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import zipfile |
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import hashlib |
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from utils.model import model_downloader, get_model |
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import requests |
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import json |
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import torch |
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import os |
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from inference import Inference |
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import gradio as gr |
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from constants import VOICE_METHODS, BARK_VOICES, EDGE_VOICES, zips_folder, unzips_folder |
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from tts.conversion import tts_infer, ELEVENLABS_VOICES_RAW, ELEVENLABS_VOICES_NAMES |
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api_url = "https://rvc-models-api.onrender.com/uploadfile/" |
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if not os.path.exists(zips_folder): |
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os.mkdir(zips_folder) |
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if not os.path.exists(unzips_folder): |
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os.mkdir(unzips_folder) |
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def get_info(path): |
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path = os.path.join(unzips_folder, path) |
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try: |
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a = torch.load(path, map_location="cpu") |
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return a |
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except Exception as e: |
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print("*****************eeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrr*****") |
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print(e) |
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return { |
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} |
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def calculate_md5(file_path): |
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hash_md5 = hashlib.md5() |
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with open(file_path, "rb") as f: |
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for chunk in iter(lambda: f.read(4096), b""): |
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hash_md5.update(chunk) |
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return hash_md5.hexdigest() |
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def compress(modelname, files): |
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file_path = os.path.join(zips_folder, f"{modelname}.zip") |
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compression = zipfile.ZIP_DEFLATED |
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if not os.path.exists(file_path): |
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with zipfile.ZipFile(file_path, mode="w") as zf: |
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try: |
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for file in files: |
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if file: |
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zf.write(unzips_folder if ".index" in file else os.path.join(unzips_folder, file), compress_type=compression) |
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except FileNotFoundError as fnf: |
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print("An error occurred", fnf) |
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else: |
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with zipfile.ZipFile(file_path, mode="a") as zf: |
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try: |
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for file in files: |
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if file: |
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zf.write(unzips_folder if ".index" in file else os.path.join(unzips_folder, file), compress_type=compression) |
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except FileNotFoundError as fnf: |
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print("An error occurred", fnf) |
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return file_path |
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def infer(model, f0_method, audio_file, index_rate, vc_transform0, protect0, resample_sr1, filter_radius1): |
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if not model: |
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return "No model url specified, please specify a model url.", None |
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if not audio_file: |
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return "No audio file specified, please load an audio file.", None |
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inference = Inference( |
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model_name=model, |
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f0_method=f0_method, |
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source_audio_path=audio_file, |
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feature_ratio=index_rate, |
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transposition=vc_transform0, |
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protection_amnt=protect0, |
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resample=resample_sr1, |
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harvest_median_filter=filter_radius1, |
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output_file_name=os.path.join("./audio-outputs", os.path.basename(audio_file)) |
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) |
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output = inference.run() |
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if 'success' in output and output['success']: |
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print("Inferencia realizada exitosamente...") |
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return output, output['file'] |
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else: |
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print("Fallo en la inferencia...", output) |
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return output, None |
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def post_model(name, model_url, version, creator): |
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modelname = model_downloader(model_url, zips_folder, unzips_folder) |
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if not modelname: |
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return "No se ha podido descargar el modelo, intenta con otro enlace o intentalo más tarde." |
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model_files = get_model(unzips_folder, modelname) |
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if not model_files: |
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return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo más tarde." |
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if not model_files.get('pth'): |
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return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo más tarde." |
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md5_hash = calculate_md5(os.path.join(unzips_folder,model_files['pth'])) |
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zipfile = compress(modelname, list(model_files.values())) |
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a = get_info(model_files.get('pth')) |
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file_to_upload = open(zipfile, "rb") |
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info = a.get("info", "None"), |
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sr = a.get("sr", "None"), |
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f0 = a.get("f0", "None"), |
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data = { |
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"name": name, |
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"version": version, |
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"creator": creator, |
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"hash": md5_hash, |
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"info": info, |
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"sr": sr, |
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"f0": f0 |
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} |
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print("Subiendo archivo...") |
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response = requests.post(api_url, files={"file": file_to_upload}, data=data) |
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result = response.json() |
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if response.status_code == 200: |
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result = response.json() |
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return json.dumps(result, indent=4) |
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else: |
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print("Error al cargar el archivo:", response.status_code) |
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return result |
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def search_model(name): |
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web_service_url = "https://script.google.com/macros/s/AKfycbyRaNxtcuN8CxUrcA_nHW6Sq9G2QJor8Z2-BJUGnQ2F_CB8klF4kQL--U2r2MhLFZ5J/exec" |
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response = requests.post(web_service_url, json={ |
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'type': 'search_by_filename', |
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'name': name |
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}) |
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result = [] |
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response.raise_for_status() |
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json_response = response.json() |
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cont = 0 |
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result.append("""| Nombre del modelo | Url | Epoch | Sample Rate | |
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| ---------------- | -------------- |:------:|:-----------:| |
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""") |
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yield "<br />".join(result) |
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if json_response.get('ok', None): |
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for model in json_response['ocurrences']: |
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if cont < 20: |
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model_name = str(model.get('name', 'N/A')).strip() |
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model_url = model.get('url', 'N/A') |
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epoch = model.get('epoch', 'N/A') |
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sr = model.get('sr', 'N/A') |
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line = f"""|{model_name}|<a>{model_url}</a>|{epoch}|{sr}| |
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""" |
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result.append(line) |
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yield "".join(result) |
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cont += 1 |
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def update_tts_methods_voice(select_value): |
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if select_value == "Edge-tts": |
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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) |
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elif select_value == "Bark-tts": |
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return gr.Dropdown.update(choices=BARK_VOICES, visible=True), gr.Markdown.update(visible=False), gr.Textbox.update(visible=False),gr.Radio.update(visible=False) |
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elif select_value == 'ElevenLabs': |
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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) |
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elif select_value == 'CoquiTTS': |
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return gr.Dropdown.update(visible=False), gr.Markdown.update(visible=False), gr.Textbox.update(visible=False), gr.Radio.update(visible=True) |
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