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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-01-20 02:51:09 +00:00
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RVC-Boss
99
infer-web.py
99
infer-web.py
@@ -21,6 +21,7 @@ warnings.filterwarnings("ignore")
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torch.manual_seed(114514)
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from i18n import I18nAuto
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import ffmpeg
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from MDXNet import MDXNetDereverb
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i18n = I18nAuto()
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i18n.print()
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@@ -82,7 +83,7 @@ import gradio as gr
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import logging
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from vc_infer_pipeline import VC
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from config import Config
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from infer_uvr5 import _audio_pre_
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from infer_uvr5 import _audio_pre_,_audio_pre_new
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from my_utils import load_audio
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from train.process_ckpt import show_info, change_info, merge, extract_small_model
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@@ -133,7 +134,7 @@ for root, dirs, files in os.walk(index_root, topdown=False):
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index_paths.append("%s/%s" % (root, name))
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uvr5_names = []
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for name in os.listdir(weight_uvr5_root):
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if name.endswith(".pth"):
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if name.endswith(".pth")or "onnx"in name:
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uvr5_names.append(name.replace(".pth", ""))
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@@ -150,6 +151,7 @@ def vc_single(
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filter_radius,
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resample_sr,
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rms_mix_rate,
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protect
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): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
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global tgt_sr, net_g, vc, hubert_model, version
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if input_audio_path is None:
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@@ -197,6 +199,7 @@ def vc_single(
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resample_sr,
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rms_mix_rate,
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version,
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protect,
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f0_file=f0_file,
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)
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if resample_sr >= 16000 and tgt_sr != resample_sr:
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@@ -232,6 +235,7 @@ def vc_multi(
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filter_radius,
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resample_sr,
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rms_mix_rate,
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protect
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):
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try:
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dir_path = (
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@@ -262,6 +266,7 @@ def vc_multi(
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filter_radius,
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resample_sr,
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rms_mix_rate,
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protect
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)
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if "Success" in info:
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try:
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@@ -288,12 +293,16 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
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save_root_ins = (
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save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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)
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pre_fun = _audio_pre_(
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agg=int(agg),
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model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
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device=config.device,
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is_half=config.is_half,
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)
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if(model_name=="onnx_dereverb_By_FoxJoy"):
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pre_fun=MDXNetDereverb(15)
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else:
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func=_audio_pre_ if "DeEcho"not in model_name else _audio_pre_new
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pre_fun = func(
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agg=int(agg),
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model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
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device=config.device,
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is_half=config.is_half,
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)
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if inp_root != "":
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paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
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else:
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@@ -336,8 +345,12 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
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yield "\n".join(infos)
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finally:
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try:
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del pre_fun.model
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del pre_fun
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if (model_name == "onnx_dereverb_By_FoxJoy"):
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del pre_fun.pred.model
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del pre_fun.pred.model_
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else:
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del pre_fun.model
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del pre_fun
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except:
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traceback.print_exc()
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print("clean_empty_cache")
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@@ -790,7 +803,7 @@ def train_index(exp_dir1, version19):
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faiss.write_index(
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index,
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"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
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% (exp_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
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)
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# faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
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infos.append("adding")
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@@ -801,11 +814,11 @@ def train_index(exp_dir1, version19):
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faiss.write_index(
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index,
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"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
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% (exp_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
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)
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infos.append(
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"成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
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% (n_ivf, index_ivf.nprobe,exp_dir1, version19)
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)
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# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
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# infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
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@@ -1030,7 +1043,7 @@ def train1key(
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faiss.write_index(
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index,
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"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
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% (model_log_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
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)
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yield get_info_str("adding index")
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batch_size_add = 8192
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@@ -1039,11 +1052,11 @@ def train1key(
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faiss.write_index(
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index,
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"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
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% (model_log_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
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)
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yield get_info_str(
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"成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
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% (n_ivf, index_ivf.nprobe, exp_dir1,version19)
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)
