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Runtime error
Runtime error
xJuuzouYTx
commited on
Commit
•
925d97e
1
Parent(s):
97fc03f
[ADD] youtube video download as wav
Browse files- .gitignore +1 -0
- app.py +62 -157
- audio_enhance/functions.py +24 -0
- tts/constants.py → constants.py +3 -0
- models/model.py +160 -0
- requirements.txt +6 -1
- tts/conversion.py +13 -6
- tts/test.py +1 -0
.gitignore
CHANGED
@@ -4,6 +4,7 @@ __pycache__
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/audios/
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/audio-outputs/
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/LOGS
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/RUNTIME
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*.pyd
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hubert_base.pt
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/audios/
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/audio-outputs/
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/LOGS
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/yt_videos
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/RUNTIME
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*.pyd
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hubert_base.pt
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app.py
CHANGED
@@ -1,166 +1,48 @@
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import gradio as gr
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from inference import Inference
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import os
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import
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import
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from
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import
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import
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import
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from
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api_url = "https://rvc-models-api.onrender.com/uploadfile/"
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zips_folder = "./zips"
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unzips_folder = "./unzips"
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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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# Select the compression mode ZIP_DEFLATED for compression
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# or zipfile.ZIP_STORED to just store the file
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compression = zipfile.ZIP_DEFLATED
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# Comprueba si el archivo ZIP ya existe
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if not os.path.exists(file_path):
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# Si no existe, crea el archivo ZIP
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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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# Agrega el archivo al archivo ZIP
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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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# Si el archivo ZIP ya existe, agrega los archivos a un archivo ZIP existente
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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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# Agrega el archivo al archivo ZIP
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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):
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print("****", audio_file)
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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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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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return output, output['file']
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else:
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return
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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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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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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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"
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# Comprobar la respuesta
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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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response.raise_for_status() # Lanza una excepción en caso de error
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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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return
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return
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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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with gr.Blocks() as app:
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gr.HTML("<h1> Simple RVC Inference - by Juuxn 💻 </h1>")
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""", visible=False)
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tts_method.change(fn=update_tts_methods_voice, inputs=[tts_method], outputs=[tts_model, tts_msg, tts_api_key, tts_coqui_languages])
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with gr.Tab("Modelos"):
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gr.HTML("<h4>Buscar modelos</h4>")
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import gradio as gr
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import os
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from constants import VOICE_METHODS, BARK_VOICES, EDGE_VOICES
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import platform
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from models.model import *
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from tts.conversion import COQUI_LANGUAGES
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import pytube
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import os
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import traceback
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from pydub import AudioSegment
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# from audio_enhance.functions import audio_enhance
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def convert_yt_to_wav(url):
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if not url:
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return "Primero introduce el enlace del video", None
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try:
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print(f"Convirtiendo video {url}...")
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# Descargar el video utilizando pytube
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video = pytube.YouTube(url)
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stream = video.streams.filter(only_audio=True).first()
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video_output_folder = os.path.join(f"yt_videos") # Ruta de destino de la carpeta
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audio_output_folder = 'audios'
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print("Downloading video")
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video_file_path = stream.download(output_path=video_output_folder)
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print(video_file_path)
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file_name = os.path.basename(video_file_path)
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audio_file_path = os.path.join(audio_output_folder, file_name.replace('.mp4','.wav'))
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# convert mp4 to wav
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print("Converting to wav")
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sound = AudioSegment.from_file(video_file_path,format="mp4")
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sound.export(audio_file_path, format="wav")
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if os.path.exists(video_file_path):
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os.remove(video_file_path)
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return "Success", audio_file_path
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except ConnectionResetError as cre:
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return "Se ha perdido la conexión, recarga o reintentalo nuevamente más tarde.", None
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except Exception as e:
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return str(e), None
