef
7 changed files with 3 additions and 2384 deletions
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833
infer-web.py
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@ -6,13 +6,6 @@ now_dir = os.getcwd()
sys.path.append(now_dir)
load_dotenv()
from infer.modules.vc.modules import VC
from infer.modules.uvr5.modules import uvr
from infer.lib.train.process_ckpt import (
change_info,
extract_small_model,
merge,
show_info,
)
from i18n.i18n import I18nAuto
from configs.config import Config
from sklearn.cluster import MiniBatchKMeans
@ -451,8 +444,8 @@ def change_version19(sr2, if_f0_3, version19):
to_return_sr2,
)
def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
# f0method8,pretrained_G14,pretrained_D15
def change_f0(if_f0_3, sr2, version19):
path_str = "" if version19 == "v1" else "_v2"
return (
{"visible": if_f0_3, "__type__": "update"},
@ -461,324 +454,7 @@ def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D
)
# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
def click_train(
exp_dir1,
sr2,
if_f0_3,
spk_id5,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
):
# 生成filelist
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
os.makedirs(exp_dir, exist_ok=True)
gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
feature_dir = (
"%s/3_feature256" % (exp_dir)
if version19 == "v1"
else "%s/3_feature768" % (exp_dir)
)
if if_f0_3:
f0_dir = "%s/2a_f0" % (exp_dir)
f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
names = (
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
)
else:
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
[name.split(".")[0] for name in os.listdir(feature_dir)]
)
opt = []
for name in names:
if if_f0_3:
opt.append(
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
% (
gt_wavs_dir.replace("\\", "\\\\"),
name,
feature_dir.replace("\\", "\\\\"),
name,
f0_dir.replace("\\", "\\\\"),
name,
f0nsf_dir.replace("\\", "\\\\"),
name,
spk_id5,
)
)
else:
opt.append(
"%s/%s.wav|%s/%s.npy|%s"
% (
gt_wavs_dir.replace("\\", "\\\\"),
name,
feature_dir.replace("\\", "\\\\"),
name,
spk_id5,
)
)
fea_dim = 256 if version19 == "v1" else 768
if if_f0_3:
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
)
else:
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
)
shuffle(opt)
with open("%s/filelist.txt" % exp_dir, "w") as f:
f.write("\n".join(opt))
logger.debug("Write filelist done")
# 生成config#无需生成config
# cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
logger.info("Use gpus: %s", str(gpus16))
if pretrained_G14 == "":
logger.info("No pretrained Generator")
if pretrained_D15 == "":
logger.info("No pretrained Discriminator")
if version19 == "v1" or sr2 == "40k":
config_path = "v1/%s.json" % sr2
else:
config_path = "v2/%s.json" % sr2
config_save_path = os.path.join(exp_dir, "config.json")
if not pathlib.Path(config_save_path).exists():
with open(config_save_path, "w", encoding="utf-8") as f:
json.dump(
config.json_config[config_path],
f,
ensure_ascii=False,
indent=4,
sort_keys=True,
)
f.write("\n")
if gpus16:
cmd = (
'"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
% (
config.python_cmd,
exp_dir1,
sr2,
1 if if_f0_3 else 0,
batch_size12,
gpus16,
total_epoch11,
save_epoch10,
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
1 if if_save_latest13 == i18n("是") else 0,
1 if if_cache_gpu17 == i18n("是") else 0,
1 if if_save_every_weights18 == i18n("是") else 0,
version19,
)
)
else:
cmd = (
'"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
% (
config.python_cmd,
exp_dir1,
sr2,
1 if if_f0_3 else 0,
batch_size12,
total_epoch11,
save_epoch10,
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
1 if if_save_latest13 == i18n("是") else 0,
1 if if_cache_gpu17 == i18n("是") else 0,
1 if if_save_every_weights18 == i18n("是") else 0,
version19,
)
)
logger.info("Execute: " + cmd)
p = Popen(cmd, shell=True, cwd=now_dir)
p.wait()
return "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"
# but4.click(train_index, [exp_dir1], info3)
def train_index(exp_dir1, version19):
# exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
exp_dir = "logs/%s" % (exp_dir1)
os.makedirs(exp_dir, exist_ok=True)
feature_dir = (
"%s/3_feature256" % (exp_dir)
if version19 == "v1"
else "%s/3_feature768" % (exp_dir)
)
if not os.path.exists(feature_dir):
return "请先进行特征提取!"
listdir_res = list(os.listdir(feature_dir))
if len(listdir_res) == 0:
return "请先进行特征提取！"
infos = []
npys = []
for name in sorted(listdir_res):
phone = np.load("%s/%s" % (feature_dir, name))
npys.append(phone)
big_npy = np.concatenate(npys, 0)
big_npy_idx = np.arange(big_npy.shape[0])
np.random.shuffle(big_npy_idx)
big_npy = big_npy[big_npy_idx]
if big_npy.shape[0] > 2e5:
infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
yield "\n".join(infos)
try:
big_npy = (
MiniBatchKMeans(
n_clusters=10000,
verbose=True,
batch_size=256 * config.n_cpu,
compute_labels=False,
init="random",
)
.fit(big_npy)
.cluster_centers_
)
except:
info = traceback.format_exc()
logger.info(info)
infos.append(info)
yield "\n".join(infos)
np.save("%s/total_fea.npy" % exp_dir, big_npy)
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
infos.append("%s,%s" % (big_npy.shape, n_ivf))
yield "\n".join(infos)
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
# index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
infos.append("training")
yield "\n".join(infos)
index_ivf = faiss.extract_index_ivf(index) #
index_ivf.nprobe = 1
index.train(big_npy)
faiss.write_index(
index,
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
)
infos.append("adding")
yield "\n".join(infos)
batch_size_add = 8192
for i in range(0, big_npy.shape[0], batch_size_add):
index.add(big_npy[i : i + batch_size_add])
faiss.write_index(
index,
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
)
infos.append(
"成功构建索引 added_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
)
try:
link = os.link if platform.system() == "Windows" else os.symlink
link(
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
"%s/%s_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (
outside_index_root,
exp_dir1,
n_ivf,
index_ivf.nprobe,
exp_dir1,
version19,
),
)
infos.append("链接索引到外部-%s" % (outside_index_root))
