app.py

import gradio as gr
import os
from constants import VOICE_METHODS, BARK_VOICES, EDGE_VOICES
import platform
from models.model import *
from tts.conversion import COQUI_LANGUAGES
import pytube
import os
import traceback
from pydub import AudioSegment
# from audio_enhance.functions import audio_enhance

def convert_yt_to_wav(url):
    if not url:
        return "Please enter the video link", None
    
    try:
        print(f"Converting video {url}...")
        # Download the video using pytube
        video = pytube.YouTube(url)
        stream = video.streams.filter(only_audio=True).first()
        video_output_folder = os.path.join(f"yt_videos")  # Destination folder path
        audio_output_folder = 'audios'

        print("Downloading video")
        video_file_path = stream.download(output_path=video_output_folder)
        print(video_file_path)

        file_name = os.path.basename(video_file_path)
        
        audio_file_path = os.path.join(audio_output_folder, file_name.replace('.mp4','.wav'))
        # Convert mp4 to wav
        print("Converting to wav")
        sound = AudioSegment.from_file(video_file_path, format="mp4")
        sound.export(audio_file_path, format="wav")
        
        if os.path.exists(video_file_path):
            os.remove(video_file_path)
            
        return "Success", audio_file_path
    except ConnectionResetError as cre:
        return "Connection lost, please refresh or try again later.", None
    except Exception as e:
        return str(e), None
    
with gr.Blocks() as app:
    gr.HTML("<h1> Simple RVC Inference - by Juuxn 💻 </h1>")
    
    gr.HTML("<h4> The current space uses only CPU, so it's only for inference. It is recommended to duplicate the space to avoid issues with processing queues. </h4>")
    
    gr.Markdown("Simple RVC GPU Inference on Colab: [![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/drive/1NKqqTR04HujeBxzwe7jbYEvNi8LbxD_N?usp=sharing)")
    gr.Markdown(
        "[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/juuxn/SimpleRVC?duplicate=true)\n\n"
    ) 
    
    gr.Markdown("Collection of models you can use: RVC + AI Kits. **[RVC Community Models](https://docs.google.com/spreadsheets/d/1owfUtQuLW9ReiIwg6U9UkkDmPOTkuNHf0OKQtWu1iaI)**")
    
    with gr.Tab("Inference"):
        model_url = gr.Textbox(placeholder="https://huggingface.co/AIVER-SE/BillieEilish/resolve/main/BillieEilish.zip", label="Model URL", show_label=True)
        with gr.Row():
            with gr.Column():
                audio_path = gr.Audio(label="Audio File", show_label=True, type="filepath")
                index_rate = gr.Slider(minimum=0, maximum=1, label="Search feature ratio:", value=0.75, interactive=True)
                filter_radius1 = gr.Slider(minimum=0, maximum=7, label="Filter (breathing roughness reduction)", value=3, step=1, interactive=True)
            with gr.Column():
                f0_method = gr.Dropdown(choices=["harvest", "pm", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe"], 
                                    value="rmvpe", 
                                    label="Algorithm", show_label=True)
                vc_transform0 = gr.Slider(minimum=-12, label="Number of semitones, up an octave: 12, down an octave: -12", value=0, maximum=12, step=1)
                protect0 = gr.Slider(
                    minimum=0, maximum=0.5, label="Protect voiceless consonants and breathing sounds. 0.5 to disable.", value=0.33,
                    step=0.01,
                    interactive=True,
                )
                resample_sr1 = gr.Slider(
                    minimum=0,
                    maximum=48000,
                    label="Resample the output audio to the final sampling rate. 0 for no resampling.",
                    value=0,
                    step=1,
                    interactive=True,
                )
                 
