| 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'<a href="{x}" target="_blank">{x}</a>' | |
| ) | |
| 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=[], | |
| ) | |