mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-05-06 20:01:37 +08:00
feat(infer): add model hash identification
and optimize infer-web ui
This commit is contained in:
源文雨
parent
7e48279c6c
commit
b9ad0258ae
104
infer-web.py
104
infer-web.py
@ -10,13 +10,12 @@ load_dotenv("sha256.env")
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if sys.platform == "darwin":
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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from infer.modules.vc import VC
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from infer.modules.vc import VC, show_info
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from infer.modules.uvr5.modules import uvr
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from infer.lib.train.process_ckpt import (
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change_info,
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extract_small_model,
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merge,
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show_info,
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)
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from i18n.i18n import I18nAuto
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from configs.config import Config
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@ -838,6 +837,7 @@ with gr.Blocks(title="RVC WebUI") as app:
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clean_button.click(
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fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean"
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)
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modelinfo = gr.Textbox(label=i18n("模型信息"))
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with gr.TabItem(i18n("单次推理")):
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with gr.Group():
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with gr.Row():
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@ -846,24 +846,23 @@ with gr.Blocks(title="RVC WebUI") as app:
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label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"),
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value=0,
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)
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input_audio0 = gr.Textbox(
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input_audio0 = gr.File(
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label=i18n(
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"输入待处理音频文件路径(默认是正确格式示例)"
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"待处理音频文件"
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),
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placeholder="C:\\Users\\Desktop\\audio_example.wav",
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)
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file_index1 = gr.Textbox(
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label=i18n(
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"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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),
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placeholder="C:\\Users\\Desktop\\model_example.index",
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interactive=True,
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file_types=["audio"]
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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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file_index1 = gr.File(
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label=i18n(
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"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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),
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)
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with gr.Column():
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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,rmvpe效果最好且微吃GPU"
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@ -876,8 +875,6 @@ with gr.Blocks(title="RVC WebUI") as app:
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value="rmvpe",
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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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@ -928,6 +925,10 @@ with gr.Blocks(title="RVC WebUI") as app:
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),
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visible=False,
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)
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but0 = gr.Button(i18n("转换"), variant="primary")
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vc_output2 = gr.Audio(
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label=i18n("输出音频(右下角三个点,点了可以下载)")
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)
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refresh_button.click(
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fn=change_choices,
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@ -935,19 +936,8 @@ with gr.Blocks(title="RVC WebUI") as app:
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outputs=[sid0, file_index2],
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api_name="infer_refresh",
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)
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# file_big_npy1 = gr.Textbox(
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# label=i18n("特征文件路径"),
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# value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
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# interactive=True,
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# )
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with gr.Group():
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with gr.Column():
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but0 = gr.Button(i18n("转换"), variant="primary")
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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(
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label=i18n("输出音频(右下角三个点,点了可以下载)")
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)
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but0.click(
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vc.vc_single,
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@ -981,36 +971,28 @@ with gr.Blocks(title="RVC WebUI") as app:
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label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"),
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value=0,
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)
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dir_input = gr.Textbox(
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label=i18n(
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"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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),
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placeholder="C:\\Users\\Desktop\\input_vocal_dir",
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)
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inputs = gr.File(
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file_count="multiple",
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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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opt_input = gr.Textbox(
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label=i18n("指定输出文件夹"), value="opt"
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)
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file_index3 = 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=True,
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)
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file_index4 = 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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f0method1 = gr.Radio(
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file_index3 = gr.File(
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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,rmvpe效果最好且微吃GPU"
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"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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),
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choices=(
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["pm", "harvest", "crepe", "rmvpe"]
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if config.dml == False
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else ["pm", "harvest", "rmvpe"]
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),
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value="rmvpe",
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interactive=True,
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)
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format1 = gr.Radio(
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label=i18n("导出文件格式"),
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choices=["wav", "flac", "mp3", "m4a"],
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value="wav",
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interactive=True,
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)
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refresh_button.click(