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with gr.Blocks() as app:
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gr.HTML("<h1> Simple RVC Inference - by Juuxn 💻 </h1>")
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""", visible=False)
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tts_method.change(fn=update_tts_methods_voice, inputs=[tts_method], outputs=[tts_model, tts_msg, tts_api_key, tts_coqui_languages])
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with gr.TabItem("Youtube"):
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gr.Markdown("## Convertir video de Youtube a audio")
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with gr.Row():
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yt_url = gr.Textbox(
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label="Url del video:",
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placeholder="https://www.youtube.com/watch?v=3vEiqil5d3Q"
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)
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yt_btn = gr.Button(value="Convertir")
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with gr.Row():
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yt_output1 = gr.Textbox(label="Salida")
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yt_output2 = gr.Audio(label="Audio de salida")
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yt_btn.click(fn=convert_yt_to_wav, inputs=[yt_url], outputs=[yt_output1, yt_output2])
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# with gr.TabItem("Mejora de audio"):
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# enhance_input_audio = gr.Audio(label="Audio de entrada")
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# enhance_output_audio = gr.Audio(label="Audio de salida")
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# btn_enhance_audio = gr.Button()
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# # btn_enhance_audio.click(fn=audio_enhance, inputs=[enhance_input_audio], outputs=[enhance_output_audio])
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with gr.Tab("Modelos"):
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gr.HTML("<h4>Buscar modelos</h4>")
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audio_enhance/functions.py
ADDED
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import torchaudio
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import numpy as np
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import torchaudio.transforms as T
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from df import enhance, init_df
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df_sr = 48000
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model, df_state, _ = init_df()
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def audio_enchance(input_audio):
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extension = input_audio.split('.')[-1]
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if extension not in ['wav', 'mpeg', 'ogg']:
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return "El formato del audio no es valido, usa wav, mpeg o ogg", None
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else:
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noisy_audio, sr = torchaudio.load(input_audio)
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print("np.shape(noisy_audio)", np.shape(noisy_audio))
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if sr != df_sr:
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resampler = T.Resample(orig_freq=sr, new_freq=df_sr)
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noisy_audio = resampler(noisy_audio)
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output_audio = enhance(model, df_state, noisy_audio)
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return np.shape(noisy_audio), noisy_audio
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tts/constants.py → constants.py
RENAMED
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VOICE_METHODS = ["Edge-tts", "CoquiTTS", "ElevenLabs",]
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BARK_VOICES = [
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zips_folder = "./zips"
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unzips_folder = "./unzips"
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VOICE_METHODS = ["Edge-tts", "CoquiTTS", "ElevenLabs",]
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BARK_VOICES = [
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models/model.py
ADDED
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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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4 |
+
import requests
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5 |
+
import json
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6 |
+
import torch
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7 |
+
import os
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8 |
+
from inference import Inference
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9 |
+
import gradio as gr
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10 |
+
from constants import VOICE_METHODS, BARK_VOICES, EDGE_VOICES, zips_folder, unzips_folder
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11 |
+
from tts.conversion import tts_infer, ELEVENLABS_VOICES_RAW, ELEVENLABS_VOICES_NAMES
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+
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13 |
+
api_url = "https://rvc-models-api.onrender.com/uploadfile/"
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14 |
+
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15 |
+
if not os.path.exists(zips_folder):
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16 |
+
os.mkdir(zips_folder)
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17 |
+
if not os.path.exists(unzips_folder):
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18 |
+
os.mkdir(unzips_folder)
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19 |
+
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20 |
+
def get_info(path):
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21 |
+
path = os.path.join(unzips_folder, path)
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22 |
+
try:
|
23 |
+
a = torch.load(path, map_location="cpu")
|
24 |
+
return a
|
25 |
+
except Exception as e:
|
26 |
+
print("*****************eeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrr*****")
|
27 |
+
print(e)
|
28 |
+
return {
|
29 |
+
|
30 |
+
}
|
31 |
+
def calculate_md5(file_path):
|
32 |
+
hash_md5 = hashlib.md5()
|
33 |
+
with open(file_path, "rb") as f:
|
34 |
+
for chunk in iter(lambda: f.read(4096), b""):
|
35 |
+
hash_md5.update(chunk)
|
36 |
+
return hash_md5.hexdigest()
|
37 |
+
|
38 |
+
def compress(modelname, files):
|
39 |
+
file_path = os.path.join(zips_folder, f"{modelname}.zip")
|
40 |
+
# Select the compression mode ZIP_DEFLATED for compression
|
41 |
+
# or zipfile.ZIP_STORED to just store the file
|
42 |
+
compression = zipfile.ZIP_DEFLATED
|
43 |
+
|
44 |
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# Comprueba si el archivo ZIP ya existe
|
45 |
+
if not os.path.exists(file_path):
|
46 |
+
# Si no existe, crea el archivo ZIP
|
47 |
+
with zipfile.ZipFile(file_path, mode="w") as zf:
|
48 |
+
try:
|
49 |
+
for file in files:
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50 |
+
if file:
|
51 |
+
# Agrega el archivo al archivo ZIP
|
52 |
+
zf.write(unzips_folder if ".index" in file else os.path.join(unzips_folder, file), compress_type=compression)
|
53 |
+
except FileNotFoundError as fnf:
|
54 |
+
print("An error occurred", fnf)
|
55 |
+
else:
|
56 |
+
# Si el archivo ZIP ya existe, agrega los archivos a un archivo ZIP existente
|
57 |
+
with zipfile.ZipFile(file_path, mode="a") as zf:
|
58 |
+
try:
|
59 |
+
for file in files:
|
60 |
+
if file:
|
61 |
+
# Agrega el archivo al archivo ZIP
|
62 |
+
zf.write(unzips_folder if ".index" in file else os.path.join(unzips_folder, file), compress_type=compression)
|
63 |
+
except FileNotFoundError as fnf:
|
64 |
+
print("An error occurred", fnf)
|
65 |
+
|
66 |
+
return file_path
|
67 |
+
|
68 |
+
def infer(model, f0_method, audio_file):
|
69 |
+
print("****", audio_file)
|
70 |
+
inference = Inference(
|
71 |
+
model_name=model,
|
72 |
+
f0_method=f0_method,
|
73 |
+
source_audio_path=audio_file,
|
74 |
+
output_file_name=os.path.join("./audio-outputs", os.path.basename(audio_file))
|
75 |
+
)
|
76 |
+
output = inference.run()
|
77 |
+
if 'success' in output and output['success']:
|
78 |
+
return output, output['file']
|
79 |
+
else:
|
80 |
+
return
|
81 |
+
|
82 |
+
|
83 |
+
def post_model(name, model_url, version, creator):
|
84 |
+
modelname = model_downloader(model_url, zips_folder, unzips_folder)
|
85 |
+
model_files = get_model(unzips_folder, modelname)
|
86 |
+
|
87 |
+
if not model_files:
|
88 |
+
return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo más tarde."