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yield get_info_str(i18n("全流程结束！"))
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@@ -1161,8 +1174,8 @@ with gr.Blocks() as app:
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value="E:\\codes\\py39\\test-20230416b\\todo-songs\\冬之花clip1.wav",
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)
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f0method0 = gr.Radio(
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label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"),
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choices=["pm", "harvest"],
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label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
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choices=["pm", "harvest","crepe"],
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value="pm",
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interactive=True,
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)
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@@ -1197,9 +1210,10 @@ with gr.Blocks() as app:
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minimum=0,
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maximum=1,
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label=i18n("检索特征占比"),
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value=0.76,
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value=0.88,
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interactive=True,
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)
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with gr.Column():
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resample_sr0 = gr.Slider(
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minimum=0,
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maximum=48000,
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@@ -1215,9 +1229,17 @@ with gr.Blocks() as app:
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value=1,
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interactive=True,
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)
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protect0 = gr.Slider(
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minimum=0,
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maximum=0.5,
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label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
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value=0.33,
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step=0.01,
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interactive=True,
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)
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f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
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but0 = gr.Button(i18n("转换"), variant="primary")
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with gr.Column():
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with gr.Row():
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vc_output1 = gr.Textbox(label=i18n("输出信息"))
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vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))
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but0.click(
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@@ -1235,6 +1257,7 @@ with gr.Blocks() as app:
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filter_radius0,
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resample_sr0,
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rms_mix_rate0,
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protect0
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],
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[vc_output1, vc_output2],
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)
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@@ -1249,8 +1272,8 @@ with gr.Blocks() as app:
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)
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opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt")
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f0method1 = gr.Radio(
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label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"),
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choices=["pm", "harvest"],
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label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
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choices=["pm", "harvest","crepe"],
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value="pm",
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interactive=True,
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)
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@@ -1285,6 +1308,7 @@ with gr.Blocks() as app:
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value=1,
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interactive=True,
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)
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with gr.Column():
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resample_sr1 = gr.Slider(
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minimum=0,
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maximum=48000,
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@@ -1300,6 +1324,14 @@ with gr.Blocks() as app:
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value=1,
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interactive=True,
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)
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protect1 = gr.Slider(
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minimum=0,
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maximum=0.5,
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label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
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value=0.33,
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step=0.01,
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interactive=True,
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)
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with gr.Column():
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dir_input = gr.Textbox(
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label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"),
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@@ -1308,8 +1340,9 @@ with gr.Blocks() as app:
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inputs = gr.File(
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file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
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)
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but1 = gr.Button(i18n("转换"), variant="primary")
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vc_output3 = gr.Textbox(label=i18n("输出信息"))
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with gr.Row():
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but1 = gr.Button(i18n("转换"), variant="primary")
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vc_output3 = gr.Textbox(label=i18n("输出信息"))
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but1.click(
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vc_multi,
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[
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@@ -1326,14 +1359,26 @@ with gr.Blocks() as app:
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filter_radius1,
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resample_sr1,
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rms_mix_rate1,
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protect1
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],
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[vc_output3],
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)
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with gr.TabItem(i18n("伴奏人声分离")):
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with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
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with gr.Group():
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gr.Markdown(
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value=i18n(
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"人声伴奏分离批量处理, 使用UVR5模型. <br>不带和声用HP2, 带和声且提取的人声不需要和声用HP5<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)"
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"人声伴奏分离批量处理， 使用UVR5模型。 <br>"
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"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>"
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"模型分为三类： <br>"
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"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>"
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"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> "
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"3、去混响、去延迟模型（by FoxJoy）：<br>"
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" (1)MDX-Net:对于双通道混响是最好的选择，不能去除单通道混响；<br>"
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" (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>"
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"去混响/去延迟，附：<br>"
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"1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>"
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"2、MDX-Net-Dereverb模型挺慢的；<br>"
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"3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。"
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)
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)
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with gr.Row():
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@@ -1384,7 +1429,7 @@ with gr.Blocks() as app:
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exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test")
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sr2 = gr.Radio(
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label=i18n("目标采样率"),
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choices=["32k", "40k", "48k"],
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choices=["40k", "48k"],
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value="40k",
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interactive=True,
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)
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