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@ -1026,6 +1008,18 @@ with gr.Blocks(title="RVC WebUI") as app:
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# )
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with gr.Column():
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f0method1 = 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,rmvpe效果最好且微吃GPU"
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),
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choices=(
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["pm", "harvest", "crepe", "rmvpe"]
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if config.dml == False
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else ["pm", "harvest", "rmvpe"]
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),
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value="rmvpe",
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interactive=True,
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)
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resample_sr1 = gr.Slider(
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minimum=0,
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maximum=48000,
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@ -1070,20 +1064,14 @@ with gr.Blocks(title="RVC WebUI") as app:
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value=1,
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interactive=True,
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)
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with gr.Row():
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dir_input = gr.Textbox(
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label=i18n(
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"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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),
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placeholder="C:\\Users\\Desktop\\input_vocal_dir",
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format1 = gr.Radio(
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label=i18n("导出文件格式"),
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choices=["wav", "flac", "mp3", "m4a"],
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value="wav",
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interactive=True,
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)
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inputs = gr.File(
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file_count="multiple",
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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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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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@ -1111,7 +1099,7 @@ with gr.Blocks(title="RVC WebUI") as app:
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sid0.change(
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fn=vc.get_vc,
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inputs=[sid0, protect0, protect1],
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outputs=[spk_item, protect0, protect1, file_index2, file_index4],
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outputs=[spk_item, protect0, protect1, file_index2, file_index4, modelinfo],
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api_name="infer_change_voice",
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)
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with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
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@ -1,16 +1,17 @@
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import os
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import sys
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import traceback
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from collections import OrderedDict
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from time import time
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import torch
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from i18n.i18n import I18nAuto
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from infer.modules.vc import model_hash_ckpt, hash_id
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i18n = I18nAuto()
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def savee(ckpt, sr, if_f0, name, epoch, version, hps):
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# add author sign
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def save_small_model(ckpt, sr, if_f0, name, epoch, version, hps):
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try:
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opt = OrderedDict()
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opt["weight"] = {}
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@ -39,28 +40,20 @@ def savee(ckpt, sr, if_f0, name, epoch, version, hps):
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hps.data.sampling_rate,
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]
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opt["info"] = "%sepoch" % epoch
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opt["name"] = name
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opt["timestamp"] = int(time())
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opt["sr"] = sr
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opt["f0"] = if_f0
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opt["version"] = version
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h = model_hash_ckpt(opt)
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opt["hash"] = h
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opt["id"] = hash_id(h)
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torch.save(opt, "assets/weights/%s.pth" % name)
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return "Success."
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except:
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return traceback.format_exc()
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def show_info(path):
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try:
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a = torch.load(path, map_location="cpu")
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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" % (
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a.get("info", "None"),
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a.get("sr", "None"),
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a.get("f0", "None"),
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a.get("version", "None"),
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)
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except:
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return traceback.format_exc()
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def extract_small_model(path, name, sr, if_f0, info, version):
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try:
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ckpt = torch.load(path, map_location="cpu")
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@ -182,9 +175,14 @@ def extract_small_model(path, name, sr, if_f0, info, version):
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if info == "":
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info = "Extracted model."
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opt["info"] = info
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opt["name"] = name
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opt["timestamp"] = int(time())
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opt["version"] = version
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opt["sr"] = sr
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opt["f0"] = int(if_f0)
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h = model_hash_ckpt(opt)
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opt["hash"] = h
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opt["id"] = hash_id(h)
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torch.save(opt, "assets/weights/%s.pth" % name)
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return "Success."
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except:
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@ -251,10 +249,15 @@ def merge(path1, path2, alpha1, sr, f0, info, name, version):
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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]
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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]
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"""
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opt["name"] = name
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opt["timestamp"] = int(time())
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opt["sr"] = sr
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opt["f0"] = 1 if f0 == i18n("是") else 0
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opt["version"] = version
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opt["info"] = info
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h = model_hash_ckpt(opt)
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opt["hash"] = h
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opt["id"] = hash_id(h)
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torch.save(opt, "assets/weights/%s.pth" % name)
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return "Success."
