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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
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Format code (#409)
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181
app.py
181
app.py
@ -1,6 +1,7 @@
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import io
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import os
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import torch
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# os.system("wget -P cvec/ https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt")
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import gradio as gr
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import librosa
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@ -20,10 +21,10 @@ from infer_pack.models import (
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)
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from i18n import I18nAuto
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logging.getLogger('numba').setLevel(logging.WARNING)
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logging.getLogger('markdown_it').setLevel(logging.WARNING)
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logging.getLogger('urllib3').setLevel(logging.WARNING)
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logging.getLogger('matplotlib').setLevel(logging.WARNING)
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logging.getLogger("numba").setLevel(logging.WARNING)
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logging.getLogger("markdown_it").setLevel(logging.WARNING)
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logging.getLogger("urllib3").setLevel(logging.WARNING)
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logging.getLogger("matplotlib").setLevel(logging.WARNING)
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i18n = I18nAuto()
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i18n.print()
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@ -44,7 +45,7 @@ for root, dirs, files in os.walk(index_root, topdown=False):
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if name.endswith(".index") and "trained" not in name:
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index_paths.append("%s/%s" % (root, name))
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def get_vc(sid):
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global n_spk, tgt_sr, net_g, vc, cpt, version
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if sid == "" or sid == []:
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@ -121,7 +122,6 @@ def load_hubert():
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hubert_model.eval()
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def vc_single(
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sid,
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input_audio_path,
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@ -144,7 +144,7 @@ def vc_single(
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try:
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audio = input_audio_path[1] / 32768.0
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if len(audio.shape) == 2:
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audio = np.mean(audio,-1)
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audio = np.mean(audio, -1)
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audio = librosa.resample(audio, orig_sr=input_audio_path[0], target_sr=16000)
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audio_max = np.abs(audio).max() / 0.95
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if audio_max > 1:
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@ -212,114 +212,111 @@ app = gr.Blocks()
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with app:
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with gr.Tabs():
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with gr.TabItem("在线demo"):
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gr.Markdown(value="""
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gr.Markdown(
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value="""
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RVC 在线demo
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""")
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"""
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)
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sid = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
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with gr.Column():
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spk_item = gr.Slider(
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minimum=0,
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maximum=2333,
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step=1,
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label=i18n("请选择说话人id"),
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value=0,
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visible=False,
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interactive=True,
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)
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minimum=0,
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maximum=2333,
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step=1,
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label=i18n("请选择说话人id"),
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value=0,
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visible=False,
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interactive=True,
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)
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sid.change(
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fn=get_vc,
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inputs=[sid],
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outputs=[spk_item],
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fn=get_vc,
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inputs=[sid],
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outputs=[spk_item],
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)
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gr.Markdown(
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value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ")
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)
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vc_input3 = gr.Audio(label="上传音频（长度小于90秒）")
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vc_transform0 = gr.Number(
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label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
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)
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vc_transform0 = gr.Number(label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0)
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f0method0 = gr.Radio(
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label=i18n(
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"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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),
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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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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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filter_radius0 = gr.Slider(
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minimum=0,
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maximum=7,
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label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
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value=3,
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step=1,
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interactive=True,
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)
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minimum=0,
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maximum=7,
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label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
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value=3,
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step=1,
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interactive=True,
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)
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with gr.Column():
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file_index1 = gr.Textbox(
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label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
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value="",
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interactive=False,
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visible=False
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)
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label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
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value="",
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interactive=False,
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visible=False,
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)
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file_index2 = gr.Dropdown(
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label=i18n("自动检测index路径,下拉式选择(dropdown)"),
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choices=sorted(index_paths),
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interactive=True,
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)
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label=i18n("自动检测index路径,下拉式选择(dropdown)"),
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choices=sorted(index_paths),
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interactive=True,
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)
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index_rate1 = gr.Slider(
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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=0.88,
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interactive=True,
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)
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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=0.88,
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interactive=True,
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)
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resample_sr0 = gr.Slider(
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minimum=0,
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maximum=48000,
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label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
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value=0,
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step=1,
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interactive=True,
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)
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minimum=0,
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maximum=48000,
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label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
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value=0,
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step=1,
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interactive=True,
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)
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rms_mix_rate0 = gr.Slider(
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minimum=0,
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maximum=1,
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label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
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value=1,
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interactive=True,
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)
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minimum=0,
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maximum=1,
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label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
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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(
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"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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),
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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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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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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(vc_single,
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[
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spk_item,
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vc_input3,
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vc_transform0,
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f0_file,
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f0method0,
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file_index1,
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file_index2,
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# file_big_npy1,
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index_rate1,
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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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but0.click(
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vc_single,
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[
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spk_item,
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vc_input3,
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vc_transform0,
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f0_file,
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f0method0,
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file_index1,
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file_index2,
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# file_big_npy1,
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index_rate1,
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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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app.launch()
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@ -9,7 +9,7 @@ import datetime
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hps = utils.get_hparams()
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os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
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n_gpus = len(hps.gpus.split("-"))
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from random import shuffle,randint
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from random import shuffle, randint
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import traceback, json, argparse, itertools, math, torch, pdb
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torch.backends.cudnn.deterministic = False
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@ -67,9 +67,10 @@ class EpochRecorder:
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def main():
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n_gpus = torch.cuda.device_count()
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if torch.cuda.is_available()==False and torch.backends.mps.is_available()==True:n_gpus = 1
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if torch.cuda.is_available() == False and torch.backends.mps.is_available() == True:
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n_gpus = 1
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = str(randint(20000,55555))
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os.environ["MASTER_PORT"] = str(randint(20000, 55555))
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children = []
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for i in range(n_gpus):
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subproc = mp.Process(
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