except:
infos.append("链接索引到外部-%s失败" % (outside_index_root))
# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
# infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
yield "\n".join(infos)
# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
def train1key(
exp_dir1,
sr2,
if_f0_3,
trainset_dir4,
spk_id5,
np7,
f0method8,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
gpus_rmvpe,
):
infos = []
def get_info_str(strr):
infos.append(strr)
return "\n".join(infos)
# step1:处理数据
yield get_info_str(i18n("step1:正在处理数据"))
[get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)]
# step2a:提取音高
yield get_info_str(i18n("step2:正在提取音高&正在提取特征"))
[
get_info_str(_)
for _ in extract_f0_feature(
gpus16, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe
)
]
# step3a:训练模型
yield get_info_str(i18n("step3a:正在训练模型"))
click_train(
exp_dir1,
sr2,
if_f0_3,
spk_id5,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
)
yield get_info_str(
i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log")
)
# step3b:训练索引
[get_info_str(_) for _ in train_index(exp_dir1, version19)]
yield get_info_str(i18n("全流程结束！"))
# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
def change_info_(ckpt_path):
if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
@ -1015,11 +691,6 @@ with gr.Blocks(title="RVC WebUI") as app:
outputs=file_index4,
api_name="infer_refresh_batch",
)
# file_big_npy2 = gr.Textbox(
# label=i18n("特征文件路径"),
# value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
# interactive=True,
# )
with gr.Column():
resample_sr1 = gr.Slider(
@ -1110,504 +781,6 @@ with gr.Blocks(title="RVC WebUI") as app:
outputs=[spk_item, protect0, protect1, file_index2, file_index4],
api_name="infer_change_voice",
)
with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
with gr.Group():
gr.Markdown(
value=i18n(
"人声伴奏分离批量处理， 使用UVR5模型。 <br>合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。 <br>模型分为三类： <br>1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点； <br>2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型； <br> 3、去混响、去延迟模型（by FoxJoy）：<br>  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；<br>&emsp;(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。<br>去混响/去延迟，附：<br>1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>2、MDX-Net-Dereverb模型挺慢的；<br>3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。"
)
)
with gr.Row():
with gr.Column():
dir_wav_input = gr.Textbox(
label=i18n("输入待处理音频文件夹路径"),
placeholder="C:\\Users\\Desktop\\todo-songs",
)
wav_inputs = gr.File(
file_count="multiple",
label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),
)
with gr.Column():
model_choose = gr.Dropdown(
label=i18n("模型"), choices=uvr5_names
)
agg = gr.Slider(
minimum=0,
maximum=20,
step=1,
label="人声提取激进程度",
value=10,
interactive=True,
visible=False, # 先不开放调整
)
opt_vocal_root = gr.Textbox(
label=i18n("指定输出主人声文件夹"), value="opt"
)
opt_ins_root = gr.Textbox(
label=i18n("指定输出非主人声文件夹"), value="opt"
)
format0 = gr.Radio(
label=i18n("导出文件格式"),
choices=["wav", "flac", "mp3", "m4a"],
value="flac",
interactive=True,
)
but2 = gr.Button(i18n("转换"), variant="primary")
vc_output4 = gr.Textbox(label=i18n("输出信息"))
but2.click(
uvr,
[
model_choose,
dir_wav_input,
opt_vocal_root,
wav_inputs,
opt_ins_root,
agg,
format0,
],
[vc_output4],
api_name="uvr_convert",
)
with gr.TabItem(i18n("训练")):
gr.Markdown(
value=i18n(
"step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. "
)
)
with gr.Row():
exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test")
sr2 = gr.Radio(
label=i18n("目标采样率"),
choices=["40k", "48k"],
value="40k",
interactive=True,
)
if_f0_3 = gr.Radio(
label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"),
choices=[True, False],
value=True,
interactive=True,
)
version19 = gr.Radio(
label=i18n("版本"),
choices=["v1", "v2"],
value="v2",
interactive=True,
visible=True,
)
np7 = gr.Slider(
minimum=0,
maximum=config.n_cpu,
step=1,
label=i18n("提取音高和处理数据使用的CPU进程数"),
value=int(np.ceil(config.n_cpu / 1.5)),
interactive=True,
)
with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理
gr.Markdown(
value=i18n(
"step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. "
)
)
with gr.Row():
trainset_dir4 = gr.Textbox(
label=i18n("输入训练文件夹路径"),
value=i18n("E:\\语音音频+标注\\米津玄师\\src"),
)
spk_id5 = gr.Slider(
minimum=0,
maximum=4,
step=1,
label=i18n("请指定说话人id"),
value=0,
interactive=True,
)
but1 = gr.Button(i18n("处理数据"), variant="primary")
info1 = gr.Textbox(label=i18n("输出信息"), value="")
but1.click(
preprocess_dataset,
[trainset_dir4, exp_dir1, sr2, np7],
[info1],
api_name="train_preprocess",
)
with gr.Group():
gr.Markdown(
value=i18n(
"step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)"
)
)
with gr.Row():
with gr.Column():
gpus6 = gr.Textbox(
label=i18n(
"以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"
),
value=gpus,
interactive=True,
visible=F0GPUVisible,
)
gpu_info9 = gr.Textbox(
label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible
)
with gr.Column():
f0method8 = gr.Radio(
label=i18n(
"选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU"
),
choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"],
value="rmvpe_gpu",
interactive=True,
)
gpus_rmvpe = gr.Textbox(
label=i18n(
"rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程"
),
value="%s-%s" % (gpus, gpus),
interactive=True,
visible=F0GPUVisible,
)
but2 = gr.Button(i18n("特征提取"), variant="primary")
info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
f0method8.change(
fn=change_f0_method,
inputs=[f0method8],
outputs=[gpus_rmvpe],
)
but2.click(
extract_f0_feature,
[
gpus6,
np7,
f0method8,
if_f0_3,
exp_dir1,
version19,
gpus_rmvpe,
],
[info2],
api_name="train_extract_f0_feature",
)
with gr.Group():
gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引"))
with gr.Row():
save_epoch10 = gr.Slider(
minimum=1,
maximum=50,
step=1,
label=i18n("保存频率save_every_epoch"),
value=5,
interactive=True,
)
total_epoch11 = gr.Slider(
minimum=2,
maximum=1000,
step=1,
label=i18n("总训练轮数total_epoch"),
value=20,
interactive=True,
)
batch_size12 = gr.Slider(
minimum=1,
maximum=40,
step=1,
label=i18n("每张显卡的batch_size"),
value=default_batch_size,
interactive=True,
)
if_save_latest13 = gr.Radio(
