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| import gradio as gr | |
| from inference import Inference | |
| import os | |
| import zipfile | |
| import hashlib | |
| from utils.model import model_downloader, get_model | |
| import requests | |
| import json | |
| from tts.constants import VOICE_METHODS, BARK_VOICES, EDGE_VOICES | |
| from tts.conversion import tts_infer | |
| api_url = "https://rvc-models-api.onrender.com/uploadfile/" | |
| zips_folder = "./zips" | |
| unzips_folder = "./unzips" | |
| if not os.path.exists(zips_folder): | |
| os.mkdir(zips_folder) | |
| if not os.path.exists(unzips_folder): | |
| os.mkdir(unzips_folder) | |
| def calculate_md5(file_path): | |
| hash_md5 = hashlib.md5() | |
| with open(file_path, "rb") as f: | |
| for chunk in iter(lambda: f.read(4096), b""): | |
| hash_md5.update(chunk) | |
| return hash_md5.hexdigest() | |
| def compress(modelname, files): | |
| file_path = os.path.join(zips_folder, f"{modelname}.zip") | |
| # Select the compression mode ZIP_DEFLATED for compression | |
| # or zipfile.ZIP_STORED to just store the file | |
| compression = zipfile.ZIP_DEFLATED | |
| # Comprueba si el archivo ZIP ya existe | |
| if not os.path.exists(file_path): | |
| # Si no existe, crea el archivo ZIP | |
| with zipfile.ZipFile(file_path, mode="w") as zf: | |
| try: | |
| for file in files: | |
| if file: | |
| # Agrega el archivo al archivo ZIP | |
| zf.write(unzips_folder if ".index" in file else os.path.join(unzips_folder, file), compress_type=compression) | |
| except FileNotFoundError as fnf: | |
| print("An error occurred", fnf) | |
| else: | |
| # Si el archivo ZIP ya existe, agrega los archivos a un archivo ZIP existente | |
| with zipfile.ZipFile(file_path, mode="a") as zf: | |
| try: | |
| for file in files: | |
| if file: | |
| # Agrega el archivo al archivo ZIP | |
| zf.write(unzips_folder if ".index" in file else os.path.join(unzips_folder, file), compress_type=compression) | |
| except FileNotFoundError as fnf: | |
| print("An error occurred", fnf) | |
| return file_path | |
| def infer(model, f0_method, audio_file): | |
| print("****", audio_file) | |
| inference = Inference( | |
| model_name=model, | |
| f0_method=f0_method, | |
| source_audio_path=audio_file, | |
| output_file_name=os.path.join("./audio-outputs", os.path.basename(audio_file)) | |
| ) | |
| output = inference.run() | |
| if 'success' in output and output['success']: | |
| return output, output['file'] | |
| else: | |
| return | |
| def post_model(name, model_url, version, creator): | |
| modelname = model_downloader(model_url, zips_folder, unzips_folder) | |
| model_files = get_model(unzips_folder, modelname) | |
| if not model_files: | |
| return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo m谩s tarde." | |
| if not model_files.get('pth'): | |
| return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo m谩s tarde." | |
| md5_hash = calculate_md5(os.path.join(unzips_folder,model_files['pth'])) | |
| zipfile = compress(modelname, list(model_files.values())) | |
| file_to_upload = open(zipfile, "rb") | |
| data = { | |
| "name": name, | |
| "version": version, | |
| "creator": creator, | |
| "hash": md5_hash | |
| } | |
| print("Subiendo archivo...") | |
| # Realizar la solicitud POST | |
| response = requests.post(api_url, files={"file": file_to_upload}, data=data) | |
| # Comprobar la respuesta | |
| if response.status_code == 200: | |
| result = response.json() | |
| return json.dumps(result, indent=4) | |
| else: | |
| print("Error al cargar el archivo:", response.status_code) | |
| return result | |
| def search_model(name): | |
| web_service_url = "https://script.google.com/macros/s/AKfycbyRaNxtcuN8CxUrcA_nHW6Sq9G2QJor8Z2-BJUGnQ2F_CB8klF4kQL--U2r2MhLFZ5J/exec" | |
| response = requests.post(web_service_url, json={ | |
| 'type': 'search_by_filename', | |
| 'name': name | |
| }) | |
| result = [] | |
| response.raise_for_status() # Lanza una excepci贸n en caso de error | |
| json_response = response.json() | |
| cont = 0 | |
| result.append("""| Nombre del modelo | Url | Epoch | Sample Rate | | |
| | ---------------- | -------------- |:------:|:-----------:| | |
| """) | |
| yield "<br />".join(result) | |
| if json_response.get('ok', None): | |
| for model in json_response['ocurrences']: | |
| if cont < 20: | |
| model_name = str(model.get('name', 'N/A')).strip() | |
| model_url = model.get('url', 'N/A') | |
| epoch = model.get('epoch', 'N/A') | |
| sr = model.get('sr', 'N/A') | |
| line = f"""|{model_name}|<a>{model_url}</a>|{epoch}|{sr}| | |
| """ | |
| result.append(line) | |
| yield "".join(result) | |
| cont += 1 | |
| def update_tts_methods_voice(select_value): | |