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except:
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@ -74,7 +74,7 @@ from infer.lib.train.losses import (
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kl_loss,
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)
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from infer.lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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from infer.lib.train.process_ckpt import savee
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from infer.lib.train.process_ckpt import save_small_model
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global_step = 0
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@ -602,7 +602,7 @@ def train_and_evaluate(
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% (
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hps.name,
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epoch,
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savee(
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save_small_model(
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ckpt,
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hps.sample_rate,
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hps.if_f0,
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@ -626,7 +626,7 @@ def train_and_evaluate(
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logger.info(
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"saving final ckpt:%s"
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% (
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savee(
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save_small_model(
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ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps
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)
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)
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@ -1,3 +1,5 @@
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from .pipeline import Pipeline
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from .modules import VC
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from .utils import get_index_path_from_model, load_hubert
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from .info import show_info
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from .hash import model_hash_ckpt, hash_id
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170
infer/modules/vc/hash.py
Normal file
170
infer/modules/vc/hash.py
Normal file
@ -0,0 +1,170 @@
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import numpy as np
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import torch
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import hashlib
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import pathlib
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from scipy.fft import fft
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from pybase16384 import encode_to_string, decode_from_string
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if __name__ == "__main__":
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import os, sys
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from configs.config import Config, singleton_variable
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from .pipeline import Pipeline
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from .utils import load_hubert
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from infer.lib.audio import load_audio
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class TorchSeedContext:
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def __init__(self, seed):
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self.seed = seed
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self.state = None
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def __enter__(self):
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self.state = torch.random.get_rng_state()
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torch.manual_seed(self.seed)
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def __exit__(self, type, value, traceback):
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torch.random.set_rng_state(self.state)
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half_hash_len = 512
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expand_factor = 65536*8
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@singleton_variable
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def original_audio_time_minus():
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__original_audio = load_audio(str(pathlib.Path(__file__).parent / "lgdsng.mp3"), 16000)
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np.divide(__original_audio, np.abs(__original_audio).max(), __original_audio)
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return -__original_audio
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@singleton_variable
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def original_audio_freq_minus():
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__original_audio = load_audio(str(pathlib.Path(__file__).parent / "lgdsng.mp3"), 16000)
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np.divide(__original_audio, np.abs(__original_audio).max(), __original_audio)
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__original_audio = fft(__original_audio)
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return -__original_audio
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def _cut_u16(n):
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if n > 16384: n = 16384 + 16384*(1-np.exp((16384-n)/expand_factor))
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elif n < -16384: n = -16384 - 16384*(1-np.exp((n+16384)/expand_factor))
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return n
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# wave_hash will change time_field, use carefully
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def wave_hash(time_field):
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np.divide(time_field, np.abs(time_field).max(), time_field)
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if len(time_field) != 48000:
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raise Exception("time not hashable")
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freq_field = fft(time_field)
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if len(freq_field) != 48000:
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raise Exception("freq not hashable")
