from i18n.i18n import I18nAuto from configs.config import Config from sklearn.cluster import MiniBatchKMeans import torch, platform import numpy as np import gradio as gr import faiss import fairseq import pathlib import json from time import sleep from subprocess import Popen from random import shuffle import warnings import traceback import threading import shutil import logging import sys from dotenv import load_dotenv from infer.modules.vc.modules import VC import shutil, glob from easyfuncs import download_from_url, CachedModels os.makedirs("dataset",exist_ok=True) model_library = CachedModels() logging.getLogger("numba").setLevel(logging.WARNING) logging.getLogger("httpx").setLevel(logging.WARNING) logger = logging.getLogger(__name__) tmp = os.path.join(now_dir, "TEMP") shutil.rmtree(tmp, ignore_errors=True) shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True) shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True) os.makedirs(tmp, exist_ok=True) os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True) os.makedirs(os.path.join(now_dir, "assets/weights"), exist_ok=True) os.environ["TEMP"] = tmp warnings.filterwarnings("ignore") torch.manual_seed(114514) config = Config() vc = VC(config) class ToolButton(gr.Button, gr.components.FormComponent): """Small button with single emoji as text, fits inside gradio forms""" def __init__(self, **kwargs): super().__init__(variant="tool", **kwargs) def get_block_name(self): return "button" weight_root = os.getenv("weight_root") index_root = os.getenv("index_root") outside_index_root = os.getenv("outside_index_root") names = [] for name in os.listdir(weight_root): if name.endswith(".pth"): names.append(name) index_paths = [] def lookup_indices(index_root): global index_paths for root, dirs, files in os.walk(index_root, topdown=False): for name in files: if name.endswith(".index") and "trained" not in name: index_paths.append("%s/%s" % (root, name)) lookup_indices(index_root) lookup_indices(outside_index_root) uvr5_names = [] for name in os.listdir(weight_uvr5_root): if name.endswith(".pth") or "onnx" in name: uvr5_names.append(name.replace(".pth", "")) def change_choices(): names = [] for name in os.listdir(weight_root): if name.endswith(".pth"): names.append(name) index_paths = [] for root, dirs, files in os.walk(index_root, topdown=False): for name in files: if name.endswith(".index") and "trained" not in name: index_paths.append("%s/%s" % (root, name)) return {"choices": sorted(names), "__type__": "update"}, { "choices": sorted(index_paths), "__type__": "update", } def clean(): return {"value": "", "__type__": "update"} def if_done(done, p): while 1: if p.poll() is None: sleep(0.5) else: break done[0] = True def if_done_multi(done, ps): while 1: # poll==None代表进程未结束 # 只要有一个进程未结束都不停 flag = 1 for p in ps: if p.poll() is None: flag = 0 sleep(0.5) break if flag == 1: break done[0] = True with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue="zinc")) as app: with gr.Row(): gr.Markdown("

RVC V2 - EASY GUI") with gr.Tabs(): with gr.TabItem("Inference"): with gr.Row(): voice_model = gr.Dropdown(label="Model Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True) refresh_button = gr.Button("Refresh", variant="primary") spk_item = gr.Slider( minimum=0, maximum=2333, step=1, label="Speaker ID", value=0, visible=False, interactive=True, ) vc_transform0 = gr.Number( label="Pitch", value=0 ) but0 = gr.Button(value="Convert", variant="primary") with gr.Row(): with gr.Column(): with gr.Row(): dropbox = gr.File(label="Drop your audio here & hit the Reload button.") with gr.Row(): record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath") with gr.Row(): paths_for_files = lambda path:[os.path.abspath(os.path.join(path, f)) for f in os.listdir(path) if os.path.splitext(f)[1].lower() in ('.mp3', '.wav', '.flac', '.ogg')] input_audio0 = gr.Dropdown( label="Input Path", value=paths_for_files('audios')[0] if