import os, sys, shutil import tempfile import gradio as gr import pandas as pd import requests import wget from core import run_download_script from assets.i18n.i18n import I18nAuto from rvc.lib.utils import format_title i18n = I18nAuto() now_dir = os.getcwd() sys.path.append(now_dir) gradio_temp_dir = os.path.join(tempfile.gettempdir(), "gradio") if os.path.exists(gradio_temp_dir): shutil.rmtree(gradio_temp_dir) def save_drop_model(dropbox): if "pth" not in dropbox and "index" not in dropbox: raise gr.Error( message="The file you dropped is not a valid model file. Please try again." ) else: file_name = format_title(os.path.basename(dropbox)) if ".pth" in dropbox: model_name = format_title(file_name.split(".pth")[0]) else: if "v2" not in dropbox: model_name = format_title( file_name.split("_nprobe_1_")[1].split("_v1")[0] ) else: model_name = format_title( file_name.split("_nprobe_1_")[1].split("_v2")[0] ) model_path = os.path.join(now_dir, "logs", model_name) if not os.path.exists(model_path): os.makedirs(model_path) if os.path.exists(os.path.join(model_path, file_name)): os.remove(os.path.join(model_path, file_name)) shutil.move(dropbox, os.path.join(model_path, file_name)) print(f"{file_name} saved in {model_path}") gr.Info(f"{file_name} saved in {model_path}") return None def search_models(name): url = f"https://cjtfqzjfdimgpvpwhzlv.supabase.co/rest/v1/models?name=ilike.%25{name}%25&order=created_at.desc&limit=15" headers = { "apikey": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6ImNqdGZxempmZGltZ3B2cHdoemx2Iiwicm9sZSI6ImFub24iLCJpYXQiOjE2OTUxNjczODgsImV4cCI6MjAxMDc0MzM4OH0.7z5WMIbjR99c2Ooc0ma7B_FyGq10G8X-alkCYTkKR10" } response = requests.get(url, headers=headers) data = response.json() if len(data) == 0: gr.Info(i18n("We couldn't find models by that name.")) return None else: df = pd.DataFrame(data)[["name", "link", "epochs", "type"]] df["link"] = df["link"].apply( lambda x: f'{x}' ) return df json_url = "https://huggingface.co./IAHispano/Applio/raw/main/pretrains.json" def fetch_pretrained_data(): response = requests.get(json_url) response.raise_for_status() return response.json() def get_pretrained_list(): data = fetch_pretrained_data() return list(data.keys()) def get_pretrained_sample_rates(model): data = fetch_pretrained_data() return list(data[model].keys()) def download_pretrained_model(model, sample_rate): data = fetch_pretrained_data() paths = data[model][sample_rate] pretraineds_custom_path = os.path.join("rvc", "pretraineds", "pretraineds_custom") os.makedirs(pretraineds_custom_path, exist_ok=True) d_url = f"https://huggingface.co./{paths['D']}" g_url = f"https://huggingface.co./{paths['G']}" gr.Info("Downloading Pretrained Model...") print("Downloading Pretrained Model...") wget.download(d_url, out=pretraineds_custom_path) wget.download(g_url, out=pretraineds_custom_path) def update_sample_rate_dropdown(model): return { "choices": get_pretrained_sample_rates(model), "value": get_pretrained_sample_rates(model)[0], "__type__": "update", } def download_tab(): with gr.Column(): gr.Markdown(value=i18n("## Download Model")) model_link = gr.Textbox( label=i18n("Model Link"), placeholder=i18n("Introduce the model link"), interactive=True, ) model_download_output_info = gr.Textbox( label=i18n("Output Information"), info=i18n("The output information will be displayed here."), value="", max_lines=8, interactive=False, ) model_download_button = gr.Button(i18n("Download Model")) model_download_button.click( fn=run_download_script, inputs=[model_link], outputs=[model_download_output_info], api_name="model_download", ) gr.Markdown(value=i18n("## Drop files")) dropbox = gr.File( label=i18n( "Drag your .pth file and .index file into this space. Drag one and then the other." ), type="filepath", ) dropbox.upload( fn=save_drop_model, inputs=[dropbox], outputs=[dropbox], ) gr.Markdown(value=i18n("## Search Model")) search_name = gr.Textbox( label=i18n("Model Name"), placeholder=i18n("Introduce the model name to search."), interactive=True, ) search_table = gr.Dataframe(datatype="markdown") search = gr.Button(i18n("Search")) search.click( fn=search_models, inputs=[search_name], outputs=[search_table], ) search_name.submit(search_models, [search_name], search_table) gr.Markdown(value=i18n("## Download Pretrained Models")) pretrained_model = gr.Dropdown( label=i18n("Pretrained"), info=i18n("Select the pretrained model you want to download."), choices=get_pretrained_list(), value="Titan", interactive=True, ) pretrained_sample_rate = gr.Dropdown( label=i18n("Sampling Rate"), info=i18n("And select the sampling rate."), choices=get_pretrained_sample_rates(pretrained_model.value), value="40k", interactive=True, allow_custom_value=True, ) pretrained_model.change( update_sample_rate_dropdown, inputs=[pretrained_model], outputs=[pretrained_sample_rate], ) download_pretrained = gr.Button(i18n("Download")) download_pretrained.click( fn=download_pretrained_model, inputs=[pretrained_model, pretrained_sample_rate], outputs=[], )