label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),
choices=[i18n("是"), i18n("否")],
value=i18n("否"),
interactive=True,
)
if_cache_gpu17 = gr.Radio(
label=i18n(
"是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速"
),
choices=[i18n("是"), i18n("否")],
value=i18n("否"),
interactive=True,
)
if_save_every_weights18 = gr.Radio(
label=i18n(
"是否在每次保存时间点将最终小模型保存至weights文件夹"
),
choices=[i18n("是"), i18n("否")],
value=i18n("否"),
interactive=True,
)
with gr.Row():
pretrained_G14 = gr.Textbox(
label=i18n("加载预训练底模G路径"),
value="assets/pretrained_v2/f0G40k.pth",
interactive=True,
)
pretrained_D15 = gr.Textbox(
label=i18n("加载预训练底模D路径"),
value="assets/pretrained_v2/f0D40k.pth",
interactive=True,
)
sr2.change(
change_sr2,
[sr2, if_f0_3, version19],
[pretrained_G14, pretrained_D15],
)
version19.change(
change_version19,
[sr2, if_f0_3, version19],
[pretrained_G14, pretrained_D15, sr2],
)
if_f0_3.change(
change_f0,
[if_f0_3, sr2, version19],
[f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15],
)
gpus16 = gr.Textbox(
label=i18n(
"以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"
),
value=gpus,
interactive=True,
)
but3 = gr.Button(i18n("训练模型"), variant="primary")
but4 = gr.Button(i18n("训练特征索引"), variant="primary")
but5 = gr.Button(i18n("一键训练"), variant="primary")
info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10)
but3.click(
click_train,
[
exp_dir1,
sr2,
if_f0_3,
spk_id5,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
],
info3,
api_name="train_start",
)
but4.click(train_index, [exp_dir1, version19], info3)
but5.click(
train1key,
[
exp_dir1,
sr2,
if_f0_3,
trainset_dir4,
spk_id5,
np7,
f0method8,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
gpus_rmvpe,
],
info3,
api_name="train_start_all",
)
with gr.TabItem(i18n("ckpt处理")):
with gr.Group():
gr.Markdown(value=i18n("模型融合, 可用于测试音色融合"))
with gr.Row():
ckpt_a = gr.Textbox(
label=i18n("A模型路径"), value="", interactive=True
)
ckpt_b = gr.Textbox(
label=i18n("B模型路径"), value="", interactive=True
)
alpha_a = gr.Slider(
minimum=0,
maximum=1,
label=i18n("A模型权重"),
value=0.5,
interactive=True,
)
with gr.Row():
sr_ = gr.Radio(
label=i18n("目标采样率"),
choices=["40k", "48k"],
value="40k",
interactive=True,
)
if_f0_ = gr.Radio(
label=i18n("模型是否带音高指导"),
choices=[i18n("是"), i18n("否")],
value=i18n("是"),
interactive=True,
)
info__ = gr.Textbox(
label=i18n("要置入的模型信息"),
value="",
max_lines=8,
interactive=True,
)
name_to_save0 = gr.Textbox(
label=i18n("保存的模型名不带后缀"),
value="",
max_lines=1,
interactive=True,
)
version_2 = gr.Radio(
label=i18n("模型版本型号"),
choices=["v1", "v2"],
value="v1",
interactive=True,
)
with gr.Row():
but6 = gr.Button(i18n("融合"), variant="primary")
info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
but6.click(
merge,
[
ckpt_a,
ckpt_b,
alpha_a,
sr_,
if_f0_,
info__,
name_to_save0,
version_2,
],
info4,
api_name="ckpt_merge",
) # def merge(path1,path2,alpha1,sr,f0,info):
with gr.Group():
gr.Markdown(
value=i18n("修改模型信息(仅支持weights文件夹下提取的小模型文件)")
)
with gr.Row():
ckpt_path0 = gr.Textbox(
label=i18n("模型路径"), value="", interactive=True
)
info_ = gr.Textbox(
label=i18n("要改的模型信息"),
value="",
max_lines=8,
interactive=True,
)
name_to_save1 = gr.Textbox(
label=i18n("保存的文件名, 默认空为和源文件同名"),
value="",
max_lines=8,
interactive=True,
)
with gr.Row():
but7 = gr.Button(i18n("修改"), variant="primary")
info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
but7.click(
change_info,
[ckpt_path0, info_, name_to_save1],
info5,
api_name="ckpt_modify",
)
with gr.Group():
gr.Markdown(
value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)")
)
with gr.Row():
ckpt_path1 = gr.Textbox(
label=i18n("模型路径"), value="", interactive=True
)
but8 = gr.Button(i18n("查看"), variant="primary")
info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show")
with gr.Group():
gr.Markdown(
value=i18n(
"模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况"
)
)
with gr.Row():
ckpt_path2 = gr.Textbox(
label=i18n("模型路径"),
value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth",
interactive=True,
)
save_name = gr.Textbox(
label=i18n("保存名"), value="", interactive=True
)
sr__ = gr.Radio(
label=i18n("目标采样率"),
choices=["32k", "40k", "48k"],
value="40k",
interactive=True,
)
if_f0__ = gr.Radio(
label=i18n("模型是否带音高指导,1是0否"),
choices=["1", "0"],
value="1",
interactive=True,
)
version_1 = gr.Radio(
label=i18n("模型版本型号"),
choices=["v1", "v2"],
value="v2",
interactive=True,
)
info___ = gr.Textbox(
label=i18n("要置入的模型信息"),
value="",
max_lines=8,
interactive=True,
)
but9 = gr.Button(i18n("提取"), variant="primary")
info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
ckpt_path2.change(
change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
)
but9.click(
extract_small_model,
[ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
info7,
api_name="ckpt_extract",
)
with gr.TabItem(i18n("Onnx导出")):
with gr.Row():
ckpt_dir = gr.Textbox(
label=i18n("RVC模型路径"), value="", interactive=True
)
with gr.Row():
onnx_dir = gr.Textbox(
label=i18n("Onnx输出路径"), value="", interactive=True
)
with gr.Row():
infoOnnx = gr.Label(label="info")
with gr.Row():
butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
butOnnx.click(
export_onnx, [ckpt_dir, onnx_dir], infoOnnx, api_name="export_onnx"
)
tab_faq = i18n("常见问题解答")
with gr.TabItem(tab_faq):
try:
if tab_faq == "常见问题解答":
with open("docs/cn/faq.md", "r", encoding="utf8") as f:
info = f.read()
else:
with open("docs/en/faq_en.md", "r", encoding="utf8") as f:
info = f.read()
gr.Markdown(value=info)
except:
gr.Markdown(traceback.format_exc())
if config.iscolab:
app.queue(concurrency_count=511, max_size=1022).launch(share=True)
else:

517
infer/lib/train/data_utils.py
View File

@ -1,517 +0,0 @@
import os
import traceback
import logging
logger = logging.getLogger(__name__)
import numpy as np
import torch
import torch.utils.data
from infer.lib.train.mel_processing import spectrogram_torch
from infer.lib.train.utils import load_filepaths_and_text, load_wav_to_torch
class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
"""
1) loads audio, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
def __init__(self, audiopaths_and_text, hparams):
self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self._filter()