|
89 |
+
|
90 |
+
if not model_files.get('pth'):
|
91 |
+
return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo más tarde."
|
92 |
+
|
93 |
+
md5_hash = calculate_md5(os.path.join(unzips_folder,model_files['pth']))
|
94 |
+
zipfile = compress(modelname, list(model_files.values()))
|
95 |
+
|
96 |
+
a = get_info(model_files.get('pth'))
|
97 |
+
file_to_upload = open(zipfile, "rb")
|
98 |
+
info = a.get("info", "None"),
|
99 |
+
sr = a.get("sr", "None"),
|
100 |
+
f0 = a.get("f0", "None"),
|
101 |
+
|
102 |
+
data = {
|
103 |
+
"name": name,
|
104 |
+
"version": version,
|
105 |
+
"creator": creator,
|
106 |
+
"hash": md5_hash,
|
107 |
+
"info": info,
|
108 |
+
"sr": sr,
|
109 |
+
"f0": f0
|
110 |
+
}
|
111 |
+
print("Subiendo archivo...")
|
112 |
+
# Realizar la solicitud POST
|
113 |
+
response = requests.post(api_url, files={"file": file_to_upload}, data=data)
|
114 |
+
result = response.json()
|
115 |
+
|
116 |
+
# Comprobar la respuesta
|
117 |
+
if response.status_code == 200:
|
118 |
+
result = response.json()
|
119 |
+
return json.dumps(result, indent=4)
|
120 |
+
else:
|
121 |
+
print("Error al cargar el archivo:", response.status_code)
|
122 |
+
return result
|
123 |
+
|
124 |
+
|
125 |
+
def search_model(name):
|
126 |
+
web_service_url = "https://script.google.com/macros/s/AKfycbyRaNxtcuN8CxUrcA_nHW6Sq9G2QJor8Z2-BJUGnQ2F_CB8klF4kQL--U2r2MhLFZ5J/exec"
|
127 |
+
response = requests.post(web_service_url, json={
|
128 |
+
'type': 'search_by_filename',
|
129 |
+
'name': name
|
130 |
+
})
|
131 |
+
result = []
|
132 |
+
response.raise_for_status() # Lanza una excepción en caso de error
|
133 |
+
json_response = response.json()
|
134 |
+
cont = 0
|
135 |
+
result.append("""| Nombre del modelo | Url | Epoch | Sample Rate |
|
136 |
+
| ---------------- | -------------- |:------:|:-----------:|
|
137 |
+
""")
|
138 |
+
yield "<br />".join(result)
|
139 |
+
if json_response.get('ok', None):
|
140 |
+
for model in json_response['ocurrences']:
|
141 |
+
if cont < 20:
|
142 |
+
model_name = str(model.get('name', 'N/A')).strip()
|
143 |
+
model_url = model.get('url', 'N/A')
|
144 |
+
epoch = model.get('epoch', 'N/A')
|
145 |
+
sr = model.get('sr', 'N/A')
|
146 |
+
line = f"""|{model_name}|<a>{model_url}</a>|{epoch}|{sr}|
|
147 |
+
"""
|
148 |
+
result.append(line)
|
149 |
+
yield "".join(result)
|
150 |
+
cont += 1
|
151 |
+
|
152 |
+
def update_tts_methods_voice(select_value):
|
153 |
+
if select_value == "Edge-tts":
|
154 |
+
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)
|
155 |
+
elif select_value == "Bark-tts":
|
156 |
+
return gr.Dropdown.update(choices=BARK_VOICES, visible=True), gr.Markdown.update(visible=False), gr.Textbox.update(visible=False),gr.Radio.update(visible=False)
|
157 |
+
elif select_value == 'ElevenLabs':
|
158 |
+
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)
|
159 |
+
elif select_value == 'CoquiTTS':
|
160 |
+
