import zipfile import hashlib from utils.model import model_downloader, get_model import requests import json import torch import os from inference import Inference import gradio as gr from constants import VOICE_METHODS, BARK_VOICES, EDGE_VOICES, zips_folder, unzips_folder from tts.conversion import tts_infer, ELEVENLABS_VOICES_RAW, ELEVENLABS_VOICES_NAMES api_url = "https://rvc-models-api.onrender.com/uploadfile/" if not os.path.exists(zips_folder): os.mkdir(zips_folder) if not os.path.exists(unzips_folder): os.mkdir(unzips_folder) def get_info(path): path = os.path.join(unzips_folder, path) try: a = torch.load(path, map_location="cpu") return a except Exception as e: print("*****************eeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrr*****") print(e) return { } 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, index_rate, vc_transform0, protect0, resample_sr1, filter_radius1): print("****", audio_file) inference = Inference( model_name=model, f0_method=f0_method, source_audio_path=audio_file, feature_ratio=index_rate, transposition=vc_transform0, protection_amnt=protect0, resample=resample_sr1, harvest_median_filter=filter_radius1, 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 "Failed", None 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 más tarde." if not model_files.get('pth'): return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo más tarde." md5_hash = calculate_md5(os.path.join(unzips_folder,model_files['pth'])) zipfile = compress(modelname, list(model_files.values())) a = get_info(model_files.get('pth')) file_to_upload = open(zipfile, "rb") info = a.get("info", "None"), sr = a.get("sr", "None"), f0 = a.get("f0", "None"), data = { "name": name, "version": version, "creator": creator, "hash": md5_hash, "info": info, "sr": sr, "f0": f0 } print("Subiendo archivo...") # Realizar la solicitud POST response = requests.post(api_url, files={"file": file_to_upload}, data=data) result = response.json() # 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 excepción en caso de error json_response = response.json() cont = 0 result.append("""| Nombre del modelo | Url | Epoch | Sample Rate | | ---------------- | -------------- |:------:|:-----------:| """) yield "
".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}|{model_url}|{epoch}|{sr}| """ result.append(line) yield "".join(result) cont += 1 def update_tts_methods_voice(select_value): if select_value == "Edge-tts": return gr.Dropdown.update(choices=EDGE_VOICES, visible=True, value="es-CO-GonzaloNeural-Male"), gr.Markdown.update(visible=False), gr.Textbox.update(visible=False),gr.Radio.update(visible=False) elif select_value == "Bark-tts": return gr.Dropdown.update(choices=BARK_VOICES, visible=True), gr.Markdown.update(visible=False), gr.Textbox.update(visible=False),gr.Radio.update(visible=False) elif select_value == 'ElevenLabs': return gr.Dropdown.update(choices=ELEVENLABS_VOICES_NAMES, visible=True, value="Bella"), gr.Markdown.update(visible=True), gr.Textbox.update(visible=True), gr.Radio.update(visible=False) elif select_value == 'CoquiTTS': return gr.Dropdown.update(visible=False), gr.Markdown.update(visible=False), gr.Textbox.update(visible=False), gr.Radio.update(visible=True)