def _filter(self):
"""
Filter text & store spec lengths
"""
# Store spectrogram lengths for Bucketing
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
# spec_length = wav_length // hop_length
audiopaths_and_text_new = []
lengths = []
for audiopath, text, pitch, pitchf, dv in self.audiopaths_and_text:
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
audiopaths_and_text_new.append([audiopath, text, pitch, pitchf, dv])
lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
self.audiopaths_and_text = audiopaths_and_text_new
self.lengths = lengths
def get_sid(self, sid):
sid = torch.LongTensor([int(sid)])
return sid
def get_audio_text_pair(self, audiopath_and_text):
# separate filename and text
file = audiopath_and_text[0]
phone = audiopath_and_text[1]
pitch = audiopath_and_text[2]
pitchf = audiopath_and_text[3]
dv = audiopath_and_text[4]
phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
spec, wav = self.get_audio(file)
dv = self.get_sid(dv)
len_phone = phone.size()[0]
len_spec = spec.size()[-1]
# print(123,phone.shape,pitch.shape,spec.shape)
if len_phone != len_spec:
len_min = min(len_phone, len_spec)
# amor
len_wav = len_min * self.hop_length
spec = spec[:, :len_min]
wav = wav[:, :len_wav]
phone = phone[:len_min, :]
pitch = pitch[:len_min]
pitchf = pitchf[:len_min]
return (spec, wav, phone, pitch, pitchf, dv)
def get_labels(self, phone, pitch, pitchf):
phone = np.load(phone)
phone = np.repeat(phone, 2, axis=0)
pitch = np.load(pitch)
pitchf = np.load(pitchf)
n_num = min(phone.shape[0], 900) # DistributedBucketSampler
# print(234,phone.shape,pitch.shape)
phone = phone[:n_num, :]
pitch = pitch[:n_num]
pitchf = pitchf[:n_num]
phone = torch.FloatTensor(phone)
pitch = torch.LongTensor(pitch)
pitchf = torch.FloatTensor(pitchf)
return phone, pitch, pitchf
def get_audio(self, filename):
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError(
"{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate
)
)
audio_norm = audio
# audio_norm = audio / self.max_wav_value
# audio_norm = audio / np.abs(audio).max()
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if os.path.exists(spec_filename):
try:
spec = torch.load(spec_filename)
except:
logger.warning("%s %s", spec_filename, traceback.format_exc())
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
return spec, audio_norm
def __getitem__(self, index):
return self.get_audio_text_pair(self.audiopaths_and_text[index])
def __len__(self):
return len(self.audiopaths_and_text)
class TextAudioCollateMultiNSFsid:
"""Zero-pads model inputs and targets"""
def __init__(self, return_ids=False):
self.return_ids = return_ids
def __call__(self, batch):
"""Collate's training batch from normalized text and aduio
PARAMS
------
batch: [text_normalized, spec_normalized, wav_normalized]
"""
# Right zero-pad all one-hot text sequences to max input length
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
)
max_spec_len = max([x[0].size(1) for x in batch])
max_wave_len = max([x[1].size(1) for x in batch])
spec_lengths = torch.LongTensor(len(batch))
wave_lengths = torch.LongTensor(len(batch))
spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
spec_padded.zero_()
wave_padded.zero_()
max_phone_len = max([x[2].size(0) for x in batch])
phone_lengths = torch.LongTensor(len(batch))
phone_padded = torch.FloatTensor(
len(batch), max_phone_len, batch[0][2].shape[1]
) # (spec, wav, phone, pitch)
pitch_padded = torch.LongTensor(len(batch), max_phone_len)
pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
phone_padded.zero_()
pitch_padded.zero_()
pitchf_padded.zero_()
# dv = torch.FloatTensor(len(batch), 256)#gin=256
sid = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
spec = row[0]
spec_padded[i, :, : spec.size(1)] = spec
spec_lengths[i] = spec.size(1)
wave = row[1]
wave_padded[i, :, : wave.size(1)] = wave
wave_lengths[i] = wave.size(1)
phone = row[2]
phone_padded[i, : phone.size(0), :] = phone
phone_lengths[i] = phone.size(0)
pitch = row[3]
pitch_padded[i, : pitch.size(0)] = pitch
pitchf = row[4]
pitchf_padded[i, : pitchf.size(0)] = pitchf
# dv[i] = row[5]
sid[i] = row[5]
return (
phone_padded,
phone_lengths,
pitch_padded,
pitchf_padded,
spec_padded,
spec_lengths,
wave_padded,
wave_lengths,
# dv
sid,
)
class TextAudioLoader(torch.utils.data.Dataset):
"""
1) loads audio, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
def __init__(self, audiopaths_and_text, hparams):
self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self._filter()
def _filter(self):
"""
Filter text & store spec lengths
"""
# Store spectrogram lengths for Bucketing
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
# spec_length = wav_length // hop_length
audiopaths_and_text_new = []
lengths = []
for audiopath, text, dv in self.audiopaths_and_text:
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
audiopaths_and_text_new.append([audiopath, text, dv])
lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
self.audiopaths_and_text = audiopaths_and_text_new
self.lengths = lengths
def get_sid(self, sid):
sid = torch.LongTensor([int(sid)])
return sid
def get_audio_text_pair(self, audiopath_and_text):
# separate filename and text
file = audiopath_and_text[0]
phone = audiopath_and_text[1]
dv = audiopath_and_text[2]
phone = self.get_labels(phone)
spec, wav = self.get_audio(file)
dv = self.get_sid(dv)
len_phone = phone.size()[0]
len_spec = spec.size()[-1]
if len_phone != len_spec:
len_min = min(len_phone, len_spec)
len_wav = len_min * self.hop_length
spec = spec[:, :len_min]
wav = wav[:, :len_wav]
phone = phone[:len_min, :]
return (spec, wav, phone, dv)
def get_labels(self, phone):
phone = np.load(phone)
phone = np.repeat(phone, 2, axis=0)
n_num = min(phone.shape[0], 900) # DistributedBucketSampler
phone = phone[:n_num, :]
phone = torch.FloatTensor(phone)
return phone
def get_audio(self, filename):
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError(
"{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate
)
)
audio_norm = audio
# audio_norm = audio / self.max_wav_value
# audio_norm = audio / np.abs(audio).max()
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if os.path.exists(spec_filename):