return gr.Dropdown.update(visible=False), gr.Markdown.update(visible=False), gr.Textbox.update(visible=False), gr.Radio.update(visible=True)
|
requirements.txt
CHANGED
@@ -172,4 +172,9 @@ validators
|
|
172 |
#git+https://github.com/suno-ai/bark.git
|
173 |
#tortoise-tts
|
174 |
#git+https://github.com/neonbjb/tortoise-tts.git
|
175 |
-
neon-tts-plugin-coqui
|
|
|
|
|
|
|
|
|
|
|
|
172 |
#git+https://github.com/suno-ai/bark.git
|
173 |
#tortoise-tts
|
174 |
#git+https://github.com/neonbjb/tortoise-tts.git
|
175 |
+
neon-tts-plugin-coqui
|
176 |
+
deepfilternet
|
177 |
+
librosa
|
178 |
+
matplotlib
|
179 |
+
maturin
|
180 |
+
#git+https://github.com/microsoft/[email protected]
|
tts/conversion.py
CHANGED
@@ -10,8 +10,16 @@ import asyncio
|
|
10 |
from elevenlabs import voices, generate, save
|
11 |
from elevenlabs.api.error import UnauthenticatedRateLimitError
|
12 |
# Not working in windows
|
13 |
-
|
14 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
15 |
|
16 |
# Elevenlabs
|
17 |
ELEVENLABS_VOICES_RAW = voices()
|
@@ -24,10 +32,6 @@ def get_elevenlabs_voice_names():
|
|
24 |
|
25 |
ELEVENLABS_VOICES_NAMES = get_elevenlabs_voice_names()
|
26 |
|
27 |
-
# CoquiTTS
|
28 |
-
COQUI_LANGUAGES = list(CoquiTTS.langs.keys())
|
29 |
-
coquiTTS = CoquiTTS()
|
30 |
-
|
31 |
def tts_infer(tts_text, model_url, tts_method, tts_model, tts_api_key, language):
|
32 |
if not tts_text:
|
33 |
return 'Primero escribe el texto que quieres convertir.', None
|
@@ -68,6 +72,9 @@ def tts_infer(tts_text, model_url, tts_method, tts_model, tts_api_key, language)
|
|
68 |
# api.TextToSpeech()
|
69 |
|
70 |
if tts_method == "CoquiTTS":
|
|
|
|
|
|
|
71 |
print(tts_text, language)
|
72 |
# return output
|
73 |
coquiTTS.get_tts(tts_text, converted_tts_filename, speaker = {"language" : language})
|
|
|
10 |
from elevenlabs import voices, generate, save
|
11 |
from elevenlabs.api.error import UnauthenticatedRateLimitError
|
12 |
# Not working in windows
|
13 |
+
import platform
|
14 |
+
|
15 |
+
COQUI_LANGUAGES = []
|
16 |
+
if platform.system() != 'Windows':
|
17 |
+
from neon_tts_plugin_coqui import CoquiTTS
|
18 |
+
|
19 |
+
# CoquiTTS
|
20 |
+
COQUI_LANGUAGES = list(CoquiTTS.langs.keys())
|
21 |
+
coquiTTS = CoquiTTS()
|
22 |
+
|
23 |
|
24 |
# Elevenlabs
|
25 |
ELEVENLABS_VOICES_RAW = voices()
|
|
|
32 |
|
33 |
ELEVENLABS_VOICES_NAMES = get_elevenlabs_voice_names()
|
34 |
|
|
|
|
|
|
|
|
|
35 |
def tts_infer(tts_text, model_url, tts_method, tts_model, tts_api_key, language):
|
36 |
if not tts_text:
|
37 |
return 'Primero escribe el texto que quieres convertir.', None
|
|
|
72 |
# api.TextToSpeech()
|
73 |
|
74 |
if tts_method == "CoquiTTS":
|
75 |
+
if platform.system() == 'Windows':
|
76 |
+
return "Funcionalidad no disponible en windows", None
|
77 |
+
|
78 |
print(tts_text, language)
|
79 |
# return output
|
80 |
coquiTTS.get_tts(tts_text, converted_tts_filename, speaker = {"language" : language})
|
tts/test.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
from neon_tts_plugin_coqui import CoquiTTS
|