File size: 6,571 Bytes
925d97e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a87192b
925d97e
 
 
 
 
a87192b
 
 
 
 
925d97e
 
 
 
 
 
a87192b
925d97e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
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 "<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.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)