try:
spec = torch.load(spec_filename)
except:
logger.warning("%s %s", spec_filename, traceback.format_exc())
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
return spec, audio_norm
def __getitem__(self, index):
return self.get_audio_text_pair(self.audiopaths_and_text[index])
def __len__(self):
return len(self.audiopaths_and_text)
class TextAudioCollate:
"""Zero-pads model inputs and targets"""
def __init__(self, return_ids=False):
self.return_ids = return_ids
def __call__(self, batch):
"""Collate's training batch from normalized text and aduio
PARAMS
------
batch: [text_normalized, spec_normalized, wav_normalized]
"""
# Right zero-pad all one-hot text sequences to max input length
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
)
max_spec_len = max([x[0].size(1) for x in batch])
max_wave_len = max([x[1].size(1) for x in batch])
spec_lengths = torch.LongTensor(len(batch))
wave_lengths = torch.LongTensor(len(batch))
spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
spec_padded.zero_()
wave_padded.zero_()
max_phone_len = max([x[2].size(0) for x in batch])
phone_lengths = torch.LongTensor(len(batch))
phone_padded = torch.FloatTensor(
len(batch), max_phone_len, batch[0][2].shape[1]
)
phone_padded.zero_()
sid = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
spec = row[0]
spec_padded[i, :, : spec.size(1)] = spec
spec_lengths[i] = spec.size(1)
wave = row[1]
wave_padded[i, :, : wave.size(1)] = wave
wave_lengths[i] = wave.size(1)
phone = row[2]
phone_padded[i, : phone.size(0), :] = phone
phone_lengths[i] = phone.size(0)
sid[i] = row[3]
return (
phone_padded,
phone_lengths,
spec_padded,
spec_lengths,
wave_padded,
wave_lengths,
sid,
)
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
"""
Maintain similar input lengths in a batch.
Length groups are specified by boundaries.
Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
It removes samples which are not included in the boundaries.
Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
"""
def __init__(
self,
dataset,
batch_size,
boundaries,
num_replicas=None,
rank=None,
shuffle=True,
):
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
self.lengths = dataset.lengths
self.batch_size = batch_size
self.boundaries = boundaries
self.buckets, self.num_samples_per_bucket = self._create_buckets()
self.total_size = sum(self.num_samples_per_bucket)
self.num_samples = self.total_size // self.num_replicas
def _create_buckets(self):
buckets = [[] for _ in range(len(self.boundaries) - 1)]
for i in range(len(self.lengths)):
length = self.lengths[i]
idx_bucket = self._bisect(length)
if idx_bucket != -1:
buckets[idx_bucket].append(i)
for i in range(len(buckets) - 1, -1, -1): #
if len(buckets[i]) == 0:
buckets.pop(i)
self.boundaries.pop(i + 1)
num_samples_per_bucket = []
for i in range(len(buckets)):
len_bucket = len(buckets[i])
total_batch_size = self.num_replicas * self.batch_size
rem = (
total_batch_size - (len_bucket % total_batch_size)
) % total_batch_size
num_samples_per_bucket.append(len_bucket + rem)
return buckets, num_samples_per_bucket
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = []
if self.shuffle:
for bucket in self.buckets:
indices.append(torch.randperm(len(bucket), generator=g).tolist())
else:
for bucket in self.buckets:
indices.append(list(range(len(bucket))))
batches = []
for i in range(len(self.buckets)):
bucket = self.buckets[i]
len_bucket = len(bucket)
ids_bucket = indices[i]
num_samples_bucket = self.num_samples_per_bucket[i]
# add extra samples to make it evenly divisible
rem = num_samples_bucket - len_bucket
ids_bucket = (
ids_bucket
+ ids_bucket * (rem // len_bucket)
+ ids_bucket[: (rem % len_bucket)]
)
# subsample
ids_bucket = ids_bucket[self.rank :: self.num_replicas]
# batching
for j in range(len(ids_bucket) // self.batch_size):
batch = [
bucket[idx]
for idx in ids_bucket[
j * self.batch_size : (j + 1) * self.batch_size
]
]
batches.append(batch)
if self.shuffle:
batch_ids = torch.randperm(len(batches), generator=g).tolist()
batches = [batches[i] for i in batch_ids]
self.batches = batches
assert len(self.batches) * self.batch_size == self.num_samples
return iter(self.batches)
def _bisect(self, x, lo=0, hi=None):
if hi is None:
hi = len(self.boundaries) - 1
if hi > lo:
mid = (hi + lo) // 2
if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
return mid
elif x <= self.boundaries[mid]:
return self._bisect(x, lo, mid)
else:
return self._bisect(x, mid + 1, hi)
else:
return -1
def __len__(self):
return self.num_samples // self.batch_size

58
infer/lib/train/losses.py
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import torch
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
dr = dr.float()
dg = dg.float()
r_loss = torch.mean((1 - dr) ** 2)
g_loss = torch.mean(dg**2)
loss += r_loss + g_loss
r_losses.append(r_loss.item())
g_losses.append(g_loss.item())
return loss, r_losses, g_losses
def generator_loss(disc_outputs):
loss = 0
gen_losses = []
for dg in disc_outputs:
dg = dg.float()
l = torch.mean((1 - dg) ** 2)
gen_losses.append(l)
loss += l
return loss, gen_losses
def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
"""
z_p, logs_q: [b, h, t_t]
m_p, logs_p: [b, h, t_t]
"""
z_p = z_p.float()
logs_q = logs_q.float()
m_p = m_p.float()
logs_p = logs_p.float()
z_mask = z_mask.float()
kl = logs_p - logs_q - 0.5
kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
kl = torch.sum(kl * z_mask)
l = kl / torch.sum(z_mask)
return l

127
infer/lib/train/mel_processing.py
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import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
import logging
logger = logging.getLogger(__name__)
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
"""
PARAMS
------
C: compression factor used to compress
"""
return torch.exp(x) / C
def spectral_normalize_torch(magnitudes):
return dynamic_range_compression_torch(magnitudes)
def spectral_de_normalize_torch(magnitudes):
return dynamic_range_decompression_torch(magnitudes)
# Reusable banks
mel_basis = {}
hann_window = {}
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
"""Convert waveform into Linear-frequency Linear-amplitude spectrogram.