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np.add(time_field, original_audio_time_minus(), out=time_field)
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np.add(freq_field, original_audio_freq_minus(), out=freq_field)
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hash = np.zeros(half_hash_len//2*2, dtype='>i2')
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d = 375 * 512 // half_hash_len
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for i in range(half_hash_len//4):
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a = i*2
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b = a+1
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x = a + half_hash_len//2
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y = x+1
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s = np.average(freq_field[i*d:(i+1)*d])
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hash[a] = np.int16(_cut_u16(round(32768*np.real(s))))
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hash[b] = np.int16(_cut_u16(round(32768*np.imag(s))))
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hash[x] = np.int16(_cut_u16(round(32768*np.sum(time_field[i*d:i*d+d//2]))))
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hash[y] = np.int16(_cut_u16(round(32768*np.sum(time_field[i*d+d//2:(i+1)*d]))))
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return encode_to_string(hash.tobytes())
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def audio_hash(file):
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return wave_hash(load_audio(file, 16000))
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def model_hash(config, tgt_sr, net_g, if_f0, version):
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pipeline = Pipeline(tgt_sr, config)
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audio = load_audio(str(pathlib.Path(__file__).parent / "lgdsng.mp3"), 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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np.divide(audio, audio_max, audio)
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audio_opt = pipeline.pipeline(load_hubert(config.device, config.is_half), net_g, 0, audio,
|
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[0, 0, 0], 6, "rmvpe", "", 0, if_f0, 3, tgt_sr, 16000, 0.25,
|
||||
version, 0.33)
|
||||
opt_len = len(audio_opt)
|
||||
diff = 48000 - opt_len
|
||||
n = diff//2
|
||||
if n > 0:
|
||||
audio_opt = np.pad(audio_opt, (n, n))
|
||||
elif n < 0:
|
||||
n = -n
|
||||
audio_opt = audio_opt[n:-n]
|
||||
h = wave_hash(audio_opt)
|
||||
del pipeline, audio, audio_opt
|
||||
return h
|
||||
|
||||
def model_hash_ckpt(cpt):
|
||||
from infer.lib.infer_pack.models import (
|
||||
SynthesizerTrnMs256NSFsid,
|
||||
SynthesizerTrnMs256NSFsid_nono,
|
||||
SynthesizerTrnMs768NSFsid,
|
||||
SynthesizerTrnMs768NSFsid_nono,
|
||||
)
|
||||
config = Config()
|
||||
with TorchSeedContext(114514):
|
||||
tgt_sr = cpt["config"][-1]
|
||||
if_f0 = cpt.get("f0", 1)
|
||||
version = cpt.get("version", "v1")
|
||||
synthesizer_class = {
|
||||
("v1", 1): SynthesizerTrnMs256NSFsid,
|
||||
("v1", 0): SynthesizerTrnMs256NSFsid_nono,
|
||||
("v2", 1): SynthesizerTrnMs768NSFsid,
|
||||
("v2", 0): SynthesizerTrnMs768NSFsid_nono,
|
||||
}
|
||||
net_g = synthesizer_class.get(
|
||||
(version, if_f0), SynthesizerTrnMs256NSFsid
|
||||
)(*cpt["config"], is_half=config.is_half)
|
||||
|
||||
del net_g.enc_q
|
||||
|
||||
net_g.load_state_dict(cpt["weight"], strict=False)
|
||||
net_g.eval().to(config.device)
|
||||
if config.is_half:
|
||||
net_g = net_g.half()
|
||||
else:
|
||||
net_g = net_g.float()
|
||||
|
||||
h = model_hash(config, tgt_sr, net_g, if_f0, version)
|
||||
|
||||
del net_g
|
||||
|
||||
return h
|
||||
|
||||
def model_hash_from(path):
|
||||
cpt = torch.load(path, map_location="cpu")
|
||||
h = model_hash_ckpt(cpt)
|
||||
del cpt
|
||||
return h
|
||||
|
||||
def _extend_difference(n, a, b):
|
||||
if n < a: n = a
|
||||
elif n > b: n = b
|
||||
n -= a
|
||||
n /= (b-a)
|
||||
return n
|
||||
|
||||
def hash_similarity(h1: str, h2: str) -> int:
|
||||
h1b, h2b = decode_from_string(h1), decode_from_string(h2)
|
||||
if len(h1b) != half_hash_len*2 or len(h2b) != half_hash_len*2:
|
||||
raise Exception("invalid hash length")
|
||||
h1n, h2n = np.frombuffer(h1b, dtype='>i2'), np.frombuffer(h2b, dtype='>i2')
|
||||
d = 0
|
||||
for i in range(half_hash_len//4):
|
||||
a = i*2
|
||||
b = a+1
|
||||
ax = complex(h1n[a], h1n[b])
|
||||
bx = complex(h2n[a], h2n[b])
|
||||
if abs(ax) == 0 or abs(bx) == 0: continue
|
||||
d += np.abs(ax - bx)
|
||||
frac = (np.linalg.norm(h1n) * np.linalg.norm(h2n))
|
||||
cosine = np.dot(h1n.astype(np.float32), h2n.astype(np.float32)) / frac if frac != 0 else 1.0
|
||||
distance = _extend_difference(np.exp(-d/expand_factor), 0.5, 1.0)
|
||||
return round((abs(cosine) + distance) / 2, 6)
|
||||
|
||||
def hash_id(h: str) -> str:
|
||||
return encode_to_string(hashlib.md5(decode_from_string(h)).digest())[:-1]
|
||||
50
infer/modules/vc/info.py
Normal file
50
infer/modules/vc/info.py
Normal file
@ -0,0 +1,50 @@
|
||||
import traceback
|
||||
from i18n.i18n import I18nAuto
|
||||
from datetime import datetime
|
||||
import torch
|
||||
|
||||
from .hash import model_hash_ckpt, hash_id
|
||||
|
||||
i18n = I18nAuto()
|
||||
|
||||
def show_model_info(cpt, show_long_id=False):
|
||||
try:
|
||||
h = model_hash_ckpt(cpt)
|
||||
id = hash_id(h)
|
||||
idread = cpt.get("id", "None")
|
||||
hread = cpt.get("hash", "None")
|
||||
if id != idread:
|
||||
id += "("+i18n("实际计算")+"), "+idread+"("+i18n("从模型中读取")+")"
|
||||
if not show_long_id: h = i18n("不显示")
|
||||
elif h != hread:
|
||||
h += "("+i18n("实际计算")+"), "+hread+"("+i18n("从模型中读取")+")"
|
||||
txt = f"""{i18n("模型名")}: %s
|
||||
{i18n("封装时间")}: %s
|
||||
{i18n("信息")}: %s
|
||||
{i18n("采样率")}: %s
|
||||
{i18n("音高引导(f0)")}: %s
|
||||
{i18n("版本")}: %s
|
||||
{i18n("ID(短)")}: %s
|
||||
{i18n("ID(长)")}: %s""" % (
|
||||
cpt.get("name", "None"),
|
||||
datetime.fromtimestamp(float(cpt.get("timestamp", 0))),
|
||||
cpt.get("info", "None"),
|
||||
cpt.get("sr", "None"),
|
||||
i18n("有") if cpt.get("f0", 0) == 1 else i18n("无"),
|
||||
cpt.get("version", "None"),
|
||||
id, h
|
||||
)
|
||||
except:
|
||||
txt = traceback.format_exc()
|
||||
|
||||
return txt
|
||||
|
||||
def show_info(path):
|
||||
try:
|
||||
a = torch.load(path, map_location="cpu")
|
||||
txt = show_model_info(a, show_long_id=True)
|
||||
del a
|
||||
except:
|
||||
txt = traceback.format_exc()
|
||||
|
||||
return txt
|
||||
BIN
infer/modules/vc/lgdsng.mp3
Normal file
BIN
infer/modules/vc/lgdsng.mp3
Normal file
Binary file not shown.