len(paths_for_files('audios')) > 0 else '', choices=paths_for_files('audios'), # Only show absolute paths for audio files ending in .mp3, .wav, .flac or .ogg allow_custom_value=True ) with gr.Row(): audio_player = gr.Audio() input_audio0.change( inputs=[input_audio0], outputs=[audio_player], fn=lambda path: {"value":path,"__type__":"update"} if os.path.exists(path) else None ) record_button.stop_recording( fn=lambda audio:audio, #TODO save wav lambda inputs=[record_button], outputs=[input_audio0]) dropbox.upload( fn=lambda audio:audio.name, inputs=[dropbox], outputs=[input_audio0]) with gr.Column(): with gr.Accordion("Change Index", open=False): file_index2 = gr.Dropdown( label="Change Index", choices=sorted(index_paths), interactive=True, value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else '' ) index_rate1 = gr.Slider( minimum=0, maximum=1, label="Index Strength", value=0.5, interactive=True, ) vc_output2 = gr.Audio(label="Output") with gr.Accordion("General Settings", open=False): f0method0 = gr.Radio( label="Method", choices=["pm", "harvest", "crepe", "rmvpe"] if config.dml == False else ["pm", "harvest", "rmvpe"], value="rmvpe", interactive=True, ) filter_radius0 = gr.Slider( minimum=0, maximum=7, label="Breathiness Reduction (Harvest only)", value=3, step=1, interactive=True, ) resample_sr0 = gr.Slider( minimum=0, maximum=48000, label="Resample", value=0, step=1, interactive=True, visible=False ) rms_mix_rate0 = gr.Slider( minimum=0, maximum=1, label="Volume Normalization", value=0, interactive=True, ) protect0 = gr.Slider( minimum=0, maximum=0.5, label="Breathiness Protection (0 is enabled, 0.5 is disabled)", value=0.33, step=0.01, interactive=True, ) if voice_model != None: vc.get_vc(voice_model.value,protect0,protect0) file_index1 = gr.Textbox( label="Index Path", interactive=True, visible=False#Not used here ) refresh_button.click( fn=change_choices, inputs=[], outputs=[voice_model, file_index2], api_name="infer_refresh", ) refresh_button.click( fn=lambda:{"choices":paths_for_files('audios'),"__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac' inputs=[], outputs = [input_audio0], ) refresh_button.click( fn=lambda:{"value":paths_for_files('audios')[0],"__type__":"update"} if len(paths_for_files('audios')) > 0 else {"value":"","__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac' inputs=[], outputs = [input_audio0], ) with gr.Row(): f0_file = gr.File(label="F0 Path", visible=False) with gr.Row(): vc_output1 = gr.Textbox(label="Information", placeholder="Welcome!",visible=False) but0.click( vc.vc_single, [ spk_item, input_audio0, vc_transform0, f0_file, f0method0, file_index1, file_index2, index_rate1, filter_radius0, resample_sr0, rms_mix_rate0, protect0, ], [vc_output1, vc_output2], api_name="infer_convert", ) voice_model.change( fn=vc.get_vc, inputs=[voice_model, protect0, protect0], outputs=[spk_item, protect0, protect0, file_index2, file_index2], api_name="infer_change_voice", ) with gr.TabItem("Download Models"): with gr.Row(): url_input = gr.Textbox(label="URL to model", value="",placeholder="https://...", scale=6) name_output = gr.Textbox(label="Save as", value="",placeholder="MyModel",scale=2) url_download = gr.Button(value="Download Model",scale=2) url_download.click( inputs=[url_input,name_output], outputs=[url_input], fn=download_from_url, ) with gr.Row(): model_browser = gr.Dropdown(choices=list(model_library.models.keys()),label="OR Search Models (Quality UNKNOWN)",scale=5) download_from_browser = gr.Button(value="Get",scale=2) download_from_browser.click( inputs=[model_browser], outputs=[model_browser], fn=lambda model: download_from_url(model_library.models[model],model), ) if config.iscolab: app.queue(concurrency_count=511, max_size=1022).launch(share=True) else: app.queue(concurrency_count=511, max_size=1022).launch( server_name="0.0.0.0", inbrowser=not config.noautoopen, server_port=config.listen_port, quiet=True, )