Args:
y :: (B, T) - Audio waveforms
n_fft
sampling_rate
hop_size
win_size
center
Returns:
:: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
"""
# Window - Cache if needed
global hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
wnsize_dtype_device = str(win_size) + "_" + dtype_device
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
dtype=y.dtype, device=y.device
)
# Padding
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
# Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
spec = torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
)
# Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6)
return spec
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
# MelBasis - Cache if needed
global mel_basis
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(
sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
dtype=spec.dtype, device=spec.device
)
# Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
melspec = spectral_normalize_torch(melspec)
return melspec
def mel_spectrogram_torch(
y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
):
"""Convert waveform into Mel-frequency Log-amplitude spectrogram.
Args:
y :: (B, T) - Waveforms
Returns:
melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
"""
# Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)
# Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame)
melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax)
return melspec

261
infer/lib/train/process_ckpt.py
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@ -1,261 +0,0 @@
import os
import sys
import traceback
from collections import OrderedDict
import torch
from i18n.i18n import I18nAuto
i18n = I18nAuto()
def savee(ckpt, sr, if_f0, name, epoch, version, hps):
try:
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt.keys():
if "enc_q" in key:
continue
opt["weight"][key] = ckpt[key].half()
opt["config"] = [
hps.data.filter_length // 2 + 1,
32,
hps.model.inter_channels,
hps.model.hidden_channels,
hps.model.filter_channels,
hps.model.n_heads,
hps.model.n_layers,
hps.model.kernel_size,
hps.model.p_dropout,
hps.model.resblock,
hps.model.resblock_kernel_sizes,
hps.model.resblock_dilation_sizes,
hps.model.upsample_rates,
hps.model.upsample_initial_channel,
hps.model.upsample_kernel_sizes,
hps.model.spk_embed_dim,
hps.model.gin_channels,
hps.data.sampling_rate,
]
opt["info"] = "%sepoch" % epoch
opt["sr"] = sr
opt["f0"] = if_f0
opt["version"] = version
torch.save(opt, "assets/weights/%s.pth" % name)
return "Success."
except:
return traceback.format_exc()
def show_info(path):
try:
a = torch.load(path, map_location="cpu")
return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % (
a.get("info", "None"),
a.get("sr", "None"),
a.get("f0", "None"),
a.get("version", "None"),
)
except:
return traceback.format_exc()
def extract_small_model(path, name, sr, if_f0, info, version):
try:
ckpt = torch.load(path, map_location="cpu")
if "model" in ckpt:
ckpt = ckpt["model"]
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt.keys():
if "enc_q" in key:
continue
opt["weight"][key] = ckpt[key].half()
if sr == "40k":
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 10, 2, 2],
512,
[16, 16, 4, 4],
109,
256,
40000,
]
elif sr == "48k":
if version == "v1":
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 6, 2, 2, 2],
512,
[16, 16, 4, 4, 4],
109,
256,
48000,
]
else:
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[12, 10, 2, 2],
512,
[24, 20, 4, 4],
109,
256,
48000,
]
elif sr == "32k":
if version == "v1":
opt["config"] = [
513,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 4, 2, 2, 2],
512,
[16, 16, 4, 4, 4],
109,
256,
32000,
]
else:
opt["config"] = [
513,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 8, 2, 2],
512,
[20, 16, 4, 4],
109,
256,
32000,
]
if info == "":
info = "Extracted model."
opt["info"] = info
opt["version"] = version
opt["sr"] = sr
opt["f0"] = int(if_f0)
torch.save(opt, "assets/weights/%s.pth" % name)
return "Success."
except:
return traceback.format_exc()
def change_info(path, info, name):
try:
ckpt = torch.load(path, map_location="cpu")
ckpt["info"] = info
if name == "":
name = os.path.basename(path)
torch.save(ckpt, "assets/weights/%s" % name)
return "Success."
except:
return traceback.format_exc()
def merge(path1, path2, alpha1, sr, f0, info, name, version):
try:
def extract(ckpt):
a = ckpt["model"]
opt = OrderedDict()
opt["weight"] = {}
for key in a.keys():
if "enc_q" in key:
continue
opt["weight"][key] = a[key]
return opt
ckpt1 = torch.load(path1, map_location="cpu")
ckpt2 = torch.load(path2, map_location="cpu")
cfg = ckpt1["config"]
if "model" in ckpt1:
ckpt1 = extract(ckpt1)
else:
ckpt1 = ckpt1["weight"]
if "model" in ckpt2:
ckpt2 = extract(ckpt2)
else:
ckpt2 = ckpt2["weight"]
if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
return "Fail to merge the models. The model architectures are not the same."