@ -16,7 +16,7 @@ from infer.lib.infer_pack.models import (
|
||||
SynthesizerTrnMs768NSFsid,
|
||||
SynthesizerTrnMs768NSFsid_nono,
|
||||
)
|
||||
|
||||
from .info import show_model_info
|
||||
from .pipeline import Pipeline
|
||||
from .utils import get_index_path_from_model, load_hubert
|
||||
|
||||
@ -136,6 +136,7 @@ class VC:
|
||||
to_return_protect1,
|
||||
index,
|
||||
index,
|
||||
show_model_info(self.cpt)
|
||||
)
|
||||
if to_return_protect
|
||||
else {"visible": True, "maximum": n_spk, "__type__": "update"}
|
||||
@ -158,6 +159,8 @@ class VC:
|
||||
):
|
||||
if input_audio_path is None:
|
||||
return "You need to upload an audio", None
|
||||
elif hasattr(input_audio_path, "name"):
|
||||
input_audio_path = str(input_audio_path.name)
|
||||
f0_up_key = int(f0_up_key)
|
||||
try:
|
||||
audio = load_audio(input_audio_path, 16000)
|
||||
@ -170,6 +173,7 @@ class VC:
|
||||
self.hubert_model = load_hubert(self.config.device, self.config.is_half)
|
||||
|
||||
if file_index:
|
||||
if hasattr(file_index, "name"): file_index = str(file_index.name)
|
||||
file_index = (
|
||||
file_index.strip(" ")
|
||||
.strip('"')
|
||||
@ -207,12 +211,12 @@ class VC:
|
||||
else:
|
||||
tgt_sr = self.tgt_sr
|
||||
index_info = (
|
||||
"Index:\n%s." % file_index
|
||||
"Index: %s." % file_index
|
||||
if os.path.exists(file_index)
|
||||
else "Index not used."
|
||||
)
|
||||
return (
|
||||
"Success.\n%s\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs."
|
||||
"Success.\n%s\nTime: npy: %.2fs, f0: %.2fs, infer: %.2fs."
|
||||
% (index_info, *times),
|
||||
(tgt_sr, audio_opt),
|
||||
)
|
||||
|
||||
@ -47,3 +47,4 @@ ffmpy==0.3.1
|
||||
python-dotenv>=1.0.0
|
||||
av
|
||||
torchfcpe
|
||||
pybase16384
|
||||
|
||||
@ -45,3 +45,4 @@ ffmpy==0.3.1
|
||||
python-dotenv>=1.0.0
|
||||
av
|
||||
torchfcpe
|
||||
pybase16384
|
||||
|
||||
@ -53,3 +53,4 @@ av
|
||||
FreeSimpleGUI
|
||||
sounddevice
|
||||
torchfcpe
|
||||
pybase16384
|
||||
|
||||
@ -46,3 +46,4 @@ torchfcpe
|
||||
ffmpy==0.3.1
|
||||
python-dotenv>=1.0.0
|
||||
av
|
||||
pybase16384
|
||||
|
||||
@ -46,3 +46,4 @@ torchfcpe
|
||||
ffmpy==0.3.1
|
||||
python-dotenv>=1.0.0
|
||||
av
|
||||
pybase16384
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user