| if select_value == "Edge-tts": | |
| return gr.update(choices=EDGE_VOICES) | |
| elif select_value == "Bark-tts": | |
| return gr.update(choices=BARK_VOICES) | |
| with gr.Blocks() as app: | |
| gr.HTML("<h1> Simple RVC Inference - by Juuxn 馃捇 </h1>") | |
| with gr.Tab("Inferencia"): | |
| model_url = gr.Textbox(placeholder="https://huggingface.co/AIVER-SE/BillieEilish/resolve/main/BillieEilish.zip", label="Url del modelo", show_label=True) | |
| audio_path = gr.Audio(label="Archivo de audio", show_label=True, type="filepath", ) | |
| f0_method = gr.Dropdown(choices=["harvest", "pm", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe"], | |
| value="rmvpe", | |
| label="Algoritmo", show_label=True) | |
| # Salida | |
| with gr.Row(): | |
| vc_output1 = gr.Textbox(label="Salida") | |
| vc_output2 = gr.Audio(label="Audio de salida") | |
| btn = gr.Button(value="Convertir") | |
| btn.click(infer, inputs=[model_url, f0_method, audio_path], outputs=[vc_output1, vc_output2]) | |
| with gr.TabItem("TTS"): | |
| with gr.Row(): | |
| tts_text = gr.Textbox( | |
| label="Texto:", | |
| placeholder="Texto que deseas convertir a voz...", | |
| lines=6, | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| tts_model_url = gr.Textbox(placeholder="https://huggingface.co/AIVER-SE/BillieEilish/resolve/main/BillieEilish.zip", label="Url del modelo RVC", show_label=True) | |
| with gr.Column(): | |
| tts_method = gr.Dropdown(choices=VOICE_METHODS, value="Edge-tts", label="M茅todo TTS:", visible=False) | |
| tts_model = gr.Dropdown(choices=EDGE_VOICES, label="Modelo TTS:", visible=True, interactive=True) | |
| tts_method.change(fn=update_tts_methods_voice, inputs=[tts_method], outputs=[tts_model]) | |
| with gr.Row(): | |
| tts_vc_output1 = gr.Textbox(label="Salida") | |
| tts_vc_output2 = gr.Audio(label="Audio de salida") | |
| tts_btn = gr.Button(value="Convertir") | |
| tts_btn.click(fn=tts_infer, inputs=[tts_text, tts_model_url, tts_method, tts_model], outputs=[tts_vc_output1, tts_vc_output2]) | |
| with gr.Tab("Recursos"): | |
| gr.HTML("<h4>Buscar modelos</h4>") | |
| search_name = gr.Textbox(placeholder="Billie Eillish (RVC v2 - 100 epoch)", label="Nombre", show_label=True) | |
| # Salida | |
| with gr.Row(): | |
| sarch_output = gr.Markdown(label="Salida") | |
| btn_search_model = gr.Button(value="Buscar") | |
| btn_search_model.click(fn=search_model, inputs=[search_name], outputs=[sarch_output]) | |
| gr.HTML("<h4>Publica tu modelo</h4>") | |
| post_name = gr.Textbox(placeholder="Billie Eillish (RVC v2 - 100 epoch)", label="Nombre", show_label=True) | |
| post_model_url = gr.Textbox(placeholder="https://huggingface.co/AIVER-SE/BillieEilish/resolve/main/BillieEilish.zip", label="Url del modelo", show_label=True) | |
| post_creator = gr.Textbox(placeholder="ID de discord o enlace al perfil del creador", label="Creador", show_label=True) | |
| post_version = gr.Dropdown(choices=["RVC v1", "RVC v2"], value="RVC v1", label="Versi贸n", show_label=True) | |
| # Salida | |
| with gr.Row(): | |
| post_output = gr.Markdown(label="Salida") | |
| btn_post_model = gr.Button(value="Publicar") | |
| btn_post_model.click(fn=post_model, inputs=[post_name, post_model_url, post_version, post_creator], outputs=[post_output]) | |
| # with gr.Column(): | |
| # model_voice_path07 = gr.Dropdown( | |
| # label=i18n("RVC Model:"), | |
| # choices=sorted(names), | |
| # value=default_weight, | |
| # ) | |
| # best_match_index_path1, _ = match_index( | |
| # model_voice_path07.value | |
| # ) | |
| # file_index2_07 = gr.Dropdown( | |
| # label=i18n("Select the .index file:"), | |
| # choices=get_indexes(), | |
| # value=best_match_index_path1, | |
| # interactive=True, | |
| # allow_custom_value=True, | |
| # ) | |
| # with gr.Row(): | |
| # refresh_button_ = gr.Button(i18n("Refresh"), variant="primary") | |
| # refresh_button_.click( | |
| # fn=change_choices2, | |
| # inputs=[], | |
| # outputs=[model_voice_path07, file_index2_07], | |
| # ) | |
| # with gr.Row(): | |
| # original_ttsvoice = gr.Audio(label=i18n("Audio TTS:")) | |
| # ttsvoice = gr.Audio(label=i18n("Audio RVC:")) | |
| # with gr.Row(): | |
| # button_test = gr.Button(i18n("Convert"), variant="primary") | |
| # button_test.click( | |
| # tts.use_tts, | |
| # inputs=[ | |
| # text_test, | |
| # tts_test, | |
| # model_voice_path07, | |
| # file_index2_07, | |
| # # transpose_test, | |
| # vc_transform0, | |
| # f0method8, | |
| # index_rate1, | |
| # crepe_hop_length, | |
| # f0_autotune, | |
| # ttsmethod_test, | |
| # ], | |
| # outputs=[ttsvoice, original_ttsvoice], | |
| # ) | |
| app.queue(concurrency_count=511, max_size=1022).launch() | |
| #share=True |