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt1.keys():
# try:
if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
opt["weight"][key] = (
alpha1 * (ckpt1[key][:min_shape0].float())
+ (1 - alpha1) * (ckpt2[key][:min_shape0].float())
).half()
else:
opt["weight"][key] = (
alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
).half()
# except:
# pdb.set_trace()
opt["config"] = cfg
"""
if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000]
elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
"""
opt["sr"] = sr
opt["f0"] = 1 if f0 == i18n("是") else 0
opt["version"] = version
opt["info"] = info
torch.save(opt, "assets/weights/%s.pth" % name)
return "Success."
except:
return traceback.format_exc()

483
infer/lib/train/utils.py
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@ -1,483 +0,0 @@
import argparse
import glob
import json
import logging
import os
import subprocess
import sys
import shutil
import numpy as np
import torch
from scipy.io.wavfile import read
MATPLOTLIB_FLAG = False
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logger = logging
def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
##################
def go(model, bkey):
saved_state_dict = checkpoint_dict[bkey]
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items(): # 模型需要的shape
try:
new_state_dict[k] = saved_state_dict[k]
if saved_state_dict[k].shape != state_dict[k].shape:
logger.warning(
"shape-%s-mismatch. need: %s, get: %s",
k,
state_dict[k].shape,
saved_state_dict[k].shape,
) #
raise KeyError
except:
# logger.info(traceback.format_exc())
logger.info("%s is not in the checkpoint", k) # pretrain缺失的
new_state_dict[k] = v # 模型自带的随机值
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict, strict=False)
else:
model.load_state_dict(new_state_dict, strict=False)
return model
go(combd, "combd")
model = go(sbd, "sbd")
#############
logger.info("Loaded model weights")
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
if (
optimizer is not None and load_opt == 1
): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
# try:
optimizer.load_state_dict(checkpoint_dict["optimizer"])
# except:
# traceback.print_exc()
logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
return model, optimizer, learning_rate, iteration
# def load_checkpoint(checkpoint_path, model, optimizer=None):
# assert os.path.isfile(checkpoint_path)
# checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
# iteration = checkpoint_dict['iteration']
# learning_rate = checkpoint_dict['learning_rate']
# if optimizer is not None:
# optimizer.load_state_dict(checkpoint_dict['optimizer'])
# # print(1111)
# saved_state_dict = checkpoint_dict['model']
# # print(1111)
#
# if hasattr(model, 'module'):
# state_dict = model.module.state_dict()
# else:
# state_dict = model.state_dict()
# new_state_dict= {}
# for k, v in state_dict.items():
# try:
# new_state_dict[k] = saved_state_dict[k]
# except:
# logger.info("%s is not in the checkpoint" % k)
# new_state_dict[k] = v
# if hasattr(model, 'module'):
# model.module.load_state_dict(new_state_dict)
# else:
# model.load_state_dict(new_state_dict)
# logger.info("Loaded checkpoint '{}' (epoch {})" .format(
# checkpoint_path, iteration))
# return model, optimizer, learning_rate, iteration
def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
saved_state_dict = checkpoint_dict["model"]
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items(): # 模型需要的shape
try:
new_state_dict[k] = saved_state_dict[k]
if saved_state_dict[k].shape != state_dict[k].shape:
logger.warning(
"shape-%s-mismatch|need-%s|get-%s",
k,
state_dict[k].shape,
saved_state_dict[k].shape,
) #
raise KeyError
except:
# logger.info(traceback.format_exc())
logger.info("%s is not in the checkpoint", k) # pretrain缺失的
new_state_dict[k] = v # 模型自带的随机值
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict, strict=False)
else:
model.load_state_dict(new_state_dict, strict=False)
logger.info("Loaded model weights")
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
if (
optimizer is not None and load_opt == 1
): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
# try:
optimizer.load_state_dict(checkpoint_dict["optimizer"])
# except:
# traceback.print_exc()
logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
return model, optimizer, learning_rate, iteration
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
logger.info(
"Saving model and optimizer state at epoch {} to {}".format(
iteration, checkpoint_path
)
)
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
torch.save(
{
"model": state_dict,
"iteration": iteration,
"optimizer": optimizer.state_dict(),
"learning_rate": learning_rate,
},
checkpoint_path,
)
def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
logger.info(
"Saving model and optimizer state at epoch {} to {}".format(
iteration, checkpoint_path
)
)
if hasattr(combd, "module"):
state_dict_combd = combd.module.state_dict()
else:
state_dict_combd = combd.state_dict()
if hasattr(sbd, "module"):
state_dict_sbd = sbd.module.state_dict()
else:
state_dict_sbd = sbd.state_dict()
torch.save(
{
"combd": state_dict_combd,
"sbd": state_dict_sbd,
"iteration": iteration,
"optimizer": optimizer.state_dict(),
"learning_rate": learning_rate,
},
checkpoint_path,
)
def summarize(
writer,
global_step,
scalars={},
histograms={},
images={},
audios={},
audio_sampling_rate=22050,
):
for k, v in scalars.items():
writer.add_scalar(k, v, global_step)
for k, v in histograms.items():
writer.add_histogram(k, v, global_step)
for k, v in images.items():
writer.add_image(k, v, global_step, dataformats="HWC")
for k, v in audios.items():
writer.add_audio(k, v, global_step, audio_sampling_rate)
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
f_list = glob.glob(os.path.join(dir_path, regex))
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
x = f_list[-1]
logger.debug(x)
return x
def plot_spectrogram_to_numpy(spectrogram):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger("matplotlib")
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
plt.colorbar(im, ax=ax)
plt.xlabel("Frames")
plt.ylabel("Channels")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def plot_alignment_to_numpy(alignment, info=None):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger("matplotlib")
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.imshow(
alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
)
fig.colorbar(im, ax=ax)
xlabel = "Decoder timestep"
if info is not None:
xlabel += "\n\n" + info
plt.xlabel(xlabel)
plt.ylabel("Encoder timestep")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
def load_filepaths_and_text(filename, split="|"):
try:
with open(filename, encoding="utf-8") as f:
filepaths_and_text = [line.strip().split(split) for line in f]
except UnicodeDecodeError:
with open(filename) as f:
filepaths_and_text = [line.strip().split(split) for line in f]
return filepaths_and_text
def get_hparams(init=True):