        # Output
        with gr.Row():
            vc_output1 = gr.Textbox(label="Output")
            vc_output2 = gr.Audio(label="Output Audio")
                            
        btn = gr.Button(value="Convert")
        btn.click(infer, inputs=[model_url, f0_method, audio_path, index_rate, vc_transform0, protect0, resample_sr1, filter_radius1], outputs=[vc_output1, vc_output2])
        
    with gr.TabItem("TTS"):
        with gr.Row():
            tts_text = gr.Textbox(
                label="Text:",
                placeholder="Text you want to convert to speech...",
                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="RVC Model URL", show_label=True)
                
            with gr.Row():
                tts_method = gr.Dropdown(choices=VOICE_METHODS, value="Edge-tts", label="TTS Method:", visible=True)
                tts_model = gr.Dropdown(choices=EDGE_VOICES, label="TTS Model:", visible=True, interactive=True)
                tts_api_key = gr.Textbox(label="ElevenLabs API Key", show_label=True, placeholder="4a4afce72349680c8e8b6fdcfaf2b65a", interactive=True, visible=False)
            
            tts_coqui_languages = gr.Radio(
                label="Language",
                choices=COQUI_LANGUAGES,
                value="en",
                visible=False
            )
            
            tts_btn = gr.Button(value="Convert")
                
            with gr.Row():
                tts_vc_output1 = gr.Textbox(label="Output")
                tts_vc_output2 = gr.Audio(label="Output Audio")   
            
        tts_btn.click(fn=tts_infer, inputs=[tts_text, tts_model_url, tts_method, tts_model, tts_api_key, tts_coqui_languages], outputs=[tts_vc_output1, tts_vc_output2])
        
        tts_msg = gr.Markdown("""**I recommend creating an Eleven Labs account and entering your API key; it's free and you have a limit of 10k characters per month.** <br/>
                ![Imgur](https://imgur.com/HH6YTu0.png)
                """, visible=False)
        
        tts_method.change(fn=update_tts_methods_voice, inputs=[tts_method], outputs=[tts_model, tts_msg, tts_api_key, tts_coqui_languages])
    
    with gr.TabItem("Youtube"):
        gr.Markdown("## Convert YouTube video to audio")
        with gr.Row():
            yt_url = gr.Textbox(
                label="Video URL:",
                placeholder="https://www.youtube.com/watch?v=3vEiqil5d3Q"
            )
        yt_btn = gr.Button(value="Convert")
                
        with gr.Row():
            yt_output1 = gr.Textbox(label="Output")
            yt_output2 = gr.Audio(label="Output Audio")   
            
        yt_btn.click(fn=convert_yt_to_wav, inputs=[yt_url], outputs=[yt_output1, yt_output2])
         
    with gr.Tab("Models"):
        gr.HTML("<h4>Search models</h4>")
        search_name = gr.Textbox(placeholder="Billie Eilish (RVC v2 - 100 epoch)", label="Name", show_label=True)
         # Output
        with gr.Row():
            search_output = gr.Markdown(label="Output")
            
        btn_search_model = gr.Button(value="Search")
        btn_search_model.click(fn=search_model, inputs=[search_name], outputs=[search_output])
        
        gr.HTML("<h4>Submit your model</h4>")
        post_name = gr.Textbox(placeholder="Billie Eilish (RVC v2 - 100 epoch)", label="Name", show_label=True)
        post_model_url = gr.Textbox(placeholder="https://huggingface.co/AIVER-SE/BillieEilish/resolve/main/BillieEilish.zip", label="Model URL", show_label=True)
        post_creator = gr.Textbox(placeholder="Discord ID or link to creator's profile", label="Creator", show_label=True)
        post_version = gr.Dropdown(choices=["RVC v1", "RVC v2"], value="RVC v1", label="Version", show_label=True)
        
         # Output
        with gr.Row():
            post_output = gr.Markdown(label="Output")
            
        btn_post_model = gr.Button(value="Post")
        btn_post_model.click(fn=post_model, inputs=[post_name, post_model_url, post_version, post_creator], outputs=[post_output])
                
    app.queue(concurrency_count=200, max_size=1022).launch()
    #share=True