"""
todo:
结尾七人组：
保存频率、总epoch done
bs done
pretrainG、pretrainD done
卡号：os.en["CUDA_VISIBLE_DEVICES"] done
if_latest done
模型：if_f0 done
采样率：自动选择config done
是否缓存数据集进GPU:if_cache_data_in_gpu done
-m:
自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files done
-c不要了
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"-se",
"--save_every_epoch",
type=int,
required=True,
help="checkpoint save frequency (epoch)",
)
parser.add_argument(
"-te", "--total_epoch", type=int, required=True, help="total_epoch"
)
parser.add_argument(
"-pg", "--pretrainG", type=str, default="", help="Pretrained Generator path"
)
parser.add_argument(
"-pd", "--pretrainD", type=str, default="", help="Pretrained Discriminator path"
)
parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
parser.add_argument(
"-bs", "--batch_size", type=int, required=True, help="batch size"
)
parser.add_argument(
"-e", "--experiment_dir", type=str, required=True, help="experiment dir"
) # -m
parser.add_argument(
"-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
)
parser.add_argument(
"-sw",
"--save_every_weights",
type=str,
default="0",
help="save the extracted model in weights directory when saving checkpoints",
)
parser.add_argument(
"-v", "--version", type=str, required=True, help="model version"
)
parser.add_argument(
"-f0",
"--if_f0",
type=int,
required=True,
help="use f0 as one of the inputs of the model, 1 or 0",
)
parser.add_argument(
"-l",
"--if_latest",
type=int,
required=True,
help="if only save the latest G/D pth file, 1 or 0",
)
parser.add_argument(
"-c",
"--if_cache_data_in_gpu",
type=int,
required=True,
help="if caching the dataset in GPU memory, 1 or 0",
)
args = parser.parse_args()
name = args.experiment_dir
experiment_dir = os.path.join("./logs", args.experiment_dir)
config_save_path = os.path.join(experiment_dir, "config.json")
with open(config_save_path, "r") as f:
config = json.load(f)
hparams = HParams(**config)
hparams.model_dir = hparams.experiment_dir = experiment_dir
hparams.save_every_epoch = args.save_every_epoch
hparams.name = name
hparams.total_epoch = args.total_epoch
hparams.pretrainG = args.pretrainG
hparams.pretrainD = args.pretrainD
hparams.version = args.version
hparams.gpus = args.gpus
hparams.train.batch_size = args.batch_size
hparams.sample_rate = args.sample_rate
hparams.if_f0 = args.if_f0
hparams.if_latest = args.if_latest
hparams.save_every_weights = args.save_every_weights
hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
hparams.data.training_files = "%s/filelist.txt" % experiment_dir
return hparams
def get_hparams_from_dir(model_dir):
config_save_path = os.path.join(model_dir, "config.json")
with open(config_save_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.model_dir = model_dir
return hparams
def get_hparams_from_file(config_path):
with open(config_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
return hparams
def check_git_hash(model_dir):
source_dir = os.path.dirname(os.path.realpath(__file__))
if not os.path.exists(os.path.join(source_dir, ".git")):
logger.warning(
"{} is not a git repository, therefore hash value comparison will be ignored.".format(
source_dir
)
)
return
cur_hash = subprocess.getoutput("git rev-parse HEAD")
path = os.path.join(model_dir, "githash")
if os.path.exists(path):
saved_hash = open(path).read()
if saved_hash != cur_hash:
logger.warning(
"git hash values are different. {}(saved) != {}(current)".format(
saved_hash[:8], cur_hash[:8]
)
)
else:
open(path, "w").write(cur_hash)
def get_logger(model_dir, filename="train.log"):
global logger
logger = logging.getLogger(os.path.basename(model_dir))
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
if not os.path.exists(model_dir):
os.makedirs(model_dir)
h = logging.FileHandler(os.path.join(model_dir, filename))
h.setLevel(logging.DEBUG)
h.setFormatter(formatter)
logger.addHandler(h)
return logger
class HParams:
def __init__(self, **kwargs):
for k, v in kwargs.items():
if type(v) == dict:
v = HParams(**v)
self[k] = v
def keys(self):
return self.__dict__.keys()
def items(self):
return self.__dict__.items()
def values(self):
return self.__dict__.values()
def __len__(self):
return len(self.__dict__)
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, value):
return setattr(self, key, value)
def __contains__(self, key):
return key in self.__dict__
def __repr__(self):
return self.__dict__.__repr__()

108
infer/modules/uvr5/modules.py
View File

@ -1,108 +0,0 @@
import os
import traceback
import logging
logger = logging.getLogger(__name__)
import ffmpeg
import torch
from configs.config import Config
from infer.modules.uvr5.mdxnet import MDXNetDereverb
from infer.modules.uvr5.vr import AudioPre, AudioPreDeEcho
config = Config()
def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
infos = []
try:
inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
save_root_vocal = (
save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
)
save_root_ins = (
save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
)
if model_name == "onnx_dereverb_By_FoxJoy":
pre_fun = MDXNetDereverb(15, config.device)
else:
func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho
pre_fun = func(
agg=int(agg),
model_path=os.path.join(
os.getenv("weight_uvr5_root"), model_name + ".pth"
),
device=config.device,
is_half=config.is_half,
)
is_hp3 = "HP3" in model_name
if inp_root != "":
paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
else:
paths = [path.name for path in paths]
for path in paths:
inp_path = os.path.join(inp_root, path)
need_reformat = 1
done = 0
try:
info = ffmpeg.probe(inp_path, cmd="ffprobe")
if (
info["streams"][0]["channels"] == 2
and info["streams"][0]["sample_rate"] == "44100"
):
need_reformat = 0
pre_fun._path_audio_(
inp_path, save_root_ins, save_root_vocal, format0, is_hp3=is_hp3
)
done = 1
except:
need_reformat = 1
traceback.print_exc()
if need_reformat == 1:
tmp_path = "%s/%s.reformatted.wav" % (
os.path.join(os.environ["TEMP"]),
os.path.basename(inp_path),
)
os.system(
'ffmpeg -i "%s" -vn -acodec pcm_s16le -ac 2 -ar 44100 "%s" -y'
% (inp_path, tmp_path)
)
inp_path = tmp_path
try:
if done == 0:
pre_fun._path_audio_(
inp_path, save_root_ins, save_root_vocal, format0
)
infos.append("%s->Success" % (os.path.basename(inp_path)))
yield "\n".join(infos)
except:
try:
if done == 0:
pre_fun._path_audio_(
inp_path, save_root_ins, save_root_vocal, format0
)
infos.append("%s->Success" % (os.path.basename(inp_path)))
yield "\n".join(infos)
except:
infos.append(
"%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
)
yield "\n".join(infos)
except:
infos.append(traceback.format_exc())
yield "\n".join(infos)
finally:
try:
if model_name == "onnx_dereverb_By_FoxJoy":
del pre_fun.pred.model
del pre_fun.pred.model_
else:
del pre_fun.model
del pre_fun
except:
traceback.print_exc()
if torch.cuda.is_available():
torch.cuda.empty_cache()
logger.info("Executed torch.cuda.empty_cache()")
yield "\n".join(infos)